摘要翻译：
免费试用促销是软件即服务（SaaS）行业中常用的客户获取策略，用户可以在有限的时间内免费试用产品。我们研究试用长度如何影响用户的反应性，并试图量化免费试用促销的个性化长度带来的收益。我们的数据来自一家领先的SaaS公司进行的大规模现场实验，新用户被随机分配到7、14或30天的免费试用。首先，我们表明，对所有消费者的7天试用是最好的统一政策，订阅量增加了5.59%。接下来，我们开发了一个三管齐下的个性化政策设计和评估框架。利用我们的框架，我们开发了基于线性回归、lasso、CART、随机森林、XGBoost、因果树和因果森林的七种个性化目标策略，并使用逆倾向得分(IPS)估计器评估它们的性能。我们发现基于lasso的个性化策略性能最好，其次是基于XGBoost的个性化策略。相比之下，基于因果树和因果林的策略表现不佳。然后，我们将一个方法在设计策略时的有效性与其在不过度拟合的情况下充分个性化处理的能力联系起来（即，捕获虚假的异质性）。接下来，我们根据用户的最佳试用时间对其进行细分，并在此背景下对用户行为的驱动因素得出一些实质性的见解。最后，我们表明，旨在最大化短期转换的政策在长期结果上也表现良好，如消费者忠诚度和盈利能力。
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英文标题：
《Design and Evaluation of Personalized Free Trials》
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作者：
Hema Yoganarasimhan, Ebrahim Barzegary, Abhishek Pani
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  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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PDF链接：
https://arxiv.org/pdf/2006.13420