This is a source code for my Medium blog post Data Driven Cistomer Segmentation.

I believe that the best way to learn about marketing data science is to work through examples.

Customer segmentation is the process of dividing customers into groups based on common characteristics, which allows companies market each group effectively and appropriately.

For this analysis I am using a public dataset from UCI Machine Learning Repositiry, which can found here. This dataset contains information on transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

You need an installation of Python, plus the following libraries:

• numpy
• pandas
• matplotlib.pyplot
• seaborn
• sklearn

• We applied k-means clustering to understand the difference between segments of an online retail shop
• Using Elbow method and Silhouette coefficient, we found the optimal number of segments, which is 4
• We looked at the most popular items bought by the group of high-value customers, which may help to sell the same/similar items to this high-value group and increase conversion.