20 May 2026
A leading streaming platform, managing six concurrent voice of customer surveys across subscription and content partner programs, was sitting on a rich seam of cancellation feedback it could not fully exploit.
Its churn surveys, lapsed customer research, and satisfaction tracking were generating thousands of open-ended verbatim responses every quarter. The churn rates were visible. The reasons behind them were not.
The Challenge
The business had been collecting churn data at scale for years, using Qualtrics to gather open-ended verbatim comments from subscribers.
But its text analytics capability hadn’t kept pace with the volume. Reading through responses manually was impractical, plus Qualtrics’ built-in text analysis tools weren’t sophisticated enough to identify the patterns that mattered.
“We have all this cancellation data – but we don’t really understand why customers are leaving.”
Customer Retention Leader
Three specific research programmes were generating data that wasn’t being properly used:
Churn surveys from cancelled subscribers – detailed feedback from customers who had left, containing clear signals about friction, dissatisfaction and decision drivers — but analyzed only superficially.
At-risk subscriber intercepts – feedback from current users identified as a high churn risk, where timely analysis could have supported retention offers but reporting was too slow to be actionable.
Subscription satisfaction tracking – ongoing sentiment data across price sensitivity, content gaps and service issues, sitting largely unread beyond headline net satisfaction scores.
Reporting across all three programs was ad hoc. Different business units – subscription marketing, content partnerships and customer success – were working in silos, with no standardized approach to analyzing or sharing churn signals. There was no fixed cadence for insight delivery, which meant leadership was making retention decisions without a consistent evidence base.
The Solution
The company deployed Relative Insight across all three research programs, establishing a unified, automated approach to churn driver analysis for the first time.
Churn driver discovery – Verbatims from canceled subscribers are now processed through Relative Insight’s comparative methodology, surfacing the specific language-level drivers of cancellation and identifying themes customers mention most frequently
At-risk subscriber profiling – Intercept feedback is analyzed to identify the specific friction points and sentiment patterns most associated with imminent cancelation – turning reactive survey data into an early-warning retention intelligence tool
Satisfaction trend tracking – Subscription sentiment data is analyzed on a rolling basis, with automated monthly reports delivered to stakeholders on a fixed schedule – replacing ad hoc analysis with a dependable insight cadence
Standardised churn framework – A unified methodology has been rolled out across all three research programs, enabling consistent comparison of churn drivers across customer segments and channels for the first time
The automated reporting capability was central to the value realised. Monthly churn theme reports now land with leadership on a fixed schedule, framing not just what the churn rate is but, specifically, what is moving it and why.
The Results
2-3 weeks
Time to quarterly churn reports
6+
Concurrent surveys automated
Real-time
Churn signal detection
“The insights come out automatically focused on what’s changed and what’s driving churn – and they arrive on schedule. Now we can act on retention opportunities before customers leave “
Customer Retention Director
Key Takeaway
Scaling voice of customer programs across multiple teams and survey types requires more than collecting feedback at volume. It demands a unified analytical framework that can surface insights consistently, detect emerging churn drivers in real time and deliver findings on a schedule that enables action.
By automating the discovery and tracking of churn drivers across all six surveys, this streaming platform transformed fragmented feedback into a strategic retention intelligence tool. What was once a manual, siloed exercise – consuming weeks of analytical effort across multiple teams – now runs automatically, surfaces unexpected patterns and delivers insight when it matters most.
The result: a retention team that doesn’t just understand its churn problem, but can anticipate and prevent it.
