Applicable Segment(s):
Grocery
Impacted Function(s):
Marketing, Data
Solution Area:
Real-time CDP
Identifying personas with micro-segmentation
Company
A large American supermarket chain.
The Challenge
The company was driving generic campaigns across the customer base due to a lack of insights into individual customer behavior, tastes, and preferences. This led to poor customer engagement and low conversions from campaigns.
The Approach
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The company deployed a Real-time CDP. Its intelligence layer, supported by machine learning algorithms, helped create granular customer segments by applying RFM (recency, frequency, monetary) modeling.
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This helped gain a deep understanding of a customer’s journey, identify products of interest, and utilize propensity models to gauge the likelihood to respond, buy, and churn.
The Result
- The company deployed a Real-time CDP. Its intelligence layer, supported by machine learning algorithms, helped create granular customer segments by applying RFM (recency, frequency, monetary) modeling.
- This helped gain a deep understanding of a customer’s journey, identify products of interest, and utilize propensity models to gauge the likelihood to respond, buy, and churn.
Armed with the insights, the grocer was able to hyper-personalize marketing campaigns with respect to each customer’s preferences, transactional behavior, lifecycle stage, and promotional activity.
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