Abstract:
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%.
