A weekly save queue focused on the saveable accounts typically removes 10–25% of voluntary churn within the first year. The uplift compounds into every downstream revenue forecast.
The model flags risk well before the cancellation email arrives. Your team gets a window long enough to have a real conversation, not a goodbye.
Save effort stops being sprayed across the whole book. The top-quartile risk-adjusted accounts get the top-quartile attention, and the genuinely gone get closed politely.
Every save attempt is logged and scored. The model learns which plays work on which segments. Year two is meaningfully better than year one.
Churn & Retention Intelligence is a weekly churn-risk score for every customer, paired with a reason code and a prioritised save action routed to the account manager or service team. It gives mid-market businesses with a CRM, a billing system, and spreadsheets an early-warning system for retention, so effort goes to saveable accounts before renewal, not after.
How many of your customers will quietly leave this quarter, and do you know which ones? Most mid-market retention programs answer that question in arrears. The cancellation email arrives, the renewal lapses, and the account manager writes a post-mortem that nobody reads.
The data needed to see it coming already sits across the business. Billing knows when the direct debit failed. The product knows when logins dropped. Support knows when the ticket volume spiked. Finance knows when the plan was downgraded. Each signal is half a story on its own. Together they are the story.
The gap is not data. It is the absence of a system that stitches the signals into one weekly view, ranks the accounts by saveable value, and puts each one in front of the person who can actually save it. Without that system, retention effort gets sprayed across the whole book. The saveable accounts blend in with the unsaveable ones. The board hears about churn after it has landed in the revenue line.
Every Monday, your account managers open their CRM and see a ranked list of customers the model flagged as at risk this week. Each row carries a risk score, the top three reasons the model is worried (failed payment, usage collapse, key contact gone), and a suggested save play grounded in what worked on similar accounts last quarter. The billing system feeds in overnight. The support platform feeds in overnight. The model re-scores weekly.
Your CRO sees the same list rolled up by segment and value band. The head of customer success sees the saveable pipeline, not just the leaving pipeline.
These are the signals the model ingests on most engagements. Not all apply to every business, and the weighting is learned from your own history, not assumed.
| Signal | What it typically means |
|---|---|
| Payment delay or failed direct debit | Financial stress or a deliberate off-ramp. Strong leading indicator in subscriptions and memberships. |
| Usage drop versus the customer's own baseline | The value is slipping. Absolute usage matters less than the break in pattern. |
| Support ticket volume spike | Something is broken for them. Unresolved tickets two weeks in are the real warning, not the spike itself. |
| NPS or CSAT drop | A stated intent signal. Rarely enough on its own, powerful when paired with a usage drop. |
| Contract renewal window approaching | Risk concentrates in the 90 days before renewal. The model weights signals more heavily inside that window. |
| Key contact churn at the customer | The champion left. Retention odds fall sharply if nobody picks up the relationship within 30 days. |
| Plan downgrade or seat reduction | A soft cancellation. Often precedes a hard one by a quarter. |
| Competitor engagement signals | Inbound from a competitor's domain, or a pricing enquiry. Hard to capture, high signal when you can. |
| Engagement decay with your content or comms | Emails stop getting opened. Webinars stop getting attended. The relationship is cooling. |
| Invoice disputes or credit notes rising | A commercial signal that procurement is looking at the line item. |
We map the churn definition that matters commercially: voluntary cancellation, non-renewal, downgrade below threshold, or silent attrition.
Data is unified in the AIS from billing, CRM, product usage, support, and NPS, with identity resolution across systems.
A churn model is trained on two to three years of historical churn to learn the signals that predict it for your book.
Each customer is scored weekly, with the top reasons for the score surfaced alongside so the action is obvious, not opaque.
A prioritised save queue is routed into the CRM, ranked by saveable lifetime value, not raw risk probability.
Save plays are logged against outcomes so the model learns which interventions work, on which segments, with which reason codes.
The leadership view reports cohort churn, model precision, and dollars saved each quarter against an agreed baseline.
Predictive models, recommendation systems, forecasting, and MLOps, delivered with a ruthless ROI focus.
Learn moreThe operating layer your AI, automation, and analytics run on. Deployed in two weeks.
Learn moreClose the gap between what your data knows and what your business does. The architecture from insight through decision to action.
Learn moreScore every lead and account by likelihood to convert, then push the ranked list into the CRM your reps already open.
40%
YoY revenue lift, same sales team
One recommended action per customer, grounded in their own data, delivered where your reps already work.
5-15%
lift in cross-sell revenue per customer contact
Ask your business a question. Get a cited answer before the meeting ends.
Minutes
from question to cited answer, across 29 sites
30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.
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