How to Regain Lost Customers in Electronic Commerce: An Empirical Study from China

Bo Xu; Bingjia Shao

How to Regain Lost Customers in Electronic Commerce: An Empirical Study from China

Bo Xu^1* and Bingjia Shao²

School of Management, Fudan University, Shanghai, PR China
College of Economics and Business Administration, Chongqing University, Chongqing, China

Corresponding Author: Bo Xu, Associate Professor, School of Management, Fudan University, Shanghai, PR China, Tel: 86-21-25011229; E-mail: bxu@fudan. edu.cn

Received June 10, 2015; Accepted November 10, 2015; Published November 12, 2015

Citation: Xu B, Shao B (2015) How to Regain Lost Customers in Electronic Commerce: An Empirical Study from China. J Internet Bank Commer 20:136.

Copyright: © 2015 Xu B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Electronic commerce has experienced rapid growth in last years. B2C electronic commerce often has a high customer loss rate. To regain the lost customers is a major concern of online vendors. This paper investigates the strategies for winning back lost customers (price promotion and relationship investment) through field experiment on a major B2C website in China. Research findings indicate the two strategies are effective in regaining lost customers online, depending on the customers’ prior relationship time with the vendor, and lapse time from the vendor. The study has contribution to electronic commerce and has practical implications for B2C vendors on customer management.

Keywords

Electronic commerce; Customer regaining; Price promotion; Relationship investment

INTRODUCTION

With the development of the Internet, electronic commerce has experienced rapid growth in last decade. Electronic commerce provides much value to consumers, which may reach far more vendors online than before. However, it also offers challenge to online business because in electronic commerce consumers may easily stop the relationship with a business and switch to other vendors. Thus, electronic commerce often suffers higher customer loss rate than traditional offline business (Sheth, 1999). How to regain or win back lost customers is a very important issue to online business.

There is a clear distinction between customer regaining and the long-established concept of customer retention. According to Stauss and Friege [1] customer regaining is defined as “rebuilding the relationship with customers who explicitly quit the business relationship”. Similarly, Thomas et al. [2] conceptualize customer regaining as “the process of firms’ revitalizing relationships with customers who have defected”. Customer regaining provides firms with high economic benefits. Research found that an average firm has a 60-70 percent probability of successfully selling again to active buyers, a 20-40 percent probability of successfully selling to lost customers, and only a 5-20 percent probability of making a successful sale to new customers [3]. Thus, business pays much attention to lost customers, which are former customers no longer active, and emphasize on getting them back [4]. Compared with traditional offline business, electronic commerce is often characterized with more intensive competition, thus how to gain or regain customers is a major concern for electronic commerce [5,6].

Despite the importance of regaining lost customers, there has been few research on customer regaining in electronic commerce. Researchers made a lot efforts to investigate how to attract new customers or retain existing customers to online vendors [7], but did not tell how to get the customers back when they are lost. In past years, researchers in marketing came to recognize that customer win-back is as important as customer retention, and should also be an important part of a customer relationship management strategy. For example, Thomas et al. [2] studied the pricing strategy for customer regaining. Homburg et al. [8] investigated how to revive the relationships of lost customers with a business from equity perspective. Tokman et al. [4] studied the factors driving win-back offer effectiveness, and indicated how the price and service benefits provided in the win-back offer may affect customers’ switch-back intention. However, all these studies were based on traditional offline business, did not suggest how to deal with lost customers in electronic commerce. As online business, electronic commerce is different from traditional business in various aspects. To fill the research gap, in this paper we investigate the effectiveness of pricing and relationship based win-back strategies in regaining lost customers in B2C online business. The results contribute to electronic commerce research and have practical implications for online vendors.

The remainder of this paper is organized as follows. First, we review the literature on customer regaining. Second, the research model and hypotheses are developed, followed by an explanation of the field experiment used to empirically test the hypotheses. We then present the results of our data analyses. Finally, we conclude with a discussion of the findings and their theoretical and practical implications.

Literature Review

Activities in the domain of customer relationship management can focus on acquiring, maintaining, and winning back customers. Research on customer relationship management has essentially focused on retaining existing customers, such as the relationship between customer satisfaction and loyalty [9,10] and long-term relationships between customers and a company [11,12]. Although customer regaining is another important area in customer relationship management, and companies are increasingly emphasizing activities which aim at winning back lost customers [13], there has been much fewer research on customer regaining compared with customer retention.

A lost customer is one who had established a relationship with the company but now has terminated the relationship [8]. Lost customers have much value to a company and it is important to get them back [14]. Currently, only a few academic studies specifically investigate the regaining of lost customers. First, Stauss and Friege [1] developed a conceptual basis for regain management aimed at winning back lost customers by supporting their views with illustrative cases. Following Stauss and Friege’s work, Helfert et al. [15] developed a five-step process framework for customer regain management. Neither of these studies provides an empirical basis for understanding of customer regaining and win-back offer. The study by Thomas et al. [2] is a very important step towards a theoretically and empirically grounded understanding of customer regaining. It examines the effects of pricing on customer recapture likelihood and on the duration of the revitalized relationship. Different from Thomas et al. [2] that focus on price promotion as the specific instrument for regaining lost customers, Tokman et al. [4] indicated that win-back offer effectiveness is determined by price and service benefits provided in the win-back offer, depending on customer’s reasons for leaving, social capital and service importance. Furthermore, Homburg et al. [8] found that the success of customer regaining depends not only on the offer made to lost customers but also on the interaction with the lost customers during the revival activities and on the process of revival activities. Customers’ perceived justice is critical to the effectiveness of the win-back strategy. Research also found that customer’s purchase likelihood declines as the length of time since the customer’s previous purchase increases, which is called “recency trap” in Neslin et al. [16]. Based on the reasons for termination, Schroder et al. [17] identified different types of lost customers, and argued that they may have different likelihood to revive the relationship with previous vendors. Kumar et al. [18] conducted a comprehensive study to confirm that the likelihood of regaining a lost customer is related to the customer’s previous experience with the vendor, reasons for defection, and the win-back offer. A win-back offer that bundles price discount with service upgrade would be most effective.

Electronic commerce is generally categorized into three major types: business-to-consumer (B2C), business-to-business (B2B), and consumer-to-consumer (C2C). Online customer relationship management is particularly important to the B2C electronic commerce. Currently, research on customer relationship management in electronic commerce has focused on online customer acquisition and retention. For example, Gefen et al. [13] studied customers’ purchasing behavior from online vendors based on trust and TAM model. Pavlou and Mendel [19] provided a comprehensive understanding of customers’ adoption of B2C electronic commerce by extending the theory of planned behavior. Campbell et al. [20] developed the electronic commerce attraction model to investigate how customer perceptions of a website influence attraction toward the system. There is also some research on how to retain existing customers to online vendors. For example, Devaraj et al. [21] investigated antecedents to customers’ satisfaction, and consequently long-term relationships with B2C vendors. Li et al. [7] studied consumers’ commitment to an online vendor and how to enhance customer loyalty through the commitment. Kim et al. [22] revealed how customers’ successful longterm relationships with an online vendor are formed through trust and satisfaction.

Compared with traditional business, electronic commerce is characterized with far more choices, and customers can easily switch from a vendor to the alternatives [23,24]. Thus customer regaining is critical to electronic commerce. However, through literature review we find that electronic commerce researchers has paid most attention to customer attraction and retention, but has largely ignored customer regaining. Although there are a few academic papers about customer win-back in marketing research as stated above, they are all based on traditional offline business. Naumann et al. [25] explored the causes of defection among service customers, but it is based on B2B context. It is still unclear what strategy is effective for regaining lost customers in particularly B2C electronic commerce.

The investment model has been adopted as a theoretical basis to understand the relationship between buyer and seller in marketing research [26]. Initially developed as a framework for exploring interpersonal relationship, the model was successfully applied to other settings, such as relationship between employees and organization, and relationship between buyer and seller. According to the theory, investments are either intrinsic, which is intangible, or extrinsic, which is tangible. In business context, the investments are often characterized as either social (intrinsic) or economic (extrinsic) [27]. In marketing research, extrinsic or economic investment is financially-based, such as a price promotion. Intrinsic or social investment is based on social relationship, and also known as relationship investment, which is often emotional effort made by sellers to buyers [28]. Both extrinsic and intrinsic investments are effective in maintaining the relationship between sellers and buyers [7,28]. For customer regaining, although some previous research indicated that such investment as price discount may help regain lost customers [2,18] there has been no study to integrate the two types of investments to offer a better understanding of customer regaining, particularly in electronic commerce context.

To investigate how to regain lost customers in B2C electronic commerce, in this paper based on the investment model two different win-back instruments, i.e., price promotion and relationship investment, are chosen. Through a field experiment we study how the two different methods are effective in regaining lost customers, and the moderating effects of customer’s tenure with the online vendor and lapsed duration after he became lost.

Research Model and Hypotheses

Based on the investment model, two types of customer win-back strategies used by B2C companies are considered in this study. The two strategies are price promotion and relationship investment. To regain lost customers, companies often offer a lower price to them in order to get them back [2]. In addition, companies may make relationship investment in customers through preferential treatment, interpersonal communication, or by sending mail or email message to customers. The relationship investment may also be effective in regaining lost customers. The research model is shown in Figure 1.

The dependent variable in the model is lost customer win-back. It refers to the degree that lost customers become active again and the extent to which the lost customer revive the relationship with an online vendor. Relationship time and lapse time are moderating factors.

Price promotion

Price promotion is a popular strategy used by companies to regain lost customers. Stauss and Friege [1] indicated that pricing is a major reason for customers to leave a company, which is consistent with the general laws of supply and demand that higher prices lead to lower demand. Tokman et al. [4] also indicated that most customers defect because of better price availability. Thus, to regain lost customers companies may choose to offer low prices. Research indicates that consumers use prior prices in the formation of reference prices, which have a significant impact on demand [29]. For customer win-back, the logical reference point is the customer’s last price paid before lapse. The lower the reacquisition price offering compared with the last price paid, the higher the probability of reacquisition [2]. In electronic commerce, customers have access to much more information about the price difference among different vendors on the Internet. Thus pricing strategy is more widely used and effective in electronic commerce than in offline context. If an online vendor offer lower prices to lost customers, it would be effective to regain the customers.

Hypothesis 1: Price promotion positively associates with customer win-back.

Relationship investment

Besides price promotion, customers also care about non-financial offers from the company. The theory of relationship marketing indicates that the phenomenon of reciprocity is also present in consumer-firm relationships. To retain customers and enhance customer loyalty, companies may make investment in their relationships with the existing customers. And customers would demonstrate loyalty in reciprocation of the relationship investment [30]. Kang and Ridgway [31] argue that consumers feel obligated to pay back the company’s “friendliness.” Research indicates that the mail message to keep customers informed, the communication with customers in a personal and warm way, and the preferential treatment to customers are all effective in retaining customers for a company [32]. In addition, the company employees’ empathy, respect and response are effective in reviving the relationships of lost customers with the company [8]. In electronic commerce, online vendors may track customers and send email or short message to lost customers to show its caring or empathy in a personal way. It is reasonable to believe that relationship investment would be effective in winning back lost customers in electronic commerce.

Hypothesis 2: Relationship investment positively associates with customer win-back.

Relationship time

Relationship time refers to the time of a customer’s relationship with a company before his last purchase, which is also termed as a customer’s first tenure with a firm in Thomas et al. [2]. Relationship time is related to a lost customer’s prior experience with a company. Research on relationship marketing indicates that customers with long time of relationship with a firm are less likely to defect and more intended to continue to purchase from the company [7,33]. Moon and Bonney [28] found that the length and intensity of relationship by sellers and buyers positively affect the relationship commitment between them. For lost or defected customers, Thomas et al. [2] found that the length of their previous relationship with the firm is positively related to the reacquisition probability and the subsequent tenure in a reinitiated relationship. Homburg et al. [8] also found that the length of their prior relationship with the firm positively affects the revival performance. Thus the relationship time plays a significant role in the winning back process. In B2C electronic commerce, it is reasonable to believe that the two customer win-back strategies, i.e., price promotion and relationship investment, depend on the lost customers’ previous relationship time with the company. If a lost customer has had a long time relationship with the firm, he may have more inclination to revive the relationship. Thus price promotion and relationship investment by the firm will be more effective in the win-back performance for customers who had long previous relationship with the company. We have the following hypotheses:

Hypothesis 3a: Relationship time moderates the relationship between price promotion and customer win-back. When relationship time is long, the effect of price promotion on customer win-back will be stronger than the case when relationship time is short.

Hypothesis 3b: Relationship time moderates the relationship between relationship investment and customer win-back. When relationship time is long, the effect of relationship investment on customer win-back will be stronger than the case when relationship time is short.

Lapse time

Lapse time refers to the amount of time elapsed since the lost customer’s last purchase (or the length of the lapse) from the company. Prior research has asserted that with passage of time, customers adapt to the new level of service provided by the switched-to firm. In addition, customers who have switched to a new firm after having experienced another firm’s service exhibit higher levels of loyalty and repeat patronage to the switched-to firm than do customers who had never experienced another provider [34]. Tokman et al. [4] also indicated that new service provider’s social capital with the customer is negatively associated with the customer’s switch-back intention. Social capital is determined by the relationship time between the customer and new service provider. Thus lost customers with long lapse time have higher cost to switch back to the original vendors. Thomas et al. [2] explained this phenomenon using the theory of cognitive dissonance, and argued that the decision to reinitiate a relationship after a long lapse results in a greater amount of dissonance relative to the decision to reinitiate a relationship after a shorter lapse. Therefore, longer lapse time will lead to lower probability of customer regaining. In B2C electronic commerce, if a customer has long lapse time from an online vendor, it would be difficult and unlikely for him to switch back to the vendor. Thus the price promotion and relationship investment by the firm will not be effective in winning back the customer. We have the following hypotheses:

Hypothesis 4a: Lapse time moderates the relationship between price promotion and customer win-back. When lapse time is long, the effect of price promotion on customer win-back will be weaker than the case when lapse time is short.

Hypothesis 4b: Lapse time moderates the relationship between relationship investment and customer win-back. When lapse time is long, the effect of relationship investment on customer win-back will be weaker than the case when lapse time is short.

Research Method

Experiment and data collection

Data were collected from www.epetbar.com, which is one of the largest online vendors for pet products in China. Founded in 2009, this B2C company had over 200 thousand customers, and the annual sales reached 30 million Yuan RMB by 2012. Data for this study were collected during the period from August to December 2012. We chose one popular product sold by the website, i.e., pet food, for study. The normal purchase period for pet food was 42 days. Based on the statistics by the company, customers who had not purchased for over 42 days ever came back. Thus customers who had not purchased for over 42 days were considered as lost or defected by the company. In August 2012, 19775 lost customers were identified, and a field experiment was conducted on them. For the experiment, the lost customers were randomly assigned to three categories as shown in Table 1: (1) price promotion only; (2) relationship investment only; (3) both price promotion and relationship investment. The observation period was the four months following the experiment until December 2012. The customers were observed for their purchases from the website in that period.

Operationalization of constructs

Win-back strategy: Two win-back strategies were used in this study, which are price promotion and relationship investment. Price promotion was operationalized by sending electronic coupon to customers. With the coupon, customers may purchase pet food on the website with a 10 percent discount. Relationship investment was operationalized by sending short message to customers through their mobile phone number. The content of the message was a reminder of the pet epidemic at that time and knowledge tip about how to prevent the pet from it. Thus it showed the company’s caring and empathy to the customers.

Relationship time: Relationship time is the lost customer’s prior tenure with the website. It was operationalized as the length of time (number of days) between the customer’s last and first purchase from the website.

Lapse time: Lapse time is the time elapsed since the customer’s last purchase. It was operationalized as the length of time (number of days) after the customer’s last purchase till the time of experiment.

Customer win-back: After using the win-back strategies, some customers switched back and purchased from the website again. Customer win-back was operationalized as the times the customer purchased on the website in the observational period.

There were totally 19775 records in the data set. The sample statistics is shown in Table 2.

In this table, for relationship investment and price promotion, 0 means not using the strategy, 1 means using the strategy.

The correlations among the variables are shown in Table 3.

Data Analyses and Results

To test the main effect of price promotion, we conducted one sample t test for the first group of customers, which includes 6958 customers receiving price promotion in the experiment as shown in Table 1. The results are shown in Table 4.

The mean value of customer win-back for this group is 1.00. The t test results indicate it is significantly larger than 0. Thus it can be concluded that price promotion has significant effect on customer winback. Hypothesis 1 was supported.

To test the moderating effects of relationship time and lapse time, we set up the following regression model:

W=β₁PP+β₂PP*RT+β ₃PP*LT+ξ (1)

W is customer win-back, it is the dependent variable. PP is price promotion, RT is relationship time, LT is lapse time. PP*RT is interaction term of price promotion and relationship time; PP*LT is interaction term of price promotion and lapse time. Regression analysis was conducted for the data of price promotion group. The results are shown in Table 5.

The results show that price promotion has significant effect on customer win-back. This is consistent with the t test results in Table 4, supporting Hypothesis 1. Relationship time positively moderates the relationship between price promotion and customer win-back. Thus Hypothesis 3a was supported. Lapse time negatively moderates the relationship between price promotion and customer win-back. Thus Hypothesis 4a was supported.

To test the main effect of relationship investment, we conducted one sample t test for the second group of customers, which includes 7271 customers receiving relationship investment in the experiment as shown in Table 1. The results are shown in Table 6.

The mean value of customer win-back for this group is 0.16. The t test results indicate it is significantly larger than 0. Thus it can be concluded that relationship investment has significant effect on customer win-back. Hypothesis 2 was supported.

To test the moderating effects of relationship time and lapse time, we set up the following regression model:

W=β₁RI+β₂RI*RT+β₃RI*LT+ξ (2)

W is customer win-back, it is the dependent variable. RI is relationship investment, RT is relationship time, LT is lapse time. RI*RT is interaction term of relationship investment and relationship time; RI*LT is interaction term of relationship investment and lapse time. Regression analysis was conducted for the data of relationship investment group. The results are shown in Table 7.

The results show that relationship investment has significant effect on customer win-back. This is consistent with the t test results in Table 6, supporting Hypothesis 2. Relationship time positively moderates the relationship between relationship investment and customer win-back. Thus Hypothesis 3b was supported. However, the moderating effect of Lapse time on the relationship between relationship investment and customer win-back is not significant. Hypothesis 4b was not supported.

As stated above, the mean value of customer win-back for the price promotion group is 1.00; the mean value of customer win-back for the relationship investment group is 0.16. To test the difference between these two groups, we further conducted an independent samples t test. The results indicate there is significant difference in customer winback between these two groups. Price promotion is more significant in winning back lost customers than relationship investment. The results are shown in Table 8.

The mean value of customer win-back for the price promotion and relationship investment group (group of lost customers receiving both price promotion and relationship investment, the third category in Table 1) is 1.51. We conducted independent samples t tests between this group and the price promotion group, and between this group and the relationship investment group, respectively. The results indicate that adopting price promotion and relationship investment together is more effective in winning back lost customers than using just one of the two strategies. The results are shown in Tables 9 and 10.

Discussion

In this study, we investigated the customer win-back strategies for B2C electronic commerce. A field experiment was conducted on a major online B2C vendor of pet food in China. The lost customers on this website were divided into three groups. Two strategies was used in the experiment, with price promotion for the first group, relationship investment for the second group, and both of the two strategies for the third group. The results show that these two strategies are effective in regaining lost customers for B2C electronic commerce, and price promotion is more effective than relationship investment. This suggests that in electronic commerce context customers care about not only financial benefit, but also emotional caring and empathy from the vendors. The results also show that using both of the strategies is better that using one of them.

Relationship time moderates the effects of price promotion and relationship investment. If a lost customer has had long prior relationship with the vendor, it would be easier and more effective to reacquire the customer by price promotion or relationship investment. Lapse time moderates the effect of price promotion. If a customer elapsed long from the vendor, it would be less likely to reacquire the customer through price promotion.

This study has contribution to electronic commerce research. Currently research focuses on customer acquisition and retention in B2C electronic commerce. There has been few research on online customer regaining. This study filled this research gap by investigating the strategies to win back lost customers in B2C context. This study also has contribution to marketing research on customer relationship management. Previous research on customer reacquisition in marketing mostly focused on the pricing strategy. This paper extends the research by exploring the effect of relationship investment as strategy for regaining lost customers, and revealing the moderating effects of relationship time and lapse time in the process.

This study has practical implications for customer management in B2C electronic commerce. To regain lost customers online vendors may offer lower price or provide coupons to them. In addition, online vendors should know that relationship investment is also important. They may communicate with the lost customers to transfer caring and empathetic information, thus the customers understand the vendors’ benevolence and reciprocate by switching back to the vendors. When applying the strategies, it is necessary to pay more attention to the lost customers with long relationship time or short lapse time, because it is more likely to get them back, and they may have more purchase and provide more revenue after coming back. To reacquire the customers with short relationship time or long lapse time, it often requires more effort from the vendors.

Conclusion

In this paper we study regaining lost customers in electronic commerce through a field experiment on a major B2C website in China. The research findings indicate that two strategies, i.e., price promotion and relationship investment, are effective in winning back the lost customers, depending on the customers’ prior relationship time with the vendor and their lapse time from the vendor. This study contributes to electronic commerce research and provides practical implications for online customer management in B2C context.