Type-II error is to contact a customer who is not ready to purchase and is costly ... gap between the importance of website elements in view of the customers that ...
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Maik Hammerschmidt and Tobias Donnevert
Combining Customer Metrics: The Customer Value-Customer Equity-Framework A company’s online marketing investments play a pivotal role for building and maintaining relationship. Over the past years online marketing investments have in average increase by 35% per year (BVDW 2010). 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 value-adding services. Thus, organizational deficiencies and shortfalls in designing and operating websites are seen as a main source of poor performance of relationship marketing (Donthu 2001; Zeithaml, Parasuraman, and Malhotra 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, Watson, and Goodhue 2002). The sales loss due to inadequate website design amounts to several billion dollars per year (Rust and Lemon 2001). Similar to products and services, websites have to bed designed in a way that they offer added value to the customer (Novak, Hoffman, and Yung 2000). Thus, it is necessary for marketing managers to design website aligned to the requirements and needs of their customers and not based on technical feasibility (Thompson, Hamilton, and Rust 2005). The ability to meet expectations and needs of customers and hence assuring closeness to the customer reflects the effectiveness of online marketing (Parasuraman, Zeithaml and Malhotra 2005). Given the multitude and heterogeneity of customer needs the ability to prioritize these needs based on an appropriate metric is a key prerequisite for achieving superior effectiveness of website design and hence online marketing. Such an economic evaluation makes it possible to identify those
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website elements and features that should be implemented first and on which resources should be concentrated. The most appropriate metric for prioritizing customers and consequently customer requirements is customer profitability (Reinartz and Kumar 2000). Venkatesan, Kumar, and Bohling (2007, p. 579f.) nicely put this issue: “Given an unlimited marketing budget, managers can contact all their customers at every time period. Such a strategy minimizes the Type-I error of not contacting a customer who could have potentially provided revenue. However, minimizing Type-I error also maximizes a so-called Type-II error. A Type-II error is to contact a customer who is not ready to purchase and is costly in terms of adversely affecting the bottom line. When faced with a limited marketing budget, the tradeoffs between Type-I error and Type-II error are highlighted, and managers are forced to prioritize their communication strategies towards customers who are expected to provide the highest growth in cash flows, i.e., customer selection.” Literature typically proposes that the internet is a cost-minimizing instrument for personalized (i.e., customer-specific) communication leading to the opportunity to mass customize while keeping investments low. Hence, one would infer that the issue of trading off Type-I and Type-II errors are negligible. Although this might be the case for mailings, in case of websites low marginal costs can only be ascertained with an undifferentiated communication; using a differentiated approach for addressing customers (personalization) will probably enhance marginal costs significantly (Schlosser, White, and Lloyd 2006). For example, continuously adapting a single website e.g., due to new or improved product offers already requires intense investments (e.g., for coordinating the content, deciding on the mode of information, testing, approvals by business departments etc.). Respective investments explode if firms have to adjust the content for different target groups. As marketing budgets are limited in online marketing as well, a differentiated communication approach and hence a prioritization of customers is inevitable in order to master the trade-off between Type I and Type II error
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(effectiveness of online marketing). In view of the problems discussed previously, prioritizing customers and thus selectively addressing customers is particularly crucial on the internet. To systematically 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, Zeithaml, and Berry (1985). First, there might be a gap between customer expectations and IT and marketing manager’s perceptions of these customer expectations (information gap). 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 unlimited possibilities to design a website (Grönroos et al. 2000). Thus, without an appropriate metric for investigating the needs and requirements of customers 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 perceived customer value (utility) 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 online marketing context. 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 customer benefit-customer equity matrix. By applying this concept, first it is possible to unveil the wants and needs of online customers ("value to the customer") by clustering them into benefit segments. Second, by assessing the equity of each benefit
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segment ("value of the customer") the website can be designed according to the benefit expectations of valuable segments. Both steps are necessary to effectively translating perceived customer value into customer profitability.
Conceptual Framework Benefit Segmentation 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 and Perdue 1992; Tynan and Drayton 1987). A customer with stable socio-demographic characteristics might assign completely different benefits to one and the same product depending on situational, usage or time-related factors. In other words, the same product might create different value to a customer based on the factors mentioned above. In order to take this into account, market segmentation should be conducted independent of personal characteristics or „objective“ functional product features. Only if firms consider the value perceptions of different product or service characteristics they can link the product or service (object) to the customer (subject). However, identifying benefit segments is only a necessary condition for achieving superior marketing performance. Marketers need a metric to valuate customers and to select the “right segment(s)” or to decide which needs have to be prioritized 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 and allocate resources accordingly. This makes it possible for companies to implement a differentiated services and instruments for different tiers of their customer base. Such differentiation of marketing efforts is supposed to lead to higher firm profits as marketing efforts become more focused when they are employed to
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meet (and preferably exceed) the expectations of the top-tier customers (Rust, Lemon, and Zeithaml 2004). The high profit relevance of customer prioritization is supported by the finding that the relationships with bottom-tier customers - which are given lower priority - are not negatively affected (Homburg, Droll, and Totzek 2008).
Customer Valuation As we aim to support future marketing decisions we need to predict the profitability potential of the benefit segment members, i.e., a profitability measure that covers future stages of the customer life cycle is needed. Prior research has proposed two fundamental categories of predictors of long-term customer profitability (Berger et al. 2002; Malthouse and Blattberg 2005; Venkatesan and Kumar 2004): socio-economic and purchase behavior/transaction related predictors. In the first category occupation and age have shown to be the most powerful predictors of customer profitability (Cooil et al. 2007; Venkatesan, Kumar, and Bohling 2007). In the second category, the three dimensions recency, purchase volume and willingness to pay are distinguished (Venkatesan, Kumar, and Bohling 2007). In our study we capture recency by the age of the previously purchased car. The older the currently used car, the higher the probability of a replacement purchase (Bayus 1991). For capturing the dimension purchase volume we used type of the previously purchased brand. If the brand of the firm considered in this study (a luxury car manufacturer) was purchased the highest profit score was assigned as the potential for buying the same brand in the future is highest. Similar brands received the same score. For low-tier brands (volume brands) lower scores were assigned (Verhoef, Langerak, and Donkers 2007). The second indicator of purchase volume was type of currently used car. If the currently owned car was a high-price car model a high score was assigned. As the third indicator for purchase volume we used number of cars owned in household. This indicator is appropriate as for households that own multiple cars the likelihood of buying the focal high-tier car brand as a second or third car (e.g., for other
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family members) is higher and hence projected purchase volume is higher. For capturing willingness to pay we used the price of the currently owned car. In addition we considered the condition of the currently used car (new or second hand car) as purchasing a new car reflects a greater willingness to pay a price premium for quality and reliability. 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, Blattberg, and Fox 2004). This belief is consistent with other research findings. For example, Bolton, Kannan and Bramlett (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, Kannan, and Bramlett 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 regression-based scoring model because it allows to include non-monetary predictor variables which were identified as important in the previous section (behavioral, socio-demographic). Moreover, using a scoring model they can be weighted flexibly according to their importance. As discussed previously, predictors of future purchase behavior have to be integrated when determining customer lifetime value. Scoring models have been successfully employed for predicting customer spendings in many industries, e.g., car manufacturers or internet companies (Rossi, McCulloch, and Allenby 1996, Yang and Allenby 2003). Recently, also “new economy” firms like Amazon have begun to use scoring models. Surprisingly, there is little academic research to date that examines whether and how scoring models can be applied on the internet (Bolton, Lemon,
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and Verhoef 2004). Thus, there is a call for scientific studies that apply scoring models in online-marketing (Bolton, Lemon and Verhoef 2004; Boyer and Hult 2005). Although benefit segmentation and customer valuation are well established concepts, the usefulness of a combined application of both concepts to segment and valuate users of a website has not been empirically demonstrated so far. This is surprising because the internet 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 customers according to their needs and then calculate customer equity for each benefit segment by aggregating the individual customer profitability of the segment members (Bauer and Hammerschmidt 2005). This linkage results in a benefit-equity matrix.
Study Design and Methodology Identifying Value-Creating Website Features 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 and Arnett (2000) and Yoo and Donthu (2001). The authors suggest four basic categories of features that provide benefit for customers: information and service quality, usability and security (system use), fun/playfulness and design. Based on an extensive literature review (Barnes and Vidgen 2001; Liu and Arnett 2000; Novak, Hoffman, and Yung 2000; Parasuraman, Zeithaml, and Malhotra 2005; Shankar, Smith, and Rangaswamy 2003; Srinivasan, Anderson, and Ponnavolu 2002; Szymanski and Hise 2000; Yoo and Donthu 2001; Zeithaml, Parasuraman, and Malhotra 2002; Wolfinbarger and Gilly 2003) a large quantity of possible value-creating website features were obtained for these four categories. This procedure was supplemented by expert interviews and website analyses.
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As a consequence, 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 and 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 and 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 and Hauser 1993).
Data Collection To collect the data for benefit segmentation we administered the questionnaire with the identified 55 website features to a random sample of visitors of a large multinational car manufacturer’s website in Germany. This process yielded a total of 2,161 usable questionnaires. Following the previous argumentation with respect to measuring the importance of the website attributes, 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
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important). The frequency distribution for each attribute did not show a tendency to an “inflation of demands” in the importance ratings. A second part of the questionnaire related to the predictor variables that were necessary to determine the future revenue potential of each respondent of the main study. As conceptually discussed above we collected three groups of predictor variables: socio-demographic (age), socio-economic (occupation; number of cars owned by customer) and behavioral [previous purchase (index of brand, type and age of the recently purchased car); whether the previously purchased car was a new or second-hand car].
Methodology Hierarchical Cluster Analysis for Identifying Benefit Segments. Hierarchical cluster analysis using the 13 standardized benefit dimensions was applied to identify benefit segments. The main drawback of hierarchical cluster analysis is that the determination of the number of clusters might be arbitrary and subjective. We attenuate these shortcomings by using probabilistic techniques for determining the optimal number of clusters (Cubic Clustering Criterion and Pseudo-F-Criterion). 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 and 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 again using squared Euclidean distance. In order to reduce the subjectivity when determining the number of clusters we used the following procedure in step 2: Considering the dendrograms we found that a solution with 4, 5 and 6 clusters could be justified. To decide on the optimal number we used the Cubic Clustering Criterion (CCC) and Pseudo-F-Criterion (PSF). The local
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maximums of CCC and PSF consistently indicated an optimal solution with 5 clusters. In addition, we examined the interpretability of the three possible solutions. All three solutions were calculated and described according to passive segmentation criteria (sociodemographics, usage behavior, brand loyalty and repurchase rates). In cooperation with marketing and market research experts of the focal automotive company the 5 cluster solution was deemed optimal. For each of the five identified clusters we determined the centroids (cluster means). Third, the cluster centroids for each benefit dimension were used as the starting point for KMeans thereby refining the Ward-solution until the distance to the respective centroid was minimal for all customers. Regression-Based Scoring Model for Predicting Customer Profitability. To calibrate the scoring model we run a regression analysis based on data of 2,182 existing automobile owners which were contained in the database of the car manufacturer and on which we had access. This calibration sample contained customers of the focal manufacturer as well as buyers of competing brands. The buyers are distributed equally across different car categories. Using the data of the calibration sample we run a regression analysis with the predictor variables as independent variables and revenue as dependent variable. We used revenue as the basis for valuating customers because due to the integration of customers of competitors we had no access on internal cost data for the different car manufacturers. Moreover, it seems unrealistic to estimate customer-specific marketing and sales cost based on predictors. It is difficult to reliably predict future marketing and sales costs of prospective customers as it is unknown how many mailings will be sent out to these customers or how long a service employee will interact with the customer. In addition, cost data for previous periods are by definition non available for future customers. Thus, it seems prudent to capture the economic worth of customers through revenues (Cornelsen 2003; Pels
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and Jaconelli 1990). Even though the firm’s products are durable goods with long replacement cycles, they require constant maintenance, which provided the variance required in modeling customer revenues. Thus, we included after-sales revenues (equipment, repair services, maintenance etc.) in addition to direct revenues from car purchases in order to assure sufficient variance in customer valuation and a nuanced gradation of purchase intensity. As we consider different brand and car tiers (volume, premium, luxury) we comprehensively cover the German automobile market and ascertain a broad spectrum of customer values. We used another 2,182 customers of the car manufacturer’s database as a holdout sample and re-estimated the regression model for this sample. For all predictors the coefficients were significant at p < .001 with except of “number of cars owned” (p < .10). The model had an R2 of 35.3% in the calibration sample and 35% in the holdout (validation) sample which is satisfactory given the cut-off levels proposed in the literature (Gelbrich 2001). The VIF (variance inflation factor) does not indicate multi-collinearity problems. Plotting the residuals against the estimated values of the dependent variable shows that heteroskedasticity is not a threat. As all independent variables exhibit high predictive validity for future revenue they were included in the scoring model.
Data Analysis and Results Extracting Benefit Dimensions For conducting meaningful benefit segmentation and to reduce complexity of the analysis the website features had to be condensed to fundamental benefit dimensions. Using exploratory factor analysis with Varimax rotation we extracted 13 benefit dimensions (e.g., product information, multimedia experiences, online shopping opportunities, navigation and ease of use) which explained 67% of the variance. The extracted dimensions are shown in Table 1. All factors displayed high reliability with Cronbachs alpha ranging from .67 to .93.
12 TABLE 1 Extracted Benefit Dimensions Benefit Dimensions
Website Features
1. Product Information on New Cars and Test Drive
Information on New Cars Car Configurator Car Presentation in 3D Information on Extra Equipment, Accessories and Personalization Online Material and Ordering Catalogues Possibility for Online-Booking of Test Drives
2. Multimedia Experience
Car Videos Car Sounds High Res Car Pictures 3D Animations Flash Animations Visualization of Technical Details Screen Saver Virtual Plant Tour Virtual Trade Fair Visits
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Financial Service and Leasing Offers
Financing and Leasing Decision Tools Information on Financial Services / Leasing Online Financial Application
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Information on Second Hand Cars
Information on Second Hand Cars Information on Stock and Assortment of Car Dealers Information on Repair and Maintenance
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Information on Clubs and Classics
Information on Clubs Information on Motor Sports Information on Restauration Information on Predecessor Models Information on Current Events Information on Newsletter
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Offer of Owner Site
Exclusive Owner Community Information for Customers via E-Mail Contact Form
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Information on Jobs and Career Opportunities
Information on Jobs and Career Opportunities Information on Firm
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Challenge
Games Guestbook Chatroom Electronic Postcards Fan Area
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Online Shopping Opportunities (Accessories)
Assortment of Online Shop
10. Online Shopping Opportunities (Travel and Customer Magazines)
Assortment Travelling Different Payment Forms Order Tracking Online Subscription Customer Magazine
13 11. Online Shopping Opportunities (Cars)
Online Order / Purchase of New Car Online Order Second Hand Car
12. Navigation and Ease of Use
Navigation Design Alternative Modes of Navigation Guided Tours Personalized Web Content Adjusted to Usage Behavior
13. Structure and Clarity of Website
No Flash Moduls Downward Compatibility Path Navigation Speed of Loading Website
Benefit Segmentation of Customers Table 2 shows the five clusters and their expected benefits in terms of the website features which are perceived as most important. Obviously, customers differ significantly according to their sought benefits. Not surprisingly, only the requirements related to usability are quite similar across all clusters. It has to be noted that features with low importance should not be eliminated from further consideration as they may represent minimum or basic requirements (Kano 1984). Here, providing a quality level that is in line with market standards is necessary. However, firms should avoid further investments into these features as for these factors quality improvements show diminishing returns to satisfaction; hence, resources should be reallocated to important features. We identify two undifferentiated clusters containing customers who either have no demands (cluster 1; „Passionless User“) or perceives most features as highly important (cluster 5; „Fans“). In between, we extract three clusters with “differentiated” benefit requirements. The first differentiated segment is primarily interested in information supporting car purchase and is labeled “Car Shoppers“. A second differentiated segment („Experiential Shopper“) assigns high importance to multimedia and fun-related website elements. The members oft he „Symbolic Shopper“ segment show high interest in symbolic or status-related products; that is
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new cars and merchandise products that communicate the brand logo of the manufacturer considered in this study. TABLE 2 Description of Benefit Segments Benefit Segment
Benefits
1. Passionless User (“I want nothing“)
No feature is of significant importance
2. Car Shopper (“No frills”)
Straight forward users; just want information about new cars as well as finance and leasing; they use owner community
3. Experiential Shopper (“Entertain me”)
Want information about new and used cars; book trips; use multimedia elements
4. Symbolic Shopper (“See and be seen”)
Want information about new cars and buy merchandising accessories communicating the brand logo
5. Fans (“I want everything”)
Nearly all features are important; the love the entire website
Valuation of Customers The Beta coefficients obtained by the regression analysis outlined above were used to determine the relative importance weights for the predictors which are as follows: previous purchase1 (.40); occupation (.24); new/second-hand car (.18); age (.12) and number of cars in household (.06). The high weight for previous purchase is consistent with the findings on the importance of previous purchase experiences for future purchase decisions discussed above. That is, the probability of repurchasing a similar product type (e.g., sports car) or similar brand (e.g., premium brand) increases with the length and intensity of using them in the past (Thomas, Blattberg, and Fox 2004). For each level of the predictor variables a certain score was assigned and weighted with the Beta coefficients. The scoring model was then applied to predict the revenue potential of the respondents of our survey. Using this approach we can transfer knowledge on existing 1
The variable „previous purchase“ is an index of the predictors “currently owned brand”, “type of currently owned car”, “price of currently owned car” and “age of currently owned car”.
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customers profiled in the database to unknown and prospective customers. Specifically, we use the scoring model to predict the revenue potential of the (prospective) customers contained in our study as a function of the socio-economic and purchase behavior-related predictors. By multiplying the achieved score for each predictor variable with the respective importance weight determined by regression 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" customers (all customers with scores ranging in the upper third of the scale) and "cold" customers (the remaining customers).
Integrating Benefit Segmentation and Customer Valuation After closing the information gap by identifying the major benefits relevant for the different segments and valuating website users applying the scoring model we now have to integrate the results so marketers can prioritize benefit segments. This allows closing the second gap (implementation gap). The integration of the two metrics leads to the benefit–equity matrix shown in Figure 1.
16 FIGURE 1 Benefit-Equity Matrix
Combining both dimensions results in ten 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. For example, 29% of the valuable users are “Car Shoppers”. The bottom right-hand figures show the size of each cell (i.e., of each benefitequity combination). Summed vertically these figures yield the size of each equity segment; for example the equity segment “hot users” contains 40% of all customers. Summed horizontally we obtain the size of each benefit segment (i.e., the benefit segment “Car Shoppers” covers 20% of all customers). The final column exhibits the fraction of members of a specific benefit segment that is hot; and logically also the percentage of cold members in this segment. For example, the fraction of hot users in the benefit segment “Car Shopper”
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amounts to 60% implying a fraction of 40% cold users. Hence, the metric in the final column indicates the equity of each benefit segment (i.e., segment-specific customer profitability).
Discussion Research Implications By using the benefit-equity matrix developed in this study marketers can answer two central questions: First, are valuable customers 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 customers in each benefit segment? In this context also the size of the benefit segments (see the bottom 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 customers. Thus, only three segments exhibit high fractions of valuable customers. Considering the distribution of hot customers 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 follow a single-segment strategy, i.e., 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 other segments. Would the website be tailored on the needs and requirements of “Car Shoppers”, the needs of approximately 70% of hot customers would be ignored. In contrast, an undifferentiated “mass strategy” would likewise be inappropriate because there are features (e.g., compatibility or challenge) that do not create value for all segments. According to our results a selective strategy, i.e., addressing segments
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2, 3 and 4 is beneficial. Firms should assure that the website is particularly attractive to these customer groups. Concerning the second question, targeting segment 2 (Car Shoppers) would yield the most effective employment of marketing resources, because this segment contains the highest share of valuable customers, resulting in the lowest risk of spillover effects. Thus, the probability to reach a valuable customer is highest in this segment. Additionally, this segment exhibits a sufficient size (20% of all customers). Finally, the cell of “Valuable Car Shoppers” is the third largest of all cells (12%). Hence, addressing the “Car Shopper” segment would yield the highest return on marketing investment. In sum, superior marketing effectiveness can be achieved if a website is designed in a way that needs of the high-tier customers are fulfilled.
Managerial Implications The following key implications can be derived from our combined benefit segmentationcustomer valuation framework: There is no dominance of one benefit segment regarding customer equity. Thus, by following a single segment strategy, the company would forgo the economic potential of other segments. For example, targeting the segment “Car Shoppers” exclusively would lead to ignoring the needs of 71% of hot customers. Nevertheless, our results clearly show that customer equity of a segment depends on how differentiated their benefit requirements are. Thus, unfocused segments which expect everything (“Fans”) or nothing (“Passionless Users”) from the website contain significantly smaller shares of valuable customers. Contrary, segments with differentiated usage behavior have a significantly higher revenue potential. The probability to catch a valuable customer is highest for customers who are interested in product information and online-shop offers.
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Clearly, the segment of “Car Shoppers”, which consists to 60% of hot customers, 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 customer, 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, Janiszewski, and Neumann 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 individual needs of different segments simultaneously. However, firms should keep in mind that previous studies by marketing research institutes have shown that it takes in average 3 years until a segment-specific orientation of a website has been successfully implemented (Saeed, Grover and Hwang 2005). Moreover, Poole (2001, p. 1) points out that „E-tailers often realize that they’ve plunked down millions of dollars to build a site that leaves visitors muddled and annoyed“. Thus, long-term investments into experience-based knowledge and capabilities of employees responsible for designing and restructuring websites notion are necessary to achieve personalized websites (Saeed, Grover and Hwang 2005). In addition, before launching new Website features like personalized avatars expensive tests and interactions with different functional areas within a firm are necessary. For example, a sophisticated usability test with only 200 users (provided e.g., by established Web marketing agencies like Vividence) cost in average $35,000. McKinsey &
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Company reported that reorganizing customer services on its Website resulted in a loss of $16 million (Meuter et al. 2005); possibly because they were not aligned tom the right customer segments. Taking into account these enormous efforts it becomes apparent that also on the internet not all segments can be addressed economically meaningful. Hence, prioritizing valuable segments is imperative also in online marketing. In line with the findings of Reinartz, Thomas and Kumar (2005) we suggest that „marketing overspendings“ on unattractive segments will probably result in significant ROI decline. The same holds true for „underspending“ on valuable segments. In sum, this study shows clearly the value of combining established metrics to solve an emarketing problem, in our case to ensure not only customer benefit orientation but additionally marketing profitability. Conducting an integrated analysis of customer benefits ("value to the customer") and customer equity ("value of the customer") an optimal allocation of marketing resources can be reached by identifying which benefit segments to prioritize.
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