Project Management IV Research Paper

Organizing a Framework for Customer Value Management. . . . Kukkonen

Marketing Management Journal, Spring 2018 60

INTRODUCTION

Due to changing habits of media consumption and digitalization of the media industry, new managerial focus on customer relationships is well justified (Aitamurto & Lewis, 2012; Hakaniemi, 2014). Due to the rapid digitalization of the business in media industry, managing the value of online customer relationships has become especially relevant from the company perspective. The vast supply of news and other content, often free of charge on the Internet, has changed the habits of news consumption. Consumers now have the power to choose, and customer loyalty is ever more difficult to achieve. Consumers of news have forced a media company to shape the marketing strategy towards a more customer-centric direction (Aitamurto & Lewis, 2012). Digitalization of communication has set a challenge for many companies, as online customers expect companies to meet them through digital channels with new rules for engagement (Lipiäinen, 2014). Despite the change in news consumption and consumer expectations, it seems that many companies still

focus on one-directional communications with established digitals tools. The advances in digital marketing and measurement tools remain largely unexploited as companies have simply lacked the resources and knowledge to fully exploit the advantages of the fast- developing digital environment (Royle & Laing, 2014; Järvinen et al. 2012).

The study focuses on building a basis for customer value management (Verhoef & lemon, 2013) for online news channels. Customer value management aims to maximize the value of a company’s customer base by analysing individual level data on customers. The resulting information is used to acquire and retain customers and to drive customer behaviour with the developed marketing strategies in such a way that the value of all current and future customers is maximized (Morgan & Hunt, 1994; Verhoef & Lemon, 2013). With this aim, the study is also in line with O’Reilly and Paper, (2009) encouraging managerial focus on CRM to build a long-term vision rather than short-run profits. Despite the obvious recent academic interest in customer value management (Verhoef, et al., 2007; Kumar, 2010; Parvatiyar & Sheth, 2002; O’Reilly & Paper, 2009), the discussion of the

The Marketing Management Journal Volume 28, Issue 1, Pages 60-79 Copyright © 2018, The Marketing Management Association All rights of reproduction in any form reserved

ORGANIZING A FRAMEWORK FOR CUSTOMER VALUE MANAGEMENT IN ONLINE MEDIA RELATIONSHIPS

ELINA KUKKONEN, Aalto University Executive Education

The study aims at building a basis for customer value management (CRM) for online news channels and encourages managerial focus on customer relationship management to build a long-term vision rather than short-run profits of customer relationships. Today managers wish to consider also non- monetary social rewards for co-creating value and to enhance customer relationships (Vargo & Lusch, 2010). Online relationships have become multi-faceted, and customers create value for the company in online transactions but also in many other ways, which also need consideration in order to understand the full potential of an online customer. Based on academic literature and prior research on CRM, the study deepens understanding of the value creation of online customer relationships and suggests monetary, social as well as visitor value to be gained for the company. The article takes a stand on the wide availability of customer data, which enables companies to have a truly holistic view of customer relationships, including new relational and economic aspects of customer value. Furthermore, the article considers the availability of monetary and relational data on customer relationships as a major asset for the development of customer value management in online environments. The managerial contribution of the paper is in building a holistic view of customer relationship value in the online context, recognising the relational value components as one of the key aspects of online relationship, and in presenting nine manageable value generating components.

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overall value of online customer relationships seems somewhat incomplete. An inclusive perspective to customer value in online relationships, including both the relational and economic components of value, has so far been adopted by relatively few studies (Kumar et al., 2010; Weinstein, 2002; Reinartz et al., 2000). However, in these studies of customer relationship value, some relevant components contributing to online relationship value have been neglected. Furthermore, the availability of monetary and behavioural data is a major asset for customer value management in online business environments. Therefore, this conceptual study contributes to prior academic research by extending new components in the discussion of customer relationship value and considers customer engagement value as a holistic view of customer value management in online relationships.

Customer engagement (Brodie et al., 2011) is a strategic way of a valuing each customer relationship, acknowledging the various aspects of value created and enduring over time for the company (Yang & Coffey, 2014; Schau & Arnould, 2009; Kumar et al., 2010). Online customers are argued to be valuable for the firm, as a buyer but also as a co-creator, an actor (Vargo & Lusch, 2008; Vargo & Lusch, 2010), and part of a network (Ford, 2011; Heinonen et al., 2013).

The paper answers one of the most critical managerial questions in the news industry: “How is the value created for a company through online customer relationships, and how could the value be measured and managed?” To answer this question, the study will first discuss the concept of customer engagement value as a desired outcome of customer relationship management (CRM) in the online environment. Then, the distinction between the concepts of visitor value and monetary and social value is defined. Considering the variety of research in the area of customer value, these concepts formerly seemed somewhat blurred.

The contribution of the paper is in conceptualizing the value of online customer relationships in the media industry based on the economic and relational value. The value of customer behaviour is analysed in terms of measurable monetary, social and visiting

activities. Recognising social and visitor value (Wang et al., 2004, Lin, 2004) of online customers is seen as one of the key aspects to holistic value of online customer relationships. By presenting nine manageable online relationship value generating components, the paper contributes to customer value management in the online environment. Implications for management are given in form of a framework to help measure and manage customer relationships according to value potential and benefit of each customer for the company.

The structure of the paper is as follows: first the literature review looks at prior research on customer relationship management, customer engagement and customer value management relevant to this study. Table 1 summarizes these studies. The section that follows presents the three concepts of value in online relationships, and the value components in the online context are organised as generating either of social, monetary or visitor value for the relationship. Then a framework is presented for managing the relationship value in online news channels. The last section discusses the implications of the study and suggests some avenues for further research.

LITERATURE REVIEW

From CRM to Managing Customer Engagement

An incisive argument by Kumar could be adopted as a leading idea for the article: “When customer engagement is effectively tracked and managed, firms will increase their profits” (Kumar, 2015, p.5).

The literature defines CRM as “a comprehensive strategy and process of acquiring, retaining and partnering with customers to create superior value for the company and the customer” (Parvatiyar & Sheth, 2002, p.5). Prior academic research pertinent to this study typically emphasizes one aspect of the value of a relationship at a time (Schmitt, et al., 2011; Leone et al., 2006). The literature on customer relationship management broadly recognizes the monetary value of customer-firm relationship, and discusses customer profitability (Gupta et al., 2004; Rust

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et al., 2004; Pfeifer et al., 2004), customer life- time value (Kumar, 2010; Reinartz & Kumar, 2000; Venkatesan & Kumar, 2004; Gupta et al., 2006) and customer equity (Zeithaml et al., 2001;Weinstein, 2002; Verhoef, 2003; Bijmolt et al., 2010). The literature discusses the social value of online relationships in terms of referral value (Rust et al., 2004; Kumar, 2010; Schmitt et al., 2011), co-creation value (Prahalad & Ramaswamy, 2004; Vargo et al., 2008; Payne et al., 2009), influencer value (Ots, 2009; Kumar, 2010; Choudhury & Harrigan, 2014) and customer knowledge value (Allee, 2000; Bueren et al., 2013). Customer value management acknowledges the customer heterogeneity, exploits the available data and focuses on increasing customer value as a key objective (Verhoef & Lemon, 2013). The information of customer analysis is used to acquire and retain customers and to drive customer engagement behaviour with marketing strategies to maximize the value of all current and future customers (Verhoef & Lemon, 2013). As Mersey et al., (2010) argue that in an online news channel context, positive customer experience leads to higher engagement of the audience, which is positively associated with readership and leads to profits (Picard, 2008, Holmberg, 2013).

The value of online user behaviour is discussed in the literature in terms of recency, frequency and monetary value, and a RFM model (Gupta & Zeithaml, 2006) is presented in several studies. More recent studies, also well in line with this study, have discovered the concept of customer engagement value in an online environment (Calder et al., 2009; Yang & Coffey, 2014). The level of online engagement value can be realized through multiple participatory elements in the relationship as well as the recency, frequency and volume aspects. The academic discussion on customer engagement value (CEV) also touches upon the economic value of the relationship (Venkatesan & Kumar, 2004). Therefore, the literature sees purchasing behaviour, referral behaviour and user volume as the major manifestations of customer engagement, the level of which can also be calculated. Customer engagement as a concept (Verhoef, Reinartz, & Krafft, 2010; van Doorn et al., 2010b) seems a strategic way of looking at online customer relationships.

According to Kumar et al. (2010), customer value in customer relationship management is driven by the nature and intensity of customer engagement regarding the firm and its product or service offerings. Their study depicts several components of customer relationship value, but ignores some components, found especially relevant in this paper due to its industry perspective. The literature on online customer engagement relevant to this study is summarised in Table 1 below.

The literature on customer engagement value is extensive (Table 1); nevertheless, academic attention towards customer engagement as a separate construct seems somewhat limited (Verhoef et al., 2007). It is notable that prior literature on customer value often takes the perspective of value received by the customer (Cronin et al., 2000; Sirdeshmukh et al., 2002; Payne et al, 2007). Here the primary focus is on the value gained from customer relationship for the company. Prior studies on customer engagement value from the perspective of media-customer relationships appear to be scarce. As Brodie et al. (2011, p.262) state, “future research is required to explore the focal networked dynamics across different engagement contexts”. And according to the authors, research specifically addressing engagement in the online environment is expected to generate further insights into the Customer Engagement (CE) concept. This research addresses the expressed need for further research by responding directly to context-specific managerial needs (Brodie et al., 2011) and the measurement of CE (Bolton, 2011). It seems that most of the research in this area has been done in connection with three areas: usage and gratification studies, taxonomical studies and media-market matching studies (Heo & Cho, 2009). Media business is still largely based on readership (Siles & Boczkowski, 2012), and here customer engagement is strongly associated with readership. In the literature, customer engagement is defined as going beyond transactions, beyond purchases and resulting from motivational drivers. Examples of customer engagement in the media context are mainly behavioural expressions, both

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TABLE 1: Literature Review of Online Customer Engagement Relevant to the News Industry

Author Data Main Findings

Bijmolt et al., 2010 Conceptual study Classification of analytics to examine customer engagement behaviour. Customer engagement may be generated in different stages of the customer life cycle: customer acquisition, customer development (growth), and customer retention (churn and win-back).

Bowden, 2009 Conceptual study Views CE as a psychological process, which drives customer loyalty. Proposes a framework for segmenting customer–brand relationships based on customer engagement and loyalty.

Brodie et al., 2011 Theoretical analysis of the CE concept.

Provides a general definition of the conceptual domain of customer engagement and future research implications based on five fundamental propositions drawn from the literature.

Bueren et al, 2013 Case studies of three European companies in the financial sector

Focuses on how concepts of Knowledge Management (KM) can be applied within the area of CRM. Describes cases in which the performance of six CRM sub processes is improved by applying the CKM model. Four knowledge aspects: content, competence, composition and collaboration support the CRM sub –processes.

Calder et al., 2009 Users of 11 sites, a median sample size of n= 1,141 and a total sample size of n=11,541

Shows that online media involves a distinct form of engagement, which has its own impact on advertising effectiveness.

Cui et al., 2015 A large direct marketing dataset from a U.S.-based catalo company consisting of 106,284 consumers with 361 variables for each customer.

Addresses the popularity of direct marketing as a tool for promotion and customer relationship management. Focuses on the need of methods of intelligent decision support for customer selection and augmenting the profitability of targeted marketing with limited resources. Suggests a POCO -model for targeting the high value customers.

Cvijikj & Michahelles, 2013

Quantitative analysis of gathered dataset consisting of posts obtained from 100 sponsored FMCG brand pages.

Analysed the characteristics of the content created by companies as factors that might influence the level of online engagement on Facebook brand pages. As a result, companies should prepare clear engagement strategies for appropriate content type, media type and posting time in order to increase the level of engagement.

Heinonen et al., 2013 A conceptual analysis. Focuses on customer-company interactions, extending the value construct through a customer dominant value perspective, recognizing value as multi-contextual and dynamic.

Huang et al., 2013 An analysis of online search consumer data (n=216) from China.

Suggests positive impacts of social identification on behavioural engagements of information seeking, interaction sharing, and knowledge creating, and purchasing intention.

Kumar et al., 2010 Conceptual study Presents a framework for determining total customer engagement value (CEV) based on four components: customer lifetime value (CLV), customer referral value (CRV), customer influencer value (CIV), and customer knowledge value (CKV).

Kumar & Mirchandani, 2012

Case study of Hokey Pokey Ice Cream company.

Presents customer influencer effect (CIE) and customer influencer value (CIV) as major determinants for social media campaign success. Case study showed increases of 49% in brand awareness, 83% in ROI and 40% in the growth rate of sales revenue. Seven step approach for social media analysis.

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Author Data Main Findings

Lin, 2003 A survey study in a quota sample of 180 respondents attending an e-commerce exposition in Taiwan. Online traveling services and (VOD) presented the e-service categories of the survey.

The empirical study suggests that commitment plays a crucial intervening role in the relationship of customer satisfaction and perceived value to loyalty. Loyalty and commitment should develop if the formation of customer satisfaction, trust, and perceived value is appropriately managed.

Manganari, et al.,2012)

241 business school students took part in an experimental study on travel website.

Online retailers in the travel sector should focus on enhancing consumer perception of control during the online shopping trip. Enabling consumers to become actors in the online store increases positive perceptions.

Malthouse et al., 2013

Conceptual study Examines how CRM needs to adapt to the rise of social media. Explores the pitfalls of the convergence, such as organization`s lack of control over message diffusion, big and unstructured data sets, privacy, security, shortage of qualified manpower, measuring the ROI, and strategies for managing employees, customers and content marketing.

Pai & Tsai, 2011 Quantitative analysis of 537 responses on email questionnaire. Data obtained from three large Taiwanese online retailing stores.

Virtual community participation significantly enhances loyalty intentions, through both social mechanisms (via community identification) and psychological mechanisms (via trust and satisfaction). Community identification is a pivotal factor for enhancing customer loyalty intentions.

Payne, et al., 2009 Case study in car industry. Several personal interviews of different focus groups, and an analysis of a customer survey, customer blogs, company data, website.

The study examines the co-creation of value in the context of service-dominant logic, proposes a conceptual model of co-creation for managing brand relationship experiences and develops a case study about an innovative service which utilizes opportunities for co-creation that reflect changing consumer preferences and new developments in mobile technology.

Rishika et al., 2013 Customer data of a large specialty firm with wine and like products in the north- eastern United States.

Investigates the intensity of the relationship between the firm and its customers as captured by customers’ visit frequency. Finds links between customer´s social media participation and the frequency of customer visits to company site as well as the profitability of customers.

Rohm, et al., 2011 Questionnaire completed by 563 customers of Dutch Bank.

Quantitative analysis revealed that company-designed online agents can effectively serve as customer socialization agents, influencing customer attitudes and behaviours and satisfying the demands of new generations of customers for richer online interactions.

Sashi, 2012 Conceptual study The study develops a model of the customer engagement cycle with connection, interaction, satisfaction, retention, loyalty, advocacy, and engagement as stages in the cycle. Four types of relationships emerge: transactional customers, delighted customers, loyal customers, and fans.

Scarpi, 2012 An online questionnaire to customers of Italian electronic e-retailer. Quantitative analysis of 300 respondents.

Consumers with hedonic orientation, enjoy browsing, have a high profit potential and seem to form a valuable customer base. The data showed that consumer enjoyment on the internet and of the internet translates into higher profits for the retailer: fun does pay off for online retailers.

TABLE 1 (continued)

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positive and negative. A concrete example of a positive expression is posting a brand message or a blog on the site (Calder & Malthouse, 2004). An example of a negative engagement behaviour is organizing public actions against a media company (Van Doorn et al., 2010). Bunker et al., (2013) suggest that involvement, rather than solely motivating consumers “to like a company” would be the key to success in online relationships. Calder et al. (2009) discuss further the concept of media engagement, focusing on the consumer’s psychological experience while consuming media. They distinguish media engagement from mere liking, implying that engagement among media audiences, readers, is a stronger state of connectedness between the customer and the media than liking alone.

To position media engagement as an industry specific term in to the broader picture of customer engagement seems justifiable. The definitions of customer engagement in the literature similarly to Calder and Malthouse (2008), and Calder, Malthouse and Schaedel (2009) reflect behavioural or psychological aspects and concrete actions made in the relationship as well as the level of customer-

firm connectedness. Kumar et al. (2010, p.297) describe “the active interactions of a customer with a firm, and with other customers, whether transactional or non-transactional in nature” and Van Doorn et al. state that ”engagement is what occurs when a prospective consumer’s mind is turned on to a brand idea enhanced by the surrounding context” (2010, p. 254). Martin et al., (2014) found that connectedness to other brand users has a positive impact on brand commitment. Users who feel linked to brands users are more committed to that brand than other consumers. According to Sashi (2012, p.257), “customer engagement seems to go beyond awareness, beyond purchase, beyond satisfaction, beyond retention, and beyond loyalty”. In many ways customer engagement represents the evolution of marketing from market orientation to relationship marketing. The Value Components of an Online Relationship Measuring and managing customer value have traditionally focused on customer acquisition and retention, and increasing customer spending with a company over time (Kumar,

Schau, 2009 Empirical, ethnographic, analysis of nine online brand communities.

Collaborative consumption and value creation in brand communities has consistency. The study organizes the current knowledge of collective brand-based actions, and suggests what is needed to support collaboration.

Sridhar & Srinivasan, 2012

Data from 7,499 consumers’ online ratings and reviews of 114 hotels in Boston and Honolulu. Quantitative analysis.

Identify a moderating role for social influence from the online reviewer community on the effects of a reviewer’s product experience on his or her online product rating.

van Doorn et al., 2010b

Conceptual study Addresses “customer engagement behaviours (CEB)”, which result from motivational drivers, including WOM activity, C2C- interactions and/or blogging activity. A theoretical model is developed linking customer engagement behaviours to specific customer-, firm-, and contextual antecedents and consequences. A difference to customer attitudes such as trust, satisfaction and commitment is explained.

Yang & Coffey, 2014 National panel survey of broadband users (N = 200) investigates the use of interactive features on internet video sites.

Audience interactivity is related to audience value. The study defined audience interactivity based on people’s frequency in using interactive features. Three groups were identified—non-interactive audience, average audience, and interactive audience of which interactive audiences are younger, more engaged online, and have higher electronic word-of-mouth value than non-interactive audiences.

TABLE 1 (continued)

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2008b). By viewing customers as assets, and systematically managing these assets, a firm can identify the most appropriate marketing actions to acquire, maintain and enhance customer relationships, and thereby, maximize financial returns (Berger et al., 2002).

In the socially networked online environment, the concept of customer engagement value broadens the perspective of the company from economic and purchasing value of customers to social behaviour such as customer influence value, customer referral value and customer knowledge value (van Doorn et al., 2010; Kumar et al., 2010).

The important aspect to online relationships adopted in this section is that the value of a customer is not limited to the profit from each transaction. Instead, the value of customer relationships is created and weighted differently based on several monetary, social and visitor value components. Through social media activities, firms influence customer engagement and are able to increase visit intensity and customer profitability (Rishika et al., 2013)

This research argues, that customer engagement value (Verhoef et al., 2010) offers an important aspect to measuring and managing customer relationship value in digital business environment. For an online media it offers a holistic view to online relationships, including the transaction value, and the behavioural manifestations of a customer with a rather indirect impact on firm performance.

Next, the three concepts of value in online relationships – monetary, social and visitor value presented in Figure 1 – are discussed and existing managerially relevant metrics suitable

to measure the presented value components are suggested. Subsequently, a framework for online customer value management is presented in detail.

Monetary Value

In academic literature on customer relationship management, Customer Value is most often presented as customer purchase value (Kumar et al., 2004; Tsai, 2005; Moe & Fader, 2004, Fader et al., 2005) and customer life-time value (Venkatesan & Kumar, 2004; Reinartz & Kumar, 2003; Berger & Nasr, 1998). Discussions of monetary value in customer- firm relationships is often geared around customer profitability (Pfeifer, 2005), customer life-time value measurement or customer equity (Lemon et al., 2001) and the overall economic value of the customer base (Berger et al., 2002). Customer lifetime value as a metric for selecting customers and designing marketing programs is strongly supported in academic literature, and the amount of academic research on customer life-time value is quite exhaustive (Reinartz & Kumar, 2003; Venkatesan & Kumar, 2004; Kumar, 2010; Rust et al., 2004). A great amount of empirical evidence of the usefulness of customer life-time value as a metric compared with other customer based metrics can also be found (Venkatesan & Kumar, 2004). Based on prior research, a general assumption can be drawn that the current customers of a company also provide the most reliable source for future revenues and profits (Rust et al, 2004, Gupta et al., 2006). The customer life-time value (CLV) metric in customer relationship management has gained a firm position, and theoretically CLV could be thought to include all aspects of value creation by the customer. Nevertheless, in practice it is

FIGURE 1: The Concepts of Value in Online Relationships

Monetary Value Social Value Visitor Value

Total Customer Value

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frequently measured only via actual purchase behaviour. CLV calculations are also primarily based on the customer’s transaction behaviour (Gupta et al., 2006; Rust et al., 2004), and this is explained resulting from the frequency of category purchases or the average quantity of the purchase (Kumar et al., 2010).

The monetary value of an online news channel subscriber has traditionally referred to the economic value of the subscription last purchased by a customer, and reported in terms of audience revenue or net value of subscriptions (State of the Media). Similarly, the total value of the subscriber base in the media industry is generated by summing up the economic value of effective subscriptions at the time of measurement. Similarly to the e- commerce field (Schefter & Reichheld, 2000), in the news industry, retention and subscriber CLV have been secondary to acquisition of new customers (Chen & Hitt, 2002). However, a shift in strategy towards customer life-time management and long-term, economically viable customer relationships provides a clear business opportunity for the industry with a base of digital customer relationships. In the context of an online news channel with a paywall (e.g. FT.com, NYT.com, and WSJ.com) or other restricted access to content, the registration of an online customer is the beginning of a customer relationship. The moment of online registration could be considered the starting point and basis for customer life-time measurement.

Social Value Social media technologies have revolutionized the way businesses and consumers interact. The customers who expect interactions among themselves in these networks also expect a similar level of interaction from their business counterparts (Choudhury & Harrigan, 2014). The literature widely discusses customer referral value (Rust et al.,, 2004, Kumar et al., 2010, Schmitt, Skiera, & Bulte, 2011) co- creation value (Prahalad & Ramaswamy, 2004; Vargo et al., 2008, Payne et al., 2007) and influencer value (Kumar et al., 2010). Customer knowledge value (Allee, 2000, Bueren et al., 2013) is also a relevant social value aspect of online relationships.

Customer referral value is a component of social customer value and particularly useful in the online news industry. Customer referrals to journalistic content or brand are expressions of engagement of customers to an online news channel. Engaged customers more often buy the product, recommend the product to others and bring in new prospects (Kumar et al., 2010). However it is notable that customers do not necessarily have to be buyers of the product to be able to refer to it, and thus create value for the company (van Doorn et al., 2010). In the literature, customer referral value is described as current customers converting prospects in their social network, both online and offline, into actual customers for which they are rewarded (Kumar et al., 2010, p.1). However, in this paper referral value relates to the non- incentivized referral of new customers. New visitors are invited to an online news channel by other readers referring to articles or newsfeeds of personal interest (Venkatesan & Kumar, 2004). All visitors to an online news channel brought in organically or by referrals, are valuable in two respects: first, every visitor becomes a part of the total audience inventory sold to advertisers, and second, he/she is also a potential new subscriber. Self-motivated referrals by customers are of particular value for an online news channel. Recommendations, as links to news items shared in social networks of customers carry the potential of bringing in new audience and customers paying for the news. Customer referral value can be calculated by using a customer’s actual past referral behaviour (Kumar et al., 2007), and if referrals are not firm-initiated or rewarded, the actual acquisition cost per one customer is zero.

Customer knowledge value is also a social component of customer value (Venkatesan & Kumar, 2004). Successful companies, such as Amazon, have realized that customers are more knowledgeable than one might think, and consequently seek knowledge through direct interaction with customers (Gibbert et al., 2002). The literature also points out that customer-driven companies need to harness the knowledge of those who buy their products (Gibbert et al. 2002; Baker, 2000; Davenport and Klahr, 1998). Customer Knowledge Management (CKM) is seen as a strategic

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process where customers as passive recipients of products and services transition to become active recipients and are seen as knowledge partners for the company (Gibbert et al., 2002). Existing literature suggests that customer-to- customer know-how exchange is reliant on the interaction between the company and its customers and the antecedents of know-how exchange arise from the motivation, opportunity and ability (Gruen et al.,2006; Siemsen et al., 2008) of customers to use and share the knowledge value for the benefit of others. Constructive criticism or complementary comments by readers are examples of customer knowledge value in the media industry. Customers could, if encouraged to, complement the news, and enrich the content with their specific knowledge, and the outcome would be a more comprehensive coverage of the topic for others (Meadows, 2012). However, the industry has not succeeded in encouraging this kind of participatory journalism, and as a result, participation opportunities and user-generated content remain rather scarce (Nguyen, 2008). Again, this is not surprising considering the recent studies (Järvinen et al., 2012; Royle & Laing, 2014) identifying skills gap in companies, lack in know-how and in allocation of human resources to take proper advantage of available digital marketing and communications tools. The options for enhancing the social value of a media audience (Bruns, 2004) lies in encouraging interactivity with customers to, for example, comment on subjects by simply asking: “Do you have more information on this issue?” or offering evaluation options on stories such as “did you find this story useful? Why or why not?” These kinds of feedback loops are also depicted in the customer value/retention model by Weinstein (2002).

Customer influencer value indicates the customer’s ability to influence other customers to increase purchases, retention and share of wallet through word of mouth as well as bringing in new prospects (Venkatesan & Kumar, 2004; Brodie et al., 2013; Hennig- Thurau et al., 2010; van Doorn et al., 2010). Each time a customer voluntarily generates WOM about the firm and its products, influencer value is increased. If the WOM generated by the customer to others in the network is positive (negative), and/or gets

(obstructs) others in the network to become customers and purchase (refuse) additional products, his or her influencer value increases (decreases). Determining the influencer value of an individual customer, the size of his/her personal network is one important indicator. However, it is also plausible that an individual can have a very large network of online social acquaintances but is not necessarily influential within the network (Kumar et al., 2010). The type of network the customer is part of also matters, as some networks are more valuable for the company in the form of a target market of potential customers than others. The personal status of a customer as an authority or a specialist in a field is significant in determining the knowledge value as well as the influencer value of an individual.

Companies can achieve a competitive advantage by leveraging customer competence (Prahalad & Ramaswamy,2004). Customers can actually be a valuable source of new product ideas (Birkinshaw et al., 2006). Hodis et al., (2015) find that co-creation is one of the most underused components among brands. They present that with engaged consumers, crowd- sourcing efforts such as product testing, new logos, changes to advertisements and trial run advertisements could be facilitated with moderate costs. Customer co-creation value in the news industry can occur through shared inventiveness, co-design, or shared production of content with the online audience. In customer co-creation, the customer participates spontaneously in the creation of the core offering itself (Lusch & Vargo, 2006). Such behaviour as making suggestions to improve the consumption experience, helping and coaching service providers and helping other customers to consume better are all aspects of co-creation, and hence customer engagement behaviour. Normann and Ramírez (1993) argue that the goal of business is not to create value for customers but rather “to mobilize customers to co-create value”(Steinman et al., 2000, p.69). According to Bendapudi and Leone, (2003), encouraging customer participation may represent the next frontier in competitive effectiveness and reflects a major shift from a product-centric to a service-centric logic for marketing (Vargo et al., 2008).

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Overall, this service-dominant logic views customers as proactive co-creators of experience rather than as passive receivers of value, and views companies as facilitators of the value co-creation process rather than as producers of standardized value (Prahalad & Ramaswamy, 2000; Payne et al., 2007). For companies operating in the online environment, co-creation can also help to understand the customers’ points of view and to better identify their needs and wants (Lusch and Vargo 2006). The internet can serve as a platform for such collaboration with customers, providing opportunities to easily offer suggestions and input for the firm (Sawhney et al., 2005). The co-creation value of customers in online news channels can be measured by the activity rate of customers taking part in content creation. Co- creation of content can be realised by producing editorial material as blogs, opinions or columns (Meadows, 2012; Kumar et al., 2010). Customers enriching the journalistic content of online news channels by commenting on stories are co-creators. Karlsson (2010) suggests transparency as a new norm for journalism and explains the transparency of journalism as meaning the various ways of making it possible for online news audiences to get involved in the news production process. Bruns (2004) lists the means of delivering transparency and indicates that users should be able participate in every stage of news production, from gathering news to reporting, publishing, analysis and discussion. Visitor Value Visitor value of customers in online environment refers to the frequency of visits and the time spent on the site (Wang et al., 2004). Visitor value is an important indicator of engagement (Calder et al., 2009; Moe & Fader, 2001) and in literature also positively linked to CLV (Venkatesan & Kumar, 2004). Considering the low conversion rate of online visits compared to the offline environment (Moe & Fader, 2004; Cui et al. 2015), it is highly important to consider the value of customer visits as foundation to the customer- firm relationship online (Bucklin & Sismeiro, 2009; Lin, 2004). Regardless of whether a customer purchased on site, his visits to a website may have significant value to firms (Agichtein et al., 2006; Yu et al., 2006; Huang

et al. 2009). In the case of commercial online news channels, the visitor value of customers is obvious as every time a customer visits the site he/she creates page impressions, which are the currency of advertising sales for the site. Thus, the value of each visitor depends for example on how much he/she browses the content on the site, producing more page impressions every time a new content page is opened. However, other optional or additional metrics for determining visitor value of online customers exist. In this study the relevant components generating visitor value for an online news media present the frequency of use, recency of use and the volume of use of an individual user. These components are considered to add relational as well as indirectly monetary value to the online relationship and to strongly reflect the level of audience engagement (Yang & Coffey, 2014). The outcome of high visitor value of users in the online news channel context lies in the potential of active customers for content sales (customers who visit most often are more likely to buy) and in the increase of inventory for advertisement sales in the form of page impressions generated by individuals visiting the site and browsing the content and directly convertible to monetary value. High visitor value of customers also reflects high levels of audience engagement, as customers who return to the site frequently and spend a lot of time browsing the content are considered as highly engaged customers.

To draw a clear distinction between the visitor value and monetary or social value of a customer, the outcome of visitor value is the value from the use of the service generated by an individual user during the online visit in the process of consuming the content. Therefore, it does not indicate the social behaviour described earlier as sharing, commenting on nor recommending content or products, neither does it indicate the online purchasing process of customers. The literature makes similar distinctions, as the recency, frequency and monetary (RFM) model (Bijmolt et al., 2010) is presented as a measurement of individual use, that effectively differentiates customer contributions to the business (Ho et al., 2002; Chen et al., 2005). Data and analytics on customer user behaviour is described as

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generally the most effective predictive data in customer relationship management in online environments (Rud, 2001). Next, the three relevant components of visitor value and related metrics for online relationship management in the media-industry are briefly explained.

Volume of use of an online customer indicates either high or low interest in the content of the site, and therefore, the potential or non- potential for purchases. The high user volume of an online news channel also reflects high engagement of customers and is also an important parameter for advertising sales. Volume of use is commonly measured on the basis of session duration, page views per session and visits per visitor. These three metrics and their definitions come from traditional web analytics (Burby et al., 2007). Session duration is commonly thought of as the amount of time a visitor spends browsing the web site. Page views per session is defined by the Web Analytics Association as “the number of page views in a reporting period divided by the number of visits in the same reporting period” (Burby et al., 2007, p.26). Page views per session is often considered a measure of engagement as it means that a customer has or has not browsed the site further and viewed content beyond what is on the landing page. However, this metric is not indisputable for level of engagement or user volume especially in the case of e-commerce sites, as numerous page views made by customers can also be an indication of poor website design rather than high interest in the products. Number of page views (or page impressions) per customer as a metric combined with information on type of content browsed would, however, give a better view of the level of user volume and engagement.

Frequency of use of online service refers to the number of visits made by a customer to a specific website, and reflects the visitor’s interest in a product or service. Frequency of use is measured by number of visits by an individual during a specific period (a week, a month, a year). Frequency of use is an important component of behavioural customer value. As mentioned, customers who visit a market participant’s website frequently are more likely to pay for the content versus customers who have made just a few visits. In

academic literature on retailing, purchasing history is used for predicting future customer activities. The assumption underlying this framework is that customers are most likely to reduce their frequency of purchase before terminating a relationship (Lemon et al., 2001). This assumption is interesting and plausible from the point of view of an online news channel and could be applied in terms of “frequency of visits” also to different phases in an online channel-customer relationship and relationship life cycles.

Recency of use reflects the interval between the time of the most recent online activity, transaction or other activity, and the time of evaluation (Schlosser et al., 2006; Ho et al., 2002). The Web Analytics Association defines the calculation of recency as the time since a unique visitor performed a specific action of interest to the analyst (Suite, 2008). Recency of visit also seems to be one of the most powerful predictors of future behaviour in an online customer, as the more recently a customer has done something, the more he/she is likely to do it again. Recency can predict the likelihood of purchases, repeat visits, social actions and just about any action-oriented customer behaviour, and therefore, has a high potential value (Venkatesan & Kumar, 2004). In the news industry, high recency of use per customer for an online site can indicate satisfaction, retention and loyalty of customers (New York Times, 2014). Customers who visited the online news site in the last week are much more likely to visit this week than customers who signed in 30 days ago. Recent customers are also the most likely to contribute to profits in the future by responding to promotions or simply just coming back by themselves. Vice versa, a high recency customer who stops visiting the site, indicates that other alternatives have been found that fulfil the same motivation for use or his or her needs have changed. In the customer relationship management context, quite extensive attention has been paid to customer metrics with a strong focus on purchase behaviour (Bolton et al., 2004; Verhoef, 2003, Verhoef, 2002). However, visitor value of a relationship indicates user behaviour (Malthouse et al., 2013; Salo & Karjaluoto, 2007), and reflects the activeness and volume of use of online news or service

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site of an individual, also recognised in prior literature on big data-based marketing (Brynjolfsson, 2013; Chen et al., 2005), analytics (Burby et al., 2007) and data mining (Chen & Popovich, 2003). In the following, Table 2 presents a framework for customer relationship management in an online news channel based on the components of customer value and suggested online metrics.

DISCUSSION AND IMPLICATIONS

The managerial view of customer relationship management for an online news channel is one that nurtures a loyal customer base of

subscribers, yet much of the results of this review are applicable to the management of any e-commerce context. Due to the digitalization of content, there exists a great opportunity for online news channels to strengthen customer relationships, yet the basic laws of building loyalty have not changed. An online news channel can operate as any e-commerce platform similar to Amazon.com and initiate a spiral of economic advantages by encouraging the value generating behaviour of customers. The increased loyalty of the customer base would make the business more profitable and enable media companies to compensate their employees more generously, provide investors with superior cash flows, and reinvest more

TABLE 2: Framework for Managing Customer Value in Online News Channel Relationship

Life-time value

Transaction value

Monetary

Value

Referral value

Knowledge value Social

Value

Volume of use

Frequency of use Visitor

Value

Recency of use Time of last visit

Number of visits

Time on site, page

impressions

Influencer value

Knowledge sharing comments,

Number of shared

stories

Economic value of the

last purchase

Purchases since the time of

registration

Size and nature of personal network

Co-creation value Number of comments, blogs

Concept of Customer Value

Components of Customer Value

Online User Metrics

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aggressively in the customer relationships enhancing the value delivered to, and experience by, the customers This research offers an answer to one of the most intriguing questions in the online business environment: “How would a company be able to retain and nurture online customer relationships for more profitable customer base?” Based on a literature review, focusing on the aspects of customer engagement value, the paper develops a framework for managing customer value in the online environment, especially in online news channels. The outcome is a holistic perspective on customer relationship value, recognising the economic and the relational value of online customers. Concrete tools and examples of online metrics are provided for managers to manage customer engagement value and loyalty of online customer relationships.

The theoretical contribution of the paper is threefold: first, the literature review shows that in online relationship management, traditional CRM and prior research on customer value management seem inadequate to recognize constantly evolving and multi-faceted value potential of online customers. According to prior research and the literature analysed in this paper, the management of customer engagement value seems to provide the most definitive view of customer relationship management in the online environment. The paper presents customer engagement value (CEV) as the desired outcome of online customer relationship management.

The second contribution is the holistic view of customer value and particularly the concept of the behavioural value of customers. Online user behaviour has been somewhat neglected in prior studies of customer value management, but in the context of online media, seems extremely relevant. Today, visitor value is easily measurable and as explained has an indisputable effect on the customer value of any online business and especially in the news industry. The user behaviour of an online customer directly affects the user volume of the site, and therefore, the value generated in the customer-media relationship. Examples of dimensions of online user behaviour that have an impact on customer value in online

environment (but are not of social or monetary value) are presented in the form of frequency of visits, length of visit, time of visit and page impressions.

The third contribution is the demonstration of the six value-generating components of the online relationship, on which customer value management can be built upon. The components of online relationship value are organised into three separate concepts, and the creation of monetary, social and visitor value of online relationships is discussed according to prior academic research.

The managerial implications of the study are also threefold. First, the paper organises online customer management activities into three categories, all aimed at growth of the value in the relationships. To maximize the value of customers for a company, managers should pursue all aspects of customer engagement value: the monetary, social and visitor value of relationships. Second, as the paper recognizes the full value potential of online customers, and explains the value creation mechanism of each value component in the relationship, it enables marketers to redirect marketing resources to measurable value-generating activities in the relationship. For example, if the customer base of an online news channel consists of high monetary value, (the majority of its customers are already paying for the content), but is limited in size and in terms of traffic (low visitor value of customers), marketing activities should focus on gaining higher user volume per customer, which would generate more inventory (page impressions) to be sold to the advertisers. Higher user volume would also be an initiative worth exploring for customer- company interactions and for customer- customer interactions. Frequency of visits, length of visit and number of page impressions generated by a customer usually indicate high or low interest towards the content of the site, and therefore, the potential or non-potential for paying for the service or content. Socially non- active customers, yet with high user volume, could be wheedled in to conversations and other engaging activities among customers in order to gain referral value, knowledge value and even influencer value in the relationship.

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In summary, customers clearly are valuable for the company from many different perspectives, and this potential should be fully exploited across industries operating in the online environment. As Hodis et al., (2015) state, for marketing professionals and scholars, the changing role of consumers from passive recipient to active contributor of brand related content (Prahalad & Ramaswamy, 2004) is a critical turning point where the marketing strategy for the socially connected consumer is shaped. In the news industry, where companies depend on large audiences and advertising revenue, customer engagement value is simply produced by activating the users of the site, by engaging them into conversations and commenting on content and by encouraging them to share and recommend the news in their own network. The three concepts of value presented here, monetary, social and visitor value, should help any online manager to organise the management of customer relationships in order to gain more value of each customer relationship in digital business environment.

Future Research and Limitations of the Study

This paper provides a promising basis for future research on customer value management research and the presented framework for analysing and managing customer engagement value has already been processed to another academic paper. The limitation of this study is the conceptual view of value management, and therefore, measuring the customer level value of relationships and determining the value structure of a firm’s online customer base with real-life data have formed the natural next steps of this study. A future study on value-based segmentation as a basis for online management programs of each customer segment seemed like an interesting initiative to implement. The significant developments in data mining techniques (Mierzejewska & Shaver, 2014; Phelan, McCarthy, Bennett, & Smyth, 2011) and availability of relational data of online customers is easily accessible for any company operating online. Company specific customer data combined with audience data of news channels would offer an interesting avenue for customer relationship research in online environment.

Third, the full value of online customer relationships can be measured according to the presented value components and the suggested variables. Measurement combines traditional CRM information on customer purchases with information on customer behaviour easily tracked using generic online customer level analytic tools. Therefore, the objectives for growing total customer value can be set and measured accordingly. The target of growing the total value of customer relationships should be established and the stakes set to realize the greatest potential of each relationship. A simple fictional calculation case can further clarify the managerial implications of this research for determining customer relationship value in online news channel context. First we assume, that based on company CRM system customer A has bought an online subscription value of 10€ per month. The same customer A visits the site daily and on each visit browses the content further from the landing page, generating altogether 5000 impressions in one month. He /she also actively shares news links from the news site to his/her own social network in Facebook, generating through links new incoming traffic of 100 persons in one month. Thus, the absolute monetary value of customer A is 10€ in one month. The visitor value of this customer could be calculated based on advertising prices of display ad formats. One of the common display advertising formats is “Giant box” (468 x400px), which are sold based on the number of page views. If the list price for this ad format is 20€ per 1000 page views, customer A has generated absolute visitor value of 100€ ((5000 / 1000) x 20€). New incoming traffic is highly valuable for most of the companies operating online in terms of potential subscribers as well as in form of ad inventory. Customer A has brought in 100 new visitors through shared links. These persons have generated at least one page impression each by landing the news site. If calculated in the same manner the value of these visits are (100/1000 x 20€) 2€ in one month. By summing up the monetary, visitor and social value (10€ + 20€ + 2€) of customer A, he/she obtains customer relationship value for the company of net worth of 32€ in one month period.

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ACKNOWLEDGEMENT

The author gratefully appreciates the grant of Research Foundation of Central Association of Communications and the Foundation of Åkerlund, as well as the comments received for this manuscript from Professor Jari Salo, University of Oulu, Oulu Business School and from Lecturer, Ms Ilona Mikkonen, Aalto University and also from Senior Lecturer, Mr Sami Kajalo, Aalto University.

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