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[Lecture, Dec 03] High Value Customer Acquisition & Retention Modelling – A Scalable Data Mashup Approach

time: 2019-11-28

Title: High Value Customer Acquisition & Retention Modelling – A Scalable Data Mashup Approach

Speaker: Dr. Kajanan Sangaralingam, Mobilewalla

Time: 10:00-11:30, December 3, 2019

Venue: Zhuanghui Hall, Building 12, Wushan Campus


Introduction to the speaker:

Kajanan is a Doctoral graduate from the National University of Singapore. He is passionate about solving real business problems using innovative approaches. Currently working as a Lead Data Scientist at Mobilewalla, headquartered in the USA. Before Mobilewalla Kajanan has worked as a Senior Data Scientist at Singapore Telecommunications. His research interest lies in proposing novel data science and research-based solutions to various business problems using business data analytics and machine learning. Kajanan is skilled in storage, retrieval and analysis of big data. Conversant with processing and analyzing the large volume of unstructured data (text) as well. He did his PhD at NUS and his Bachelor at University of Moratuwa, Sri Lanka. He also has 4 years of working experience as a Senior Software Engineer and Software Engineer in different industries.


Abstract:

Identifying valuable customers as well as retaining them has become key component for any business to succeed in this competitive market. Businesses have also realized that relying solely on its own transactional data, might not be sufficient any longer, to meet the required objectives. There is a need to partner and leverage the power of big data available from the external data sources to add more value. In this paper, we are detailing the methodology of mashing up Mobilewalla’s high scale mobile consumer data with one of the world’s largest online food delivery company in order to revamp their retention and acquisition strategy. In this deployment, Mobilewalla has helped the client, a) to identify the new potential high impact customers from Mobilewalla ecosystem, and b) to predict the unfavorable transitions such as high impact customers getting churned or falling into low impact category. We observed that correctly identified high impact customers by Mobilewalla’ customer acquisition model had 21.41% higher average revenue per user (ARPU) than the expected ARPU from high impact customers. Further, the customer retention model can help the client to spend 80% of their retention budget dollars optimally.