A subscription business was consistently losing customers without any advance warning. By the time managers identified an account as at risk, the customer had often already made the decision to leave, and the opportunity to intervene had passed. Our Agentic AI development team analysed the client’s CRM data and product usage patterns, identified the behavioural signals that reliably preceded cancellation, and built a monitoring agent that tracks account health on an ongoing basis. Each week, the sales team receives a prioritised list of at-risk customers, giving them the lead time they need to act before churn becomes inevitable.
Subscription Services
Agentic AI Development
We connected the agent to the client’s CRM and product usage data, establishing a reliable pipeline that gave the system full visibility over the signals most relevant to predicting customer disengagement.
Our team worked with the client to identify the specific usage behaviours and engagement patterns that historically preceded cancellation. We then trained the agent to detect and weight these signals accurately across the full customer base.
We built a weekly reporting layer that surfaces at-risk accounts in a clear, prioritised format, designed to give managers exactly what they needed to act before a customer reached the point of leaving.
We deployed the agent into the client’s live environment, reviewed the quality and accuracy of early outputs, and made adjustments to ensure the weekly reports were reliable and immediately usable by the sales team.
The client held a large volume of customer data, much of which had no bearing on churn prediction. A key part of our consultancy was determining which signals carried predictive value and building the Agentic AI solutions system to focus exclusively on those, keeping the model accurate and the outputs actionable rather than overwhelming.
The agent’s role was to inform and equip the sales team, not to contact customers directly. We designed the system with a clear handoff point, ensuring that every customer interaction remained human-led and that the agent functioned purely as an intelligence layer supporting those decisions.
Churn looked different across different customer segments, and a single threshold applied uniformly would have produced unreliable results. We worked with the client to understand how risk varied by account type and usage pattern, and built the model to reflect that nuance rather than treating every customer the same way.
