How to Combine Customer Equity and Customer Value in an Online Environment Tobias J. Donnevert, Maik Hammerschmidt, Hans H. Bauer, University of Mannheim Abstract The annual web sales loss due to inadequate Website design amounted to several billion dollars. Consequently, investigating Website visitors’ needs and targeting the most attractive segments are two fundamental tasks for relationship marketing. This paper integrates the established concepts of benefit segmentation and customer valuation. Based on data of 2,161 website users we conduct a hierarchical cluster analysis and combine the results with equity segmentation. As a result we develop the “user benefit- user equity- framework”. Our results show, that there is no dominance of one benefit segment regarding user equity. However, segments with differentiated benefit requirements exhibit significantly higher customer equity than unfocused segments. Thus, a selective strategy is promising. Introduction A company’s Website assumes a pivotal role in relationship marketing. Online marketing investments will increase by 35% in 2007 (BVDW 2007). Websites are the critical interface between a buyer and a seller. Furthermore, as the factors “physical facilities” and “appearance of personnel” are irrelevant on the Internet, the website is the vehicle for delivering valueadding services. Thus, organizational deficiencies and shortfalls in designing and operating websites are seen as a main source of poor performance of Internet relationship marketing (Donthu 2001; Zeithaml et al. 2002). Inadequate website design leads to a decrease in the number of visitors, shorter visiting times, abandoning of Internet shopping carts and lower intention to revisit and recommend the site (Donthu 2001; Loiacono et al. 2002). To explore the reasons why many firms struggle to design Internet offerings in a way that creates good customer appeal and assures profitability, we draw on the Gap model by Parasuraman et al. (1985). First, there might be a gap between customer expectations and IT and Marketing manager’s perceptions of customer expectations (information g a p ) . The information gap becomes particularly crucial in online environments due to the fact that wants and needs on any given attribute vary heavily across online users and contexts. At the same time there are limitless possibilities to design a website (Grönroos et al. 2000). Thus, without an appropriate metric for investigating the needs and requirements of online users the probability to achieve a match between customer expectations and management perceptions of these expectations is marginal at best. Second, there might be a gap between the importance of website elements in view of the customers that should be prioritized and the management’s assumptions of the importance of website features (implementation gap). Thus, marketers need a metric to prioritize the needs of online customers. In order to close the information gap, we highlight customer value/benefit as the appropriate metric. For closing the implementation gap, decisions should be based on customer profitability (equity). To address both gaps, this paper transfers the established concepts of benefit segmentation and customer valuation to the Internet. Moreover, in order to achieve long term marketing performance both gaps have to be closed simultaneously. Thus, both metrics must be combined in the analysis. To integrate both metrics we develop the user 3038 benefit- user equity- matrix. Applying this concept, it is possible to unveil the wants and needs of website visitors ("value to the user") by clustering them into benefit segments. Second, by assessing the equity of each benefit segment ("value of the user") the website can be designed according to the benefit expectations of valuable segments. Conceptual Framework Benefit segmentation has become the preferred technique for market segmentation because it segments customers on the basis of their needs. The fulfillment of benefit expectations through products or services entails the driver of purchasing a product or patronizing particular suppliers (Loker & Perdue 1992; Tynan & Drayton 1987). But unveiling benefit segments is only a necessary condition. Marketers need a metric to valuate customers and to select the “right segment(s)” or to decide which needs to consider as more important if they are conflicting. The metric to prioritize segments should be customer profitability in order to target segments that most contribute to the enhancement of firm value. As we aim to support future marketing decisions we need to predict the profitability potential of the benefit segment members. Prior research has shown that often a few predictors suffice to get valid estimations of customer profitability (Malthouse & Blattberg 2005). Here, socio-demographic, economic and purchase behavior related predictors are suggested in the literature (Venkatesan & Kumar 2004; Berger et al. 2002). Although weaknesses in those ex post measures have been identified, empirical evidence shows that customers who have bought most recently and more frequently in the past have the highest monetary value and are more likely to respond favorably to subsequent offers (Thomas et al. 2004). This belief is consistent with other research findings. For example, Bolton et al. (2000) find that experience with a product or service, measured by the number of prior transactions, is strongly associated with a higher likelihood of repatronage and accelerating repurchases in the future. The high predictive validity of ex post measures can be explained by the customer’s desire to maintain the status quo (Bolton et al. 2000). Thus, it can be asserted that previous-period measures drive customer expectations and intentions and therefore drive future purchase behavior. To predict future purchases we apply a scoring model because it allows to include such non- monetary variables (behavioral, socio-demographic), which can be weighted flexibly according to their importance. Scoring models have been successfully employed for predicting customer spendings in many industries, e.g. car manufacturers or Internet companies (Rossi et al. 1996, Yang & Allenby 2003). Nevertheless, there is little academic research to date that examines their applicability in an online context (Bolton et al. 2004). Although benefit segmentation and customer valuation are well established concepts, the usefulness of a combined application of both concepts to segment and valuate visitors of a website has not been empirically demonstrated so far. This is surprising because the WWW is a medium very much in line with the fragmented nature of modern markets. This underscores the marketer’s need for understanding how website users differ in their behavior and in the value they create for the company (Sen et al. 1998). Therefore, in this paper we cluster visitors of a website according to their needs and calculate user equity for each benefit segment. This linkage results in a user benefit- user equity- matrix. 3039 Study Design Item generation and scale development: To conduct benefit segmentation we first derived fundamental benefit dimensions of a website. To generate value-creating website features we followed the framework proposed by Liu & Arnett (2000) and Yoo & Donthu (2001). The authors suggest four basic categories of features that provide benefit for users: Information and Service Quality, Usability and Security (System Use), Fun/Playfulness and Design. Based on an extensive literature review (Barnes & Vidgen 2001; Liu & Arnett 2000; Novak et al. 2000; Para-suraman et al. 2005; Shankar et al. 2003; Srinivasan et al. 2002; Szymanski & Hise 2000; Yoo & Donthu 2001; Zeithaml et al. 2002; Wolfinbarger & Gilly 2003) a large quantity of possible website features were obtained for these four categories. This procedure was supplemented by expert interviews and website analyses. A set of 110 items representing all facets of a website formed the initial pool of website attributes. Using insights from focus group discussions and expert interviews we eliminated confusing or redundant items; reworded some others in order to improve clarity. This procedure resulted in 55 items that entered the final questionnaire. The importance of website features was used to measure the perceived benefits delivered by the features (Gustafsson & Johnson 2004). For determining attribute importance, two general approaches are supposed in the literature: direct importance ratings and indirect, statistical determination (revealed importance). Counter to the mantra of the superiority of statistical importance estimation, the results of the meta-analysis conducted by Gustafsson & Johnson (2004) show that direct importance ratings entail a higher reliability than indirect measures because they provide more stable weights. Moreover, direct measures are more correlated with preferences than statistically derived measures. Finally, direct ratings are more future-related as they show a higher predictive validity for customer loyalty than statistical estimations. These evidences are consistent with other findings which show that both types of importance evaluation lead to similar results in most cases (Griffin & Hauser 1993). Data Collection and analysis: To collect the data we administered the questionnaire to a random sample of visitors of a large multinational car manufacturer’s website in the USA and in Germany. This process yielded a total of 2,161 usable questionnaires (1,047 from the USA, 1,114 from Germany). Following the previous argumentation, we asked respondents to judge the relative importance of each attribute directly on a 7-point scale ranging from 1 (not at all important) to 7 (extremely important). The frequency distribution for each attribute did not show a tendency to an “inflation of demands” in the importance ratings. Using exploratory factor analysis w e extracted 13 benefit dimensions (e.g., product information, multimedia experiences, shopping possibilities, ease of navigation) which explained 67% of the variance. All factors displayed high reliability with Cronbachs alpha. Hierarchical cluster analysis using the 13 standardized benefit dimensions was applied to identify benefit segments. We chose a three step approach combining different techniques in order to overcome the problems of outlier sensitivity and subjective determination of the number of clusters (Green & Krieger 1995). First, we identified and eliminated outliers using Single-Linkage algorithm (nearest neighbor) whereby Squared Euclidean distances were used as proximity measure. Second, Ward's procedure identified the optimal number and centroids of clusters. Third, after optimizing results from Ward's procedure using K-Means five clusters could be identified. To predict the revenue potential of each respondent we collected three groups of predictor variables that have been shown to be valid predictors of customer profitability (Venkatesan & Kumar 2004; Malthouse 2002; Reinartz & Kumar 2003): socio-demographic (age), socioeconomic (occupation; number of cars owned by customer) and behavioral (recent purchase 3040 [index of brand, type and age of the recently purchased car]; whether it was a new or secondhand car. To calibrate the scoring model we run a regression analysis based on data of 2,182 automobile owners which were contained in the database of the car manufacturer. This sample contained customers of the manufacturer and buyers of competing brands. The buyers are distributed equally across different car categories. We used another 2,182 customers of this database as a holdout sample. We selected the revenue as our dependent variable and the predictors as independent variables. Even though the firm’s products are durable goods, they require constant maintenance, which provided the variance required in modeling customer revenues. For all predictors the coefficients were significant at p < 0.001 with except of “number of cars owned” (p = 0.1). The model had an R2 of 35.3% in the calibration sample and 35% in the holdout (validation) sample. As all variables show a high predictive validity they were included in the scoring model. The Beta coefficients were used to obtain t h e relative importance weights for the predictors which are as follows: recent purchase (0.40); occupation (0.24); new/second-hand car (0.18); age (0.12) and number of cars owned (0.06). For each level of the predictor variables a certain score was assigned. The scoring model was then applied to predict the revenue potential of the respondents of our survey. Empirical Results Benefit segmentation: Table 1 shows the five clusters and their expected benefits in terms of the website features which are perceived as most important. Obviously, users differ significantly according to their sought benefits. Not surprisingly only the requirements related to usability are quite similar across all clusters. We identify two undifferentiated clusters who either has no demands (cluster 1) or perceives most features as highly important (cluster 5). In between, we extract three clusters with “differentiated” benefit requirements. Cluster Benefits 1. Passionless User (“I want nothing“) No feature is of significant importance 2. Car Shoppers (“No Frills”) Straight users, just want information about new cars as well as finance and leasing; they use owner community 3. Experiential Shoppers Want information about new and used cars; book trips; use multimedia elements 4. Symbolic Shoppers Want information about new cars and buy merchandising accessories showing the brand logo 5. Fans (“I want everything”) Nearly all features are important, they love the web site Table 1: Benefit Cluster Valuation of website visitors: We use the scoring model to predict the revenue potential of prospective customers. By multiplying the achieved score with the importance weight for each variable and summing up over all variables a total revenue score was calculated for each respondent. Based on the scores respondents were classified into two groups: "hot" users (all users with scores ranging in the upper third of the scale) and "cold" users (the remaining users). Integrating benefit segmentation and valuation: After closing the information gap by identifying the major benefits relevant for the different user segments and valuating website visitors using the scoring model we now have to integrate the results so marketers can 3041 prioritize benefit segments. This allows closing the second gap (implementation gap). The combination of the two metrics leads to the user benefit–user equity–matrix shown in Figure 1. The combination of both dimensions results in nine cells, each representing different combinations of benefit and equity segments. The top left-hand figures in both equity segment columns have to be read “vertically” and show the distribution of valuable (hot) and less attractive (cold) users across the benefit segments. By using this matrix marketers can answer two central questions: First, are valuable users concentrated on certain benefit segments? Second, how precisely can each segment be addressed from a value-based perspective, i.e. what is the share of hot users in each benefit segment? In this context also the size of the benefit segments (see the down right-hand figures) is relevant which indicates whether addressing the segment is economically viable. Concerning the first question the results clearly show that segments with differentiated benefit requirements and therefore focused usage behavior exhibit above average customer equity. Contrarily, undifferentiated segments contain significantly less valuable users. Considering the distribution of hot users across the five benefit segments (column 2) it can be seen that there is no dominance of one benefit segment with respect to customer equity. Thus, it seems inappropriate to a priori align the website exclusively to a single segment (e.g., car shoppers). Obviously, when following this strategy the firm would forgo high economic potential. In contrast, an undifferentiated “mass strategy” would likewise be inappropriate because there are features that do not create value for all segments. According to our results a selective strategy, i.e. addressing segments 2, 3 and 4 is beneficial. Firms should assure that the website is particularly attractive to these user groups. Concerning the second question, targeting segment 2 (car shoppers) would yield the most efficient employment of marketing resources, because this segment contains the highest share of valuable users, resulting in the lowest risk of spillover effects. Additionally, this segment exhibits a sufficient size (20% of all users). Finally, the cell of “valuable car shoppers” is the third largest of all cells (12%). 5. Discussion The following key findings can be derived from our combined benefit segmentation-user valuation-framework: There is no dominance of one benefit segment regarding user equity. Thus, by following a single segment strategy, the company would forgo the economic potential of other segments. Would the website only target the segment “Car Shoppers”, the needs of 71% of hot users would be ignored. 3042 Figure 1: User Benefit–User Equity–Matrix Nevertheless, our results clearly show that user equity of a segment depends on how differentiated their benefit requirements a r e . Thus, unfocused segments which expect everything or nothing from the website contain significantly smaller shares of valuable users. Contrary, segments with differentiated usage behavior have a significantly higher revenue potential. Clearly, the segment of “Car Shoppers”, which consists to 60% of hot users, is the most attractive one. The “Experiential Shoppers” occur as a second segment to which marketing resources should be allocated. Targeting these segments entails the highest probability of addressing a valuable user, and marketing resources can be employed with the smallest spillover effects. Thus, these segments yield the highest "return on marketing investments". Targeting both attractive segments could be implemented through personalization and differentiation. Using guided tours, quick links or virtual shopping agents (so called avatars), visitors can be directed to their desired website features or information, without aligning the site exclusively to the needs of one or two clusters. Especially, avatars as identification figures, website guides, conversation partners or individual recommenders have the potential to fulfill the consumer’s desire for a more individual and interpersonal shopping experience ( Holzwarth et al. 2006). The possibility to choose from different avatars (e.g., attractive vs. expert avatar) or to configure a personalized avatar makes it possible to fulfill the needs of different segments simultaneously. In general, this study shows clearly the value of combining established metrics to solve an e- marketing problem, in our case to ensure not only customer benefit orientation but additionally marketing efficiency. 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