The solutions that stood out
The Elbow method was used to determine the number of clusters and the K-means algorithm was used to form clusters based on features such as age, number of products purchased, number of visits, discount and promotional codes used, monthly total and average selling price, etc. Once the clusters were identified, product recommendation was made by predicting buying patterns of each customer using the Apriori method.
Clusters were formed based on the following features using the K-means algorithm :
- Season, temperature, and climate: This gave an idea about which city to cluster during a particular season.
- Religions: This gave insight into the percentage of people belonging to a particular religion.
- Supermarkets, income, literacy and density: This was formed to gain insights and form clusters based on competitor supermarkets, average income of the population, literacy rate, and population density.
Based on the inferences drawn from these clusters, a recommender system was created to recommend products using a recommendation system based on product users.
RFM(Recency, Frequency and Monetary value) analysis was used to predict past purchase behaviour to segment customers. Based on this, customers were segmented into
- Best customers: Customers who bought most recently, most often and spent the most
- Loyal customers: Customers who bought most recently
- Big spenders: Customers who spent the most
- Almost lost and lost customers: Customers who haven’t purchased for some time but have purchased and spent the most in the past
- Lost customers who spend less: Customers who purchased long ago and have spent very less on the purchase
Once the clusters were formed, product recommendations were made using the GraphLab framework.