In today’s competitive business landscape, understanding and retaining customers is paramount. Churn prediction has always been a critical endeavor for companies. Though traditional methods have long played a role in churn prediction, acknowledging their limitations such as inconsistent responses from customers and lack of data in real-time, and exploring innovative alternatives is key. In this blog, we’ll explore how businesses can leverage’s conversation insights to achieve early churn prediction, surpassing the limitations of traditional methods.

Understanding the Challenge

Traditional churn prediction methods primarily rely on numerical metrics like Customer Satisfaction (CSAT), Net Promoter Score (NPS), and issue resolution data. While these metrics offer valuable insights, they often miss the “why” behind customer decisions, for example the subtleties of customer sentiments hidden within conversations. Knowing the “why” enables companies to make informed improvements to their products, services, or customer experiences, addressing specific pain points and tailoring retention strategies more effectively. This knowledge not only helps in retaining existing customers but also informs future business decisions to prevent churn and foster long-term customer loyalty.

Businesses have relied on post-call analysis to gauge customer satisfaction, predict churn, and devise retention strategies. However, this approach is reactive by nature. Here are the problems with this approach: 

Time delay due to survey based data collection: CSAT and NPS scores are often collected  quarterly/annually after an interaction has occurred, which means that by the time a low score is received, the customer might already be on the brink of churn or churned. Predicting churn in such cases becomes more reactive than proactive.

Inconsistent Responses: Besides customers not providing responses in the survey, the surveys are incomplete and they might not always provide accurate or honest CSAT and NPS scores. Factors like mood, time constraints, and the phrasing of the survey questions can influence their responses, leading to skewed scores that don’t accurately reflect their true sentiments.

The Advantage: Unveiling the Power of Conversation Insights

Elevate your customer retention strategy with’s conversation insights. goes beyond mere transcription, extracting vital emotions, intentions, topics, and sentiments from customer conversations. Imagine seamlessly integrating this invaluable data, including call scores and trackers, with domain-specific metrics. The result? A comprehensive and dynamic understanding of customer interactions that empowers businesses to proactively predict churn and more precisely, tailor actions to foster lasting customer relationships. This proactive approach empowers businesses to address concerns before they escalate.

Let’s explore how’s conversation insights transforms churn prediction with an example:

A customer contacts a subscription-based company’s customer support regarding a delayed order delivery. Traditionally, the representative would address the issue, inquire about satisfaction, and conclude the call. The NPS or CSAT scores for these calls are analyzed periodically and this post-call analysis might identify a lower CSAT/NPS score which makes the customer likely to churn. On the contrary, conversation insights are collected for every interaction and provide more nuanced insights, context behind the insights such as the intent and unsaid emotions hidden between the lines which helps businesses tailor customer interactions. Conversation insights includes keywords pre-set by businesses such as competitor mentions and sentiment analysis which provides early dissatisfaction signs even before the customer provides a low CSAT/NPS scores. This dynamic analysis enables the generation of churn signals while the conversation is still ongoing leading personalized retention strategies. 

With the churn signals from conversation insights, representatives can address the delayed delivery issue more promptly, offer solutions that specifically counter the competitor’s appeal, and ensure that the customer’s concerns are met before they escalate. This proactive approach not only resolves the immediate issue but also prevents potential churn, fostering customer loyalty and satisfaction. 

By empowering businesses to intervene proactively, address concerns, and build enduring relationships, this technology heralds a new era in customer retention. With, businesses can not only predict churn but also shape a future where customer satisfaction takes center stage, driving sustained growth and success.

Avatar photo
Suprabath Chakilam
Product Manager - Applied AI/ML

Suprabath is currently working on building products to make machines understand human language better than humans. He has experience in building SaaS and e-commerce products. His area of expertise is making code prototypes and experimenting on new technologies, business analysis, and problem solving.