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首页> 外文期刊>Journal of Intelligent Information Systems >Data augmentation by predicting spending pleasure using commercially available external data
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Data augmentation by predicting spending pleasure using commercially available external data

机译：通过使用商业上可获得的外部数据预测支出的愉悦度来增强数据

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摘要

Since customer relationship management (CRM) plays an increasingly important role in a company's marketing strategy, the database of the company can be considered as a valuable asset to compete with others. Consequently, companies constantly try to augment their database through data collection themselves, as well as through the acquisition of commercially available external data. Until now, little research has been done on the usefulness of these commercially available external databases for CRM. This study will present a methodology for such external data vendors based on random forests predictive modeling techniques to create commercial variables that solve the shortcomings of a classic transactional database. Eventually, we predicted spending pleasure variables, a composite measure of purchasing behavior and attitude, in 26 product categories for more than 3 million respondents. Enhancing a company's transactional database with these variables can significantly improve the predictive performance of existing CRM models. This has been demonstrated in a case study with a magazine publisher for which prospects needed to be identified for new customer acquisition.
机译：由于客户关系管理（CRM）在公司的营销策略中扮演着越来越重要的角色，因此公司的数据库可以被视为与他人竞争的宝贵资产。因此，公司不断尝试通过自身收集数据以及通过获取市售外部数据来扩充其数据库。到目前为止，关于这些可用于CRM的外部数据库的实用性的研究很少。这项研究将为此类外部数据供应商提供一种基于随机森林预测建模技术的方法，以创建可解决传统交易数据库缺点的商业变量。最终，我们预测了消费愉悦度变量（一种购买行为和态度的综合度量），用于超过300万名受访者的26种产品类别。使用这些变量增强公司的交易数据库可以显着提高现有CRM模型的预测性能。与一家杂志出版商进行的案例研究已经证明了这一点，对于新客户的获取，需要为其确定潜在客户。

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