Predicting likelihood to recommend and likelihood to churn based on cumulative experience of online services: modeling transitions in customer attitudes and behaviours

根据在线服务的累积经验预测推荐的可能性和流失的可能性：对客户态度和行为的转变进行建模

• 批准号: 477935-2014
• 负责人: Chignell, Mark
• 金额: $ 5.19万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=599704
• 关键词: Predicting; likelihood; recommend; churn; based

Customers want high quality, and high bandwidth services, at competitive pricing. In practice there are engineering tradeoffs between guaranteeing increasing levels of quality and the associated costs. What is the relationship between cumulative service quality and resulting customer satisfaction as reflected in attitudes and behaviors such as the net promoter score (or likelihood to recommend, L2R). We will carry out a series of experiments and observational studies where our goal will be to develop a family of models, ultimately for each family of wireless services, that predict key attitude scores and L2R based on patterns of quality of experience over time. Using controlled experiments and field and longitudinal studies we will investigate how cumulative experience translates into attitudes towards services based on the history of frustration and satisfaction experienced. We will develop models that predict the impact of cumulative experience in different contexts and will examine how changes in quality of experience lead to changes in attitudes, and finally decisions, that are reflected in L2R judgments (and discontinuation of service in some cases). An important part of the research will involve controlled experiments that can test hypotheses about causal relationships. Thus our strategy will be not only to develop models, but also to test their predictions in formal experiments, with analysis of variance carried out on the experimental results to identify causal factors that influence L2R. The outcomes of this research will also include guidelines for monitoring and managing service delivery so as to increase satisfaction and loyalty, and reduce frustration and churn on the other.

客户希望以具有竞争力的价格获得高质量和高带宽服务。在实践中，在保证不断提高的质量水平和相关成本之间存在着工程权衡。累积的服务质量和由此产生的客户满意度之间的关系是什么，反映在态度和行为上，如净推荐分数（或推荐可能性，L2R）。我们将进行一系列实验和观察性研究，我们的目标是开发一系列模型，最终针对每个无线服务系列，根据随时间推移的体验质量模式预测关键态度评分和L2R。使用控制实验和现场和纵向研究，我们将调查如何累积的经验转化为基于历史的挫折和满意度的服务态度。我们将开发模型，预测在不同的情况下积累的经验的影响，并将研究如何在经验的质量变化导致态度的变化，并最终决定，这是反映在L2R的判断（在某些情况下停止服务）。研究的一个重要部分将涉及控制实验，可以测试因果关系的假设。因此，我们的策略将不仅是开发模型，但也要在正式的实验中测试他们的预测，方差分析的实验结果进行，以确定影响L2R的因果因素。这项研究的成果还将包括监测和管理服务提供的指导方针，以提高满意度和忠诚度，并减少另一方面的挫折感和流失。