Customer Relationship Summary

Often times, simple changes create massive impact. Last year I worked in the marketing department of Jumia.com, an online retail company that offers various services ranging from travel booking to food delivery. I led a 3-member Customer Relationship Management (CRM) team responsible for increasing repeat purchase and drive sales growth.

The team used a basic direct marketing tool to inform customers about discounts and promotions via email and text. The tool did not allow customization of timing and content of promotional messages. Thus, customer engagement was low, and website traffic and conversion from this channel was a fraction of what it needed to be.

I was tasked to customize direct marketing to drive repeat purchase, automate CRM

I examined five relevant case studies from other industries and markets to learn best practices. These were the three key learnings: 1) all customers are unique but many clump together into shared patterns, 2) customers are bombarded with too many messages and majority of the promotional messages are lost in the clutter, and 3) the best way to engage customers is to target the right prospect with the right message at the right time.

Second, I conducted analysis on the data. After evaluating various alternatives to segment the customers, I selected Receny, Frequency and Monetary (RFM) analysis. (The RFM model is a widely used marketing technique that quantitatively determines the best customers by clustering them into meaningful and measurable groups.) The fundamental premise underlying my analysis was that customers who have purchased recently, more frequently and in larger amounts are more likely to respond to our offering rather than other customers.

This technique created 6 unique groups of customers distinguishable from one another. After studying these groups, I could see interesting patterns emerge. For example, there was a small group of highly profitable customers that contributed to the majority of the revenue. In other words, just 24% of the customers contributed 75% of the revenue. Moreover, customers within a particular segment shared similar demographics but responded to promotional efforts

As a result, CRM can analyze these segments anytime from different angles using filters including dates, country of origin or order value. I also introduced new metrics and performance indicators such as segment-wise retention rate and placed them on this dashboard to help evaluate progress of each segment overtime.

This process innovation was successful because it was customer-centric. The impact on customer satisfaction was significant. Moreover, it enabled the company to better retain its loyal, most profitable customers and reduce time and money spent on unprofitable ones. It also drove quantifiable results. After the project went live, the website traffic from direct marketing went up by more than 30% and the conversion rate improved by 45% in only the first month. This tool is on track to increase revenue by approximately 8% by Dec 2016, and is currently being replicated in other subsidiaries of