Hema Yoganarasimhan, Ebrahim Barzegary, Abhishek Pani
arXiv 24 Jun 2020 · Statistics — Machine Learning · 3 citations (OpenAlex)
arXiv:2006.13420 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S. Athey and G. Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.843 | 4 | 3 | 75% |
| 2 | N. Fong, Y. Zhang, X. Luo, and X. Wang (2019) Targeted promotions on an e-book platform: Crowding out, heterogeneity, and opportunity costs | 0.843 | 3 | 3 | 100% |
| 3 | T. Guo, S. Sriram, and P. Manchanda (2017) The effect of information disclosure on industry payments to physicians | 0.843 | 3 | 3 | 100% |
| 4 | O. Rafieian (2019) Optimizing user engagement through adaptive ad sequencing | 0.843 | 3 | 3 | 100% |
| 5 | O. Rafieian and H. Yoganarasimhan (2020) Targeting and privacy in mobile advertising | 0.843 | 3 | 3 | 100% |
| 6 | S. Wager and S. Athey (2017) Estimation and inference of heterogeneous treatment effects using random forests | 0.843 | 3 | 3 | 100% |
| 7 | D. Dey, A. Lahiri, and D. Liu (2013) Consumer learning and time-locked trials of software products | 0.644 | 2 | 2 | 100% |
| 8 | M. Dudḱ, J. Langford, and L. Li (2011) Doubly robust policy evaluation and learning | 0.644 | 2 | 2 | 100% |
| 9 | D. G. Horvitz and D. J. Thompson (1952) A generalization of sampling without replacement from a finite universe | 0.644 | 2 | 2 | 100% |
| 10 | O. Rafieian (2019) Revenue-optimal dynamic auctions for adaptive ad sequencing | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.