By creating customer segmentation and comparing to general population, one can know which part of the general population are more likely to be customer and which part are not. I analyzed demographics data for customers of a mail-order sales company in Germany, comparing it to demographics information for the general population.
Firstly, I used different approaches to pre-process the data, and then I used unsupervised learning techniques, PCA (Principle Componets Analysis) and k-NN (k-nearest neighbor algorithm), to perform customer segmentation and to identify the core customer traits of the company.
Secondly, with demographics information for targets of a marketing campaign for the company, I used different models to predict which individuals are most likely to convert into customers.
Finally, I also tested the model in competition through kaggle, where the competition is here.
Since the dataset offered by AZ Direct GmbH is private and cannot be published, only the meta-data is uploaded here.
- Arvato_Project_Workbook.ipynb: The main analysis file
- DIAS_Attributes_Values_2017.xlsx: the meta-data (a detailed mapping of data values for each feature in alphabetical order)
- DIAS_Information_Levels_Attributes_2017.xlsx: the meta-data (a top-level list of attributes and descriptions, organized by informational category)
- predic_result_lr.csv - the rediction results (used logistic regression)
- predic_result_lr_PCA20.csv - the rediction results (used logistic regression and 20 PCA componets only)
- predic_result_rf.csv - the rediction results (used random forest)
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Segementation part:
Using PCA and KMeans, I got the results as follows.- The population who tend to be customer: The people who may be adults and usually shop online.
- The population who might not be customer: The people who are not rich, nor focusing on the brand of cars. However, they tend to use cars often. Some of them might have some anti-society characteristics.
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Supervised Learning Prediction part:
The results were not good. For the three models (logistic regression, random forest and k-NN), none of them has good precision even used GridSearch.
Everything need to follow Udacity's Terms of Use and other policies. The use of the AZ Direct GmbH data is solely to complete the data mining task which is part of the Unsupervised Learning and Capstone projects for the Udacity Data Science Nanodegree program. Using the AZDirect GmbH data in any other context is prohibited.
If you have any comments, ideas or feedback, feel free to leave your message either here or the post at medium. And thank your in advance !