Predicting 4G Adoption with Apache Spark
Companies have long realized the value of targeting the right customer with the right product. However, this request has never been so inevitable as in the era of big data. Thanks to the tractability of the customers' behavior, the preference information for each individual is collected and updated by the firm in a timely fashion. In this study, we developed a targeting strategy for telecommunication companies to facilitate the adoption of 4G technology. Utilizing the most up to date machine learning technique and the information about individual's local network, we set up a prediction model of consumer adoption behavior. We then applied the model to the real world and conduct field experiment. We worked with the largest telecommunication company in China and used Apache Spark to analyze the data from the complete customer based of a 2nd tie city in eastern China. In the experiment group, we asked the company to use the list we generated as the targets and in the control group, the company used the existing targeting strategy. The results demonstrated the effectiveness of the proposed approach comparing to existing models.