Customer Retention and Churn Prevention Questions

Focuses on diagnosing at-risk accounts and designing concrete retention playbooks to prevent churn. Expect to assess root causes for churn such as product-market misfit, dissatisfaction with service, organizational changes at the customer, pricing or budget pressures, and competitive threats. Candidates should demonstrate a structured approach: identify at-risk signals, prioritize accounts by value and risk, build tailored action plans with short term quick wins and longer term investments, define escalation protocols for high value accounts, coordinate cross functional actions, and track outcomes using retention KPIs such as churn rate, renewal rate, and net revenue retention. The topic also covers designing customer success interventions, relationship building, friction removal, and criteria for deciding when to divest versus invest in renewal.

HardTechnical

0 practiced

You must choose between a rule-based scoring system (business rules) and an ML-based risk scoring model for a client who cares about transparency and has limited labeled churn history. Compare pros and cons from the perspectives of accuracy, explainability, maintenance, time-to-value, and operational cost. Recommend an approach and outline a migration plan from rules to ML if applicable.

MediumTechnical

0 practiced

You have product-event logs, support tickets, billing history, NPS surveys, and CRM attributes. Describe how you'd build an account-level feature set for a churn prediction model. List at least 15 features across usage, engagement, support, commercial, and sentiment domains, and explain how you would compute or aggregate each feature (e.g., last_30d_active_users, support_escalation_rate_90d).

HardTechnical

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Design a governance process for offering retention discounts and credits that prevents perverse incentives and abuse. Describe approval flows, thresholds for discounts (who can approve what), monitoring to detect abuse patterns, and how to report discount impact on renewals and NRR monthly.

MediumTechnical

0 practiced

Sketch a REST event schema and API contract that product teams should emit to support real-time churn risk scoring and playbook triggers. Include required fields, an example JSON payload, idempotency considerations, rate limits, and guidance about PII handling and minimal metadata needed for scoring decisions.

HardTechnical

0 practiced

Design a data pipeline and feature store that supports both batch and real-time churn scoring, ensuring strong feature consistency between offline training and online scoring. Specify storage choices for raw events, transformed features, serving store, streaming/batch frameworks, transformation patterns, materialization strategies, and latency requirements for online scoring.

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