2017 Russell Ackoff Doctoral Student Fellowship Proposal

Transcription

1 2017 Russell Ackoff Doctoral Student Fellowship Proposal Can Firms Improve the Performance of Referral Programs by Inducing Reciprocity from Customers? Evidence from a Field Experiment Research Focus: Referral Programs, Reciprocity, Behavioral Economics Yupeng Chen 4th Year Doctoral Student Marketing Department The Wharton School The University of Pennsylvania Faculty Advisor: Professor Raghuram Iyengar Marketing Department The Wharton School The University of Pennsylvania 1

2 Goals of the Proposed Research Referral programs, in which a firm s existing customers are rewarded for bringing in new customers, have been widely used by firms as an indispensable tool to acquire new customers and have fueled the phenomenal growth of many companies, including Dropbox, Uber, and Airbnb. In the case of Dropbox, for instance, a current user receives 500 MB free storage space for each friend being invited to sign up for and install Dropbox. According to Drew Houston, Co-Founder and CEO of Dropbox, this referral program was largely responsible for the growth in Dropbox s user base from 100, 000 registered users in September 2008 to more than 4 million in January Compared to other customer acquisition methods, referral programs enjoy superior targetability and cost effectiveness (Mummert 2000), and research has shown that customers acquired through referral programs can be more valuable than customers acquired through other methods (Schmitt et al. 2011). Given the critical role that referral programs play in firms customer acquisition practice, it is of particular interest for firms to identify strategies that can improve the performance of their referral programs. In this research, I propose and empirically evaluate a strategy to enhance the effectiveness of referral programs by inducing and leveraging positive reciprocity from existing customers toward the firm. Reciprocity, defined as the behavioral phenomenon of people responding toward (un)kind treatment likewise, even in the absence of reputational concerns in Kube et al. (2012), has long been argued to be deeply embedded in and have substantial implications for social and economic interactions (Cialdini 1992, Fehr and Gachter 2000). My proposed strategy is motivated by the rich behavioral economics literature on reciprocity (Fehr et al. 1993, Berg et al. 1995, Fehr et al. 1997, Fehr and Falk 1999) and particularly recent field evidence suggesting that positive reciprocity from an economic agent can be elicited and leveraged to increase the agent s provision of goods that are desired by the other party (Gneezy and List 2006, Falk 2007, Kube et al. 2012). Leveraging insights from this literature, I hypothesize that a firm can improve the performance of its referral program by employing appropriate marketing tools to induce positive reciprocity from existing customers and channel such reciprocity toward their participation of the referral program. In particular, the motivation to reciprocate to the firm could make an existing customer more likely to refer friends, and, when they decide to refer, refer more friends and friends of higher value to the firm. Planned Methodology To examine whether and to what extent the effectiveness of referral programs can be improved by inducing and leveraging positive reciprocity from existing customers, I conducted a randomized field experiment in collaboration with a leading Chinese online financial services company. The company offers customers financial deposit services in which they can invest money and earn interest, and it has maintained a referral program which rewards existing customers for inviting friends to open accounts at the company. For the experiment, I included more than 90,000 existing customers in a campaign inviting them to refer their 2

3 friends, and randomly assigned them to a control condition and two treatment conditions. The regular referral program was implemented in all three conditions, and the only difference across conditions was how customers were approached at the beginning of the campaign. Specifically, customers in the control condition received a text message inviting them to refer friends; customers in the first treatment condition received a gift - a coupon for their next investment - and a text message inviting them to refer friends; and customers in the second treatment condition received a text message notifying them about the value that the company had created for them and inviting them to refer friends. Both treatment conditions were designed to elicit reciprocity from customers, and they aimed to provide converging evidence that reciprocity from customers can be triggered and channeled to improve the performance of referral programs. The campaign lasted for two weeks, at the end of which the gift coupon in the first treatment condition expired. Tracking the investment behavior of new customers acquired via referral during the campaign for an 8-week period, I find that both the gift treatment and the notification treatment have economically and statistically significant effects on the performance of the referral program, increasing the investments brought in by the referral program (i.e., investments made by new customers) by more than 150%. A closer look at the data reveals that these effects are not due to the two treatment conditions yielding more newly acquired customers through the referral program - the numbers of new customers acquired in all three conditions are statistically indistinguishable - but are primarily driven by the higher average investment made by new customers acquired in the two treatment conditions compared to that in the control condition. As the next step, I plan to do the following: (1) track the investment behavior of newly acquired customers for a longer time horizon to measure their value in the long run, (2) conduct a thorough analysis of the data to rule out alternative explanations so as to ensure proper interpretation of the treatment effects as the result of reciprocity from existing customers toward the company, and (3) explore the mechanism through which reciprocity from existing customers led to an improved performance of the referral program. Why Funding is Being Sought I hope to utilize the Ackoff Fellowship to support my attendance at two conferences: MIT Conference on Digital Experimentation 2017 (scheduled in Oct. 2017) and 2018 Bass UTD-FORMS Conference (scheduled in early 2018), to both of which I plan to submit the current research project for presentation. Both conferences are highly regarded in the field and the opportunity of presenting at these two conferences and receiving feedback from their participants could greatly benefit this research project. 3

4 Specifics of how the Funds will be Used I am seeking financial support totaling $2650 from the Ackoff Fellowship and a detailed breakdown of the budget is provided in the section below. The funds will be used for two conference attendances: MIT Conference on Digital Experimentation 2017 (scheduled in Oct. 2017) and 2018 Bass UTD-FORMS Conference (scheduled in early 2018), to both of which I plan to submit the current research project for presentation. Budget Details for Anticipated Expenses Registration fee for MIT Conference on Digital Experimentation 2017: $200. Airfare and other travel expenses (e.g., taxi) between Philadelphia and Boston: $600. Lodging expense in Boston (2 nights): $500. Registration fee for 2018 Bass UTD-FORMS Conference: $50. Airfare and other travel expenses (e.g., taxi) between Philadelphia and Dallas: $700. Lodging expense in Dallas (3 nights): $600. TOTAL: $2650. Other Current Funding Sources The Marketing Department provides an annual research funding of $1000 to each doctoral student. I plan to use this research funding, together with the Ackoff Fellowship (if granted), to cover research and travel expenses of the current project and other related projects. 4

5 Statement from the Faculty Advisor, Professor Raghuram Iyengar I certify that I have read the proposal and will be in residence to supervise the proposed research over the forthcoming year. Professor Raghuram Iyengar 5

6 References Berg, Joyce, John Dickhaut, Kevin McCabe Trust, Reciprocity, and Social History. Games and Economic Behavior 10(1) Cialdini, Robert Social Motivations to Comply: Norms, Values, and Principles. Jeffrey A. Roth, John T. Scholz, eds., Taxpayer Compliance, vol. 2. University of Pennsylvania Press, Falk, Armin Gift Exchange in the Field. Econometrica 75(5) Fehr, Ernst, Armin Falk Wage Rigidity in a Competitive Incomplete Contract Market. Journal of Political Economy 107(1) Fehr, Ernst, Simon Gachter Fairness and Retaliation: The Economics of Reciprocity. Journal of Economic Perspectives 14(3) Fehr, Ernst, Simon Gachter, Georg Kirchsteiger Reciprocity as a Contract Enforcement Device: Experimental Evidence. Econometrica 65(4) Fehr, Ernst, Georg Kirchsteiger, Arno Riedl Does Fairness Prevent Market Clearing? An Experimental Investigation. Quarterly Journal of Economics 108(2) Gneezy, Uri, John A. List Putting Behavioral Economics to Work: Testing for Gift Exchange in Labor Markets Using Field Experiments. Econometrica 74(5) Kube, Sebastian, Michel Andre Marechal, Clemens Puppe The Currency of Reciprocity: Gift Exchange in the Workplace. American Economic Review 102(4) Malmendier, Ulrike, Vera L. te Velde, Roberto A. Weber Rethinking Reciprocity. Annual Review of Economics Mummert, Hallie The Year s Best Bells & Whistles. Target Marketing 23(11) 3 5. Schmitt, Philipp, Bernd Skiera, Christophe Van den Bulte Referral Programs and Customer Value. Journal of Marketing 75(1)