  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.
