Design and Evaluation of Personalized Free Trials

55 Pages Posted: 6 Aug 2020

See all articles by Hema Yoganarasimhan

Hema Yoganarasimhan

University of Washington

Ebrahim Barzegary

affiliation not provided to SSRN

Abhishek Pani

Independent

Date Written: June 2, 2020

Abstract

Free trial promotions, where users are given a limited time to try the product for free, are a commonly used customer acquisition strategy in the Software as a Service (SaaS) industry. We examine how trial length affect users' responsiveness, and seek to quantify the gains from personalizing the length of the free trial promotions. Our data come from a large-scale field experiment conducted by a leading SaaS firm, where new users were randomly assigned to 7, 14, or 30 days of free trial. First, we show that the 7-day trial to all consumers is the best uniform policy, with a 5.59% increase in subscriptions. Next, we develop a three-pronged framework for personalized policy design and evaluation. Using our framework, we develop seven personalized targeting policies based on linear regression, lasso, CART, random forest, XGBoost, causal tree, and causal forest, and evaluate their performances using the Inverse Propensity Score (IPS) estimator. We find that the personalized policy based on lasso performs the best, followed by the one based on XGBoost. In contrast, policies based on causal tree and causal forest perform poorly. We then link a method's effectiveness in designing policy with its ability to personalize the treatment sufficiently without over-fitting (i.e., capture spurious heterogeneity). Next, we segment consumers based on their optimal trial length and derive some substantive insights on the drivers of user behavior in this context. Finally, we show that policies designed to maximize short-run conversions also perform well on long-run outcomes such as consumer loyalty and profitability.

Keywords: free trials, targeting, personalization, counterfactual policy evaluation, field experiment, machine learning, policy design, digital marketing

JEL Classification: M31, L86, C14, C21, C52, C54, C55, C93

Suggested Citation

Yoganarasimhan, Hema and Barzegary, Ebrahim and Pani, Abhishek, Design and Evaluation of Personalized Free Trials (June 2, 2020). Available at SSRN: https://ssrn.com/abstract=3616641 or http://dx.doi.org/10.2139/ssrn.3616641

Hema Yoganarasimhan (Contact Author)

University of Washington ( email )

481 Paccar Hall
Seattle, WA 98195
United States

HOME PAGE: http://faculty.washington.edu/hemay/

Ebrahim Barzegary

affiliation not provided to SSRN

Abhishek Pani

Independent

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