In this project, we will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany.
These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.
- men in higher middle age
- high income and a high online affinity
- high financial interest, often save and invest money and are financially inconspicuous
- low socially, family and sensual minded and are very low dreamful
- residence length is 7-10 years
- life stage between "Families With School Age Children" and "Older Families & Mature Couples"
- young women
- low to very low income and a high online affinity
- low financial interest, are not money-saver or investors
- high socially, sensual minded, dreamful
- not religious, materialistic or tradional minded
- life stage is "Young Couples With Children"
- living in less affluent households
- share of unemployment in the community is high