Archiwa kategorii: E-business articles

My articles on e-business

Public goods on the Internet?

In 2015, Polish Wikipedia removed a significant number of users from the possibility of page editing without logging in (to be exact: more than half a million IP addresses were blocked – source).

Meanwhile, the definition of public good says that:
– consumption is non-rival,
– it is not possible to exclude anyone from the consumption of a given public good.

Despite the above-mentioned contradiction, Wikipedia and other websites (such as free content providers, e-services) can be considered public goods. The explanation of this paradox, as well as the connection of types of goods with business models of online businesses, can be found in the following article:

  • Tymoteusz Doligalski, Business Models of Internet Companies and Types of Goods Offered, Journal of Business Models (2018), Vol. 6, No. 2, pp. 32-36
  • Download: Internet business models (2018, pdf).

Business model and strategy

Excerpts from the article: T. Doligalski, Internet Business Models in the Consumer Market – a Typological Approach, „Marketing i Rynek”, 12/2018.

Downloads:

 

The notion of business model with reference to strategy and revenue model

There are numerous definitions of business model. For the purpose of this reasoning, we apply the definition referring to the systemic approach, according to which business model is a simplified image of an company, which presents its essential elements and relations between them (Doligalski, 2014). The look at the company from the perspective of business model, according to R. Amit and Ch. Zott (2010), is characterised by the analysis of how the company creates value rather than what exactly it offers to its customers and where and when it operates. It is based on the holistic approach to the company without focusing on a selected function or resource.

The notion of business model is often compared to business strategy. In management science, strategy is understood in many different ways. The most frequently indicated common features of the definitions of strategy are the specified goal, the method of its pursuit, measurability, timing, and reference to various stakeholders (depending on the type of strategy: customers, competitors, employees, shareholders). In somewhat simplified terms, it can be said that a business model presents what a company is, while a strategy describes what the company wants to achieve and how it intends to do that (Cf. Tab. 1). A disputable issue is the popularity of company attributes such as business model and strategy. It is often declared that a company is seeking its business model (meaning: it has not established the final configuration of its most significant components or a long-term form of its relationships with stakeholders yet). This approach is opposed by Ch. Baden-Fuller and M.S. Morgan (2010), who argue that each and every company has its business model. The aspect of the popularity of strategy application is also interesting. If it is assumed that a strategy is a formalised set of long-term goals and plans, then probably not all entities have it in place. If viewing this term in broader terms, that is as a general concept of operations enabling its context-related interpretation and application (Pindelski, Obłój, 2006), the use of a strategy is more common. However, such a definition of strategy brings it closer to the notion of business model, e.g. as proposed by J. Magretta (2002), according to which the notion of business model is underlain by stories of how companies operate.

 

Table 1. Business model and strategy – summary of differences and similarities

Business model Strategy
describes what a given company is specifies strategic goals and methods of their pursuit
presents a given organisation’s image captured at one point in time has a time dimension and a certain direction of changes
resembles a state resembles a flow
is often oriented towards a company’s inside, the basic logic of its operations, and creation of an economic value is often created with respect to other market players, it points to the issue of positioning and competitive advantage
each company has a business model not all companies have strategies defined as a set of long-term goals and plans in place
concerns the crucial aspects of business operations
rather unchangeable over a short period of time

Source: based on T. Doligalski, Model biznesu z perspektywy ogólnej teorii systemów, [in:] T. Doligalski (ed.), Modele biznesu w Internecie. Teoria i studia przypadków polskich firm, Wydawnictwo Naukowe PWN, Warszawa 2014, p. 22.

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Internet Business Models in the Consumer Market – a Typological Approach (pdf)

Excerpts from the article: T. Doligalski, Internet Business Models in the Consumer Market – a Typological Approach, „Marketing i Rynek”, 12/2018.

 

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This paper presents characteristics of business models adopted by Internet companies operating in the consumer market. The typology covers online vendors, e-service providers, content providers, multi-sided platforms, and community providers. The business model types are described here, also with respect to selected economic categories. Additionally, the paper discusses the notion of business models from systemic and typological perspectives and compares this term to the notion of strategy and revenue model.

 

Keywords: business model, Internet, e-commerce, e-business


Introduction

The notion of business model gained in popularity as Internet companies emerged in the late 1990s. They were characterised by an operational logic different from the one pursued by traditional companies. Since then, numerous technological innovations have been introduced as well as consumer behaviours and methods of influencing them by companies have changed. Although the basic business models adopted by Internet companies have remained fairly stable, the latter have acquired new properties along with their development and adaptation to the changing conditions. Therefore, the need to look at the current characteristics of such companies is noticeable. The aim of this paper is to fill the research gap by describing the properties of business models adopted by Internet companies operating in the consumer market.

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Business models in systemic and typological approaches

The relevant literature distinguishes various approaches to the analysis of business models. This study describes systemic and classification approaches (mainly typological ones). In the former case, it is treated as a complex system composed of correlated elements. The classification perspective, in turn, consists in grouping companies into sets comprising companies with similar characteristics.

The classification perspective resembles the ranks of organisms in biology. It is composed of two major approaches: taxonomy and typology. Taxonomy consists in grouping companies based on a quantitative analysis which takes many factors into consideration. Typology, in turn, is a qualitative deductive approach resulting in identifying ideal types, that is theoretical models with properties typical of a given group of businesses (Lambert, 2015).

In the systemic approach, a business model changes when a modification of at least one of the key elements causes different relations between them. An introduction of a new product might not lead to a change of the business model if it takes place with unchanged elements constituting the business model and the same structure. If, however, it involves a redefinition of the target group, acquiring new competencies, and a different revenue model, the business model changes.

In the typological approach described below, a change of a business model involves a principal change of the operating model or inclusion of a different model in a company’s overall activities. Amazon.com began its operations with selling books and later expanded it by selling other goods. In the typological approach, its business model, that is online vendor, remained the same. The business model changed when the company expanded its operating model by activities related to a multi-sided platform (enabling sale to other entities) and an online service (offering cloud computing services).

The systemic and typological approaches differ also with respect to competing with the use of business models. In the systemic approach, it is presented as strengthening one’s own feedbacks, weakening competitors, and transforming competitors into allies (Casadesus-Masanell, Ricart, 2011). In the typological approach, competing with business models plays a less significant role as companies applying the same business model (e.g. online shops) compete with marketing instruments rather than the with the general concept of operations, which is identical. Although competition can occur between different types of business models, this is much the same as a grocery shop competing with a multi-sided platform, such as a local marketplace.

The differences regarding the postulate of uniqueness of a business model, which is formulated by some researchers, are similar. It can be considered from the systemic point of view but, from the typological perspective, its application is limited since companies are usually characterised by one of the several identified business models. Exceptions are the situations where companies combine several types of business models in their operations.

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Typology of the Internet business models – methodological comments

A variety of classifications of Internet companies, covering both taxonomies and typologies, can be found in the relevant literature. Many of them were developed at the turn of the 21st century in response to the emergence of pure players often operating based on a different logic than that of bricks and mortars. Most of the business models distinguished at that time have still been in use. However, unsuccessful models, that is ones which are no longer applied or are unpopular, can also be identified. They include the e-mall, which was distinguished by P. Timmers in his 1998 typology as a place aggregating online shops (Timmers, 1998). A likely reason for this business model being out of use is the popularity of comparison shopping engines, which offer similar services in a form that is more attractive to the customer. The classifications designed in recent years often concern new types of companies, e.g. ones operating in the fintech sector (Gimpel, Rau, Röglinger, 2017) and entities that have lately gained popularity as objects of research, e.g. multi-sided platforms (Täuscher, Laudien, 2018).

The existing classifications differ in terms of the number of business models. The smallest ones comprise five types of entities, whereas the more expanded ones distinguish twenty or more entities (Nojszewski, 2006). Some taxonomies are created as a list of possible combinations of available components of business models (e.g. five types of revenue models, six types of goods offered, etc.). This approach results in a large number of target taxa. Identification of many business models shows, on the one hand, the diversity of the entities operating in a given sector and, on the other hand, hampers the generalisation of their characteristics and results in their superficial description.

Certain classifications of business models adopted by entities operating online assume a given component of a company as a starting point. It is a controversial approach because an analysis of a company from the angle of a business model means a look at the whole logic of an organisation’s activity rather than at its selected element. This is how Rappa distinguishes the advertising model, where he classifies also companies acting as content providers and e-services (2003). This study treats it as a revenue model which could occur in several business models.

Classifications of business models differ also in terms of the research field. Most probably, they include e-business models, while business models in electronic markets (Timmers, 1998) or companies where the Internet plays a crucial role (Afuah, Tucci, 2018, p. 18) are less frequent. It is a significant issue since the mentioned categories are not identical. Depending on the selected definition of e-business, some pure players may not be taken into account in the classification, e.g. a blog or a small news website, a retailer on an auction platform, or a community website.

This study is an attempt to look at characteristics of Internet companies from a current perspective. It has been 20 years since the first classifications were developed and although many companies still function according to a similar logic today, characteristics of some entities have changed. The research field has been limited to pure players operating in the consumer market, thus excluding bricks and clicks, bricks and mortars, and entities from the business market. The applied approach distinguishes fewer types as it focuses on an attempt to describe their characteristics in more depth with the use of economic categories rather than on a description of examples of companies. A holistic view of a company is applied, which is reflected, among others, in an unambiguous separation of the terms ‘business model’ and ‘revenue model’.

The proposed typology of business models adopted by Internet companies operating in the consumer market comprises: online vendors (online shops and retailers on e-commerce platforms), e-service providers (companies offering an automated service provided via the Internet), content providers (companies creating and publishing content online), multi-sided platforms (Internet intermediaries), and community providers (companies enabling interactions to people with shared interests). This classification coincides with the seven business models adopted by pure players as identified by Kenneth and Jane Laudon (2014, 413-416): e-tailers, transaction brokers, market creators, content providers, community providers, portals, and service providers. These typologies differ, however, in terms of not only number of models but also and foremost description of their properties.

Based on a literature review and findings of our own research on operations of companies on the Internet, which was conducted for a few years, pure (ideal) types were distinguished in the deduction process and their qualitative features were described. This approach is used in creating a typology (Lambert, 2015). The typology is complementary to the taxonomy of Polish Internet companies, which is a classification prepared with the use of questionnaire surveys and statistical analysis. The segmentation procedure enabled identification of five segments of Polish Internet companies: suppliers of unique offerings, specialised newcomers, comprehensive incumbents, productivity enhancers, and run-of-the-mill retailers (Doligalski, Zaborek, Sysko-Romańczuk, 2015).

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Online vendors

These are companies selling products through their shop or an e-commerce platform. Online vendors can be intermediaries offering products manufactured by other enterprises or, less commonly, selling products manufactured by themselves. Online retailers usually offer traditional products, digital products (e.g. e-books) and traditional services (e.g. travel services).

Online sale is characterised by low barriers to entry defined as overall costs which must be incurred to commence an activity. There are numerous solutions being ready-made online shops, where one only needs to place product descriptions and other contents. The fact that it is so easy to enter the sector results, however, in a high competitive pressure. Online vendors often function in conditions similar to perfect competition, which arises from offering homogenous products; a high number of entities operating in the market; problems with standing out; market transparency and a significant role of price as a factor influencing consumer buying behaviours (which is affected by effortless comparison of prices). On the other hand, there are factors that disturb the purity of perfect competition. These include: uniqueness or non-homogeneity of some products; the resulting difficulty in comparing them and making consumer decisions; the necessity for companies to incur expenses for promotion, and distinctive features developed over a long term. The latter can include the achieved economies of scale, recognisable and trusted brand (often outside the Internet or based on positive opinions of customers in reputation systems), integration with traditional entities, or community preparing and publishing product reviews.

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E-service provider

For the purpose of this study, e-service is defined as an automatic service provided remotely via the Internet without any direct involvement of an employee of the service provider, which requires self-service and hence it is customised. Examples of e-services include e-mail, Internet search engines, Internet banking, and network storage. The most common revenue models are sale of services, freemium, and revenue from advertising.

E-services are characterised by non-rival consumption (consumption of a certain good by one person does not reduce the consumption of it by others) and scalability, understood here as the capability of easy serving a greater number of customers. In the case of e-services, barriers to entry might occur. The major initial cost is the development of software but the achievement of critical mass, defined as acquisition of an appropriate number of customers, is not normally required. This is because it is possible to provide e-services on a small scale.

There are many related terms originating from IT. These include SaaS (software as a service), cloud as a service, and infrastructure as a service. Their common feature is that a service is rendered via the Internet based on an application running on the provider’s servers. The latter aspect is extremely important and is at times overlooked in publications on management. An e-book kept on the customer’s terminal is not an e-service but the cloud storage from which the customer downloaded it can be treated as such. By analogy, a book is not a service but the activity pursued by libraries or bookshops consisting in making it available can be considered a service.

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Content provider

It is an entity distributing and sometimes also creating content online. The scope of the content is broad and includes, among others, text, graphics, audio, and video. This type of activity is characterised by high costs of content creation and ease of its publication in various forms and channels. Hence frequent relationships between online content providers and enterprises operating in the media market.

The revenue model adopted by such companies is primarily to display advertisements while offering contents free of charge or, less commonly, to sell contents. Free access to topical contents offered, among others, by online portals reduces however consumer willingness to buy the paid contents, the paper form of which is often delayed with respect to free ones available online.

Earning revenue from advertisements along the offered contents may seem a simple revenue model. Indeed, the solutions available to publishers (here: owners of websites) make it possible to easily join advertising networks or sell side platforms, that is entities managing advertisements displayed on their customers’ websites. Moreover, the increasing popularity of automated sale of advertising space as part of the so-called programmatic advertising systems results in the growing significance of the characteristics of website visitors meeting advertiser expectations, in addition to the website’s property of being a place of advertisement display. In effect, a website with valuable contents competes in the open marketplace with many websites with poorer quality contents, which offer the possibility to reach the target group at a lower expense. Premium publishers are in a slightly better position as they make their spaces available at higher rates to a limited number of advertisers as part of the private marketplace (Dyba, 2016).

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Multi-sided platforms

Multi-sided platforms are intermediaries between various groups of customers, which provide an environment in which transactions or other types of interactions take place. Transactions can be carried out there (e.g. auction platforms, travel platforms) or at least two groups of users can be aggregated there as a result of facilitated interactions (e.g. classified ad platforms, dating services). The revenue models applied by platforms are commission fees on transactions, charges for an account with expanded features, charges for publication or promotion of an advertisement.

Achieving critical mass at the beginning of the operations of a multi-sided platform is a challenge in its management. This means the necessity to acquire a sufficient number of customers from both groups to enable interactions between them. If the initial actions are successful, it is important to establish a balance between the size of both groups of customers.

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Community providers

Community providers are companies offering people with similar ideas, identities, interests or needs various interaction opportunities, such as exchanging or sharing resources, communicating, and at times even cooperating. Hence, communities are based on interactions using the so-called value co-creation oriented towards others, that is contributing a certain workload or sharing a resource, the beneficiary of which will be the community, rather than on interactions directly related to concluding transactions.

Communities based on resource exchange comprise exchanging objects of everyday use or sharing a car during travel. There are also many communities based on communication. These include discussion forums, question and answer websites (e.g. Quora), social networking websites.

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Comparison of the identified business models adopted by Internet companies

It is worth analysing the characteristics of the distinguished business models from the angle of key discriminatory criteria, such as economies of scale, network effect, and rival consumption. It should be remembered that this description concerns archetypical business models, that is pure types, and therefore it does not reflect the full variety and complexity of real companies.

High economies of scale occur in almost all identified business models adopted by Internet companies, which arises from the capability of serving a large number of customers without a considerable increase in variable costs. An exception in this case is online retailers offering tangible products, due to the workload involved in managing each order. It is illustrated by the information about significantly increased employment before Christmas, which results in higher variable costs and hence lower economies of scale (Isidore, 2017).

Network effect is defined in this study as situation where customer value grows as a result of a greater number of customers and interactions between them (Wang, Chen, Xie, 2010; Doligalski, 2010). In the case of online vendors and content providers, it is usually low or medium since interactions between users are normally an addition to the main product (e.g. an article as a non-network element, comments to it as a network element). Interactions between customers, in turn, are the essence of the business model being multi-sided platforms and communities. E-services vary in terms of the network effect; for some, it is non-existent (e.g. a hosting account) or medium (e.g. a cloud storage), while for electronic mail the network effect is significant.

Another aspect is the issue of rival consumption. It is understood as a situation where consumption of a good by one person limits its utility to others. This is the case with traditional products offered by online shops. This type of consumption occurs also as part of multi-sided platforms when traditional products are on offer, which arises from their limited availability. Content, online service and community providers, in turn, are characterised by non-rival consumption. The consumption of the goods offered by such entities does not limit their utility to other customers; it can even increase it at times, which, however, results from the network effect.

 

Table 2. Characteristics of the identified business models adopted by Internet companies operating in the consumer market

Online vendor E-service Content provider Multi-sided platform Community provider
Nature of the undertaking a vendor offering tangible goods or traditional services a vendor offering digital products an automated service provided remotely via the Internet without any direct involvement of an employee of the service provider, which requires self-service an entity creating and distributing contents online an intermediary between various groups of customers, providing environments where transactions or other types of interactions between them take place a company offering people with similar ideas, interests or needs various interaction opportunities of collaborative nature
Example of the offered goods a book, household appliances software a search engine, a cloud storage, electronic mail electronic issues of newspapers, blogs, video on demand interactions with vendors or prospective customers interactions with people with similar interests consisting in discussion, exchange of knowledge
Most common revenue models sale of tangible products and services sale of digital products sale of e-services, freemium revenue from advertising, sale of access to contents intermediation fees advertising, freemium, access fees
Economies of scale rather low high high high high high
Network effect low low undefined low, medium high high
Prevalent nature of consumption rival non-rival non-rival non-rival rival non-rival

Source: own work.


Conclusion

A business model analysis permits a holistic view of business operations. The typological approach applied in this paper additionally enables understanding of the diversity of Internet companies active in the consumer market. The identified business models are pure types, which do not fully reflect either the complexity or the diversity of real companies but they are simplified analogues presenting their essential properties. This paper distinguishes five business models adopted by Internet companies. An area of further research could be their quantitative characteristics as well as subtypes in each of them. Other research fields deserving their own classifications are bricks and clicks as well as the application of the Internet adopted by companies operating in the business market.

 

Bibliography

  1. Afuah, , Tucci, Ch. (2001). Internet Business Models and Strategies: Text and Cases, New York: McGraw-Hill.
  2. Amit, R. H., & Zott, C. (2010). Business Model Innovation: Creating Value in Times of Change. working paper. DOI:10.2139/ssrn.1701660.
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  4. Baden-Fuller, Ch., Morgan, M.S. (2010). Business Models as Models, Long Range Planning, 43, s. 156-171. DOI: 10.1016/j.lrp.2010.02.005.
  5. Casadesus-Masanell, R., Ricart, J.E. (2011). How to design a winning business model, Harvard Business Review, 89(1–2)/2011, s. 100–107.
  6. Doligalski T. (2010). Efekty sieciowe a strategie produktowe, Marketing i Rynek, nr 11.
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  9. Doligalski, T., Zaborek, P., Sysko-Romańczuk, S. (2015). Value proposition and firm performance: segmentation of Polish online companies, International Journal of Business Performance Management, 16, nos. 2/3. DOI: 10.1504/IJBPM.2015.068733.
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[1] The author is thankful to his colleagues and students from the Warsaw School of Economics (SGH) for their critical comments on the ideas presented in this paper at successive stages of their development.

[2] For the purpose of this reasoning, digital products and online services are separated.

[3] The opinion voiced by Professor Charles Baden-Fuller at the Business Model Conference in Venice in 2017

e-Business Models and Strategies

Below you will find my articles on e-Business Models and Strategies.

Internet Business Models in the Consumer Market – a Typological Approach

What are the internet business models? This is the research question I attempt to answer in this paper. As a result I present five internet business models and describe their characteristics with reference to economic notions. Both, HTML and PDF versions of the article are available.

T. Doligalski, Internet Business Models in the Consumer Market – a Typological Approach, „Marketing i Rynek”, 12/2018.

 

Business Models of Internet Companies and Types of Goods Offered

Is Facebook a public good? Is Wikipedia one? This kind of questions led me to writing an article in which I present the relations between the business models of internet companies operating in the B2C market (online vendors, e-service providers, content providers, multi-sided platforms, community provider) and the types of goods they offer (i.e. private, club, common, and public goods). The analysis shows that internet companies provide all four types of goods distinguished in the theory of economics.

See more:  Internet Business Models and Types of Goods Offered (pdf) published in the Journal of Business Models (2018).

 

Value Creation in e-Business as a Driver of Financial Perfomance: Investigating Business Models of Polish Internet Companies

This study investigates the relationship between the ways of creating value in e-business and the level of company profitability. The theoretical basis for the study was the model elaborated by Amit and Zott. The analysis was carried out on the set of data that was collected in the questionnaire survey conducted among managers of 150 companies from the group of the largest online retailers and e-service providers. Logistic regression was used to determine the statistically significant predictors of the operating profit indicator.

The research shows that out of the four components of the Amit and Zott model only the complementarity was closely linked to improvement in financial performance. The next two items – customer lock-in and innovation – did not have predictive power, while the fourth item (customer efficiency) was negatively correlated with the financial result. The research described above show that it is difficult to establish a universal set of value-creating factors in e-business.

See more: P. Zaborek, T. Doligalski, S. Sysko-Romańczuk, Value Creation in e-Business as a Driver of Financial Perfomance: Investigating Business Models of Polish Internet Companies​, “Research on Enterprise in Modern Economy – theory and practice”, 2016 | nr 4 | 101–113.


Strategies of Value Proposition on the Internet

In view of the inadequacy of traditional value-for-customer typology in the context of online operations, it is justified to ask how companies build customer value (or value proposition) with the use of the Internet. The article is an attempt to answer this question The article outlines five strategies that can also be perceived as five customer value dimensions. These are: efficiency strategy, free value, complex solution, unique value and value co-creation.

The above strategies were used to segment Polish online companies for the purposes of a research carried out in 2012, described in the article entitled: Value proposition and business performance: segmentation of Polish online companies. Statistical analysis revealed that the level of application of particular strategies can be operationalised using single hidden variables estimated on the basis of the Likert scale, containing the determinants of the strategies employed. The outlined strategies are therefore one-dimensional constructs. Moreover, the results of factor analysis suggest that the hidden variables for the strategies are uncorrelated, which points to a possibility of independent development of individual strategies by enterprises.

See more: T. Doligalski, Strategies of Value Proposition on the Internet, „Perspectives on Innovations, Economics & Business”, Volume 5, Issue 2, 2010.

Value proposition and firm performance: segmentation of Polish online companies

The main aim of the next study was to perform a segmentation of companies by value proposition. The segmentation research drew from previously formulated strategies (see Strategies of Value Proposition on the Internet. Therefore, this article could be included in the product and market stream. Nevertheless, due to numerous references to business models, this article has been qualified as belonging to this stream. As a result of the segmentation procedure, five segments of online businesses were identified: unique solution providers, specialized juniors, comprehensive seniors, productivity facilitators and typical salespeople.  The segmentation of online companies developed through research was a prerequisite for conducting qualitative research among companies representing selected segments, as described in Business Models and Growth Modes. Qualitative Research of Polish Online Companies. These studies show that companies show similarities in terms of the following segmentation criteria: contribution to customer, offer breadth, and differences in terms of content management and customer value creation. A typical growth path model assumes expansion to foreign markets after obtaining a certain scale of sales in the domestic market.

See more: T. Doligalski, T., Zaborek, P. and Sysko-Romańczuk, S. (2015) Value proposition and firm performance: segmentation of Polish online companies​, International Journal of Business Performance Management, Vol. 16, Nos. 2/3, pp.133–148.


Doligalski, Conceptual Model of Internet-Based Customer Value Management, [in:] T. Doligalski, Internet-based Customer Value Management. Developing Customer Relationships Online, Springer, Heidelberg 2015.

In the third chapter of the monograph Internet-based Customer Value Management  I presented my own model of Internet-based customer value management. By means of the article I tried to fill the theoretical gap related to actions aimed at developing online customer relations in order to increase customer value and translating into increasing company value.  In this model, reductionist perception of customer value management through the prism of measurement and maximisation was extended to include aspects related to management based on customer value and the use of the concept of value exchange to describe customer relationships with the company. It is due to the specific nature of the Internet, perceived as an environment for customer relation development, in which the role non-material values delivered to companies by customers is much bigger than in traditional non-Internet relations. The model provides a holistic description of customer relationships – from customer needs, through resources and expertise, value communication, and customer segmentation, to corporate value.  The model of Internet-Based Customer Value Management was elaborated for a couple of years. I presented it in a number of publications, differing not only in terms of research maturity, but also the application context. In the aforementioned chapter, the model is presented in a universal sense, without specifying the sector or type of enterprise.
T. Doligalski, Internet-based Customer Value Management. Developing Customer Relationships Online, Springer , Heilderberg 2015.

 

Business Models of Internet Companies and Types of Goods Offered (pdf, 2018)


Abstract

The article presents the relations between the business models of internet companies operating in the B2C market and the types of goods they offer (i.e. private, club, common, and public goods). The analysis shows that internet companies provide all four types of goods distinguished in the theory of economics.

Key words

business models, internet, public good

Acknowledgments

The author wishes to thank everyone who submitted critical remarks on the ideas presented in this article, particularly his colleagues and students at Warsaw School of Economics.

Introduction

The purpose of this article is to relate the typology of the business models of internet companies operating in the B2C market to the types of goods they offer, as distinguished in the theory of economics.

By “internet companies” (pure players) the author understands companies whose only (or at least predominant) environment for developing relations with customers is the internet. The remaining companies can be divided into multichannel (brick-and-click) companies, i.e. those which provide value to their customers using a combination of traditional and interactive channels, and brick-and-mortar companies, which operate largely outside of the internet.

The typology of business models of internet companies operating in the B2C market used in this article includes (Doligalski, 2018): online vendors (internet stores and sellers using e-commerce platforms), e-service providers (companies which offer an automated service provided through the internet), content providers (companies which publish content on the internet), multisided platforms (internet intermediaries), and community providers (companies which allow for interactions between people who share common interests). For the purpose of this discussion, the following typology of goods will be used: public goods, common goods, club goods, and private goods.

An analysis of these business models provides a comprehensive overview of the way companies function. The typological approach used in this article additionally reveals differences in the functioning of organizations — in this particular case, internet companies operating in the consumer market. The business models distinguished above are ideal (pure) types which do not fully reflect the complexity or diversity of real-world companies. Nonetheless, as simplified analogues, they embody their most crucial properties. Knowing the ideal types within the range of business models and the types of products these business offer enables us to understand the basic logic according to which real world companies operate, even if their business models and products are hybrids of ideal models.

Typology of business models of companies in the B2C market

Online vendors are companies that deal in the sales of tangible products through an online store or an e-commerce platform. Online vendors can be middlemen who offer products that are manufactured by other companies, or, less commonly, they may sell products which they manufacture themselves. These vendors typically provide physical products, traditional services (e.g. travel packages) or digital products (e.g. software). Online vendors that offer material goods or traditional services sell private goods that are characterised by rival consumption and a feasible exclusion. Rival consumption is understood as the situation in which the consumption of a good by one person diminishes its utility to others. Paid digital products are an instance of club goods, the consumption of which is non-rival, but still remain not available to anyone.

By an e-service we understand a service which is provided remotely over the internet, based on the server of the provider, without any direct involvement of any employee of the provider. An e-service is thus an internet tool, often of an infrastructural nature, which requires self-management from the customer and offers individualized values. Examples of e-services include e-mail, internet search engines, internet banking systems, and network storage. E-Services are characterised by non-rival consumption (consumption of the good by one person does not limit its utility to others) and scalability, understood as the capability to serve a greater number of customers. However, such consumption may become rival when congestion problems occur, limiting the convenience of these internet services.

Content providers are entities that distribute content online. The scope of content provided by this type of business varies widely and includes text, graphics, audio, and video. This type of activity is characterised by the high cost of content creation and the ease of its publication in different forms and through various channels. This explains the relationships that often exists between internet content providers and enterprises in the media industry. Similarly to e-services, content is usually consumed through non-rival consumption, as long as there are no limitations to scalability.

Multi-sided platforms are intermediaries between different groups of customers, and provide an environment in which transactions or other types of interactions take place. They can enable financial transactions (e.g. auction platforms, travel platforms) or at least aggregate two groups of users, facilitating interactions between them (e.g. classified ad platforms, dating services). The product offered by these platforms is interaction with users from the complementary group; it is typically rival in nature and usually leads to a customer obtaining a private good. If — less commonly — a platform brings together consumers and sellers of digital goods, then it usually makes it possible to obtain a club type of good. The character of this interaction is thus dependent to a large degree on the type of good being offered.

Community providers are companies that offer people of similar needs, interests or identities the opportunity to enter into different kinds of interactions, such as the exchange or sharing of resources, communication, and, in some cases, cooperation. Communities are therefore based on interactions, ones that do not directly involve transactions, but instead utilise value co-creation that is oriented towards others in the community, that is the contribution of a certain user-made work or sharing a resource to benefit the community as a whole (Doligalski, 2015). Community providers thus offer non-rival interactions with other users, interactions that lead to the creation and provision of a certain good (e.g. discussions, open source software).

Often in case of multisided platforms and community providers it is difficult to unequivocally assign companies to either of the models, as they usually combine the characteristics of each, i.e. rivalry over scarce goods (e.g. private goods, position within a ranking) and cooperation between users (e.g. sharing opinions about sellers).

The relationship between business models and types of products offered

As mentioned above, online vendors offer private goods (tangible goods or traditional services) or club goods (digital products). E-Service and content providers charge fees to their customers while offering club goods. If they are offered free of charge, should they be distinguished as public goods, or at least as commons?

Public goods are characterised by two values: the impossibility of excluding anyone from consuming the good, and non-rival consumption (Adams & McCormick 2006; Kaul, Grunberg & Stern 1999). On the other hand, if rival consumption occurs, we are dealing with a common good (commons). Typical examples of public goods include lighthouses and the ozone layer, while in the case of commons, it is parks and public roads.

So do free content or e-services bear the characteristics of one of these two types of goods? The question requires us to differentiate between two criteria: the purpose and technological properties of a given good. Both free content and e-services are offered according to the principle of common accessibility. Technically there are many ways in which a person could be denied access to a website. An internet site may not be displayed to users with a particular kind of terminal (desktop or mobile), a specific browser, or a particular IP address, which is associated with the location of the user (geoblocking) or their internet provider.

So does the technical capability to block access to certain content or e-service settle the question of the character of these goods? One might argue that a similar form of denying consumption may occur in the case of a public good such as a television signal, which can theoretically be blocked for users inhabiting a particular area. Public roads are often given as one example of a common good, but in this case, exclusion may take place by limiting access to particular types of vehicles.

These ambiguous criteria make it more difficult to qualify free content and e-services. But if we assume that a search engine or the content of a particular blog is, generally speaking, available to anyone and any potential exclusions are notably rare exceptions, then these goods are of a more public than club character. This approach may seem to contradict the formal definitions of public and common goods, nonetheless these goods are often classified as elements of a continuum or as non-pure public goods (Kaul, Grunberg & Stern 1999).

On the other hand, if content and e-services are offered free of charge over the course of limited-time promotions, after which the customer is required to make a payment (e.g. Netflix), these should be classified as club goods. This situation resembles a club that allows anyone to enter in the afternoon, but charges an admission fee in the evening.

There remains the matter of qualifying goods offered by multi-sided platforms and community providers. Multi-sided platforms usually offer rival interactions with users from the other group. In some cases, access to a platform is restricted by payment (e.g. the dating website eHarmony), and thus its product should be counted as a private good. Provided that access to the platform is free, then its product — rival interaction with users from the other group — bears the characteristics of a common good. This resembles a used car market – in the first case, there is an entry fee, while in the other, there is not. In both cases buyers compete for the best used cars offered by sellers. Community providers, on the other hand, offer non-rival interactions which may lead to the creation of certain goods (discussions, open source software). Some of them are open to everyone (e.g. Twitter, open chat forums) and hence are of a public good character. There are communities with restricted access (e.g. chat groups for classmates), and these offer a club good.

While this discussion is concerned with ideal types, in practice these entities usually combine the properties of both types. Table 1. presents an attempt to associate business models of internet companies in the B2C market with the basic types of goods they offer.

Table 1. Proximal relations between business models of internet companies and the types of goods offered

Feasible exclusion Non-feasible exclusion
Rival consumption Private goods

Online vendors selling tangible products or traditional services

Multi-sided platforms with restricted access

Common goods

Multi-sided platforms with free access

Non-rival consumption Club goods

Online vendors selling digital products

Paid e-service providers

Paid content providers

Providers of communities with restricted access

Public goods

Free e-service providers

Free content providers

Providers of communities with free access


Discussion

This article presents an attempt to relate business models to the types of products offered. It combines internet companies, i.e. entities that have operated for more or less the past 20 years, with an older economic concept, namely, the typology of goods. The analysis shows that internet companies provide all four types of goods distinguished in the theory of economics.

The proposed classification is of a proximal character, as the goods offered by internet companies may not always be qualified unequivocally. Examples of goods that are difficult to categorize include e-services offered using the freemium model. A basic free version of an e-service bears the characteristics of a public good, while the paid premium version is a club good.

The classification of goods based on the criteria of rivalry and feasible exclusion does not account for revenues obtained through other channels. Thus internet content that is offered for free but allows for a display of intrusive advertisements bears the characteristics of a public good. Similarly, websites that offer free e-services, while at the same time selling — or enabling other entities to sell — their customers’ data, are classified as public goods. The definition proposed by Kaul (2001) is a contemporary attempt to approach the problem of the public good by proposing that it is inclusive (public in consumption), based on participatory decision-making and design (public in provision), and that it is just (public in benefits). Under this definition, many companies that provide their content or services free of charge would not be included in the category of public goods, though these would include both Wikipedia and open source software.

The above remarks, as well as the complexity and the hybrid character of products offered by internet companies, indicate the need to formulate a new categorization of goods, one that would better reflect the conditions of the modern economy. Such a categorization could include external effects that accompany consumption, both positive (e.g. interactions between users) and negative (e.g. congestion problems).

References

  1. Adams, Roy & McCormick, Ken (2006) Private Goods, Club Goods, and Public Goods as a Continuum, Review of Social Economy, 45:2, 192-199, DOI: 10.1080/00346768700000025
  2. Doligalski, Tymoteusz (2018) Business Models of Internet Companies: Typological Approach, working paper.
  3. Doligalski, Tymoteusz (2015) Internet-Based Customer Value Management, Springer, Heilderberg.
  4. Kaul, Inge (2001) Public Goods: Taking the Concept to the 21st Century. The Market or the Public Domain, Drache D. (comp.), London & New York: Routledge, p. 255-273.
  5. Kaul, Inge, Grunberg, Isabelle & Stern, Marc A., (1999) “Defining Global Public Goods”, in Kaul, Inge et al., Eds. (1999) Global Public Goods: International Cooperation in the 21st Century, New York: Oxford University Press, p. 2-19.

 

Predicting New Car Registrations: Nowcasting with Google Search and Macroeconomic Data

Published as: E. Tomczyk, T. Doligalski, Predicting New Car Registrations: Nowcasting with Google Search and Macroeconomic Data, [in:] Sł. Partycki (ed.),  E-społeczeństwo w Europie Środkowej i Wschodniej. Teraźniejszość i perspektywy rozwoju (e-Society in Middle and Eastern Europe. Present and Development Perspectives), Wydawnictwo KUL, Lublin 2015, p. 228-236.

Download the paper as pdf from SSRN:  Predicting New Car Registrations: Nowcasting with Google Search and Macroeconomic Data


Abstract

Based on search queries data and a macroeconomic index (PMI) we attempt to predict new car registrations. As the forecasting horizon is short, the modelling is performed in accordance with the idea of nowcasting. The study covers 48 monthly observations for sixteen car producers present on Polish market. The proposed model explains the level of new car registrations for the five major makes and allows to forecast the number of registrations for the current and next month.

Keywords: nowcasting, prediction, modelling, car, automotive, demand, registrations,  Internet, search, Google, Poland, CEE, PMI

 

Introduction

In modern economies there are several sources of data on real-time activities which may help in modelling the behaviour of various entities, such as consumers or businesses.  These sources of information include online auctions, parcel shipment companies, credit card or mobile operators, as they possess precise data on transactions in certain locations [4, p.1]. A special role among them is played by search engines which provide data on frequencies of their queries. Possibly the most popular is Google Trends presenting both number and location of chosen searches.  Availability of such data enables modelling in accordance with the idea of  nowcasting.

Nowcasting is defined as the prediction of the present, the very near future and the very recent past [2, p.4]. The reason for modelling the current or recent past events is the delay in availability of data, which in modern economies can amount to weeks or even months. The other circumstances increasing the application value of nowcasting are macroeconomic turbulences, great uncertainty and unique shocks, as they cause past values to lose their predictive power [8, p.4].  As Bańbura et al. state ‘nowcasting is based on exploitation of data which is published early and possibly at higher frequencies than the target variable of interest in order to obtain an ‘early estimate’ before the official figure becomes available’ [2, p.4].  The scope of nowcasted activities is wide and includes current changes in unemployment, private consumption or – beyond just the economic activities – development of infectious diseases [4, p. 2].

As mentioned above, search engines are a valuable source of data on various entities’ behaviour (mostly consumers’ behaviour) in modern economies. Their usefulness results from both their popularity and type of data gathered. Search engine queries, as opposed to questionnaires, are not biased by submitting false information aimed at creating a desired image. Below we present three conditions the fulfillment of which should increase the application value of nowcasting with search data.

  • The nowcasted activity is preceded with search engine queries.

The searching behaviour depends heavily upon the type of activity. Consumers are more likely to search information on products of high involvement or information related to some risk [5, p. 1340]. Under certain circumstances people tend to rely more on friends’ opinions and less on online searching information online (e.g. on local markets).

  • It is possible to identify search queries associated with the nowcasted activity.

The common problem is selection of queries not only typical for the activity, but also queries that precede it. Queries including brand or product name may be related not only to pre-purchase search, but also with post-purchase services or buying an used product. On the other hand, the list of specific queries related strictly to pre-purchase phase may be long and difficult to identify. Moreover, these phrases may be rarely entered and data on their search frequency may be not available.

(iii) Searches lead to a nowcasted activity to a similar extent.
In other words, searching consumers have similar purchasing potential. A consumer looking for information on a movie to watch in cinema is likely to do it only once (or not at all). An investor entering abbreviation of company quoted on stock exchange may in theory buy or sell any number of company’s stocks. In the first case, search queries are more likely to serve as a valuable predictor of the demand.  Demand nowcasting with Google search is probably easier on B2C than on B2B market as the size of transactions following the search varies less.  Some brands or products may however attract interests of consumers who do not intend to purchase them (e.g. prestigious brands, innovative products).

Interestingly Choi and Varian provide an example which demonstrates that the use of more sophisticated methods may help if the three above listed conditions are not met [4, p. 2].    They modeled a confidence index for Australian consumers by identification of phrase categories, which frequency of entering is correlated with the historical levels of the consumer confidence. The prediction of the consumer confidence index is based on the assumption that the identified correlations will persist in the future.

 

Nowcasting of automotive markets

We decided on car registrations as the modelled variable for the following two reasons. First, data on car registrations are available with monthly frequency thus offering relatively long time series. This is not typical; many macroeconomic data are available in quarterly or yearly intervals only. And second, scope of these data includes 20 bestselling car manufacturers in a given month and offers a wide cross-section through Polish new car market.

The customer behaviour on automotive market imperfectly meets the three conditions mentioned in the previous section. The potential buyers are likely to conduct search queries before the purchase in order to recognize the vehicle parameters or find out the dealer’s location. The names of the car makes are the phrases associated with the purchase. Unfortunately, these phrases as the broad matches may refer also to other activities (e.g. looking for spare parts). As we decided on modelling both consumer and business car registrations, a single search may lead to the purchase of more than one car. However, the great majority of Polish companies are small and medium businesses, so we can safely assume that an average transaction will include rather small number of vehicles.

The automotive market was the subject of a number of nowcasting analyses. Sun, Li, Li and Zhang  forecast automotive demand in China with macroeconomic, price, consumer and other factors (i.e. sales of competitive automobile types, advertising investment) [9, p. 431]. The category of consumer factors includes consumer satisfaction index as well as searching index based on queries of Baidu, the leading Chinese search engine. Their model offers more accurate car sales prediction than popular benchmark models, especially during market fluctuations. Researching the Chilean automotive market, Carrière-Swallow and Labbé create Google Trends Automotive Index, which together with an autoregressive component form the set of explanatory variables. The proposed model also outperforms and provides forecasts more rapidly than benchmark models [3, p. 5].

Results obtained in our previous article point to importance of autoregressive component (that is, lagged number of car registrations) and Internet search queries in modelling number of passenger car registrations in Poland. In case of four automotive brands (i.e. Fiat, Opel, Skoda, Toyota) autoregressive component and search data were two major factors influencing number of first registrations. The registrations of the remaining two brands (i.e. Peugeot, Renault) can be explained with previous level of registrations and car manufacturer web site traffic [6, p.6].

In this paper, we add a macroeconomic component to verify dependence of car registrations on the general situation in the economy. We also extend the scope of research to 16 best-selling brands on Polish market.  The purpose of this paper is to model new car registrations with the Google search and macroeconomic data and to evaluate forecasting quality of the nowcasting models.


Description of data

Our sample covers 48 monthly observations from January 2011 to December 2014.[1] Monthly data on new registrations of passenger cars are provided by the Polish Association of Automotive Industry (PAAI)[2] on the basis of the Central Register of Vehicles database administered by the Ministry of the Interior. A one-month lag is allowed by Polish law between the purchase of a private car and its registration. Current data on number of registrations becomes available on the PAAI webpage around the 5th day of the next month. Due to availability of data, the following 16 passenger car makes were included in the initial empirical analysis: BMW, Citroen, Dacia, Fiat, Ford, Honda, Hyundai, Kia, Nissan, Opel, Peugeot, Renault, Skoda, Suzuki, Toyota, and Volkswagen. Together, they constitute 85% of new car registrations in Poland.

Relative numbers of queries pertaining to car makes are defined in a way proposed by Choi and Varian [4, p.3]. Original search data is defined relative to BMW registrations in the week of June 6 –14, 2014. For aggregated monthly series, data is rescaled relative to the maximum monthly query in the period analysed (that is, number of BMW queries in June 2014, equal to 280.71) and defined in percentage terms.

Table 1 presents each car producers’ share in total volume of searches and ratio of number of searches to number of registrations (henceforth, S/R ratio). The shares of car producers in searches are calculated as the ratio of shares of particular producer to the sum of searches of all producers. Thus, they sum to 100%. S/R ratio is calculated by dividing the producer’s share in Google searches by its share in registrations.  If the ratio exceeds 1, the make is proportionally more often searched than it is registered. In case of BMW and Honda, the ratios are the highest. This illustrates that these brands to the highest extent attract interest of consumers who do not follow with actual purchase. On the other hand, Dacia, Volkswagen and Skoda are the producers who are relatively rarely searched as compared to their number of registrations. The diverse behaviour can be explained with the fact that BMW and Honda can be perceived as aspirational brands, while the latter three makes offer rather utilitarian than symbolic value, and thus draw proportionally less interest.


Table 1. Car manufacturers’ shares in Google searches and ratios of number of searches to number of registrations (S/R)

 

Shares inGoogle searches S/R ratio
BMW 12% 4.98
Citroen 8% 3.06
Dacia 4% 1.73
Fiat 8% 1.63
Ford 7% 1.47
Honda 12% 1.37
Hyundai 10% 1.16
Kia 5% 1.15
Nissan 5% 0.94
Opel 6% 0.90
Peugeot 7% 0.74
Renault 4% 0.58
Skoda 3% 0.46
Suzuki 6% 0.44
Toyota 3% 0.29
WV 1% 0.28
Total 100%

Source: authors’ calculations

Our previous results [6, p.6] suggest that web traffic variables and seasonal effects do not exhibit statistically significant or economically substantial influence on number of car registrations in Poland, and autoregressive component and search data remain the major factors in explaining the dependent variable. To extend our analysis, we add Purchasing Managers’ Index (PMI) to our set of regressors to account for the impact of macroeconomic environment on car registrations. PMI data, published by the Polish economic portal Bankier.pl,[3] becomes available on their webpage with one-month lag and free of charge. In comparison with other macroeconomic data PMI may be considered current and easily accessible, and our preliminary data analysis suggested that it is better suited to modelling car registrations than indicator of business conditions in retail trade. It also reflects the changes of activities related rather to B2B than B2C market, as opposed to data on frequency of search queries which may over-represent the consumer purchasing behaviour. We do not include explanatory variables taken from the Polish car market, for two reasons. First, autoregressive component in our models takes account of recent (lagged one month) number of car registrations; and second, car market data is not easily accessible on the Internet, and therefore less useful for the purpose of real-time analysis.

 

Estimation results

Based on the average number of registrations per month, car makes considered for empirical analysis can be grouped into three categories of car sellers: major (five makes), medium (three makes), and small (the remaining eight car makes; see Table 2).

 

Table 2. Average number of car registrations in 2011-2015

 

Make Category
of  producer
Averagemonthly

registrations

1. Skoda major 2 958
2. Volkswagen major 2 075
3. Toyota major 1 904
4. Opel major 1 763
5. Ford major 1 724
6. Renault medium 1 288
7. Hyundai medium 1 251
8. Kia medium 1 260
9. Nissan small 1 020
10. Peugeot small 999
11. Fiat small 985
12. Citroen small 825
13. Dacia small 795
14. Honda small 523
15. Suzuki small 506
16. BMW small 502

Source: authors’ calculations

 

We found that results of the subsequent stages of empirical analysis are very dependent on the number of car registrations. For medium and small producers we did not find an economically valid and statistically significant dependence of number of car registrations on PMI, and a limited one only on lagged search queries. However, for the five major players, the results look more promising.

Linear models with HAC standard errors (to account for serial correlation in the error term) have been estimated for five dependent variables describing number of first registrations of Ford, Opel, Skoda, Toyota and Volkswagen. Results are summarised in Table 3. AR(1) component (that is, number of car registrations lagged one month) and internet search data lagged two months were included on the basis of our previous analysis of car registrations data. Also, PMI data lagged two months were added; the lag is meant to account for one-month delay in publishing the index plus an additional one-month lag before it can be reflected in car registration numbers because of the delay allowed by Polish law between the purchase of a vehicle and its registration.

 

 

Table 3. Summary of estimation results

 

Ford Opel Skoda Toyota Volkswagen
constant −3778.03 ** −2451.22 ** −3339.64 ** −2667.55  * −1102.73
AR(1) 0.281 ** 0.348 ** 0.394 ** 0.457 ** 0.563 **
St-2 18.190  * 24.497 ** 69.425 ** 49.779 ** 79.434
PMIt-2 79.708 ** 39.612 ** 53.611 ** 36.241 14.579
R2 0.525 0.421 0.485 0.448 0.451
RESET p-value 0.745 0.341 0.592 0.206 0.157
normality p-value 0.084 0.016 0.956 0.900 0.101
maximum VIF 1.234 1.189 1.287 1.297 1.099
omit St-2: improved information criteria 2/3 0/3 0/3 0/3 0/3

** – coefficient statistically different from zero at 0.05 significance level; * – coefficient statistically different from zero at 0.10 significance level

Source: authors’ calculations

 

The strongest explanatory power is exhibited by the autoregressive component (that is, number of registrations lagged one month) and search queries lagged two months. Lagged dependent variable has positive and statistically significant influence in all cases, and so does lagged search data, with the sole exception of Volkswagen. Hypothesis of dependence of car registrations on Purchasing Managers’ Index is confirmed in three out of five cases: for Ford, Opel and Skoda.

All five models are characterized by satisfactory coefficients of determination, comparable to those obtained in previous research, and all are correctly specified according to the RESET test. All but one (that is, Opel) have normally distributed standard errors when tested at the 0.05 significance level, and since sample size is adequate, absence of normality of standard errors does not negatively influence estimation results. There is no multicollinearity in any of the models. In addition, we conducted the omitted variable test for lagged search queries to verify whether ignoring internet search data improves statistical quality of the estimated models. In only one case (that is, Ford) two out of three information criteria point to higher quality of the reduced model; in four remaining cases, models with search variable perform better than models without it.

 

Evaluation of forecasting quality

 

To assess forecasting quality of the models, they were re-estimated on the basis of a limited sample: from January 2011 to June 2014 (that is, on the basis of 42 observations). The remaining six months of 2014 were used to evaluate forecasting quality of the models using mean errors (ME) and mean absolute percentage errors (MAPE). Results are reported in Table 4.

 

Table 4. Measures of forecasting quality

 

Ford Opel Skoda Toyota Volkswagen
ME -160.12 316.57 -200.57 346.79 122.23
MAPE 12.8% 17.3% 15.8% 14.6% 13.8%

Source: authors’ calculations

 

 

Results presented in Table 4 suggest that number of registrations of Ford and Skoda are somewhat overestimated by the models, and those of Opel, Toyota and Volkswagen – slightly underestimated. However, size of bias does not appear substantial as compared to actual number of registrations. In-sample forecast errors as measured by mean percentage absolute errors range from 12.8% for Ford to 17.3% for Opel. As far as we are aware, there exist no similar studies for the Polish market to compare our results with but we consider them acceptable.

 

Discussion and limitations

 

Empirical analysis of passenger car registrations suggests a notable disparity between small/medium and major car producers. For the first category of car makes, we were not able to define and estimate economically meaningful and statistically significant relationships with lagged search queries and Purchasing Managers’ Index representing the macroeconomic environment. It seems that registration volumes of minor car producers are not directly influenced by aggregated economic variables, and it is generally difficult to fit a model which would meet standard quality criteria.

For the five major sellers, we find that number of registrations of Ford, Opel, Skoda and Toyota cars are adequately explained by the autoregressive component, internet search data lagged two months, and – with the exception of Toyota –  Purchasing Managers’ Index lagged two months. It appears that major new car sellers share a common pattern of dependence of their registration numbers on internet search and macroeconomic factors. It is interesting to note that Volkswagen registration numbers are statistically significantly influenced only by the autoregressive component.  It seems that web searches of this car make do not fall in step with purchasing decisions. As shown in Table 1, Volkswagen is characterized by one of the lowest ratios of number of searches to number of registrations.

Forecasting quality of the models constructed for the five major sellers seems satisfactory. We also found that among the estimated coefficients, the ones that exhibited the largest instability when shortened sample was used were those associated with lagged search variables. This may suggest that search data, being volatile and subject to major variation from month to month, reduces stability of results of econometric analysis and therefore presents additional challenges when used for nowcasting.

Estimation results show that Google search and macroeconomic data can be used to explain number of registrations of 5 major car producers in short term (i.e. one month). The estimates are based upon publicly available data and do not require any insider knowledge. The nowcasting estimation can also serve to assess the current sales level of major producers, as the sales precede the registrations with about two weeks (to be exact: from zero to four weeks). This conclusion remains in accordance with the definition of nowcasting which is the prediction of the present [4, p. 2; 2, p.4].

Nowcasting models which use search data may serve as a source of marketing insight on current trends in consumer purchasing behaviour. Knowledge of this type is difficult to acquire via traditional research methods such as surveys or in-depth interviews. As mentioned above, nowcasting with Google search data also helps to identify changes in consumer purchasing behaviour. This is especially useful for businesses requiring sustaining efficient infrastructure or extensive resource planning.

Our results, as well as conclusions of other studies [8, 7, 3] show that modelled activity is explained also with the autoregressive component (i.e. level of the activity from previous period). Thus nowcasting seems to be more feasible in industries in which data on subjects’ behaviour is recorded and publicly available. If the data is collected in higher frequencies (e.g. weeks instead of months), nowcasting models may provide faster and more accurate results.

The research is burdened with following limitations. Data on popularity of Google searches are so called “broad matches” presenting the volume of all searches including the given keyword (here: automotive make). Thus they include also queries not related with purchasing new car, but referring to e.g.  spare parts or used cars.

Furthermore, in this paper we attempt to model the number of car registrations conducted by both consumers and businesses. Many models using Google search data as explanatory variable refer to private consumption [8]. Data on registrations on separate markets are available, however in shorter time queries.  In the future it will be possible to model private car registrations and hopefully achieving better results. On the other hand, the current research reflects the entire sale volume and thus is of greater practical use.

Among other potentially productive directions of further analysis we would suggest readdressing the question of factors influencing number of registrations for smaller car producers since they seem to differ from those influencing registrations of major players; and searching for additional macroeconomic explanatory variables that would be available easily, free of charge and (almost) in real time.

 

Literature

 

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  5. Dholakia U.M. (2001). A motivational process model of product involvement and consumer risk perception, European Journal of Marketing, vol. 35 iss: 11/12, pp.1340 – 1362.
  6. Doligalski T., Tomczyk E. (2015). Nowcasting New Car Registrations with Google Search Data and Car Manufacturers’ Website Traffic. Working paper.
  7. Li N., Peng G., Chen H., Bao. J. (2013). A Prediction Study on E-commerce Orders Based on Site Search Data. 6th International Conference on Information Management, Innovation Management and Industrial Engineering, 2, 314-318.
  8. Schmidt T., Vosen S. (2009). Forecasting Private Consumption: Survey-based Indicators vs. Google Trends. Ruhr Economic Papers, 155.
  9. Sun B., Li B., Li G., Zhang K. (2013). Automobile Demand Forecasting: An Integrated Model of PLS Regression and ANFIS. Advances in Information Sciences & Service Sciences, 5(8), 429-436.

 

 

[1] With the exception of Honda registrations data which are available up to September 2014 (45 observations).

[2] Polish Association of Automotive Industry, http://www.pzpm.org.pl/en, [2015.04.02].

[3] http://www.bankier.pl/gospodarka/wskazniki-makroekonomiczne/pmi-polska-pol, [2015.03.10].

Nowcasting New Car Registrations with Google Search Data and Car Manufacturers’ Website Traffic

Tymoteusz Doligalski, Emilia Tomczyk, Nowcasting New Car Registrations with Google Search Data and Car Manufacturers’ Website Traffic, paper accepted at the 6th EMAC Regional Conference, Vienna 2015.

 

Abstract: The purpose of this paper is an attempt to nowcast (here: to predict in a short time horizon) new car registrations in Poland based on data of Google search queries and website traffic of car manufacturers. The study covers 47 monthly observations for six automotive makes. The strongest explanatory power is exhibited by the autoregressive component (number of registrations lagged one month), followed by the number of search queries. The website traffic of car manufacturers significantly influences the number of registrations in two out of six cases.

Keywords: nowcasting, prediction, car, automotive, Internet, search, Google, website traffic, Poland, CEE


Introduction

Nowcasting is defined as “the prediction of the present, the very near future and the very recent past” (Bańbura, Giannone, Modugno, and Reichlin, 2013, p. 4). The reason for nowcasting is the delay in availability of data, which in modern economy can amount to weeks or even months. The other circumstances increasing the application value of nowcasting are macroeconomic turbulences, great uncertainty and unique shocks, as they cause past values to lose their prediction power (Schmidt & Vosen, 2009).  As Bańbura, et at. state nowcasting is based on ‘exploitation of information which is published early and possibly at higher frequencies than the target variable of interest in order to obtain an ‘early estimate’ before the official figure becomes available’ (2013, p. 4).

According to Choi and Varian (2011), nowadays there are several sources of data on real time economic activities which may help in predicting the present (as opposed to predicting the future).  Possibly the most often used is data from Google Trends presenting number and location of chosen search queries.  The data may be used to identify the current changes in unemployment, private consumption or – beyond the economic activities – development of infectious diseases (Choi & Varian, 2011).  The other sources of information are parcel shipment companies or credit card operators, as they possess precise real-time data on transactions in certain locations.

There exists another source of data which can be used in nowcasting. This is the data on website traffic. Usually these data are fragmentarized. The website owner possesses thorough knowledge on his or her traffic, but does not know the traffic of other websites. There exist however research entities that provide data on traffic of various websites in a certain category. Megapanel PBI/Gemius is such a research program. It monitors online behaviour of Polish internet users, thus providing monthly data on traffic on most popular websites in Poland. This kind of data meets the above mentioned requirements of nowcasting.

The purpose of this paper is an attempt to nowcast new car registrations in Poland based on the data of Google search queries and website traffic of car manufacturers. Usefulness of web search data in predicting behaviour of economic variables has already been noted in literature (Askitas & Zimmermann, 2009; Choi & Varian, 2011; Li, Peng, Hang, Jiaxing, 2013). A few publications on nowcasting concern the automobile markets (Choi & Varian, 2011; Sun, Li, Li, Zhang, 2013; Carrière-Swallow & Labbé, 2013). Their common approach is the use of search data as the independent variable. The data on car manufacturers’ website traffic – to our best knowledge – has not served as predictor of car sales or registrations yet.

Description of data

Our sample covers 47 monthly observations from January 2011 to November 2014. Monthly data on new registrations of passenger cars[1] are provided by the Polish Association of Automotive Industry (PAAI)[2] on the basis of the Central Register of Vehicles database administered by the Ministry of the Interior. A one-month lag is allowed by Polish law between the purchase of a private car and its registration. Current data on number of registrations becomes available on the PAAI webpage around the 5th day of the next month. First registrations of makes of passenger cars included in the empirical analysis (that is, Fiat, Opel, Peugeot, Renault, Skoda, and Toyota) are coded with variables starting with the letter R; for example, R_fiat stands for the number of Fiat cars first registered in a given month. Seasonal effects are expected: so-called summer inertia, that is, lower numbers of first registrations in the summer months (June, July and August), and higher end-of-year sales in the winter months (November, December and January).

Relative numbers of queries pertaining to car makes are coded by variables starting with the letter S and defined in a way proposed by Choi and Varian: “The query index is based on query share: the total query volume for the search term in question within a particular geographic region divided by the total number of queries in that region during the time period being examined. The maximum query share in the time period specified is normalized to be 100 and the query share at the initial date being examined is normalized to be zero.” (Choi & Varian, 2011, p. 3). Original search data is defined relative to Opel sales in the week of March 31 – April 6, 2013 (the maximum query share in the period analysed). For aggregated monthly series, data is rescaled relative to the maximum monthly query in the period analysed, that is, number of Opel queries in October 2014, equal to 396.43. For example, S_fiat stands for the percentage of queries on Fiat cars in a given month relative to the maximum level of 396.43.

Traffic variables, coded with variables starting with the letter T, reflect the number of unique visitors of car manufacturers’ websites in a given month. This type of data has not been previously used to explain first registrations or other sales numbers. The source of traffic time series is Megapanel PBI/Gemius which monitors the online behaviour based on a panel of Polish internet users. The data on website traffic is subject to estimates and cannot be interpreted in a straightforward way but it allows to compare various websites visited by Polish consumers. Data provider records missing values when number of visits is lower that a minimum defined level (in case of car manufacturers, 40,000 hits); for the purpose of this paper, two approaches to missing data were undertaken:

  • imputation of the “lower bound” value of 40,000 visits,
  • calculation of an average of two months preceding and two month following a missing value; in special case of Renault, missing value for November 2014 is calculated as an average of four preceding months.

Results of empirical analysis (see next section ) show that treatment of missing values does not influence outcomes in a significant way.

We limit our dataset to internet data (that is, search and traffic data) and lagged values of the dependent variable (car registrations), omitting car market data and macroeconomic variables. The rationale for this approach is that internet data are available almost in real time, and car registrations data become accessible speedily, on the 5th of the next month. On the other hand, macroeconomic data are published with at least two-month lags which limits their application for nowcasting.

 

Empirical results

Linear models with HAC standard errors (to account for serial correlation in the error term) have been estimated for six variables describing number of first registrations: Fiat, Opel, Peugeot, Renault, Skoda, and Toyota. Results are summarised in Table 1. The strongest explanatory power is exhibited by the autoregressive component (that is, number of registrations lagged one month); it is the only regressor that is statistically significant at the 0.05 significance level in all six models, and the size of the estimated coefficients vary from 0.314 for Toyota to 0.642 for Fiat.

As far as search data is concerned, in four of the models (for Fiat, Opel, Peugeot and Skoda) search variables lagged either one (in case of Peugeot) or two months exhibit positive and statistically significant influence on the number or registrations. This result is consistent with the one-month delay between car sale and its registration allowed by Polish law, taking into account that additional delay may be expected between internet search and actual signing of the contract.


Table 1. Summary of estimation results

Fiat Opel Peugeot Renault Skoda Toyota
AR(1) 0.642 0.396 0.382 0.399 0.485 0.314
St 64.987
St-1 -25.100
St-2 25.285 22.428 49.425
Tt-1 0.749 1.085
summer dummy -348.374 -354.485
winter dummy -117.006
R2 0.485 0.371 0.368 0.301 0.503 0.533
RESET p-value 0.189 0.527 0.068 0.503 0.677 0.170
normality p-value 0.000 0.166 0.043 0.780 0.927 0.924
maximum VIF 1.004 1.081 1.657 1.000 1.154 1.557

All variables are statistically significant at 0.05 level.

 

Traffic variables do not exhibit systematic and statistically significant influence on the number of car registrations for any lag considered in the models. In two cases (that is, Peugeot and Renault) traffic variable lagged one month is statistically significant; however, these two models exhibit the lowest coefficients of determination and their descriptive value is therefore limited.

The models provide only limited support for the hypothesis of seasonal behaviour of car registrations. Summer dummy variable estimated coefficient exhibits its expected negative sign (for summer consumer inertia) and is statistically different from zero in two cases only, for Skoda and Toyota. Winter dummy variable is only statistically significant (but with negative coefficient which contradicts the expectations of high end-of-year sales) in the Peugeot model.

As far as general statistical quality of the estimated models is concerned, they are correctly specified according to the RESET test. All but two (for Fiat and Peugeot) have normally distributed standard errors, and since sample size can be considered sufficient, lack of normality does not negatively influence estimation results. There is no multicollinearity in any of the models. Treatment of missing values in traffic data does not influence the general conclusion that traffic numbers do not have statistically significant impact on the dependent variable.

 

Discussion

There are two major factors influencing number of first registrations: autoregressive component and search data. The result is coherent with Choi and Varian’s conclusion, as “simple seasonal AR models that include relevant Google Trends variables tend to outperform models that exclude these predictors by 5% to 20%.” (Choi & Varian, 2011, p. 8).

What remains to be explained are the differences in lag lengths between internet search and car registration. The lag may be non-existent (for Renault, where search variables do not exhibit significant impact at all, and Toyota, where only current value does), equal to one month (for Peugeot) or to two months (for the three remaining car makes). Certain delay between search and registration is expected but should be similar for all car makes; to explain the differences, factors such as length of order fulfilment and sales policies of car manufactures should be taken into consideration.

Econometric analysis of car registrations data suggests that there subsets of car makes may be distinguished: Fiat and Opel (of which registration numbers seem to follow similar patterns based on search data lagged two months); Skoda and Toyota (which exhibit significantly lower registrations in the summer months); and Peugeot and Renault, where influence of lagged traffic data may be observed. Otherwise, car manufacturer website traffic appears to have limited predictive value. Interestingly, in these two cases the search factor is either not-existent (Renault) or negatively correlated with number of registrations (Peugeot). For a Polish consumer these two brands are more difficult to spell than other included in the research. As we included only properly spelled brands, the search data might not have fully reflected the number of queries. The result may suggest that website traffic may be considered as a predictor when search data is unavailable or clear attribution of search queries with the nowcasted activity is problematic.

This study is burdened with the following limitations. Its purpose was to nowcast new passenger car registrations, performed both by consumers and businesses. Often Google search data is used for prediction of private consumer activities. Due to limited availability of detailed data we attempted to nowcast all passenger car registrations. It might have resulted in lower prediction quality, but it is of greater practical use as it reflects the entire sale volume.

Macroeconomic variables (e.g. consumer satisfaction index, general business conditions index) and automobile market data were not included in the research. They are published with a delay, thus do not meet nowcasting requirements.  However,  lagged data of this kind could be included into more sophisticated models.

 

Acknowledgment

The research project would not have been possible without the support of Polskie Badania Internetu Sp. z o.o (PBI). The company provided us with the data on website traffic of car manufacturers in the period from January 2011 to November 2014. The research program Megapanel PBI/Gemius presents the behaviour of Polish Internet users based on a study Net Track Millward Brown SMG/KRC, conducted on a sample selected and weighted by PBC.

 

References

Askitas N., Zimmermann K.F. (2009). Google Econometrics and Unemployment Forecasting. Applied Economics Quarterly, 55 (2), 107-120.

Bańbura M., Giannone D., Modugno M., Reichlin L. (2013). Now-Casting and the Real-Time Data Flow, Working Papers, European Central Bank, no. 1564.

Carrière-Swallow, Y., Labbé, F. (2013). Nowcasting with Google Trends in an Emerging Market.  Journal of Forecasting, 32 (4), 289–298.

Choi H., Varian H. (2011).  Predicting the Present with Google Trends. Economic Record, 88: 2–9. doi: 10.1111/j.1475-4932.2012.00809.x

Li N., Peng G., Chen H., Bao. J. (2013). A Prediction Study on E-commerce Orders Based on Site Search Data. 6th International Conference on Information Management, Innovation Management and Industrial Engineering, 2, 314-318.

Schmidt T., Vosen S. (2009). Forecasting Private Consumption:  Survey-based Indicators vs. Google Trends. Ruhr Economic Papers, 155.

Sun B.; Li B.; Li G; Zhang K. (2013). Automobile Demand Forecasting: An Integrated Model of PLS Regression and ANFIS. Advances in Information Sciences & Service Sciences,  5(8), 429-436.

[1] For legal reasons, the category of personal cars includes also cars with cargo compartment (Polish: samochody z kratką) in the period January-July 2014.

[2] Polish Association of Automotive Industry, http://www.pzpm.org.pl/en, [2015.04.02].

Value Creation in E-Business and Financial Performance: Researching Polish Online Companies with Amit and Zott’s Model.

 

Abstract:

The paper presents the outcomes of the survey of the managers of Polish internet companies with the aim of verifying to what extent the e-business value creation model developed by Amit and Zott could be used to explain different profitability levels among firms. The study included 150 businesses with the largest representation of internet retailers and service providers. The major outcome of the study was developing logistic regression model that allowed to establish which variables were statistically significant predictors of operational return on sales ratio. It implied that out of four elements comprising Amit and Zott’s model only Complementarities were closely linked with improved financial performance. The two other elements – Customer Lock-in and Novelty – were not viable predictors while the forth component (Efficiency for Customer) was negatively related to the performance metric.

Keywords: business models, value creation, e-business, e-commerce, Internet, CEE, Poland
TrackNew Technologies and E-marketing

Introduction

The objective of the study was to establish to what extent the value creation model originally proposed by R. Amit and C. Zott could be linked to financial performance of internet companies (Amit, Zott 2001). The presence of various elements of Amit and Zott’s concept was measured with Likert-type 7-point scale comprising a list of statements pertaining to the four value drivers of the model incuding: novelty, lock-in, complementarities and efficiency. According to Amit and Zott these four categories represent groups of factors that can enhance the total value created by e-business. In particular they refer the following specific kinds of business solutions:

  • Novelty is defined by the level of uniqueness of goods or services offered by a firm with regard to how the customers’ needs are satisfied and what those needs are; thus novelty can be achieved by devising new ways of satisfying existing needs or finding and addressing entirely new needs.
  • Lock-in concerns the various kinds of costs that customers need to bear if they were to replace the firm’s offering with one of competitors; it is assumed that the higher are such switching costs the customer lock-in is tighter.
  • Complementarities are determined by the extent of completeness of customer needs fulfillment by the firm and its business partners.
  • Efficiency pertains to the benefits that customers can reap from savings in time, effort and financial costs that are brought about by the firm’s offer.

There were so far only a few attempts to test the Amit and Zott’s model empirically with quantitative approach, though the outcomes were rather inconclusive and context sensitive. Christensen and Methlie (2003) having researched implementations of e-business solutions in traditional companies noted no significant improvement in financial performance, which was attributed to the early stage of the implementation and the lack of time for the positive effects to manifest. According to the later research of Amit and Zott (2007) Novelty positively influenced financial performance while Efficiency did so only during a period of resource scarcity. In another paper, the same authors studied how the fit between Novelty- and Efficiency-centred business models with different product market strategies can enhance firm performance (Amit, Zott 2007).  Using a population of general companies (including both internet enabled and traditional businesses), Malone et al. found that some models perform financially better than others (2005).

In light of the above, if the concept of the value creation model in e-business were to have considerable practical merit it could be proposed that “the increased involvement of a company in any of the four value drivers results in its improved financial performance”. To test this hypothesis it was necessary to develop adequate measures of elements of value creation model and competitive performance, which will be discussed in the subsequent section.

Research methodology

The data on characteristics of business models employed by internet companies were collected through CATI survey in August 2012. The interviewed respondents were the managers of Polish firms which were utilizing Internet as a distribution channel for retailing and services with the exception of major Internet portals, advertising and web design agencies, media brokers, telecommunications companies, banks, insurers and operators of large popular news and lifestyle portals. The exclusions were made on the assumption that those companies employed so unique and diverse value creation mechanisms that making meaningful comparisons among them might not have been possible based on survey data. The net sample of 150 units was drawn at random from a database of major internet companies compiled by the authors of the study from several available rankings and listings of various types of internet businesses operating in Poland. The final set of studied companies was made up in 57% of retailers and 43% of service providers, with 63% of them having sales of tangible products as the main revenue stream, 17.3% generating most incomes from sales of virtual products and 16% relying above all on proceeds from advertising.  Around 25% of businesses generated more than half of their sales outside of the internet.

Since they are multidimensional and difficult for direct measuring, the four sources of value in the model could be thought of as constructs or latent variables and evaluated accordingly, with appropriate multi-item Likert scales. Building on existing works in management science, both conceptual and empirical, and drawing from their own experience the authors came up with 24 statements that were listed in the Table  1.

Table 1: The list of statements of the 7-point Likert scale used for measuring the four sources of value creation

Value creation source: Novelty

Only a few firms offer solutions similar to ours
In markets we operate in we are recognized as pioneers
By offering our solutions we shape needs or behaviors of customers
In large part customers choose our solutions for the sake of their innovativeness
Our solutions have been imitated by competitors
Our key business partners do not cooperate with our competitors

Value creation source: Lock-in

Resigning from our offer and changing to our competitors’ brings about high switching costs to our customers, such as extra time, effort or financial expenses
It happens that customers are not fully satisfied with our offer but they stay with us due to switching costs
We provide our customers with personalized solutions
Most of our customers make use of our personalized solutions
We consider it important to maintain for as long as possible even these customers who are less profitable
Regular customers are rewarded through loyalty programs and other measures
We have implemented specific mechanisms for maintaining customers
Our key partners have strong impact on uniqueness of our offer for customers
An important criterion of selecting our business partners is enhancing our capacity for maintaining customers

Value creation source: Complementarities

Our offer is among the most comprehensive in the industry
Our firm has been systematically widening the extent of customer needs that we are able to satisfy
Our key partners have strong influence on how comprehensive our offer is
Our customers are choosing our offer for the attractiveness of the available complementary products
Customers try to use our solutions together to benefit from synergy effects
Value creation source: Efficiency
Our solutions allow customers to take advantage of savings in time and effort
Because of our solutions customers can solve their problems more easily
Internet grants customers more efficient access and use of our products than traditional channels
Our partners have significant contribution to savings in time and effort afforded customers by our offer

The original set of 24 individual variables was reduced with principal component analysis to a more manageable set of 8 composite variables representing various dimensions of the four theoretical constructs. Factor analysis also solved the problem of multicollinearity  among variables which tends to inflate standard errors in modeling. The 8 components were then used as independent variables in regression modeling. Using factor loadings on individual variables for interpretation, the following components were isolated: (1) novelty: customer perspective, (2) novelty: competitors and business partners perspective, (3) lock-in: personalization, (4) lock-in: exit barriers, (5) lock-in: loyalty programs, (6) lock-in: cooperation with business partner, (7) complementarities, (8) efficiency.

As can be seen, the constructs Complementarities and Efficiency were represented by a single component variable each, while the construct Novelty had two independent dimensions. The most complex in structure was Lock-in which was made up of four apparently independent aspects. The components extracted with exploratory factor analysis allowed to retain about 60% of the original variance.

As an indicator of financial performance served the operational return on sales ratio obtained by dividing operational income from transactions on the internet by sales achieved on the internet. To increase item reply rate from survey participants, the variable did not have open-ended format but was categorized with four predetermined answer classes. To be usable in binary logistic regression the variable was dichotomized with the value of 0 denoting firms with operational returns on sales no greater than 20% (67.7% of the sample) and the value of 1 indicating the companies that had the ratio of more than 20% (33.3% of the sample). The split point of 20% was chosen to yield two groups with possibly comparable sizes – it transpired that any other merger of categories in original variable would result in more disproportionate subgroups.

To obtain the model with the best fit to the empirical data and possibly the best prognostic qualities it was decided to use binary logistic regression with backward elimination of predictors to exercise more control over the suppressor effect. The original set of independent variables included 8 components representing various aspects of value creation model supplemented with several terms pertaining to different characteristics of the firms encompassing number of employees, percentage of sales from transactions on the internet, percentage of loyal customers, year of funding and year of starting operations on the internet.

Research results

The regression procedure finished at the 9th step, after eliminating 8 statistically insignificant candidates for predictors, resulting in the model with the following goodness-of-fit and pseudo R-Square measures (Table 2.)

Table 2: Goodness-of-fit measures for the outcome logistic regression model

Step Chi-square Sig. -2 log-likelihood Nagelkerke R Square
9 45.735 <0.001 145.219 .365

The overall model provides statistically significant improvement in predicting group membership of the companies (i.e. low versus high sales profitability) with error reduction of around 36.5% in comparison to the baseline solution containing only fixed term and no independent variables.  The classification table contains a comparison of groupings of firms using regression equation and the baseline setting when all observations were assigned to the more numerous category of the dependent variable.

Table 3: Outcomes of classification of the participant firms into groups with low and
high profitability using regression model and baseline knowledge of the dependent variable distribution

Observed

Expected

Operational
ROS > 20%

Percentage Correct

0

1

Baseline classification

Operational
ROS> 20%
0

100

0

100.0

1

50

0

0.0

Overall Percentage

66.7

Classification with the final model

Operational
ROS> 20%
0

86

14

86.0

1

23

27

54.0

Overall Percentage

75.3

The classification table indicates that predicting group membership of companies with the logistic regression model results in 75.3% of correctly classified cases compared to 66.7% of rightly predicted memberships when all firms were considered to be low profitability (0). Thus, the model offers a considerable improvement in predicting capacity over baseline scenario and could be potentially useful for practical applications. The variables included in the model and their coefficients are shown in the Table 4.

B

Standard Error

Wald

df

Sig.

Exp(B)

Complementarities

.603

0,284

4.521

1

.033

1.828

Percentage of total revenue from internet transaction

.600

.263

5.222

1

.022

1.823

Number of employees

.940

.188

24.908

1

.000

2.559

Efficiency

-.636

.299

4.540

1

.033

.529

Constant

-5.229

1.212

18.611

1

.000

.005

The final regression equation contains 4 statistically significant predictor variables. Interestingly, out of 8 different aspects of the value creation model only 2 display sufficiently strong association with financial performance to be used along other variables in the regression function. Quite unexpectedly, the Efficiency variable remains in negative relationship with financial performance with higher scores on Efficiency diminishing odds of a company falling into higher profitability category by the factor of 0.529. Other variables with higher values tend to increase the odds of a firm being classified as highly profitable. The strongest factor here is the number of employees which with each progressing category on the scale increases the odds by more than twofold (2.559). Involvement in Complementarities and Percentage of Total Revenues from Internet Transactions seem to be equally important stimulants of profitability with similar impact on odds ratio of 1.8 following one unit increase on their respective scales with other factors remaining equal.

The above findings seem to give only limited support to the hypothesis of the study, as only one of four drivers comprising the Amit and Zott’s value creation model in e-business (Complementarities) seems to be positively associated with financial performance of surveyed companies and another driver (Efficiency) exhibits what appears to be an inverse relationship.

Discussion

The lack of the two variables (Novelty, Lock-in) representing e-business value creation model in the regression may suggest that this concept could only have limited potential as a conceptual lens through which to explain success and failure of business models. One relevant caveat though could be the idiosyncratic features of the studied internet companies, which even though were representative for Poland, included mostly e-retailers and providers of rather uncomplicated services. It may well turn out that explanatory power of the value creation framework could be more potent if applied to analyze more complex business models, like those represented by the companies excluded from the sample.

The fact that Efficiency remained in negative relationships with operational ROS doesn’t necessarily have to imply causal association. And even if cause-and-effect link did exist it is possible to propose at least two plausible explanations.

The companies which offerings allowed the consumers to solve their problems quickly, possibly also cheaply and reliably, may have had taller operational cost structures (for instance had to employ more staff or use more expensive network infrastructure) and consequently had to accept lower sales profitability. Such situation didn’t have to entail weaker financial results and worse market position as only one metric was used to evaluate financial consequences of involvement in e-business, which is an apparent limitation of the study. It can be easily imagined that lower ROS ratio could be compensated by higher volume of sales to achieve higher profitability of the company.

Another explanation could lie in very intense competitive rivalry within the industries in which some companies had to operate. In such conditions advanced and comprehensive solutions increasing efficiency for customers could be forced upon a firm by strong pressure from rivals and as such could be a necessary measure to remain in the market and not a source of competitive advantage translating into increased mark-ups and profits. Unfortunately the data collected in the survey was not sufficient to assess the intensity of competitive struggle in each firm’s market environment.

The substantial positive effect of Complementarities on financial performance could be linked to economies of scope which result in lower cost structure and higher unit profitability due to offering wide range of products addressing a group of related needs. The other partial explanation could be offered by considerable costs of customer acquisition in internet channels.

Among all significant predictors the strongest one was the company size as measured by the number of employees with larger firms tending to display higher levels of profitability. It may imply that certain economy of scale mechanisms could be responsible but the fact that the studied businesses operated in various markets, compounded by the lack of knowledge about their pertinent characteristics, makes such a conclusion debatable.

References

  1. Amit, R., Zott, Ch. (2001). Value Creation in E-Business, Strategic Management Journal, 22 ,493-520.
  2. Amit, R., Zott, Ch. (2007). Business Model Design and the Performance of Entrepreneurial Firms, Organization Science, 2 (18), 181–199.
  3. Amit, R., Zott, Ch. (2008). The Fit between Product Market Strategy and Business Model: Implications for Firm Performance, Strategic Management Journal, 29, 1–26.
  4. Amit, R., Zott, Ch. (2012) Creating Value through Business Model Innovation, MIT Sloan Management Review, 53, 41-49.
  5. Christensen, G.E., Methlie, L.B. (2003). Value Creation in eBusiness: Exploring the Impacts of Internet-Enabled Business Conduct, 16th Bled eCommerce Conference eTransformation, Bled, Slovenia, June 9-11, 2003, 27-43.
  6. Malone, T.W. Weill, P. Lai, R.K. D’Urso, V.T. Herman, G. Apel, Th.G. Woerner, S.L. (2006), Do Some Business Models Perform Better than Others?, MIT Sloan Working Paper 4615-06.

Internet-Based Customer Portfolio Building

 

Paper accepted for 10th International Conference Marketing Trends (Paris, January 20th-20nd 2011).

Download the full pdf version of Internet-Based Customer Portfolio Building.


Abstract

This paper presents a conceptual model of Internet-based customer portfolio building. The proposed model refers to the concept of value exchange between a company and a customer, as well as to the concept of delivering values to customers. The model is made up of five stages: defining, creating, communicating and delivering values, as well as generating values for a company.

Key Words

Customer portfolio, customer relationship management, CRM, e-CRM, customer value management, customer relationships, online, Internet, e-commerce, e-business, value proposition, customer value, customer loyalty, customer trust, value exchange.

Introduction and Objectives

Contemporary companies compete in many markets. The most important of these is the market of customers. Customers provide companies with many values, among them revenues. Revenues attract participants of other markets such as shareholders, suppliers or potential employees. The area which contributes to value creation in companies migrates from fixed assets and production, to customer relationships and other market-based assets. Therefore, customers in most markets become an asset of great value to companies. Successful development of this asset through long-term and profitable customer relationships contributes to the growth of companies’ value and profits.

Changes in the theory and practice of management are accompanied by social and technological transformations. The Internet and IT have added a new dimension to customer relationships. Contrary to traditional media, the Internet has become an environment that facilitates multi-sided communication, searching for information, conducting transactions and even co-creating values. Hence, companies are able to develop to greater extent Internet-based relationships with customers, as it allows companies to grasp such benefits as cost reduction, increasing values for customers, or the acquisition of new customers.

The combination of these two areas, customer relationships and the use of the Internet, creates a great challenge for marketers and entrepreneurs. The purpose is to create long-term and profitable customer relationships, often in markets hardly accessible through traditional channels. This challenge requires a new orientation adapted to the online environment. The experiences of companies from the period of “the Internet bubble” prove also that the question of how to develop customer relationships on the Internet should still be raised.

This paper is an attempt to answer this question by proposing a model of Internet-based customer portfolio building. The proposed model refers to many marketing concepts such as value exchange perspective, market-based assets and competences, trust building, customer portfolio segmentation and customer switching costs.

Literature review

The customer relationship is presented as value exchange both in classical (Bagozzi 1975, McCarthy and Brogowicz 1981), and more recent marketing approaches (Miller and Lewis 1991, Jackson 2007, Cheng 2009). In the process of value exchange companies provide customers with values, receiving in return other values from customers. The concept of “values for customers” appears in many marketing and managerial publications (Drucker 1954, Porter 1980, Payne and Holt 2001).

The concept of values for customers has been a premise for creating models of delivering values to customers (Szymura-Tyc 2005; Kordupleski and Simpson 2003; Baker 2003). The concept of creating values for customers has also been raised in Peppers’ and Roger’s publication, in which they stress the need for individualized and cooperation-based dimensions in the relationship (1997). Cooperation with customers within the process of co-creating values may be the basis for a competitive advantage (Prahalad, Ramaswamy 2004, Tapscott, Williams 2006)

Relationships with customers should be viewed from the perspective of creating values for shareholders. This position is shared by Doyle, who in Value-based marketing subordinates marketing efficiency to maximizing return for shareholders (2004). The approach to customer relationships as to managing company’s assets has been described by Blattberg, Getz and Thomas in Customer Equity: Building and Managing Relationships as Valuable Assets (2001). Gupta and Lehman in Managing Customers as Investment proved the relationship between customer value and corporate valuation (2005). This position finds its reflection in the fifth stage of the proposed model, which is generating values for a company.

The concept of customer portfolio relatively rarely appears in marketing literature. In the article Customer Portfolio Management: Toward a Dynamic Theory of Exchange Relationships Selnes and Johnson exhibit dynamics of relationships between various segments of customers and their influence on marketing efficiency (2004). Portfolio dynamics have also been investigated by Homburg et al (2009).

In the area of developing customer relationship in the online environment there is still a lack of publications explaining this process. Amit and Zott show the source of value creation in e-business (2001). Verona and Prandelli investigate affiliation and lock-in as the source of competitive advantage of Internet companies (2002).

The concepts of value exchange and customer portfolio

In the process of value exchange companies provide a customer with values (a set of values, composition of values), receiving in return other values from a customer. The values for customers can be differently categorized. The simplest categorization includes functional and symbolic values. Xu and Cai built an e-commerce value model based on three components: the outcome value, the process value, and the shopping enjoyment (2004). Cheng et al. while researching perceived customer values in e-commerce used a modified categorization of Sheth et al. (1991), which included functional, social, emotional and epistemic values (2008).

The author proposes the following categories of values for customers delivered in the online environment: value of purpose, convenience, individualization, communications, community and safety (Figure 1.). Value of purpose results from the company’s competence to satisfy customer needs according to his or her expectations. Value of convenience refers to the customer’s perceived easiness to initiate and continue a relationship with a company. Value of individualization describes the customer’s ability to customize the received values to his or her needs. Value of communications refers to the company’s ability to provide a customer with information, which he or she currently expects, as well as the ability to listen to a customer. Value of community is the benefit resulting from communicating or interacting in a different way with other company’s customers (e.g. users of the same product). Value of safety refers to the low level of risk related to a relationship with a company.

Viewing the value exchange from the perspective of values delivered to a company by customers, the author distinguished the values in the following dimensions: revenues; data, information and knowledge; values for another group of customers (on a multi-sided market); contribution to co-creating a value process; word of mouth; company’s image; economies of scale; network effects.

Fig. 1. Conceptual model of a value exchange between a customer and a company on the
Internet

Fig. 1. Conceptual model of a value exchange between a customer and a company on the Internet

value-exchange

Among the most common types of value exchange the author distinguishes: initial, non-monetary and monetary. The essence of the initial value exchange is providing potential and current customers with information on a company and its products, trust building and enabling contact with a company. This pattern of value exchange is followed by many companies that use their websites mainly for the purpose of customer acquisition, and later develop customer relationships through traditional channels. Within the non-monetary value exchange the company offers customers some free values (e.g. articles, podcasts, videos) or services (e-mail, search engine, content publishing). The company displays advertisements to its users, and in consequence it is able to create value for another group of customers, who are advertisers and who provide the company with revenues. The monetary exchange consists of delivering values to customers, which they pay for. Companies that participate in the monetary value exchange are not only online stores, but also companies offering paid content (e.g. newspaper archives, music files) or rendering paid services (e.g. banks, financial brokers).

A customer can be defined as a company’s stakeholder who participates in an exchange of values with a company in order to have his or her needs satisfied. Consequently, a customer may not necessarily provide a company with revenues, which is exhibited by the non-monetary value exchange.

Customer portfolio is the sum total of any given company’s relationships with its customers. Customer portfolio can be described with various variables. Probably the most important is its value, which is the discounted value of benefits, which the company may derive from it, reduced by costs related to it. The other important variable is the structure of the portfolio. The structure of the portfolio can be viewed from such perspectives as values expected from, or values delivered to, a company.

From managerial perspectives information concerning customers’ responses to company’s activities is important, such as customer satisfaction, loyalty, trust or involvement. Information on the course of relationships with customers is also important (durability, frequency of purchasing or consuming values from a company). Another important category of information is the total risks resulting from customer relationships and incurred by a company (delays in payments, higher costs, revealing confidential information).

Customer portfolio building is the total of a company’s actions aimed at increasing values derived from the portfolio and resulting in values for stakeholders. Customer portfolio building should usually guarantee diversified streams of values in the long term, as various values are necessary for different operations of a company. Revenues have an effect on profitability and liquidity and as a consequence enable companies to deliver values for other stakeholders. Customers also provide a company with data, information and knowledge, which make it possible for the company to understand better their needs and thus develop innovations aimed at increasing values for customers or decreasing company’s costs. Customers recommendations passed in informal communication (word of mouth) are also important, as well as the positive image values resulting from a better perception of a company due to providing service to a prominent customer.

 

 

The model of Internet-based customer portfolio building

 

The first stage in the process of customer portfolio building is defining values, when the value proposition and target group are selected (Figure 2.). The essence of the next stage, which is creating value, is to provide the company with assets, competences and other managerial solutions necessary for the offering the chosen value proposition. Communicating values is the third stage in the process of Internet-based customer portfolio building. It consists of attracting customers to the company and trust building. Delivering values is a continuous stage including customer segmentation, value exchange, involvement and loyalty building. Customer segmentation aims at distinguishing groups of customers who share common or similar characteristics and who react to the company’s actions much the same way. The value exchange is a transactional part of the relationship during which customers receive values from the company and in return deliver other values to it. Involvement building consists of increasing the scope of values which are the subject of exchange. Loyalty building is understood as increasing customer willingness to continue the relationship with the company. The final stage in the proposed model is generating value for the company, which underlines the need to create values for shareholders based on marketing activities.

Fig. 2. Conceptual model of Internet-based customer portfolio building

 

internet-customer-portfolio

Defining values

Defining values is the first stage in the process of customer portfolio building, when decisions regarding the value proposition and the target group selection are taken. The purpose of defining value is to achieve a sustainable distinctive advantage, which is described by Doyle and Stern as a “perceived difference that leads customers in the target segment to prefer one company’s offer to those of others” (2006).

In order to select a target group selection, market segmentation should first be performed. Remaining within the concept framework of the value exchange, dimensions of segmentation can be described as values expected by customers, values delivered by customers to a company and the use of the Internet. The first dimension is closely related to the customer’s needs and expectations, the second to their potential for the company. Many studies show that the use of the Internet and adoption of Internet-related services such as e-commerce or social networking is diverse within the population of Internet users (Monroe, Sinclair, Wachinger 2009, Forrester Research 2010). Therefore, the need to take into consideration this dimension also arises.

In companies that develop customer relationships through traditional channels, adding the Internet as another marketing channel usually does not exert a breakthrough influence on the target group selection. The target group is then broadened by new customers, who, due to various constraints, have not taken advantage of company’s offer through traditional channels. These new customers might not have chosen the company’s offer before due to unfavourable location, or lack of time or mobility.

Companies usually develop relationships with customers who purchase products or services and thus provide companies with revenues and other values. This is the basic model of the value exchange between companies and their customers. The target group selection becomes more complex when a company operates on a multi-sided market, and in the process of the value exchange needs two or more distinct groups of customers (Evans 2003). There are many examples of companies acting on multi-sided markets. Internet portals deliver value to both users and advertisers. Online auctions develop a relationship with both sellers and buyers. Producers of operating systems need hardware producers, software developers and final users.

In the traditional economy a common rule of the value proposition formulation is combining a price level with values for customers. According to this rule, companies offering inferior values charge customers lower prices than companies offering superior values. Hence, several strategies concerning the value proposition can be distinguished (e.g. inferior value – low prices, superior value – high prices). The application possibilities of this rule on the Internet are limited. The rule explains well the strategies of companies taking part in a monetary value exchange, such as online stores. Among them there are companies selling a product with inferior customer service and charging a low price, as well as other companies that enrich the same product with greater customer service and expect higher prices for it.

Referring values to price level may not always be used on the Internet for several reasons. In the online environment many companies offer superior customer values for free. Examples of these companies include newspapers which publish their content free of charge and companies which offer communications services on the Internet, such as e-mail or instant messaging providers. Moreover, according to Kim, on the Internet the strategy of offering superior values and charging high prices is rarely adopted (2004). In the traditional economy this strategy is most often used when marketing high quality, well-branded products to affluent customers.

The value proposition on the Internet is influenced by: product virtualization including partial or entire digitalization and product enrichment in information (Doligalski 2006); network effect, which refers to an increase in values for a customer (utilities) when the number of other customers (users) of that product increases. (Tellis, Yin, Niraj 2009); co-creating values with customers (Prahalad, Ramaswamy 2004) and experience-based consumption (Jiang, Benbasat 2004-5). The author distinguishes strategies of the value proposition on the Internet into the following five: strategy of efficiency, free values, complete customer solutions, unique values and value co-creation. It is worth mentioning that these strategies have been formulated according to different criteria and may be merged (Doligalski 2010).

The efficiency strategy consists of offering values to customers, which are to lower their transactional and other costs, and in this way allow savings of time and money. The examples of companies adopting this strategy are online auctions. Due to supply aggregation they offer a wide range of products, which leads to lowering customer transactional costs within offer search and analyses. Many Internet companies apply the efficiency strategy while offering values related to communications. Solutions such as e-mail services, instant communicators and social networking websites also reduce the transactional costs of a customer.

The free value strategy is based on offering values to customers for which they are not charged. This strategy has been popular since the early years of commercial use of the Internet. Consequently many companies (e.g. newspapers), which usually charge customers in traditional channels, offer the same or similar values for free in the online environment, which in turn leads to problems with generating income. The strategy of free values can be a part of a broader business strategy assuming revenue generation. This can be performed twofold: revenues can be generated by another group of customers on a multi-sided market or the company can charge customers for premium values (the so called freemium strategy).

The strategy of complete customer solutions relies on offering a broad scope of values from certain categories. Internet technologies enable the presentation of a high number of products in online stores, which results from low technological constraints. Therefore, online stores often shape their offer according to the long tail rule, which assumes offering both best-sellers, as well as niche products. Moreover, many companies offer values based on economies of scope. This concept consists of offering products from different categories. An often-quoted example of a complete customer solution is Amazon.com. The company offers a wide range of products (the long tail) including niche products, and at the same time offers products from other categories such as household electronics (the economies of scope).

The next strategy formulated is the strategy of unique values. A company follows this strategy if it offers scarce values on the market. This situation is very attractive, as it allows companies to charge high prices and therefore take advantage of a high margin. The greatest disadvantages of this strategy are difficulties in creating scarce values and then sustaining the scarcity in long term. The adoption of the unique values strategy may result from innovations, privileged access to resources or operating in a niche.

The strategy of value co-creation assumes that customers actively participate in shaping the value proposition, which will be delivered to themselves or to other customers. According to Prahalad and Ramaswamy, this is the value co-creation with customers that is the essence of competition in the modern economy (2004). The scope of the strategy is broad. It includes a situation, in which a customer co-creates the composition of values with a company for him or herself. A customer may also create values orientated not at him or herself, but at other customers. In the first case, customer’s activity can be described as mass customization. According to Kleeman and Voss mass customization refers to “isolated activity of individual customers (..), not to the collective activity of many individuals” (2008). Within mass customization, the value exchange can be described as one-to-one and the participation of other customers is not required. The value co-creation orientated at other customers assumes that a customer actively participates in activities aimed at creating values for other customers. The range of these activities is also wide. They include creating and publishing content on the Internet, interaction and communication with other customers on social networking websites or software development in the Open Source movement.

Creating Values

The essence of creating value is the next stage in providing a company with assets, competences and other managerial solutions necessary for the offering the chosen value proposition.

Firm-specific assets are always closely related to its field of activity. Therefore, it is difficult to list the most common assets. However, one can try to list the Internet-specific assets, which are helpful or necessary in the customer portfolio building process:

  • brand;
  • domain;
  • reputation;
  • opinions of a company or its products published on blogs, chats, forums;
  • hyperlinks leading to a company’s website;
  • informational resources created by a company or its customers;
  • technological infrastructure;
  • relationships with supplier;
  • partnerships with other websites, portals etc.

Internet-specific assets may overlap or include some sub-assets (e.g. brand, reputation, opinions on a company). Internet-specific assets can be perceived from different perspectives such as brand equity, search engine positioning or social media marketing. From a brand equity perspective, there are assets and liabilities that add to or subtract from the value provided by a product or service (Aaker 1991). In search engine positioning, which is aimed at getting the highest position in search engines listings, other assets become crucial. They are links leading to a website, valuable domain, information resources on a website and the proper construction of a website. From the view of social media, marketing essential assets include opinion leaders who promote the company and people who join the company’s profile on a social networking website.

It is difficult to list the most common competences needed for Internet-based customer portfolio building as they are closely related to a company’s core activities. They can be categorized according to the stage of the process. Hence, the competences can be seen from the perspectives of:

  • defining values – understanding customer needs and choosing the appropriate value proposition;
  • creating values – acquisition of assets and competences required to offer the value proposition;
  • communicating values – reaching potential customers with the proper message resulting in their visit to a company’s website, as well as trust building;
  • value delivery – customer service aimed at customer satisfaction, involvement and loyalty;
  • value generation for the company – ability to derive financial benefits from customer relationships.

A similar approach is shared by Zhu, who distinguished four dimensions of e-commerce capabilities (competences), which are: information, transaction, customization and back-end integration (2004). Zhu found that in companies conducting online sales, the e-commerce competences, together with a complimentary factor which is IT intensity, positively mediate firm performance measures such as sales generation, cost reduction, asset return and inventory turnover.

Competences can be perceived also from the perspective of organizational structure. On an abstract level the competences of the Internet pure players, companies which operate only in the online environment, can be described as abilities to deliver values to customers and to satisfy their needs online. In the case of brick-and-click companies, which are multichannel players active both on the Internet and in traditional channels, an essential competence becomes the ability to manage the customer relationships through different channels. According to Lindstroem, merging different channels in the process of customer service has become a major challenge for contemporary enterprises (Lindstroem 2001).

The major assets of Amazon.com include brand, and related to it domain, technology allowing mass customization and seamless order placement, hyperlinks pointing at the company’s websites, customers who trust the company, recommend it and write reviews and the base of tens of millions of reviews written by more than five million customers (Ante 2009). Thanks to this asset, Amazon.com is not only a place where products are sold, but also where decisions on products are taken. Core competences of the company include abilities to provide customers with detailed product information, as well as the proposition of similar products to purchase, abilities to enable a seamless placement of orders, and to deliver products to customers, which requires management of sophisticated logistical processes. Thanks to these assets and competences Amazon.com was able to widen the range of products sold from books to various categories and therefore take advantage of economies of scope. Competences even on a much lower level than those of Amazon.com are not easy to build nor to acquire. This is well exhibited by the sector of Polish online groceries, some of which successfully compete on the Internet against traditional retail chains, mainly of French or German origin. The latter take advantage of their assets, such as a well-recognized and trusted brand, developed logistics infrastructure and a wide range of products. What traditional retail chains usually miss are the competences in the field of online customer service and delivery of purchased products to the customer’s location within the chosen time frame. This example shows that competences of pure players and traditional companies may be very distinct from each other.

Communicating Values

Communicating values is the third stage of the process of Internet-based customer portfolio building. It consists of attracting customers to a company and trust building. The process of attracting customers to the company is usually based on informing them of the value proposition and convincing them to visit the company’s website, or contact the company in a different way.

Within communicating values strategies of push and pull can be distinguished. The push strategy includes direct activities of a company, which are to influence potential customers to purchase a product or change the perception of a company. In the pull strategy it is the customers who initiate actions aimed at developing the relationship with a company (Kumar, Shah 2004). There is a common belief that the Internet is a pull medium. The pattern of consumer behaviour, in which customers initiate relationships by searching for information and placing enquires etc. takes place on the Internet, however this is not the only one. While increasing the Internet’s potential as an advertising medium, resulting from the growing number of its users, development of new promotional forms, targeting possibilities and more in-depth research, the Internet offers advertisers the possibility of reaching a wide audience with their messages in the push concept.

Promotion on the Internet also allows various types of message targeting to specific groups of customers as well as minimizing contact with Internet users who do not belong to the target group. Targeting may take place by considering placement of advertisements as well as based on users’ profiles or consumer behaviour, etc.

Informal communication (word of mouth, viral marketing) plays an important role in attracting customers. This phenomenon is the exchange of information concerning companies or their products, conducted by people not related to these companies. The Internet facilitates the informal communication by enabling the finding, publishing and exchanging of information. Information itself in the online environment may be transmitted through different channels such as: blogs and microblogs, forums, social networking websites, e-mail services, online communicators, video-sharing websites etc. Informal communication is also the essence of the current trend: the popularity of social networking websites.

When developing relationships with customers in the online environment, the significance of trust increases. The high level of risk perceived by customers is typical not only for beginners, but also for advanced Internet users (Forsythe 2006; Schlosser, White, Lloyd 2006). Negligence in trust building may result in a lack of it, which is often the main reason why customers tend to withhold from placing orders online, the use of e-services or submitting vulnerable information (Wang, Beaty, Foxx 2004). Erkki Liikanen, a member of the European Commission responsible for the Information Society, explained the role of trust in e-commerce without ambiguity saying “No trust, no transaction” (Kossecki, Świerczyńska-Kaczor 2004). Obłój and Capron showed that, as far as Internet auction are concerned “the ability of a reputable seller to command a price premium increases with the size of the reputation gap between the focal seller and its matched competitor” (Obłój, Capron 2002). Hence, the reputation, which is closely related to the concept of trust, allows selling at higher prices. Dellacoras presents the conclusions of much research, which shows that when it comes to online auctions the seller’s reputation positively mediates the price and the possibility of purchase (Dellacoras 2002).

Delivering values

Due to the wide scope of values that are the subject of exchange on the Internet, relationships between a company and its customers may differ from each other. Hence, the need for customer portfolio segmentation (customer profiling) arises, which allows companies to distinguish groups of customers who share similar or common characteristics and who react to company’s actions similarly.

There is a principal difference between market segmentation conducted while defining values and profiling customers. Market segmentation is aimed at the identification of groups of potential customers who the value proposition will be addressed to. Customer portfolio profiling means operational attributing current customers to segments in order to achieve better satisfaction of their needs and higher concentration on the most valuable customers. Customer portfolio segmentation has been described profoundly by many scholars (Storbacka 1997; Zeithaml, Rust, Lemon 2001; Reinartz, Kumar 2002). Customer portfolio segmentation can be based on such dimensions as values generated by customers for a company, and values expected from a company.

Value exchange is the transactional part of the relationship, during which customers receive values which are to satisfy their needs and provide companies with other values. The value exchange may take place not only on a company’s website, but also on a social networking website, an online auction, through instant communicators or e-mail.

Involvement building is understood here as an increasing in the scope of values which are the subject of exchange. Involvement building can be conducted twofold: the types of values exchanged remain the same, however the intensity of the exchange grows and the scope of values is broadened. A non-trivial task within involvement building in an online store is convincing its visitors, of whom usually only a small fraction are buyers, to make purchases and – of smaller importance – to publish their product reviews.

The last activity within value delivery is loyalty building. Customer loyalty on the Internet is usually not mediated by loyalty programs, but by the proper satisfying of customer needs. This is confirmed by conclusions in the publication 2006 Walker Loyalty Report for Online Retail (Walker Information 2006). A few loyalty leaders among e-commerce companies from various categories were identified, which were: Amazon.com, eBay, iTunes, L.L.Bean, Lands’ End, QVC, Victoria’s Secret and Walgreens. These companies outperformed the industry average in all areas including: look and feel, being trusted and safe, ease of use, personalization, uniqueness of items, display and description of items, response time and speed of site, recommendation and reviews. The concept of customer switching costs is useful in understanding customer loyalty on the Internet. These are the real or perceived costs that have to be incurred by the customer if he or she wants to change provider and do not have to be incurred if the customer stays with the provider. These costs usually arise with the customer’s growing involvement in the process of value exchange. Consequently, of the growth of customer involvement, there is higher loyalty (Chen, Hitt 2002).

Once the value exchange takes place and companies perform their cross-selling and loyalty building activities, customers’ needs and expectations may change, hence there is a need for another segmentation of customer portfolio, which may result in attributing customers to different segments. This in turn may lead to offering them a modified composition of values.

 

Value Generating for a Company

The last stage in the model of the customer portfolio building is generating values for a company. In the initial value exchange, in which the Internet is used to start the relationship, which later develops through traditional channels, companies derive benefits from customer acquisition. The benefits may result from acquisition of customers in new markets or from higher efficiency in current markets. Due to the fact that it is the customer who initiates the relationship with the company, there is customer self-selection in regard to matching the company’s value proposition to customer needs.

In a non-monetary exchange, the company provides customers with values for which they do not have to pay. The values that customers deliver to company in return are usually appreciated by another group of customers, who are the advertisers. This model is very common on the Internet, as many companies offer some values, such as content or services without charging the customer, for which they display advertisement. However, starting already from the 1990s this concept has been criticized for not being profitable enough, which is also raised in the current debate concerning free access to the press online.

Probably the most traditional model is based on monetary exchange. Customers pay for values received from companies. Online stores, paid databases and some e-services operate within this concept. Despite the simplicity of this model, there are many online stores in Poland which do not generate profits (Internet Standard 2009). In the case of paid e-services, the challenge becomes the ability to create such a value proposition, for which customers will be willing to pay, knowing that there are other free services available in the same category.

Independently from revenues, customer portfolio building on the Internet enables the acquisition of data, information and knowledge of customers, all of which are often difficult to achieve through other channels.

Research and managerial implications

The proposed model of Internet-based customer portfolio building is an integrative framework presenting the interdependence of various marketing actions orientated at the development of customer relationships. The model itself, as well as all of its stages, requires further research. The most interesting aspects – in the author’s opinion – are presented below.

What is the right approach to creating a superior value proposition on the Internet? In which regard do the online superior value propositions differ from the superior offline ones? In which way is the superior online proposition influenced by customization, network effects and digitalization?

When should customers be value co-creators? Is value co-creation a proper strategy for all companies? When do customers prefer to be co-creators and when only consumers of values?

Viewing the customer portfolio from brand and relational perspectives, which of the two interdependent assets is more important on the Internet: brand equity or customer equity? Which is more crucial in creating online brands: delivering superior values to customers or effective mass communication? Is customer equity an antecedent of brand equity in the online environment? If yes, how should it influence the marketing strategies of online companies? Compared to traditional companies, should online companies concentrate more on delivering superior values and less on mass communication, relying more on customer acquisition through positive word of mouth communication instead?

The issue of trust building on the Internet has been thoroughly researched in literature (Bart et al. 2005; Schlosser, White, Lloyd 2006; Lee, Turban 2001). Still, some questions require more research. Which factors influence a potential customer to initiate a relationship with a company? To what degree does the successful building of trust allow an increase in prices? In other words, what are trust-price strategies?

A very common challenge in customer relationships is achieving an optimum balance between customer acquisition, cross-selling (additional selling) and loyalty building in terms of a company’s expenditures, time spent etc. (Blattberg, Getz, Thomas 2001). Internet technologies are often characterized by high fixed and low marginal costs. Hence, the question arises, how these factors influence optimum balance between customer acquisition, cross-selling and loyalty building in the online environment compared to traditional business? According to Kumar, companies should concentrate more on customer profitability than on the number of its customers or their loyalty (Kumar 2008). The suggestion is then to keep rather a smaller portfolio including the most precious customers. Does the technological environment change this and allow companies to keep a big portfolio deriving benefits also from the least profitable customers?

The marketing efficiency and shareholders perspective on the customer portfolio is also of great importance. Adopting Srivastava, Shervani and Fahey’s (1998) approach, there is a need to research how various elements of the proposed model mediate the shareholder value by accelerating and enhancing cash flows, lowering the volatility and vulnerability of them and increasing the residual value.

The measurement of the customer portfolio and customer lifetime values has been comprehensively described in literature (Berger, Nasr 1998; Bauer, Hammerschmidt and Braehler 2003). Gupta and Lehman showed even direct relationships between the value of customer relationships and the company’s valuation based on examples of Internet companies (Gupta, Lehmann 2003). Still, there is a need for more studies on the dependence between the Internet-related metrics (such as unique visitors, page views etc.) and the value of the customer portfolio. Remaining in the field of marketing efficiency, a useful tool could be metrics for assessing the activities within certain stages of the model. Such metrics could help managers in assessing their activity e.g. in the area of defining values.

Finally, another field of research could be a study examining to what extent the proposed model provides insight into the process of customer portfolio building through traditional channels.

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Strategies of Value Proposition on the Internet

JEL Classifications: M31 ; and / or UDC: 389
Key words:  Value Proposition, Values for Customer, Internet, E-commerce, Online, Marketing, Customer, Strategy, Value Offerings, Value co-creation, Mass Customization, Freemium.
Abstract:  Paper presents strategies of value proposition on the Internet. Author has distinguished and described strategies of efficiency, free values, complete customer solutions, unique values and value co-creation. These strategies are different from traditional strategies of value proposition, which refer values for customers to the level of price.

PIEB

Abstract: Paper presents strategies of value proposition on the Internet. Author has distinguished and described strategies of efficiency, free values, complete customer solutions, unique values and value co-creation. These strategies are different from traditional strategies of value proposition, which refer values for customers to the level of price.

JEL Classifications: M31 ; and / or UDC: 389

Key words: Value Proposition, Values for Customer, Internet, E-commerce, Online, Marketing, Customer, Strategy, Value Offerings, Value co-creation, Mass Customization, Freemium.

Strategies of Value Proposition on the Internet

Concept of Value Proposition

The concept of value proposition is often used in marketing literature [Anderson et al. 2006, Clarke III 2001]. Value proposition is understood in this paper as a composition of values delivered to customers by a company in order to satisfy their needs. Values are delivered to customers through products or services, other instruments of marketing or in other ways (e.g. by corporate credibility). They can be differently categorized. Probably the simplest categorization of values includes functional and symbolic values.

In the traditional economy a very common rule of value proposition formulation is combining the price level with the values for customer. According to this rule, companies offering inferior values charge customers lower prices than companies offering superior values. Hence, there can be several strategies of value proposition distinguished (e.g. inferior value – low prices, superior value – high prices).

The application possibilities of this rule on the Internet are constrained. The rule explains well the strategies of companies taking part in a monetary value exchange, such as online stores. Among them there are companies selling a product with inferior customer service and charging low price and other companies that enrich the same product in great customer service and expect higher prices for it.

Referring values to price level may not always be used on the Internet for several reasons. In the online environment many companies offer superior customer values for free. To these companies belong newspapers publishing content or companies offering communications services on the Internet, such as e-mail or instant messaging providers. Moreover, according to Kim on the Internet the strategy of offering superior values and charging high prices is rarely adopted [2004]. In traditional economy this strategy is most often used when marketing high quality, well-branded products to affluent customers.

Author distinguished five following strategies of value proposition on the Internet: strategy of efficiency, free values, complete customer solutions, unique values and value co-creation. It is worth mentioning, that these strategies have been formulated according to different criteria and may be merged.

Strategy of Efficiency

Efficiency strategy consists in offering values to customers, which are to lower their transactional, interaction and other costs and in this way allow savings of time and money.

The examples of companies adopting this strategy are online auctions. Due to supply aggregation they offer wide range of products, which leads to lowering customer transactional costs within offer search and analyses. Moreover, these companies decrease also customer costs by reduction of information asymmetry. This phenomenon occurs when one of the transaction parties has greater knowledge than the other party and is able to take advantage of it. Usually, this is the seller who has greater knowledge, which is the best seen on the second-hand market.  Online auctions reduce the information asymmetry using the seller’s evaluation performed by buyers. It is worth mentioning that online auctions also deliver knowledge on buyers, which allows reducing the risk and the transactional costs of sellers. Research of Garciano and Kaplan showed that transactional costs of buying or selling a used car with the use of Internet is twice as low as without it [Zott, Amid, 2001].

Many Internet companies apply the efficiency strategy while offering values related to communications. These solutions such as e-mail services, instant communicators, social networking websites also reduce the transactional costs of a customer.

Strategy of Free Values

Free value strategy is based on offering values to customers, for which they are not charged. This strategy has been popular since the early years of commercial use of Internet. As a consequence many companies, among which newspapers, charge customers outside Internet, while offering these values for free online, which in turn leads to problems with generating income.

Strategy of free values can be a part of a broader business strategy assuming the revenue generation. This can be performed twofold: revenues can be generated by another group of customers or the company can charge customers for premium values.

The first concept assumes that the company is acting on a multisided market and needs at least two distinct groups of customers to generate revenue [Evans, 2003]. Internet portals have two distinct groups of customers. The first one are final users who take advantage of values offered by the portal for free such as news, e-mail or search engine. The other group of customers are advertisers, who provide the portal with revenues, for which they can display advertisement. In this case the free value strategy is used in order to build customer base, on which company will offer paid services for the other group of customers (advertisers).

The other method of offering free values is based on acquisition of customers, who take advantage of free values and are also offered premium values, for which they have to pay. This strategy is often called freemium, which is the composite of free and premium. This strategy may seem to be very attractive, however its biggest challenge is the necessity of offering so precious values, for which customers – who already receive free values – will be willing to pay.

Strategy of Complete Customer Solutions

Strategy of complete customer solutions relies on offering a broad scope of values from certain categories[*]. Internet technologies enable presenting a high number of products in online stores, which results from low technological constraints. As a consequence online stores often shape their offer according to the long tail rule, which assumes offering both best-sellers, as well as niche products. Moreover, many companies offer values based on the economies of scope. This concept consists in offering products from different categories. A travel agency taking advantage of economies of scope would also offer insurance, car rentals etc.

An often quoted example of complete customer solution is Amazon.com. The company offers wide range of products (long tail) including niche products, and at the same time offers products from other categories such as household electronics (economies of scope). The strategy of complete customer solutions describes well also the strategy of Google. The company delivers different sets of values (products) allowing search, exchange and management of information in the online environment.

Strategy of Unique Values

The next of formulated is the strategy of unique values. A company follows this strategy, if it offers scarce values on the market. This situation is very attractive, as it allows charging high prices and thus taking advantage of high margin. The greatest disadvantage of this strategy are difficulties in creating scarce values and then sustaining the scarcity in long term. The adoption of unique values strategy may result from innovations, privileged access to resources or operating in a niche.

Innovations in value offerings may lead to situation in which a company offers unique values to customer. Examples of companies following this strategy are Skype and Google with its search engine. A company may offer unique values which result from a privileged access to resources. This strategy is adopted by media companies, such as newspapers, TV or radio stations, that offer online content unavailable on other websites. Uniqueness of values offered may also result from operating in a niche, in which consumer needs are different and should be satisfied with different composition of values. Acting in a niche is often combined with a low competition pressure and a higher level of margin. A example for this strategy may be an online store offering shoes in large sizes.

The strategy of unique values is attractive as it allows to burden customer with higher financial and non-financial costs. It means that a company may charge higher prices or impose higher transactional costs on them (such as slowly operating website). In an opposite case, when a company offers  common values, which are also offered by a numerous number of competitors, reduction of monetary and non-monetary customer costs may be a method of increasing values for a customer. This is easily to notice in the sector of online stores offering household equipment, books, music or websites allowing hotel reservation or airline ticket purchase. In these industries, companies often compete on the Internet with low prices, which may lead to deterioration of their margin.

Strategy of Value Co-Creation

The strategy of value co-creation assumes that customers actively participate in shaping the value proposition, which will be delivered to themselves or to other customers. According to Prahalad and Ramaswany, this is the value co-creation with customers that is the essence of competition in modern economy [2004]. The scope of the strategy is broad. It includes the situation, in which a customer co-creates composition of values with a company for himself. The customers may also create values orientated not at themselves, but at other customers. In both cases, the process of value co-creation must be developed on the basis of mutual commitment [Dobiegała-Korona, 2009].

In the first case, customer’s activity can be described as mass customization. According to Kleeman and Voss mass customization refers to „isolated activity of individual customers (..), not to the collective activity of many individuals” [2008]. Within the mass customization the value exchange can be described as one-to-one and the participation of other customers is not required. The examples of implementation of mass customization are numerous. Consumers may build their own computer, change the equipment of a car or design clothes.

Value co-creation orientated at other customers assumes that a customer actively participates in activities aimed at creating values for other customers. The scope of these activities is also broad.  They include creating and publishing content on the Internet, interaction and communication with other customers in social networking websites or software development in the Open Source movement. Usually a numerous number of customers (users) create values for other more numerous group of customers – recipients.  The value exchange can be described as all-to-all, as opposed to the former one-to-one.

Conclusion

Competing on the Internet requires an adoption to the new environment.  The way in which companies shape their values propositions is also a subject of change. Author proposed in this paper five strategies of customer value proposition. The proposed strategies are an alternative to the traditional approach combining values for customer with price level.

Porter’s competitive strategies comply with the traditional approach to value proposition. According to his conclusion a company should act either as a price leader and offer low value for low prices, or as a differentiator and offer differentiated values for higher prices. New approach to value proposition on the Internet requires then new ways of achieving competitive advantage.  A research combining the proposed strategies with new approach to competitive advantage may be a continuation of this paper.

References

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Dobiegała-Korona, B., 2009., “Customer Trust”, Economics & Business Administration Journal, vol. 1, p. 121-126.

Evans, D.S, 2003. “The Antitrust Economics of Multi-Sided Platform Markets”, Yale Journal of Regulation, Summer, s. 327-379.

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Prahalad, C.K., Ramaswamy V., 2004. The Future of Competition: Co-Creating Unique Value with Customers, Harvard Business Press.


[*] According to Kaplan and Norton: „With this value proposition, customers feel that the company understands their business or personal issues and they trust the company to develop customized solutions tailored to them” [Kaplan, Norton, 2004, p.329].