Extracting Actionable Rules from Raw Data

Author(s): Nehdiii

Originally published on Towards AI.

Image by DALL-E 3

When working with products, we often encounter situations where introducing certain “rules” becomes necessary. Let me clarify what I mean by “rules” through some practical examples:

Imagine we’re facing a surge in fraudulent activity within our product, prompting the need to tighten onboarding for a specific customer segment to mitigate risk. For instance, analysis reveals that most fraudsters share common traits such as particular user agents and IP addresses originating from certain countries.Another strategy could be offering coupons to customers for use in our online store. However, we aim to target only those at risk of churning, as loyal users are likely to return without additional incentives. For example, we might identify the most promising segment as customers who joined within the past year and showed a spending drop of over 30% in the last month.Transactional businesses often serve a segment of customers that generate losses rather than profits. Take, for instance, a banking customer who completes verification and frequently contacts customer support incurring onboarding and servicing costs yet conducts minimal transactions and contributes little to no revenue. To address this, the bank might consider introducing a small monthly subscription fee for customers maintaining an account balance below… Read the full blog for free on Medium.

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Published via Towards AI