Predicting Loyal Customers for Sellers on Tmall to Increase Return on Promoting Cost

Application Area: 

Project Details

Term: 

Fall 2017

Students: 

Wendy Huang, Yu-Chih Shih, Jessy Yang, Zoe Cheng

University: 

NTHU

Presentation: 

Report: 

Sellers on E-commerce platform sometimes run big promotions (e.g., discounts or cash
coupons) on particular dates (e.g., Boxing-day Sales, "Black Friday" or "Double 11 (Nov 11th)”, in
order to attract a large number of new buyers. Unfortunately, many of the attracted buyers are
one-time deal hunters, and these promotions may have little long lasting impact on sales. To
alleviate this problem, it is important for sellers to identify who can be converted into repeated

Predicting Customer Purchase to Improve Bank Marketing Effectiveness

Application Area: 

Project Details

Term: 

Fall 2017

Students: 

Sandy Wu, Andy Hsu, Wei-Zhu Chen, Samantha Chien

University: 

NTHU

Presentation: 

Report: 

A bank marketing dataset from UCI Machine Learning Repository was adopted for this project (https://archive.ics.uci.edu/ml/datasets/bank+marketing). The dataset is about a Portuguese banking institution with records of direct marketing campaign phone calls, and the final outcomes indicating whether success campaigns are also included in a binary format (yes/no). A success campaign indicates the customer has finally subscribed a term deposit at the end of the campaign.

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