Data Source: https://archive.ics.uci.edu/ml/datasets/Online+Retail#
Backgrounds:

• Company X is an online B2B retailer sells small widgets for home/apartment decorations to other small businesses globally
• The company was founded in December 2010 and immediately attracted many business clients within first 3 months
• The monthly total sales dropped starting in Dec 2011. Since it’s the season that most people would make more purchase for gifting, the marketing and sales team were concerned about the revenue performance.
• The marketing team had analyzed all the factors and decided in order to get better conversion and retention, they need to understand the segmentation of the clients in order to create better marketing campaigns.

Methods:

• Exploratory data analysis
• K-Means clustering

Results:

• Clustered customers into 3 groups

Recommendations:

• Customers clearing all the three cut-offs (RFM) are the most reliable customers. Business should focus on making customized promotional strategies and loyalty schemes for these customers in order to retain this valuable customer base.
• Customers failing the recency criterion only are those customers who have stopped visiting the site. Business should focus on these customers and look out for the reason why they abandoned visiting the site.
• Customers clearing the recency criterion but failing frequency criterion are the new customers. Business should provide more incentives and offers to these customers and try to retain these new customers.
• Apart from segmenting customers, business can also use RFM criterion to filter out a reliable customer base and perform analysis like Market Basket Analysis to see customer buying pattern or assess the success of marketing strategies by analyzing the response of these customers.
• Company can run A/B testing on these segments in case it's making any changes in it's services or products to see how the segments react