Customer Acquisition & Retention

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.

Predicting readability of The News Lens' next online articles to enhance reader loyalty

Application Area: 

Project Details

Term: 

Fall 2017

Students: 

Uniss Tseng, Elisa Wang, Patrizia Mach, Sabrina Wei

University: 

NTHU

Presentation: 

Report: 

In order to establish a dedicated reader base, online news website The News Lens aims to drive traffic directly to their website rather than via third-party social media, such as Facebook. Establishing this goal involves selecting a list of featured articles to display on the homepage, which are most likely to be read completely and aid in establishing a reader habit to primarily use The News Lens for its insights to current events.

Identifying bookings with a high-risk of host rejection to improve customer service and customer satisfaction

Application Area: 

Project Details

Term: 

Fall 2017

Students: 

Arturo Heyner Cano Bejar, Tonny Kuo, Nick Danks, Kellan Nguyen

University: 

NTHU

Presentation: 

Report: 

AsiaYo! operates in a highly competitive industry serving as intermediary and agent between accommodation providers (hosts) and accommodation seekers (guests). High customer service is a critical business goal for AsiaYo!. Rejection of bookings by hosts can cause customer dissatisfaction and potentially loss of customers. Currently, AsiaYo! experiences a host rejection rate of 15% of orders. Rejection of a booking by the host triggers a reaction from the AsiaYo!

Predict the average recipe rating on BBC Good Food

Application Area: 

Project Details

Term: 

Fall 2015

Students: 

Claire Huang, Wan Yi Chou, Eva Shih, Pornlada Ittipornpithak

University: 

NTHU

Presentation: 

Report: 

The primary stakeholder is BBC Good Food which is a recipe website related to BBC. The
problem is that it is not the most popular website since it does not appear on the first page when user
searching in Google website by keyword as “recipe website”. Hence, the business goal of this project is
to improve the quality of recipes on the website. By doing so, we aim to attract more people to visit the
website and the analytic goal of the project is to predict the average rating value of new recipe before
publishing.

Forecasting demand for trailers for efficient use and customized service

Application Area: 

Project Details

Term: 

Fall 2014

Students: 

Yu-Ning Kao, Jou-yu Huang, Ting-Ju Wang, Chin Chang

University: 

NTHU

Presentation: 

Report: 

In this project, our data source is the stakeholders who are Jouyu’s family business. It is kind of transport industry. The business model is to transport customers’ excavators to the destination they require through the trailers whom the stakeholders own. However, since the stakeholders are used to manually record the trips that customers require, they predict the next demand only based on their experience. Without the assistant of technology, stakeholders are unable to anticipate the demand of trailers in the future accurately.

Missing marital status prediction for hypermarkets

Application Area: 

Project Details

Term: 

2013

Students: 

Sankalp Gaur, Vineet Jain, Sonali Gadekar, Harshita Jujjuru, Tushna Mistry

University: 

ISB

Presentation: 

Report: 

Business Problem
The customer database contains a field called "MARITAL_STATUS". This is an important field for business. It can help the marketing department to segment the customers and target marketing and promotional initiatives accordingly.

Classifying Biscuit Brand Switchers for Targeted marketing by a New Biscuit Manufacturer

Application Area: 

Project Details

Term: 

2013

Students: 

Archana Rajan, Kevin John, Aditi Vaish, Deepak Agnihotri, Pranav Maranganty

University: 

ISB

Presentation: 

Report: 

• The stakeholder in this data mining project is Mine Sweeper Biscuits (MSB), a premium biscuit manufacturer based out of Denmark. While MSB has entered the Indian market through retail outlets, its sales have failed to take off due to the low product trial rate among Indian consumers.

Predicting Adoption of MVAS

Application Area: 

Project Details

Term: 

2012 (Nov)

Students: 

Anand Prasad, Charanpreet Singh Arora, Dhruv Gandhi, Gagan Oberoi, Nikesh Lamba

University: 

ISB

Presentation: 

Report: 

The Indian Telecom industry is amongst the most fiercely fought services market in India with more than 10 large-scale operators providing voice and data services at highly competitive prices. The Industry has witnessed significant reduction in profit margins in recent years, with the average revenues per user from voice telephony being amongst the lowest in the world. On the other hand, margins from value added services are largely seen as the next source of growth for mobile operators.

Predicting Mobile Value-Add-Services Likelihood

Application Area: 

Project Details

Term: 

2012 (Nov)

Students: 

Sagar Gupta, Raghuraman Chandrasekhar, Yash Chandwani, Sudhanshu Dharmadhikari, Saurabh Choudhary

University: 

ISB

Presentation: 

Report: 

The Indian mobile telephony market is a classic example of a volume based strategy. Average revenue per user (ARPU) is one of the lowest, while the subscriber figures are second only to China. The market itself is highly fragmented with more than 5 players holding less than 90% of the market. In this scenario, identifying new sources of revenues is critical for survival.

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