Leveraging service recovery strategies to reduce customer churn in an emerging market

Abstract

Building on the properties of emerging markets, we investigate how a firm should align its service recovery strategies with different types of service failure to reduce customer churn in an emerging market. Using resource exchange theory and a multi-method approach, we show that the conventional wisdom related to service recovery needs to be reevaluated in emerging markets. Our results show that process failures lead to a higher likelihood of customer churn compared to outcome failures in emerging markets. Investigating service recovery mechanisms, we find that compensation is more effective in recovering from process failures than in recovering from outcome failures in emerging markets. Similarly, employee behavior has a stronger impact on mitigating the ill effects of process failures than those of outcome failures. The study contributes to the literature on service recovery and resource exchange theory and provides managerial insights for the effective management of customer churn due to service failures in emerging markets.

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Fig. 1

Notes

  1. 1.

    https://www.bbc.com/news/business-41320615.

  2. 2.

    https://www.telegraph.co.uk/finance/personalfinance/energy-bills/11802872/Npower-loses-300000-customers-and-profits-plummet-thanks-to-billing-errors.html.

  3. 3.

    http://www.airlineratings.com/news/449/how-southwest-handles-complaints-the-social-media-connection.

  4. 4.

    https://www.thehindu.com/news/cities/mumbai/Customers-head-for-the-exit-at-first-sign-of-poor-service/article10026653.ece.

  5. 5.

    http://www.mahindracomviva.com/mahindra-comviva-estimates-mexico-incurred-highest-revenue-loss-due-to-churn-in-latin-america.htm/.

  6. 6.

    https://www.daedalus.co.in/financial-services-market-india/.

  7. 7.

    https://www.financialexpress.com/economy/tapping-the-telecom-sector-for-next-phase-of-gdp-growth/980877/.

  8. 8.

    http://www.businessworld.in/article/Hospitality-Industry-In-India-A-Big-Contributor-To-Economy-s-Growth-/16-05-2017-118291/.

  9. 9.

    Details of the comments and the themes can be provided upon request. A snapshot of the comments is presented in the Web Appendix.

  10. 10.

    We conduct an additional robustness analysis with customer (dis)satisfaction as the dependent variable.

  11. 11.

    https://www.financialexpress.com/economy/tapping-the-telecom-sector-for-next-phase-of-gdp-growth/980877/.

  12. 12.

    https://www.financialexpress.com/economy/tapping-the-telecom-sector-for-next-phase-of-gdp-growth/980877/.

  13. 13.

    https://www.absolutdata.com/blog/can-data-analytics-stop-indias-telecom-churn-crisis/.

  14. 14.

    https://economictimes.indiatimes.com/opinion/interviews/knolskapes-rajiv-jayaraman-decodes-the-digital-blur/articleshow/64369415.cms.

  15. 15.

    This type of setting may sometimes create sample selection bias. To check and mitigate this potential bias, we conduct a set of behavioral experiments and find that the results are sample selection bias resistant.

  16. 16.

    Note that in the context of our data, customers have faced failure for the first time. We note that the exposition and modeling of repeat failure and its impact on churn is an important future research avenue given the data availability. However, in the behavioral experiments, we explicitly control for same.

  17. 17.

    Note that the percentage composition of outcome and process failures is different, which may bias the results. We test the proposed relationships with the behavioral experiments to remove any potential issues with the field data.

  18. 18.

    In a separate analysis, we attempted to account for the endogeneity using the Latent Instrument Variables approach; however, this approach did not return any fruitful results.

  19. 19.

    https://www2.deloitte.com/insights/us/en/focus/business-trends/2013/emerging-market-talent-strategies.html.

  20. 20.

    We have presented our results to one of the firms in the hospitality industry, which has expressed a willingness to implement our findings. We are still in negotiation with that firm about a data-sharing agreement.

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Acknowledgements

The authors would like to thank the special issue Guest Editors and the anonymous reviewers for their guidance and support in the review process. We are also grateful to the dissertation advisory committee of Sourav Bikash Borah at Indian Institute of Management, Bangalore for providing feedback in an earlier version of the manuscript. The authors would like to thank Naufel Vilcassim, Rajan Vardarajan, Jagdish Sheth, and Constantine Katsikeas for their insightful guidance at various stages during the development of the paper.

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Appendices

Appendix 1: Taxi Service Failure-Recover Scenario

Please recall the Taxi Service provider you generally use.

Failure Scenarios

Outcome Failure: Unavailable Service

You have to travel to another city for some important personal work. You have decided to book a taxi with the same service provider. You called the taxi service provider and booked an air- conditioned (AC) taxi with free Wi-Fi service to drop you at the airport. You have booked the taxi for 10 am on the day of your journey. Finally, the day of your journey arrives. The taxi arrives on time. However, as you board the taxi you have realized that it is not the type of taxi you booked. The taxi has neither air conditioning nor Wi-Fi service, which was promised while booking. Finally, your journey starts. Upon reaching the airport, you have decided to complain to the service provider.

Process Failure: Inattentive Service

You have to travel to another city for important personal work. You have decided to book a taxi with the same service provider. You called the taxi service and booked an air-conditioned (AC) taxi with free Wi-Fi service to drop you at the airport. You have booked the taxi for 10 am on the day of your journey. Finally, the day of your journey arrives. However, the taxi arrives 15 min late. As you board the taxi, you have realized that the driver is in the middle of a telephone call. The driver finishes his call after 5 min. Finally, your journey starts. However, you have realized that the AC is not switched on. You asked the driver to switch on the AC. However, he does not pay attention to your request. After a second request, he finally switches on the AC. Upon reaching the airport, you have decided to complain to the service provider.

A typical recovery profile is described below

You have called customer care and begun to narrate the entire incident. Customer service allows you to complete what you want to say. The executive replied, “We are sorry to hear about your experience. We are committed to providing excellent customer service to our customers. Let me check with the concerned department. Could you please give me some time? I will get back to you as soon as possible”. After 10 min, you have received a call from a customer service executive. The executive informed you, “We are extremely disappointed to know that your experience with us on the last trip was not satisfactory. Based on your complaint, we have investigated the problem. We accept our mistake and will try our best to avoid such mistakes in the future. We have decided to provide a 100% refund for this trip, which will be credited to your account. We hope you will give us an opportunity to serve you again”.

We changed the recovery profiles based on our intended manipulation. For compensation, customers read, “We have decided to provide a 100% refund for this trip, which will be credited to your account” (high compensation) or “We have decided to provide a 50% refund for this trip, which will be credited in your account” (medium compensation). In a no-compensation scenario, the statement was missing. Again, for TAT customers read “After 10 minutes, you have received a call from customer service executive” (faster resolution vs. “After 3 hours, you have received a call from customer service executive” (delayed resolution). Politeness and courtesy have been manipulated by many statements in the recovery profile such as “The customer service executive interrupts you when you try to complete what you want to say” (rude behavior) vs. “The customer service allows you to complete what you want to say” (polite behavior). Similarly, the customer reads that the executive replied, “We are committed to providing excellent customer service to our customers. Let me check with the concerned department. Give me some time and I will get back to you as soon as possible” (rude behavior) vs. the executive replied, “We are sorry to hear about your experience. We are committed to providing excellent customer service to our customers. Let me check with the concerned department. Could you please give me some time? I will get back to you as soon as possible” (Polite behavior).

Appendix 2: Measures

Model Constructs

Propensity to Complain (adapted from Bodey and Grace (2007)) (1-Strongly Agree, 7-Strongly Disagree); Coefficient alpha = .85.

PC1: If there is a service failure, I will complain to the company.

PC2: If I am dissatisfied with the things I buy, I will complain about them to the shop (or other suppliers) that sold them to me.

PC3: I do not hesitate to complain if I think it is warranted to do so.

PC4: Based on my past purchasing experiences, I am likely to complain in the event of dissatisfaction or service failure.

PC5: I am inclined to complain to the service provider if I am unhappy with a service.

PC6: I am usually reluctant to complain about service regardless of how bad it is (R).

PC7: I am less likely than most people to complain about unsatisfactory service (R).

Realism of the Scenario (adapted from Liao 2007) (1-Not at all, 7- Completely); Coefficient alpha = .78.

RS1: The extent to which this particular scenario “sounds realistic”. (R).

RS2: The extent to which this particular scenario “could happen in real life”. (R).

Turn Around Time (adapted from Tax et al. 1998). 7-point scale anchored at the endpoints (1- Strongly Disagree, 7- Strongly Agree); Coefficient alpha = .75.

TAT1: The taxi service provider was quick to resolve my problem.

TAT2: The length of time taken to resolve my problem was longer than necessary. (R).

Service Recovery Expectations (adapted from Hess et al. (2003)) (1- Strongly Disagree, 7- Strongly Agree); Coefficient alpha = .80.

SRE1: I expect the taxi service provider to do everything in its power to solve the problem.

SRE2: I do not expect the taxi service provider to exert much effort to solve the problem. (R).

SRE3: I expect the taxi service provider to try to make up for (providing the wrong taxi/inattentive service by the driver).

Controllability Attribution (adapted from Smith et al. (1999)) (1- Strongly Disagree, 7- Strongly Agree).

Do you think the service provider could have prevented the problem?

Severity of Failure (adapted from Mattila 2001 Basso and Pizzutti (2016)) (1-Unimportant, 7-important).

Based on your experience, how important do you feel the service failure was?

Politeness and Courtesy (adapted from Liao (2007)) (1- Strongly Disagree, 7- Strongly Agree).

The customer service representative was courteous to me.

Likelihood of Churn (adapted from (Singh 1990)) (1- Strongly Disagree, 7-Strongly Agree).

Based on my experience with service failure and recovery, I am going to end my relationship with the firm.

Appendix 3: Restaurant Failure-Recovery Scenario

Please recall a restaurant that you have visited recently.

Failure Scenario

Outcome Failure

You go to the same restaurant for lunch. You are seated at your table and the waiter comes to take the order. You select your items from the menu and place your order. The waiter informs you that the restaurant is out of the items you have selected. You have decided to complain to the manager.

Process Failure

You go to the same restaurant for lunch. You are seated at your table, but the waiter is busy talking to other waiters. Finally, after almost shouting at the top of your voice, the waiter takes the order. However, the waiter delays inordinately in delivering your food and after two reminders, finally serves you. You have decided to complain to the manager.

Service Recovery Scenario

You walk to the manager and start narrating the incident. The manager allows you to complete what you want to say. The manager replied, “We are sorry to hear about your experience. We are committed to providing excellent customer service to our customers. Let me check what I can do for you. I will get back to you as soon as possible”. After 2 min, the manager gets back to you. The manager informed you, “We are extremely disappointed to know that your experience with us in today was not satisfactory. Based on your complaint, we have investigated the problem and we have decided to provide a 100% refund for today’s lunch. We hope you will give us an opportunity to serve you again”.

We change the recovery profiles based on our intended manipulation. For compensation, customers read “we have decided to provide a 100% refund for today’s lunch” (high compensation) or “we have decided to provide a 50% refund for today’s lunch” (medium compensation). In no-compensation scenario, the statement was missing. Again, for TAT customers read “After 2 minutes, the manager gets back to you” (faster resolution) vs. “After 15 minutes, the manager gets back to you” (slower resolution). Politeness and courtesy have been manipulated by many statements in the recovery profile such as “The manager interrupts you when you try to complete what you want to say” (rude behavior) vs. “The manager allows you to complete what you want to say” (polite behavior). Similarly, the manager replied, “We are committed to providing excellent customer service to our customers. Let me check what I can do for. I will get back to you as soon as possible” (rude behavior) vs. the manager replied, “We are sorry to hear about your experience. We are committed to providing excellent customer service to our customers. Let me check what I can do for you. I will get back to you as soon as possible” (polite behavior).

Measures: All measures are similar to Experiment 1.

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Borah, S.B., Prakhya, S. & Sharma, A. Leveraging service recovery strategies to reduce customer churn in an emerging market. J. of the Acad. Mark. Sci. 48, 848–868 (2020). https://doi.org/10.1007/s11747-019-00634-0

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Keywords

  • Service recovery
  • Emerging markets
  • Type of failure
  • Multi-methods