Ignoring Your Best Customer? An Investigation of Customer Satisfaction, Customer Retention and Their Financial Impact

Transcription

1 Ignoring Your Best Customer? An Investigation of Customer Satisfaction, Customer Retention and Their Financial Impact Baohong Sun, Ronald Wilcox and Ting Zhu 1 1 Baohong Sun is an Associate Professor of Marketing at Tepper Schoo of Business of Carnegie Mellon University. Ronald Wilcox is an Associate Professor of Business Administration at The Darden School, University of Virginia and Ting Zhu is a PhD student at the Graduate School of Industrial Administration, Carnegie Mellon University. 1

2 Ignoring Your Best Customer? An Investigation of Customer Satisfaction, Customer Retention and Their Financial Impact Abstract Many organizations devote considerable amounts of money and human resources to develop systems aimed at improving customer retention and profitability. The conventional wisdoms is that if retaining the most profitable customers is a good way to increase profitability, then allocating resources to increase the satisfaction of those customers has to be a great objective. However, managers do not observe clear link between satisfaction, retention and profitability. The reason is that different customers have different preferences for convenience as well as different costs associated with switching service providers. These preference and cost heterogeneities have important implications for how companies should target their customer service efforts. In this paper, we adopt a latent class model to examine the interrelationship among satisfaction, retention and profitability. Applying the model to a data of customer satisfaction, self-reported switching propensity, and profitability provided to us by a large Midwestern bank, we make normative statements about which customers are the most critical ones for the company to satisfy and how to satisfy them. The results of this research help to explain why managers have been frustrated by the apparent lack of actionable information present in customer satisfaction data and points to more intelligent ways to use this data. Keywords: customer service, customer satisfaction, customer retention, profitability, latent class model 2

3 Assessing the value of customer satisfaction has certainly become frustrating to executives, because businesses need a quantifiable metric that is tied to actual business results. - Christopher Fay, Principal, The Parthenon Group 1. Introduction One of the most fundamental questions facing marketing managers in service organizations is how to allocate scarce marketing resources to retain their best customers. The efforts of many companies to identify and retain profitable customers have been chronicled in many well read books and business publications (Jeffrey and Franco 1996; Kotler 1994; Reichheld and Sasser 1990) and have formed the basis for a set of beliefs concerning the best business practices in this arena. This conventional wisdom can be summarized by three interrelated tenants Retaining customers is significantly less expensive than finding new ones. The best way to retain customers is to keep them ``satisfied''. Satisfying your most profitable customers is the single best way to increase profitability. Many organizations have used these ideas to justify devoting considerable amounts of money and human resources to develop systems aimed at monitoring and improving customer retention (Lowenstein 1996). More recently, companies have committed increasing amounts of resources to targeting their most profitable customers for increased satisfaction and hence retention (Jeffrey and Franco 1996). The clear logic here is that if customer retention is a good objective, then retaining your most profitable customers has to be a great objective. One of the key tools these service organizations have used to track their customer service efforts is the customer satisfaction survey. By asking customers about different aspects of their service experience organizations hope to identify which are satisfied, and hence assumed less likely to switch service providers, and which are not satisfied and assumed at risk to defect. Attempting to use these satisfaction surveys to make tactical decisions suffers from several problems. First, they are generally administered to either 3

4 all of the company's customers or some representative sample thereof. Therefore, the average customer satisfaction score provides little insight into how to increase the satisfaction of their most profitable customers. Second, the link between aggregate satisfaction scores and aggregate retention rates is quite tenuous. In 1990 the U.S. General Accounting Office (GAO) issued a report on 20 companies that had scored well in the 1988 and 1989 Malcolm Baldrige National Quality Award. Among the findings of the GAO was that while executives responding from these companies indicated that customer satisfaction had increased significantly, customer retention remained almost unchanged (Lowenstein 1996). Finally, even if one assumes that increasing customer satisfaction scores will in fact increase retention rates this is by no means a guarantee that increased retention will lead to increased profitability. To illustrate this last point consider the following simple example. In a recent conversation we had with the Marketing Vice-President of a large consumer bank this individual indicated that the much-touted 80/20 rule does not apply in banking. Rather, he said, 120% of our profits come from 80% of our customers and then we lose that extra 20% profit on the least profitable 20% of our customers.'' This executive's profitability rule is consistent with the profitability figures found in Table 1 Table 1: Profitability by Segment Percent of Customers 80% 20% Profitability per Customer $ $66.65 Now assume that the current retention rate for the more profitable customers is higher than that of the unprofitable ones. This situation is quite plausible since the unprofitable customers are likely to be more price sensitive and hence switch banks more readily to take advantage of special offers. Suppose the retention rate, defined as the percent of customers who remain after a given time period, is 90% for the profitable customers and 30% for the unprofitable ones. Suppose further that the bank has 1000 current customers. Then the profits earned from those customers who are retained is given by $1000(.2*.3*(-66.65)+.8*.9*100)=$68,001. 4

5 Finally, assume that the bank now embarks on a customer service campaign designed to increase customer satisfaction and increase retention rates. In particular, suppose they are successful at retaining half of those customers who would have previously defected. If these newly retained customers are evenly distributed across the two profitability types this would raise the retention rates of the profitable and unprofitable types to 95% and 65% respectively. The new profits earned on retained customers would be $1000(.2*.65* (-66.65)+.8*.95*100)=$67,335, less than those of the prior situation. This result arises because the bank's customer service campaign, besides retaining additional profitable customers, influenced a small but highly unprofitable segment as well. This result would become more exaggerated if the unprofitable customers were more sensitive to service issues than the profitable ones. This simple example illustrates the point that increasing customer retention, in and of itself, is not an appropriate goal and can lead to undesirable results. The deficiencies of customer service and retention programs have not escaped the notice of many industry executives. In a recent survey, a full 80% of business executives did not believe that their investments in customer service programs were paying off in higher company profitability (cite). In short, these executives believed that their efforts at increasing company profitability through improving and tracking customer satisfaction had failed. What has been missing from the above discussion is the link between customer satisfaction, retention, and firm profitability. This is the focus of our paper. We adopt a latent class model to examine the interrelationship among these three constructs. Central to our model is the observation that different customers have different preferences for convenience as well as different costs associated with switching service providers. These preference and cost heterogeneities have important implications for how companies should target their customer service efforts. To accomplish this, we use data on customer satisfaction, self-reported switching propensity, and profitability provided to us by a large Midwestern bank. 2. A Brief Overview of the Literature on Customer Satisfaction 5

6 The literature on customer satisfaction is quite diverse. Broadly speaking the this literature is defined by three interrelated streams of work; research on the antecedents of customer satisfaction, studies that examine the proper ways to measure satisfaction, and finally, research which attempts to link customer satisfaction to other variables of strategic interest to the firm (i.e. customer retention). Understanding the causal relationship between various primitive constructs and customer satisfaction has captured the interest of marketing academics for well over a decade now. Evolving out of the drive to increase product quality (Deming, 1986, Hauser and Clausing 1988) customer satisfaction became the battle cry of companies trying to compete in a newly globalized marketplace. Marketing academics followed this industry trend by probing the underlying reasons for customer satisfaction/dissatisfaction. For example, Garbarino and Johnson (1999) find that focusing on customer satisfaction is more effective for some types of customers than others. Crosby and Stephens find that though relationship marketing adds value to the life insurance package, it is not a substitute for having a strong, up-to-date core service. Readers interested this line of research should see Anderson and Sullivan (1993), Bearden and Teel (1983), Bolton (1991), Boulding, Kalra, Staelin, and Zeithaml (1993), Boulding, Kalra, and Staelin (1998), Churchill and Suprenant (1982), LeBarbera and Mazursky (1983), Oliver (1980), Oliver and Swan (1989), Tse and Wilton (1988), and Westbrook (1980). Measured customer satisfaction is very sensitive to the scales used to capture customers' responses. To this end, a number of researchers have examined the correct way to measure customer satisfaction. For those interested in measurement issues in this field we recommend Cronin and Taylor (1992), Parasuraman, Zeithaml, and Berry (1988), and Westbrook (1980). A somewhat more recent line of this literature has examined the links between customer satisfaction and constructs tied to firm performance. Hauser, Simester, and Wernerfelt (1994) link satisfaction to the incentive scheme of the salesperson. Through this link they develop optimal compensation schemes designed to make the salesperson consider the long-run implications of their behavior. Danaher and Rust (1996) reports the relationship between measured overall service quality and subsequent usage. Bolton and Lemon (1998) extend this idea by providing a more structural model of customer's post- 6

7 satisfaction-measurement usage, modeling customer's individual-level decision making. Using customer satisfaction measures from the cellular phone service provider, Bolton (1998) shows that differences in customer satisfaction explain about 26% of the variance in the duration of the customer-service provider relationship across customers. Anderson, Fornell, and Rust (1997) explore the relationship between customer satisfaction and the productivity level of the company. This allows them suggest when a company should be more concerned with high satisfaction scores and when productivity levels should be of primary importance. Reinartz and Kumar (2000) examine the profitability of long-life customers in a non-contractual setting and conclude that it is better to let go of some customers and only focus on more profitable customers. In Reinartz and Kumar (2003), they develop a hazard framework to empirically measure customer lifetime duration that integrates projected profitability. Finally, using a Markov switching matrix, Rust, Lemon and Zeithaml (2003) model customer life time value which results from the frequency of category purchases, average quantity of purchase, and brand switching pattern, combined with the firm s contribution margin. retention, defection and possible return. Their model can be used to evaluate firm s marketing efforts on the strategic improvements that generate the greatest return. This paper contributes to this most immediate stream of research. In particular, we link customer satisfaction to firm profitability at the individual customer level. This link allows us to make normative statements about which customers are the most critical ones for the company to satisfy. The results of this research help to explain why managers have been frustrated by the apparent lack of actionable information present in customer satisfaction data and points to more intelligent ways to use this data. 3. Linking Customer Satisfaction and Profitability 3.1 Customers opportunity cost of time Consumers are likely to evaluate potential service providers along a number of dimensions before making a choice. Proximity, price, courteousness of the staff, differences in the menu of services available, as well as many other factors may play a role in determining which provider is ultimately chosen. However, two factors that we believe almost always prominent determinants of any given customer s decision are the perceived quality on the service and price of that service. 7

8 Some measures to increase customer service are costless or nearly costless to a company. Assuming a company does not have to spend a great deal of money training their customer-facing employees to smile, smiles are probably appreciated by most people and not expensive. But, there are often inherent trade-offs between the quality of the service provided to customers and the cost of that service. For example, an airline can to a large extent control the amount of time a customer has to wait at the baggage checkin counter through its allocation of ticketing agents. Likewise, a bank can reduce the amount to time its customers spend on their banking by increasing staffing levels at the bank, providing on-line banking capabilities, developing simple and efficient procedures to deal with customer problems, and in many other ways as well. None of these measures to increase service quality are free to the company and in some cases they can be quite expensive. We also believe that consumers sensitivity to service quality will vary across individuals in proportion to that person s opportunity cost of time. Becker (1965), in his seminal and ultimately Nobel Laureate research on opportunity costs applied to everyday decision making, argued that there is heterogeneity across the population in people s implicit time costs owing to differences in the demographic profiles of consumers. Consumers who have many things competing for their attention (e.g. jobs, a family with young children) are less willing to spend substantial amounts of time searching for better consumption alternatives than their less-time-constrained counterparts. Becker also observed that time costs vary positively with income. High income individuals are more likely to have many things competing for their time and as such be less willing to engage in an extensive search for the lowest price relative to lower income individuals. 2 Similarly, these same high-time-cost individuals are likely to be more sensitive to the convenience aspect of service. Formalizing these ideas into hypothesis: Hypothesis 1: Consumers with a higher opportunity cost of time will be relatively less sensitive to service prices relative to those with a lower opportunity cost of time. 2 See Kim, Srinivasan, and Wilcox (1998); Narasimhan (1988); and Blattberg et. al. (1978) for empirical evidence of this argument in a marketing setting. 8

9 Hypothesis 2: Consumers with a higher opportunity cost of time will be relatively more sensitive to the perceived convenience of a service relative to those with a lower opportunity cost of time. The role of overall satisfaction itself in determining whether a customer stays with their current service provider or moves on to another may vary significantly across customers with different opportunity costs of time. Changing providers is costly in the sense that there is often some kind of fixed cost associated with a change. In keeping with our banking example, changing banks requires a nontrivial amount of inconvenience. This inconvenience is likely to be greater if the customer has a large number of accounts with the bank and/or intensely uses some of the services offered by the bank. Both the administrative hassle costs as well as the learning costs associated with switching to a new bank are likely to create some amount of inertia to not switch. This inconvenience is particularly onerous to customers with high time costs. Our banking example is by no means peculiar in this respect. Once a customer has installed an Internet service provider (ISP), say AT&T WorldNet, on their laptop it is both time consuming and tedious to go through the process of canceling that ISP and setting up another. Again, high time costs exacerbate this inconvenience. This observation leads to the following pair of testable hypotheses Hypothesis 3: Customers with higher opportunity costs of time are less likely to switch service providers. Hypothesis 4: Overall customer satisfaction will be a more powerful determinant of customer retention for customers with lower time costs and a less powerful determinant for those with higher time costs. In the language of statistics, Hypotheses 4 predicts an interaction effect between individual-level satisfaction and time costs when deciding whether to switch service providers. 3.2 Linking the implications of the hypotheses 9

10 In many respects, the value in the analysis we present arises not from the startling conclusion of any particular hypotheses but the implications of a combination of the hypotheses. The implications of these combined ideas are presented in Figure 1. Figure 1: Targeting Customer Satisfaction Efforts Higher Profitability Target for Intensive Customer Satisfaction Efforts Trapped Customers: Limited Additional Customer Satisfaction Efforts No Additional Customer Satisfaction Efforts Lower Profitability Target for Demarketing Lower Time Costs Higher Time Costs The area targeted for demarketing is simply a realization of what the bank s marketing vice-president mentioned. In this industry some nontrivial proportion of customers are not profitable, 20% by his accounting. The bank would be better off without them. No doubt this proportion is larger or smaller depending on the particular service industry under scrutiny, by the general fact remains that increased customer satisfaction efforts targeted towards all customers can have unintended negative consequences. In the upper right hand corner of Figure 1 are what we term trapped customers. They certainly are not trapped in the sense that they have no ability to change service providers. Yet, doing so would be very costly to them. Conventional wisdom would have 10

11 the service provider spending scarce marketing resources attempting to increase these customers satisfaction. They are, after all, very profitable. Our earlier arguments suggest that this may not be a wise course of action. These customers are likely to be more tolerant to some degree of dissatisfaction in order to insulate themselves from the costs of searching for and switching service providers. In that sense they are trapped by their own busy lives. What makes this particular insight even more salient is aforementioned empirical reality that income and time costs are positively related across the population. Given the, rather unsurprising, result presented later that higher income individuals tend to be more profitable for the bank and the conclusion is clear, the proportion of consumers who are both highly profitable and subject to relatively high time costs may be substantial. In terms of Figure 1, customers are not uniformly distributed over this twodimensional space. They are more likely to lie along or near dashed line than relatively far away from it. In totality, what all of this implies is that the appropriate target for scarce marketing resources brought to bear on increasing customer satisfaction is probably much smaller, and defined much differently, than many businesses currently envision. To put this framework to practical use, of course, a company must be able to identify its most profitable customers, those customers who are likely to have high time costs, and each customer s overall level of satisfaction. Only then can a company see whether the barriers to switching in their particular industry, and the consumer behaviors that flow from them, in line with the presented framework. It is that to which we will now turn our attention. We will develop a model that will both allow us to test the veracity of our stated hypotheses for this particular market and provide straightforward means for managers of other businesses to see if the framework presented here makes sense for them. 4. Methodogy 4.1 Data Description [Insert Table 2 About Here] To demonstrate the efficacy of our approach we applied this model to householdlevel data collected from a large Midwestern bank. Specifically, the bank gave us access to a customer satisfaction survey data conducted in April There are 1201 randomly selected households from the bank's customer base. In the survey, each respondent was 11

12 asked to rate from 1 to 7 their overall satisfaction towards the bank as well as subsatisfactions towards different aspects of the bank which determines the overall satisfaction, e.g. teller, branch, fee, convenience, bank hours and error resolution. They were also asked to rate from 1 to 7 their propensity of staying with this bank in the near future. We also have access to demographic information for each of the customers. In addition, we have monthly holding and transaction information for all the 20 financial products (checking, saving, brokerage, IRA, mortgage etc.) provided by the bank for each customer from July 1997 to June Finally, the bank provided us with average annual profit (net of expenses of maintaining the account) per account for each of their 20 products. Using the average profit information coupled with the account holding information, we can approximate annual profit the bank can make out of each customer. 3 The means and standard deviations of the variables we use in our estimation are reported in Table 2. On average, the customers have been with the bank for years and there is huge variation of the length of tenure with the bank. The average profit is about and the standard deviation is In order to take into account the leaving propensity, we also report the weighted average of annual profit from each customer. It is with standard deviation of Both indices indicate that there are many customers make negative contributions to the bank s profit even before we take into account the cost of their banking activities. 4.2 The Inadequacy of Aggregate Analysis [Insert Table 3 About Here] As mentioned previously, many managers have voiced their concerns over the value of customer satisfaction data. Their frustration stems from the problem of finding actionable information in the data. If a substantial proportion of unsatisfied customers do not switch and a likewise many satisfied customers do switch, what is the value of increasing satisfaction? Ignoring heterogeneity in customers' demographic profiles and 3 Ideally, profit per customer should also differ because of number of accounts, balance of each account and actual bank activities of each customer. The profit data we obtained from the bank is the average profit per dollar or per account net of operating cost. We use average profit times balance of each account or number of accounts to calculate the total profit. Because of the limited accessibility of the profit data, we can only approximate the profit per customer without taking into account the cost of their banking activities. This approximation will not affect our main results. 12

13 purchase behavior can indeed lead to ambiguous conclusions on customer retention as well as profitability. To illustrate this point, Table 3 provides the frequencies of stated switching intentions for each level of satisfaction in our bank data. For example, 26 individuals indicated were very unsatisfied (answered ``1'' on the satisfaction scale) and were also very unlikely to switch (answered ``1'' on the probability of switching scale). More generally, while casual examination of the data indicates that satisfaction and selfreported retention propensity are negatively related, there is a considerable amount of noise in the relationship between the two. For example, the italic entries indicate customers who are somewhat or very unsatisfied yet have reported that they are unlikely to switch banks. Because of the noise in this relationship a manager might be hesitant to commit substantial company resources to increasing customer satisfaction. We emphasize here that this noise exists in spite of the fact that the switching propensity is self-reported. Since actually switching banks requires some time and effort, and answering ``1'' on a questionnaire does not, it is almost certain that the number of people who are relatively unsatisfied yet do not actually switch banks will be higher than the figures reported here. Further, unexpected events may necessitate bank switching for some individuals who are highly satisfied and are now reporting that a switch is unlikely. Thus, while Table 3 does provide evidence for a substantial amount of noise in the relationship between satisfaction and switching propensity it understates the true amount of uncertainty in this relationship. The above discussion provided insight into why this type of aggregate analysis offers only a nominal picture of the relationship between satisfaction and retention. In the following sections, we present a latent class bivariate probit model to describe the relationship between satisfaction and retention and demonstrate that it is important to control for customers' heterogeneity on the time cost. Our empirical model offers a significantly better guide to managers trying to allocate their customer service resources. 4.3 Model Suppose there are i=1, I customers with the bank. Their are measured in a discrete fashion. We treat the overall satisfaction SAT ij and retention intentions RET ij as 13

14 discrete representations of the latent satisfaction (W i ) and latent utility of staying with service provider i (U i ). We allow overall satisfaction to be determined by variables representing the fee component and convenience components of the services. The estimated coefficients represent the importance of those service components on the overall satisfaction. W i = 1 COREi + α 2CONVENi + α 3TELLERi + α 4HOURi + α 5 α ERRCOPY + ξ i i We include the CORE i to represent consumer's satisfaction with the monetary aspect of service. CONVEN i, TELLER i and HOUR i are consumer satisfaction ratings on convenience, teller service and branch hours. ERRCORY i denotes whether an error recently made by the bank was solved or not. Speer (1996) shows that customers who have had a complaint successfully resolved are more satisfied. CONVEN i, HOUR i, and ERRCORY i measure consumer satisfaction towards the convenience aspects of service and TELLER i measures the interactive aspect of service. These variables are controllable by the bank. Let W * ij represent the deterministic part of the utility. The discrete * satisfaction is defined as SAT ij =1 if θ j 1 < Wij < θ j and SAT ij =0 otherwise for j=1,..7. For identification, θ 0 is normalized to be zero and θ J = +. Similarly, we define the latent utility of staying with service provider as U i = β SAT + β ACCTNBR + β MPERIOD 1 i + β SAT 4 2 i 3 i * ACCTNBRi + β 5 i i SAT * MPERIOD + ξ i i where consumer decision about switching provider is given by RET ij =1 when l < U < l and RET ij =0 otherwise for j =1,..7. l 0 and l = +. Let U * ij' j' 1 * ij' j' 0 = J represent the deterministic part of the utility. SAT ij is the overall satisfaction. ACCNBR i and MPERIOD i are number of accounts held by the household and number of years the customer staying with the bank. ACCNBR i represents the time and effort required to switch from this bank and MPERIOD i stands for customer inertia built over the years. These two variables captures the procedural switching cost involving the loss of time and effort and relational switching cost involving psychological discomfort due to the 14

15 breaking of bonds (Burnham, Frels and Mahajan 2003). We use them to represent customer switching costs. We also include the interactions of the two variables with overall satisfaction to study whether switching cost decreases the impact of satisfaction on retention propensity. We allow the error terms from the latent satisfaction and latent utility to be correlated and follow a multivariate normal distribution N ( 0, ) where is defined as 1 σ. 1 σ captures the covariance between satisfaction and retention measures. σ We follow Russell and Kamakura (1989) to define the latent class model. We assume each customer has a probability π k that s/he belongs to segment k. In order to characterize the difference in the demographic profiles of consumers in each segment, we make π k a function of some demographic variables X i. Thus, π ik = The log likelihood function is given by K K k= 1 k= 1 π ik (γ X ) = 1. k i LogLikelihood = I K J i= 1 k= 1 j= 1 π ( γ X ik k ) SAT RET i ij ij' log[ P ijj' k ( SAT ij = 1, RET ij' = 1)] where P ijj ' k denotes the joint probability of customer i from segment k with satisfaction j and switching propensity j. This is a bivariate probit model with latent classes. Note it is a recursive simultaneous equations model. Thus the coefficient of SAT in retention equation indicates the causality relationship between SAT and RETENT (Lee 1979). 4.3 Empirical Results To determine the number of segments, we estimate the model assuming 1, 2, 3 and 4 segments. The BICs are [xxx, Ting, would you please report the numbers here] indicating that the model with 2 segments fits the data the best. Thus, in the following discussion, we focus on the estimation results with two segments. [Insert Table 4 About Here] 15

16 In Table 4, we report the estimation results of the model with and without heterogeneity. First, we examine the segmentation function, we find that income, occupation and having young kids all increase the probability for a customer to belong to the first segment. This implies that, relative to customers in segment 2, customers in segment 1 are better educated, they have more advanced job and/or are more likely to have young kids in the family. These are the customers who have many things competing for their attentions. Consistent with Becker s findings, we term customers in segment 1 as the high time cost customers and those in segment 2 as the low time cost customers. Next, we compare the estimated coefficients of high time cost segment with low time cost segment in the satisfaction function. The lower threshold estimates of the low time cost segment indicate that consumers with low time cost are relatively easier to be satisfied with the bank than those with high time cost. This is because low time cost consumers can afford time and effort to search around and choose a bank they are truly happy with. With the estimates of intercepts controlling for the difference in mean satisfaction between these two groups, we now compare the coefficients of variables representing importance of fee and convenience components in determining customer overall satisfaction. Interestingly, we find high time cost customers do not care much about the fee component of the service. Instead, they are very sensitive to overall convenience, whether an error was corrected quickly and flexibility of banking hour. This is because, with limited amount of time, high time customers appreciate efforts made by the bank to save them time. Teller service has insignificant effect on the overall satisfaction of the high time cost customers. This may be due to the fact that high time cost customers are busy and more knowledgeable. They can save time by handling transaction via automatic teller machine, telephone and web. Thus they have less chance to visit the tellers and do not rely on teller service for financial advice. Different from high time cost customers, the fee component of service has significant impact on the overall satisfaction of low time cost customers. Even though all the three convenient service components have positive and significant impact on increasing overall satisfaction for low cost customers, the magnitude of the coefficients on convenience components of low time cost customers are much smaller than those of high time cost customers. This means that low time cost customers care much less about 16

17 each of the convenience components than high time cost customers. Quality of teller service has significant and the highest impact on improving customer satisfaction. This is because low time cost customers have more time to spend in the bank and they highly value their interaction with the teller. Thus, we have shown Hypothesis 1 and 2 that customers are heterogeneous in their sensitivities to monetary and convenience components of their overall satisfaction. High time cost customers attach higher weights to convenience components of services and low time cost customers have higher weight for fee that offers them higher return and customer interaction component that offers them better customer interaction. Increasing convenience improves overall customer satisfaction more for high time cost customers and lowing price and improving customer interaction work better for the low time cost customers. Now we turn to compare the coefficients between high and low time cost segments in the retention function. The higher thresholds of high time cost segments imply that these customers on average have lower intention to retain than those with low time cost. Overall satisfaction with the bank has positive and significant impact on retention for both the low time cost customers and the high time cost customers, as found by previous research. Total number of accounts holding with the bank (ACCNBR i ) significantly improves retention propensity for high time cost segment. Thus, the higher the number of accounts held by the high time cost customers with the bank, the higher the cost incurred to switch bank and the higher the probability for them to retain. This explains why some unhappy customers constantly complain but still stay with the same bank for a long time. This is because they have higher switching costs. Their higher switching cost prohibits them from switching to a bank that could make them happier. They are the ``trapped'' customers. 4 Different from high time cost customers, low time cost customers have more time to shop around and are not affected by switching costs in making their switching decisions at all. Thus, given the same overall satisfaction, customers with high time cost are less likely to switch service providers than those with low time cost. This is consistent with Hypothesis 3. 4 Total number of years with the bank (MPERIOD i ) has no significant impact on both segments. This is because customers are less likely to take into account inertia represented by tenure with the bank when stating their intention of switching. The inertia may have significant impact on actual switching behavior. And it indeed affects the effect of satisfaction on retention. 17

18 Interestingly, for high time cost customers, the interaction term of SAT i and MPERIOD i has significant and negative sign. This indicates that the length of tenure with the bank decreases the impact of satisfaction on retention propensity because of their inertia. For low time cost customers, the interaction between SAT i and ACCTNBR i is positive and significant indicating that having more accounts with the bank increase customers satisfaction sensitivity for low time cost customers. This is because having more investment with the bank increases low time cost customers sensitivity to the fee component of service, which is the key driver for their overall satisfaction. Given that the switching costs decreases the impact of satisfaction on retention for high time cost customers and increase the impact of satisfaction on retention for low time cost customers, high time cost customers are much less sensitive to satisfaction than low time customers. Thus, we have shown Hypothesis 4 that overall customer satisfaction is a much more powerful determinant of customer retention for low time customers and a less powerful determinant for high time cost customers. In summary, we demonstrate how customers with different time cost respond differently to each of the service components. Our results imply that it is important to take into account the heterogeneity of opportunity cost owing to differences in demographic profiles of customers in examining the relationship between customer satisfaction and retention propensity. Because of the heterogeneity in time cost, customers react differently to the monetary and convenience components of services. In addition, it makes satisfaction a much less effective tool in improving customer retention. Finally, it makes some customers trapped because of switching cost and less likely to switch providers given the same satisfaction. In order to demonstrate the importance of taking into account opportunity cost when modeling the relationship between satisfaction and retention, we also estimate and report the estimation results of the model without segmentation. The aggregate results lead to at least three conclusions that are different from those from segmentation results. First, we should note that the aggregate approach ignores the different effects of fee and convenience components on improving overall satisfaction and retention. Thus, using aggregate approach leads to the conclusion that in order to improve retention, the bank can adopt the same strategy to improve overall satisfaction of all customers. Second, in the satisfaction equation, the coefficient of fee component (CORE i ) is not significant 18

19 indicating that customers are not very sensitive to the monetary aspect of financial products. Relying on the aggregate approach leads us to the conclusion that customers are not sensitive to the fee component of the services, which is consistent with our observation that banks usually emphasize on improving the customers relationship and ignore the impact of fee component. Similarly, satisfaction with the teller service (TELLER i ) has no significant impact on overall satisfaction ratings. Third, the coefficient of both ACCNBR i and MPERIOD i are not significant indicating that switching costs do not affect consumer switching probability. This is consistent with the observation that most banks segment customer base by profitability and not switching cost without realizing that some profitable customers are trapped and ignoring them may not cause them to switch. Fourth, we notice that switching cost has no significant impact on the satisfaction sensitivity, as indicated by the insignificant coefficient of the interaction between SAT i and switching costs (ACCTNBR i and MPERIOD i ). Thus, the aggregate model leads to the conclusion that improving satisfaction increases retention the same way for all customers. [Insert Table 5 About Here] We have already shown that customers are heterogeneous in determining their satisfaction and resulting retention. They respond differently to firm s effort in improving overall satisfaction. Even with the same customer satisfaction, they present different propensity of switching banks. This offers important implications for banks with scarce resources to efficiently allocate their dollars to achieve higher satisfaction and retention rate that can be translate directly to the improvement of profitability. What we are interested in is whether the existence of heterogeneity affects a profit-maximizing service provider in allocating their scarce resource in improving customer satisfaction and retention in order to increase profit? In Table 5, we randomly select CONVEN i and ERRCORY i which respresent convenience comoponents of services and increase each of them by 40%. We report the percentage of change of mean satisfaction, retention propensity and profit to study the translation from improving service quality to improving profitability. Similarly, we also decrease CORE i, which represents the fee component of services, by 40%. The average satisfaction, retention and change of profitability are 19

20 reported over 100 simulations. Again, we report the simulation results for both pooled data and segmented data to show the importance of taking into account heterogeneity. As expected, improving CONVEN i improves overall satisfaction of high time cost customers by 14.6% and of low time cost customers by only 4.3%. This indicates that providing more convenience for high time cost customers is more effective in improving their overall satisfaction. The 14.6% improvement in satisfaction only translates to increase of retention by 1.4% and to increase of profit by 1.7%. This is because high time cost customers are much less sensitive to satisfaction. For low time cost customers, the 4.6% increase in satisfaction is translated into a 2.4% increase in retention intention and 2.7% increase in profitability. Similar results hold for correcting recent bank error. The bank s efforts on improving convenience and saving time are better taken by high time cost customers in increasing their satisfaction rating because they have higher preference for improved convenience. However, the effect on retention decision is mitigated because of their high switching cost prohibits them from switching to a bank they are more satisfied with. These imply that bank's effort does not translate well into increasing satisfaction and especially retention for high time cost customers. On the contrary, for low time cost customers, the bank s effort on improving convenience is less effective in improving overall satisfaction. But the increase in satisfaction is better translated into increase of retention and even profitability. When we decrease the fee by 40%, the satisfaction score of low time customers are improved by 15.5%. Since low time customers are more sensitive to satisfaction in determining retention, especially customers with many accounts with the bank, the improvement in satisfaction translates into a 8.5% increase in retention and a 10.0% increase in profit. Thus, for low time cost customers, decreasing the fee can effectively improve their overall satisfaction and hence retention intention. Note the improvement for high time cost customers should be zero because the coefficient of CORE for high time customers is insignificant. [Insert Table 6 About Here] In the above simulation, we do not distinguish profit customers from unprofitable customers. In Table 6, we divide the customers further into four segments based on time cost and profit and conducted similar simulations as in Table 5. How marketing effort is 20

21 translated into improvement in satisfaction and retention is the same as before. In terms of profit, the 1.7% improvement in profit of high time cost customers (reported in Table 5) because of the increase in convenience is contributed by high profit customers whose profit is increased by 1.8%. The low profit customers incur a profit loss of 2.7%. Similarly, improving overall satisfaction by decreasing CORE by 40% for low time cost and low profit customers incurs 10.8% loss of profit. Making the same effort for the low time cost and high profit customers improve their profit by 10.1%. Note the effect of fee component on improving overall satisfaction is insignificant as implied by the insignificance of its coefficient. Thus, customer retention improves overall profitability of the bank only for the profitable customers. Improving retention rate for unprofitable customers only makes the bank worse off, let alone the significant cost of increasing their satisfaction and retention. In Table 6, we also report the simulation results for pooled data. Increasing convenience by 40% improve overall satisfaction by 7.7%, which translate into 2.0% increase of retention intention and only 2.3% of profit increase. Similarly, increasing the satisfaction of error corrected increases the overall satisfaction by 9.4%, retention by 3.4% and profit by 3.9%. A 40% decrease in fee improve satisfaction by 10.4%, retention by 5.6% and profit by 6.0%. This echoes the findings of managers who are puzzled by the profit payoff of improving satisfaction, as discussed at the beginning of the paper. From the above analysis, we can tell that the best customers are from the Low Cost and High Profit segment. Improving customer satisfaction by offering cheaper fees for this group has a higher impact on their retention. Given that they are profitable for the bank, their decision of retaining with the bank contributes more to the increase of the bank's profit. Note that we assume from the beginning that the bank can not charge different fees to different customers. But the Low Cost customers are more fee sensitive. Finding ways to decrease the fee for this group of customer works better in improving their overall satisfaction than improving convenience. The segment the bank can pay least attention to is High Cost and Low Profit group. They are the ``trapped'' customers and any bank effort can significantly increase their satisfaction rating without significant increasing their retention propensity. With a low profit potential, the bank wastes resources if this segment is treated the same as Low Cost/High Profit segment. 21

22 4.5 Managerial Implications for Service Providers How to allocate scarce resource to improve profitability is always an issue. In common practice, it is assumed that improving overall satisfaction should increase profit. Our results indicate this is not true. It is important to understand how marketing effort translate into overall satisfaction, retention and then profitability. First, marketing effort does not translate directly to overall satisfaction. Different customers have different sensitivities to different marketing efforts. Different from frequently purchased packaged goods for which customer satisfaction is determined by price and quality of the products, service is an experience product. Customer satisfaction for services is based on many service components that require human interaction. Improving one of many service components not only needs huge capital investment but also takes longer time to affect consumer decision. So it is very important for the service provider to know the right way of improving satisfaction for different customers. In the bank application, we found it is effective to provide convenience to high time cost customers and better financial products to low time customers. If we ignore heterogeneity among customers and rely on the pooled estimation results, we may overlook the effectiveness of using CORE i as an effective tool to attract low time cost customers. Second, overall satisfaction does not translate directly to retention. It works better for customers with low time cost. Switching cost plays an important role for high time cost customers. It makes these customers trapped with the current service provider and lowers the impact of satisfaction on retention. In the banking industry, switching cost is related to the number of accounts opened or length of tenure with the bank. In other industries, switching cost could be due to other factors. For example, risk-averse customers may not want to try another Barbara because of quality uncertainty. Similarly, customers may not want to switch to new software because of the learning and unlearning required to the switching. Switching cost plays an important role in determining customer retention and undermines the importance of satisfaction on retention. Only the customers with enough time to shop around, who are less risk averse or who have higher learning abilities are more likely to retain when they are more satisfied with the service provider. Understanding the role of switching cost, the service provider can risk allocating less resource to these customers without making them leave. Increasing switching cost, especially in a way that improving convenience, can increase their retention rate. For 22

23 example, Bank of America requires direct deposit to be qualified for a certain saving account. This increases both switching cost and convenience. Third, retention does not translate directly to profit. To improve profitability, it is not wise for a service provider to treat profitable and unprofitable customers equally and make effort to keep both, unless the later are expected to generate profit in the near future (which we do not but can be incorporated in the model). A firm should shake off the unprofitable customers. Ways to improve satisfaction and retentions for profitable customers works similarly for shaking off unprofitable customers. For example, a bank can offer promotional price to customers who also purchase more profitable financial products. Then customers who are only interested in purchasing less profitable financial products and are price sensitive are more likely to switch banks. While it is easier to shake off unprofitable low time cost customers, it is not intuitive how to shake off unprofitable high time cost customers due to their high switching cost. Decreasing convenience and switch cost both will increase their chance of leaving the bank. However, the bank should carefully examine high time cost and unprofitable segment. According to Li, Sun and Wilcox (2003), high time cost customers also have higher income, higher education and better profession. They are more likely to take advantage of new investment tools provided by different providers, for example, online brokerage. They only use the minimum services provided by the bank for convenience and invest somewhere else and thus become a seemingly unprofitable customer. So instead of shaking them off, the bank can also find ways to attract them to buy more profitable products by providing similar new investment tools. As long as the bank can provide competitive returns, the high cost customers should greatly appreciate the convenience of consolidate all their investments in one bank. Thus, instead of shaking them away, the bank can transfer unprofitable customers to profitable customers, based on the fact that they prefer convenience. Fourth, given the limited marketing resource, managers should decide on how to allocate limited resources. First priority should be given to low time cost and high profit customers. Those customers are profitable but prone to switch for banks for better financial return. Their retention rate is more sensitive to satisfaction, especially those have more accounts with the bank. Efforts improving their satisfaction with the fee component of service can be more easily transferred to improvement in profit. Second 23