Customer lifetime value prediction | |
| Author | Biradar, Abhishek Reddy |
| Call Number | AIT RSPR no.IM-22-05 |
| Subject(s) | Customer relations--Management--Data processing Machine learning Neural networks (Computer science) Web applications--Development |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | Companies are spending large amounts of money on retaining and acquiring customers without knowing the amount of value they do for the company. Every customer in the market is not the same, they have their value to the company. The amount of revenue customers spend varies with other customers. To let the executives know the value of each customer, customer lifetime value is introduced. Customer lifetime value (CLTV) is defined as the average amount of revenue generated by the customer in his complete lifespan in the company. By knowing CLTV the marketers can estimate how much money is needed to spend on the customers. The objective of the study is to build an application to predict the CLTV of a customer. We implement the DNN and Xgboost models to predict the CLTV and compare them using the RMSE metric. Finally, we built a web-based application to predict the CLTV of a customer for the upcoming three months and provide recommendations based on the customer segments. The dataset is collected from the UCI repository, which contains transaction data made by customers for one year. The DNN model has scored the lowest RMSE over Xgboost. Hence, the DNN is used in application development to predict CLTV. The customers are segmented (Loyal customers, potential customers, promising customers, hibernating customers) based on their predicted CLTV value. In addition, this application provides suggestions on strategies to be implemented based on segments to improve the CLTV of a customer. |
| Year | 2022 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Vatcharaporn Esichaikul |
| Examination Committee(s) | Chutiporn Anutariya;Huynh, Trung Luong |
| Scholarship Donor(s) | AIT partial Scholarship |
| Degree | Research studies project report (M. Eng.) - Asian Institute of Technology, 2022 |