Tracking churn risk has long been one of the toughest—and most critical—challenges for CX and Operations leaders. Numerous statistics highlight the importance of reducing customer churn. One in particular never ceases to amaze me as a former contact center manager: A study by Bain & Company shows that increasing customer retention rates by just 5% can increase profits by 25% to 95%. This highlights how reducing churn—even slightly—can have a significant impact on profitability since loyal customers typically spend more, refer others, and cost less to serve.
I am thrilled to introduce you to a new capability I wish I had in managing a contact center because it would have completely changed my life: MiaRec's new customer churn score and dashboard. In this article, I will briefly explain the difference between traditional and AI-enhanced ways to measure customer churn and then show you how MiaRec is changing all that by calculating every customers' churn risk independently of key metrics, such as CSAT, NES, and NPS, and how you can visualize the aggregated risk scores in dashboards to give you immediate and actionanable insights.
Historically, calculating customer churn risk within contact centers has been notoriously difficult. Traditional approaches typically involved post-interaction surveys and heavily relied on metrics such as Customer Satisfaction (CSAT), Net Promoter Score (NPS), or Customer Effort Score (CES).
While this was somewhat helpful in gauging customer retention trends, these methods came with some fundamental challenges:
Submission Bias: Think about it: when do you complete a post-call survey? Usually, when you are over the moon happy or really upset about the quality of service or product. Customers who complete surveys usually represent extreme viewpoints.
Lack of Context: Numeric scores from surveys don't capture the nuance behind customer experiences. A dissatisfied customer rating an interaction "2 out of 10" provides little clarity about specific pain points or actionable improvements.
Not Scalable: Last but not least, very few people actually complete the surveys, making this traditional way of gathering data unscalable.
As a result, CX leaders have struggled to predict and proactively manage churn effectively. However, recent advancements in Generative AI have significantly transformed how we now approach data collection. Rather than having to ask customers to provide us with the data, we can now automatically mine for data within every single customer interaction. This allows us to detect customer sentiment, score agent performance, identify critical moments such as escalation requests, and much more.
One critical application of Generative AI in the contact center context is to automatically calculate customer churn risk after every customer interaction, eliminating submission bias and ensuring a more representative understanding of overall customer health. In addition, the AI assesses the context of the conversation, analyzes customer sentiment, and much more to provide a detailed reasoning for each churn-risk prediction.
MiaRec's approach to churn risk leverages advanced AI to analyze and score each customer interaction automatically. Rather than relying solely on traditional metrics (CSAT, NPS, CES), MiaRec evaluates churn risk based on the substance and sentiment of each conversation using a 0–100 scale:
Low Risk (Score: 1–29): Customer has a low likelihood of churning, canceling, or not buying again due to this interaction.
Medium Risk (Score: 30–79): There are signs of potential churn; the customer may not return based on this interaction. Monitoring is recommended.
High Risk (Score: 80–99): There are strong indicators of churn; immediate intervention is advised.
Churned (Score: 100): The customer explicitly requested to cancel.
If a call is not applicable for assessing churn risk, it is scored as 0.
Each score is paired with a confidence level (rated 1–10) and an explanatory comment offering clear insight into the reasoning behind the assessment.
Image: Screenshot of MiaRec's prompt to calculate the customer churn risk score based on a specific customer interaction.
You can view the churn risk score for each customer interaction in the call detail view alongside other critical metrics per call or aggregated across all (or selective) calls in a dashboard:
Image: Screenshot of MiaRec's new Customer Churn Dashboard.
As you can see from the screenshot above, you can analyze your churn risk as a snapshot or trend over time—by topics (e.g., pricing inquiries, order placements), customer segments, or even specific users.
In addition, the dashboard breaks down churn risk by agent, helping you identify if certain team members are consistently delivering a poor customer experience that may be driving customers away.
But perhaps even more powerful is how topic analysis surfaces the why behind low CSAT and high churn risk scores. By clustering churn-related conversations into specific topics—like pricing complaints, billing errors, or returns confusion—you can pinpoint recurring friction points that drive dissatisfaction.
This goes beyond individual interactions: topic-level insights help uncover broader root causes, such as confusing return policies, product quality issues, or gaps in agent training. The result? You can take targeted action to improve policies, processes, and support—before the customer walks away.
Image: Screenshot of the MiaRec Customer Churn Dashboard showing the aggregated customer churn risk scores as well as the number of calls per risk score category by agent.
MiaRec's new customer churn risk feature offers numerous benefits, for example:
Aggregate Analytics: MiaRec lets you quickly grasp churn patterns daily, weekly, or monthly, identifying trends and opportunities.
Enhanced Operational Efficiency: Managers can proactively address churn, optimizing resources and improving customer retention.
Customization: Tailor dashboards to focus on key indicators relevant to your operational goals.
Scalability: AI handles large interaction volumes easily, providing continuous churn risk assessments.
There is no question that embracing MiaRec's AI-driven churn risk calculation can significantly enhance your ability to understand, predict, and reduce customer churn, ultimately driving improved customer experiences and business outcomes. If you would like to see this new feature in action, click below to schedule a demo, and we would be more than happy to walk you through it.