Design and Evaluation of Personalized Free Trials

by   Hema Yoganarasimhan, et al.

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 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.


page 16

page 17


Active Learning for Developing Personalized Treatment

The personalization of treatment via bio-markers and other risk categori...

Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion

With the increasingly fierce market competition, offering a free trial h...

Rapidly Personalizing Mobile Health Treatment Policies with Limited Data

In mobile health (mHealth), reinforcement learning algorithms that adapt...

Personalized Policy Learning using Longitudinal Mobile Health Data

We address the personalized policy learning problem using longitudinal m...

An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest

Evidence from observational studies has become increasingly important fo...

Randomization Bias in Field Trials to Evaluate Targeting Methods

This paper studies the evaluation of methods for targeting the allocatio...

Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging

Treating children with autism spectrum disorders (ASD) with behavioral i...