Top Highlights
- Building a system to identify at-risk users with ML models (pre-churn and uplift) enables targeted retention efforts, reducing costs and increasing effectiveness.
- The pre-churn model flags users unlikely to pay soon, while the uplift model pinpoints those whose behavior can be influenced by personalized offers.
- Combining these models in a sequential system allows the company to intervene precisely where it’s most impactful, saving resources and boosting retention.
- Regular calibration and continuous data collection are crucial to keeping the system accurate and responsive over time for sustainable customer retention.
Understanding Customer Loss and Building a Risk Profile
Many fintech companies find that keeping existing customers costs less than finding new ones. Therefore, identifying users who are likely to leave becomes crucial. In digital banking, users often stop using their cards when they make no transactions for 30 days. This simple rule helps define when a customer is at risk. To predict who might leave, companies gather data like age, location, device type, and transaction history. Metrics such as how often a user transacts or how long since their last payment are especially helpful. Calendar data, like day of the week or season, also impacts user activity. Combining these insights creates a clear risk profile, which can guide targeted retention strategies. Clear identification of at-risk users allows banks to act early and prevent churn effectively.
Targeted Offers Using Machine Learning
Once a bank spots users at risk, the next step involves understanding who responds to offers. A simple cashback promotion might help some, but not all users. For this, machine learning models predict the true impact of offers on user behavior. Two types of models are often used: one identifies users likely to pay, and the other predicts which users respond to specific offers. The first model flags those with a high chance of payment, while the second suggests which of these can be influenced by incentives. Running experiments, like A/B tests, helps assess the effectiveness of targeted offers. When applied correctly, this approach reduces costs by focusing on users who are most likely to respond positively, saving marketing budgets and increasing retention. This strategy makes customer retention more predictable and efficient.
Implementing a Continuous and Adaptive Retention System
Effective retention involves a multi-step process that works over time. First, a risk model evaluates each user’s likelihood to pay soon. Those flagged as at risk then go to a second stage, where a performance uplift model estimates how much an offer will improve their chances of paying. This dual approach ensures that companies only target users most likely to change behavior. Regular updates and recalibration of models keep the system accurate as customer habits evolve. The process also includes randomized testing—some at-risk users receive offers, some do not—to refine the models continually. By focusing marketing efforts on users who respond best, banks can cut unnecessary costs and strengthen customer bonds. This systematic, data-driven approach makes ongoing retention efforts smarter and more sustainable.
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