Once we define who of your customers are at risk of churn, we can design a whole new customer retention strategy for you, based on various data-driven insights and customer behaviours. We will test out which incentives are most likely to win-back and keep your customers as active buyers.

Challenge

TradePoint wanted to see how many of its inactive customers (of the past five years) could be re-engaged with the brand and a solution to predict future customer churn.

Solution

  • We designed a machine learning model to predict customer behaviour leveraging over 15 engineered variables. The model identified 475,000 out of nearly 1 million customers at high risk of churn
  • We created an experimentally designed campaign with; 5 different sets of incentives to win-back churned customers

Challenge

TradePoint wanted to see how many of its inactive customers (of the past five years) could be re-engaged with the brand and a solution to predict future customer churn.

Solution

  • We designed a machine learning model to predict customer behaviour leveraging over 15 engineered variables. The model identified 475,000
    out of nearly 1 million customers at high risk of churn
  • We created an experimentally designed campaign with 5 different sets
    of incentives to win-back churned customers

Churn model prediction accuracy over 91%

Over £4 million additional revenue generated from previously inactive customers after the first month

£13.16 million revenue generated after the fifth month

76, 264 customers shifted from churn back to active

53% of re-activated customers were still active after six months

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