Catching hidden customer risk before it turns into churn

After closing support cases, customer satisfaction was measured only through average survey scores, which masked serious dissatisfaction expressed in comments, and ARP Ideas delivered an automated sentiment and risk analysis workflow that exposed these issues early and enabled proactive follow-up.

In the initial setup, customer service teams sent a satisfaction survey after each closed case, collecting a simple 1 to 5 rating along with an optional comment, but analysis focused almost entirely on average scores, which created a false sense of stability.
As a result, so-called silent bombs went unnoticed, where customers selected a high rating simply to close the interaction while describing serious frustration in the written feedback, such as repeated calls, poor communication, or unresolved root causes.

ARP Ideas designed a lightweight but effective workflow that analysed both quantitative and qualitative feedback immediately after survey submission.
Using Azure OpenAI for sentiment analysis and topic classification, the solution evaluated the numerical score together with the tone and content of the comment to determine whether the case should be considered OK, Risk, or Critical.

When feedback was flagged as risky or critical, the system automatically identified the underlying reason, such as response time, communication style, ownership, or lack of a lasting solution.
An alert was then sent via email or Microsoft Teams to the responsible account manager or service leader, requesting a direct follow-up with the customer.

This approach shifted feedback handling from passive reporting to active intervention.
Instead of discovering dissatisfaction weeks later through churn, complaints, or escalations to management, teams could respond the very next day, often before frustration reached public channels or executive levels.

Over time, aggregated insights made it possible to analyse trends across teams, regions, or channels, for example identifying repeated complaints about response times in a specific market.
Service managers gained concrete data on communication quality rather than relying solely on technical SLAs, while HR and training teams received clear signals that service style and empathy mattered just as much as technical resolution.

The solution was designed as a small, fast-to-deploy project, integrating an existing survey form with Azure OpenAI and simple notification channels, and could be easily extended into platforms such as Dynamics 365 for case ownership or Copilot-driven summaries if required.