Having access to customer data is only the beginning. The real value comes from turning that data into action that improves customer outcomes.
For a large insurance provider, proactively identifying customers who may be at risk of making a formal complaint became an opportunity to intervene earlier, resolve concerns and improve the overall customer experience.
Using daisee’s Complaints Predictor dashboard, the organisation was able to identify high-risk customer interactions and give teams the insight they needed to take action before concerns escalated.
The result was significant: formal complaints were reduced by 27% in just one week.
Traditional complaint management is often reactive. By the time a customer submits a formal complaint, the issue has already escalated and the organisation is left managing the consequences.
The challenge is identifying the interactions where intervention could make a difference before that happens.
daisee’s Complaints Predictor uses AI-powered conversation analytics to analyse customer interactions and identify conversations that show indicators associated with potential complaints.
Instead of waiting for a complaint to be formally recorded, teams can use these insights to prioritise interactions that may require attention.
For this insurance provider, the Complaints Predictor dashboard provided a way to identify customer interactions with a higher likelihood of escalating into a complaint.
This allowed teams to focus their attention where it could have the greatest impact.
By identifying and following up on high-risk interactions early, the organisation could:
This shift from reactive to proactive customer service can help organisations address the root cause of dissatisfaction rather than simply managing complaints after they occur.
The most compelling measure of success was the impact on formal complaints.
In just one week, the insurance provider reduced formal complaints by 27%.
The result demonstrates how operational analytics can move beyond reporting what has already happened and help contact centre teams take action that can influence future outcomes.
Rather than simply measuring complaint volumes, the organisation was able to use conversation data to identify potential risk and intervene earlier.
For contact centre leaders, the value of AI isn't simply having another dashboard or another set of metrics.
The real opportunity is connecting insight with action.
In this case, the Complaints Predictor helped the organisation follow a simple process:
Identify → Prioritise → Intervene → Improve
This approach transforms customer conversations from a source of historical data into a tool for proactive decision-making.
For insurance providers and other regulated organisations, managing complaints effectively is critical to both customer experience and operational performance.
A proactive approach can help organisations identify emerging issues earlier, improve consistency in customer service and give teams greater visibility of where intervention is needed.
It also provides an opportunity to move beyond simply asking "How many complaints did we receive?"
Instead, organisations can start asking:
"Which customers are at risk of becoming unhappy, and what can we do about it?"
That is where operational analytics can create real value.
This customer example highlights an important principle: data only creates value when it leads to action.
By using daisee's Complaints Predictor to identify potential complaint risks early, the insurance provider was able to give its teams the information they needed to intervene proactively — contributing to a 27% reduction in formal complaints in just one week.
For organisations looking to improve customer experience, reduce complaints and move towards more proactive contact centre management, AI-powered conversation analytics can provide the visibility needed to make better decisions, earlier.
Discover how daisee can help your organisation identify emerging customer risks, prioritise high-value interactions and turn conversation data into measurable improvements.
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