Is this a consulting service?

No, this is a free, interactive software tool that calculates your potential liability under the DPDP Act 2023 in 30 seconds. It provides an instant risk score and automated action plan.

What are common DPDP violations in Churn Analysis Data Audit?

Common violations include: Using customer support notes or chat transcripts (collected for service delivery) as features in churn prediction models without specific consent for this analytical purpose., Incorporating inferred sensitive signals like user sentiment or financial distress into churn models, which were derived from personal data collected for other primary purposes., Cross-linking data from various business units (e.g., sales, marketing, support, product usage) to build comprehensive churn profiles without obtaining explicit, granular consent for data aggregation and analytics..

The DPDP Audit Tool

Compliance for Churn Analysis Data Audit

📉

Churn Analysis Data Audit
Liability Check

🔎

Your churn models might be secretly violating DPDP. Using personal data for analysis beyond its original collected purpose without fresh consent is a direct route to ₹250 Crore penalties.

Why Churn Analysis Data Audit is at Risk

Product and revenue teams often enrich churn models with user behavior, support chat logs, and sentiment analysis – data often collected for other purposes like customer service or product improvement. Under **DPDP's 'purpose limitation' principle**, using this **personal data** for churn prediction without explicit, fresh consent is a serious breach. The Data Protection Board (DPB) will scrutinize whether the data processing aligns with the original, stated purpose. **Even internal analytics are not exempt** from this critical rule, especially if they leverage sensitive personal data points.

Common Violations

1.Using customer support notes or chat transcripts (collected for service delivery) as features in churn prediction models without specific consent for this analytical purpose.
2.Incorporating inferred sensitive signals like user sentiment or financial distress into churn models, which were derived from personal data collected for other primary purposes.
3.Cross-linking data from various business units (e.g., sales, marketing, support, product usage) to build comprehensive churn profiles without obtaining explicit, granular consent for data aggregation and analytics.

The Immediate Fix

Immediately audit the data inputs for *all* your churn prediction models. Identify any personal data, support notes, or behavioral signals that are not covered by the original purpose of consent. Either obtain fresh, explicit consent for these specific analytical uses or ensure such data is fully anonymized before being fed into your models.