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Data Driven Customer Engagement

Introduction
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International Coffee Chain designed a robust engagement strategy utilizing customer lifetime value and Churn Prediction to influence Customer behaviour.

Customer Lifetime Value indicates the future potential of a customer. The lifetime for every customer is usually the period that the customer continues to shop. In case of quick service restaurants, the customer can visit the store after long interval of time hence the lifetime of customer is considered as 1 year. The CLV model predicts the potential revenue that the customer can generate in the next 12 months. The customers were split into 3 groups based on the revenue generated by them.

Data Driven Customer Engagement Customer Engagement
Churn for a customer happens when they stop using the services and are no longer part of the ecosystem. However, in non-contractual retail there is no event which indicates churn. Instead the model would predict the probability for the customer to not transact in next 30 days. On considering the decision threshold at 42% the model was able to classify the customers with 96% accuracy.
Data Driven Customer Engagement Customer Engagement
Based on the predictions for Churn and CLV the customers can be segmented into the below Engagement Matrix. The objective here is to make sure that the customer is engaged but does not get accustomed to discounts. So, incentives are only given to influence changes in the behaviour.
Data Driven Customer Engagement Customer Engagement
In the nudge plan the idea is to not spam the customer but to make sure that the communication reaches just in time before the customer’s next visit. Using the historical data, the average days between transactions for every customer is identified. Using this metric, the nudge is to be executed couple of days before the probable next visit. Early and relevant nudges influence the customer to transact more often at the store
Data Driven Customer Engagement Customer Engagement
The nudges can take the form of offers, feedback on previous visits or suggestions on products that go well with the customers favourites.
Melvin Davis Vallully

Solution Architect

Suraj Sharma

Senior Software Engineer

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