Browsing by Author "LHR TP 8358"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Repeated Buyer Prediction: A Study of Repurchasing Intention of Buyer in E Commerce
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muhammad Usman; SP20-RCS-014; Dr. Hamid Turab Mirza; LHR TP 8358
    During promotions, retailers frequently obtain a huge number of new customers. However, several of the purchasers attracted are one-time hunters, and the deals are unlikely to have a long-term influence on sales. It was critical for merchants to discover who may be turned into regular loyal consumers and afterward target them to reduce promotion costs and boost return on investment (ROI). It was critical for merchants to discover who could be converted into repeat customers to solve this problem. Merchants may significantly cut promotion costs and increase the return on that investment by focusing on these prospective loyal consumers (ROI). Consumer targeting in the area of internet advertising was generally known to be difficult, especially for first-time consumers. In this work, collect a collection of merchants as well as their associated new buyers gained during the "Double 11" day offer using Tmall.com's long-term user behavior record. This experiment objective was to predict whether new clients would become loyal consumers in the future for certain merchants. In other words, this experiment must estimate the probability, which these new purchasers will buying within the similar merchants again for the next six months. This work suggested employing enhanced merged models (XGBoost as well as LightGBM and Histogram-based gradient boosting machine to forecast a repeat customer and feature engineering through extracting feasible features by which important components would be derived to train the model to prophesy the repeated buyer. These experimental findings suggest that when compared to the original models, this work-combined model may achieve significant performance increases. XGBoost accuracy was 95.97 and AUC was 0.9753. The accuracy of Light GBM was 92.58% and the AUC was 96.15%. Histogram-based gradient boosting machine training accuracy was 96.7% and the testing accuracy was 96.27%.