Development of marketing strategies and plans based on clustering, cluster analysis and segmentation.

Anton Barinov

Bachelor's thesis

Abstract:

This thesis is focused on development of marketing plans and strategies targeting specified segments of customers, based on K-means clustering algorithm and clustering results analysis. The main goal is to determine the key advantages of applying clustering methods in marketing and methodology of K-means, as well as situations in which it should be applied, and modern techniques for developing marketing strategies and plans. It studies modern approaches in marketing, specifically targeting to consumer markets, usage of customer data as segmentation variables and generational marketing. The following thesis examines the main stages of data collection, analysis and application of unsupervised machine learning clustering algorithms, segmentation, targeting and positioning, the impact of customer behavioural and demographical patterns on purchasing and brand preferences. The result of the thesis is a dataset, created for application of K-means clustering algorithm for marketing purpose, which will be executed using program code, and detailed analysis of its results. After identifying the key differences between target segments and their analysis, a marketing plan and strategy will be developed for each customer group, considering this knowledge and specifics of each segment.

Keywords

Targeting, Segmenting, K-means clustering, Machine learning, Positioning, Customer loyalty, Customer database, Generational marketing

Language used: English

Date on which the thesis was submitted / produced: 15. 3. 2021

Thesis defence

• Supervisor: Ing. Richard Selby, Ph.D.

Czech University of Life Sciences Prague

Faculty of Economics and Management

Bachelor programme / field:
Economics Policy and Administration / Business Administration