Hybrid Recommender System for Increased Efficiency of Direct Mailing Campaigns
Name
Karina Kisselite
Abstract
The utilization of predictive analytics techniques can support companies in direct marketing initiatives. The goal of this work is to identify customers who are most likely to buy specific products via personalized email campaigns. The data used in the thesis was collected from a large manufacturing corporation.
The work aims to solve this problem by building a hybrid recommender system by applying a linear combination of several models, where each of them requires the input data in different formats. Collaborative filtering and market basket analysis are used to incorporate customer purchasing data. Furthermore, usage of feature-based model allows to take into account customer attributes. Process mining is used to extract the process models of the customers' behaviour for the purpose of identifying the set of the most important features.
The experimental results show that with the best model when contacting 0.5% of loyal customers the expected success rate would be 93% instead of 15% which we would get if the emails are distributed in a random manner. In addition to this, the results suggest that loyalty of the customer is a crucial point in the performance of the model whereby leading to loyal customers being more easily predictable. Finally, the discriminative power of content-based filtering together with market basket analysis yields the best-performing model, which marketing departments could use to more effectively promote and sell products.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Anna Leontjeva
Defence year
2014