Paper Info
Title
Repeat Buyer Prediction for E-Commerce
Abstract
A large number of new buyers are often acquired by merchants during promotions. However, many of the attracted buyers are one-time deal hunters, and the promotions may have little long-lasting impact on sales. It is important for merchants to identify who can be converted to regular loyal buyers and then target them to reduce promotion cost and increase the return on investment (ROI). At International Joint Conferences on Artificial Intelligence (IJCAI) 2015, Alibaba hosted an international competition for repeat buyer prediction based on the sales data of the ``Double 11\" shopping event in 2014 at Tmall.com. We won the first place at stage 1 of the competition out of 753 teams. In this paper, we present our winning solution, which consists of comprehensive feature engineering and model training. We created profiles for users, merchants, brands, categories, items and their interactions via extensive feature engineering. These profiles are not only useful for this particular prediction task, but can also be used for other important tasks in e-commerce, such as customer segmentation, product recommendation, and customer base augmentation for brands. Feature engineering is often the most important factor for the success of a prediction task, but not much work can be found in the literature on feature engineering for prediction tasks in e-commerce. Our work provides some useful hints and insights for data science practitioners in e-commerce.
Year
DOI
Venue
2016
10.1145/2939672.2939674
KDD
Keywords
Field
DocType
Repeat Buyer Prediction,Feature Engineering,E-commerce
Data mining,Market segmentation,Return on investment,Computer science,Feature engineering,Customer base,E-commerce
Conference
Citations 
PageRank 
References 
15
0.84
5
Authors
9
Name
Order
Citations
PageRank
Guimei Liu172135.38
Tam T. Nguyen2786.79
Gang Zhao3208.59
Wei Zha4150.84
Jian-Bo Yang53832203.05
Jianneng Cao624916.00
Min Wu7335.46
Peilin Zhao8136580.09
Wei Chen9150.84