Retailer has several stores in key cities across the country and sells baby care and pregnancy care products. ... P E N
P E N S E R ANALYTICS
Case Study: Retail Predicting Customer Behaviour
Business Context & Problem Retailer has several stores in key cities across the country and sells baby care and pregnancy care products. They wanted to identify the most valuable customers based on both engagement and purchase patterns so that they cam proactively manage their customer relationships. They also wanted to compare different sets of customers and nurture the right customers earlier in their lifecycle. IT landscape of the retailer consisted of CRM, ERP and an e-commerce platform. Data capture was not an issue, but the databases were proprietary, hence the integration was an issue. Our solution was based on a three pronged approach, to provide an onetime automated solution.
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DATA COLLECTION
CUSTOMER SEGMENTATION
FRAMEWORK
Automate the data assimilation
Engagement and Purchase patterns
Enable real-time segmentation.
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Our Solution Customer Segmentation based on engagement levels, recency, purchase behaviour and demographics Client had data distributed among CRM, ERP, ecommerce among other data sources. So the task of assimilating the data was massive and complicated. All the data from ERP and the CRM solutions was brought onto a common platform and fed into our patent pending multi dimensional big data database.
“Sherlock can connect to 800 different databases from legacy ones to the new age big data” The customer behaviour was both measured individually and benchmarked against rest of the customers to capture inherent groups of customers. The purchase pattern of customers along with demographics was used to create “Behavioural Segments”. The engagement level of the customer was also measured, this when overlapped with the recency information, resulted in identifying Lapsed Customers, Advocates, Aspirationals, Practicals and New Customers.
compare different groups (based on either engagement or purchases or demographics or any other parameter) of customers on the fly, and deduce actionable insights.
“We provided a real time customer segmentation solution”
All this information was fed back into the Sherlock framework, where the client can
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