Free trial promotions are a commonly used customer acquisition strategy in the Software as a Service industry. We use data from a large-scale field experiment to study the effect of trial length on customer-level outcomes. We find that, on average, shorter trial lengths (surprisingly) maximize customer acquisition, retention, and profitability. Next, we examine the mechanism through which trial length affects conversions and rule out the demand cannibalization theory, find support for the consumer learning hypothesis, and show that long stretches of inactivity at the end of the trial are associated with lower conversions. We then develop a personalized targeting policy that allocates the optimal treatment to each user based on individual-level predictions of the outcome of interest (e.g., subscriptions) using a lasso model. We evaluate this policy using the inverse propensity score reward estimator and show that it leads to 6.8% improvement in subscription compared with a uniform 30-days for-all policy. It also performs well on long-term customer retention and revenues in our setting. Further analysis of this policy suggests that skilled and experienced users are more likely to benefit from longer trials, whereas beginners are more responsive to shorter trials. Finally, we show that personalized policies do not always outperform uniform policies, and we should be careful when designing and evaluating personalized policies. In our setting, personalized policies based on other methods (e.g., causal forests, random forests) perform worse than a simple uniform policy that assigns a short trial length to all users. This paper was accepted by Duncan Simester, marketing.
We are grateful to an anonymous firm for providing the data and to UW-Foster High-Performance Computing Lab for providing us with computing resources. We thank the participants of the 2018 Marketing Science conference and the Triennial Choice Symposium. Thanks are also due to seminar audiences at the
Dynamic discrete choice models are widely employed to answer substantive and policy questions in settings where individuals' current choices have future implications. However, estimation of these models is often computationally intensive and/or infeasible in high-dimensional settings. Indeed, even specifying the structure for how the utilities/state transitions enter the agent's decision is challenging in high-dimensional settings when we have no guiding theory. In this paper, we present a semi-parametric formulation of dynamic discrete choice models that incorporates a high-dimensional set of state variables, in addition to the standard variables used in a parametric utility function. The high-dimensional variable can include all the variables that are not the main variables of interest but may potentially affect people's choices and must be included in the estimation procedure, i.e., control variables. We present a data-driven recursive partitioning algorithm that reduces the dimensionality of the high-dimensional state space by taking the variation in choices and state transition into account. Researchers can then use the method of their choice to estimate the problem using the discretized state space from the first stage. Our approach can reduce the estimation bias and make estimation feasible at the same time. We present Monte Carlo simulations to demonstrate the performance of our method compared to standard estimation methods where we ignore the high-dimensional explanatory variable set.
Mate preference in short-term relationships and long-term ones may depend on many physical, psychological, and socio-cultural factors. In this study, 178 students (81 females) in sports and 153 engineering students (64 females) answered the systemizing quotient (SQ) and empathizing quotient (EQ) questionnaires and had their digit ratio measured. They rated their preferred mate on 12 black-line drawing body figures varying in body mass index (BMI) and waist to hip ratio (WHR) for short-term and long-term relationships. Men relative to women preferred lower WHR and BMI for mate selection for both short-term and long-term relationships. BMI and WHR preference in men is independent of each other, but has a negative correlation in women. For men, digit ratio was inversely associated with BMI (p = 0.039, B = − 0.154) preference in a short-term relationship, and EQ was inversely associated with WHR preference in a long-term relationship (p = 0.045, B = − 0.164). Furthermore, men and women in sports, compared to engineering students, preferred higher (p = 0.009, B = 0.201) and lower BMI (p = 0.034, B = − 0.182) for short-term relationships, respectively. Women were more consistent in their preferences for short-term and long-term relationships relative to men. Both biological factors and social/experiential factors contribute to mate preferences in men while in women, mostly social/experiential factors contribute to them.
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