Question
1) Best starting problem type: Finding patterns (exploratory / diagnostic analysis).
- Start by aggregating and exploring quantitative data (usage logs, watch time, session frequency, billing events, cohort churn over time) to surface obvious correlations or temporal patterns that point to likely causes of cancellations.
2) How "identifying themes" can extend the analysis:
- Apply text analysis and manual coding to cancellation reasons, customer support tickets, app reviews, and exit surveys to extract recurring themes (e.g., "price too high," "no new content," "technical problems," "too expensive for family").
- Map those themes back to customer segments and the quantitative patterns you found (e.g., are price complaints concentrated in a certain region or plan?), turn qualitative signals into structured features for further analysis.
3) Example of a "finding patterns" use case:
- Run cohort and segmentation analyses and discover that churn is highest among users who: watched <2 hours/week, used only mobile apps, and experienced >2 playback errors in the last month. You might also find churn spikes 7–14 days after a free trial ends or after a price increase. Those patterns suggest specific levers to investigate (engagement, reliability, pricing).
4) How "making predictions" helps business decisions:
- Build a churn-prediction model that scores each subscriber’s probability of canceling in the next 30 days and estimates their lifetime value. Use those scores to prioritize retention actions (e.g., targeted offers, personalized content nudges) toward high-risk, high-value users and to simulate the ROI of different retention strategies. This enables data-driven allocation of marketing/discount budgets and measurement via A/B tests. Turn into short paragraph
- Start by aggregating and exploring quantitative data (usage logs, watch time, session frequency, billing events, cohort churn over time) to surface obvious correlations or temporal patterns that point to likely causes of cancellations.
2) How "identifying themes" can extend the analysis:
- Apply text analysis and manual coding to cancellation reasons, customer support tickets, app reviews, and exit surveys to extract recurring themes (e.g., "price too high," "no new content," "technical problems," "too expensive for family").
- Map those themes back to customer segments and the quantitative patterns you found (e.g., are price complaints concentrated in a certain region or plan?), turn qualitative signals into structured features for further analysis.
3) Example of a "finding patterns" use case:
- Run cohort and segmentation analyses and discover that churn is highest among users who: watched <2 hours/week, used only mobile apps, and experienced >2 playback errors in the last month. You might also find churn spikes 7–14 days after a free trial ends or after a price increase. Those patterns suggest specific levers to investigate (engagement, reliability, pricing).
4) How "making predictions" helps business decisions:
- Build a churn-prediction model that scores each subscriber’s probability of canceling in the next 30 days and estimates their lifetime value. Use those scores to prioritize retention actions (e.g., targeted offers, personalized content nudges) toward high-risk, high-value users and to simulate the ROI of different retention strategies. This enables data-driven allocation of marketing/discount budgets and measurement via A/B tests. Turn into short paragraph
Answers
GPT-5 mini
Start by running exploratory analyses on quantitative signals (usage logs, watch time, session frequency, billing events and cohort churn over time) to surface temporal correlations and likely root causes of cancellations, then extend that by applying text analysis and manual coding to cancellation reasons, support tickets, app reviews and exit surveys to extract recurring themes (e.g., price, content, technical issues) and map those themes back to segments so qualitative signals become structured features. For example, cohort/segmentation work might reveal highest churn among users who watch <2 hours/week, use only mobile apps, and experienced >2 playback errors, with spikes 7–14 days after trial end or price hikes—pointing at engagement, reliability and pricing levers. Finally, build a churn-prediction model that scores subscribers’ 30‑day cancellation risk and estimates LTV so you can prioritize retention actions (targeted offers or content nudges) toward high‑risk, high‑value users, simulate ROI of interventions and measure impact with A/B tests.