beyond data
    Churn & Retention Intelligence

    Know which customers are leaving 30 to 90 days before they do, and what to do about each one.

    Outcomes

    What changes once this is in

    Voluntary churn down 10–25% within 12 months

    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.

    Early warning, 30 to 90 days out

    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.

    Retention effort aimed at saveable accounts

    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.

    A feedback loop that gets sharper every quarter

    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 growing 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.

    The Problem

    What usually breaks

    How many of your customers will quietly leave this quarter, and do you know which ones? Most 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.

    In Your Business

    How this lands inside your operation

    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.

    This is for you if…

    • You are a CRO or head of customer success under board pressure on retention, and you need a defensible plan.
    • You have a CRM, a billing system, and enough customer history to see patterns, even if it lives in spreadsheets today.
    • Your book is large enough that manual account review cannot cover it, but small enough that every saveable account matters.
    • You want the save effort concentrated on accounts your team can actually save, not sprayed across the whole base.
    • You are ready to act on a weekly queue, not a quarterly report.

    This probably isn't the right fit if…

    • You have fewer than 12 months of clean customer history. The model needs something to learn from.
    • You want a dashboard that reports churn after the fact, without anyone acting on the signal.
    • Your churn is mostly involuntary (card declines, death, business closure) and the commercial lever is payment recovery, not retention.
    What we watch

    Early-warning signals we watch

    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.

    SignalWhat it typically means
    Payment delay or failed direct debitFinancial stress or a deliberate off-ramp. Strong leading indicator in subscriptions and memberships.
    Usage drop versus the customer's own baselineThe value is slipping. Absolute usage matters less than the break in pattern.
    Support ticket volume spikeSomething is broken for them. Unresolved tickets two weeks in are the real warning, not the spike itself.
    NPS or CSAT dropA stated intent signal. Rarely enough on its own, powerful when paired with a usage drop.
    Contract renewal window approachingRisk concentrates in the 90 days before renewal. The model weights signals more heavily inside that window.
    Key contact churn at the customerThe champion left. Retention odds fall sharply if nobody picks up the relationship within 30 days.
    Plan downgrade or seat reductionA soft cancellation. Often precedes a hard one by a quarter.
    Competitor engagement signalsInbound from a competitor's domain, or a pricing enquiry. Hard to capture, high signal when you can.
    Engagement decay with your content or commsEmails stop getting opened. Webinars stop getting attended. The relationship is cooling.
    Invoice disputes or credit notes risingA commercial signal that procurement is looking at the line item.
    How It Works

    The engagement, step by step

    1. 1

      We map the churn definition that matters commercially: voluntary cancellation, non-renewal, downgrade below threshold, or silent attrition.

    2. 2

      Data is unified in the AgileData platform from billing, CRM, product usage, support, and NPS, with identity resolution across systems.

    3. 3

      A churn model is trained on two to three years of historical churn to learn the signals that predict it for your book.

    4. 4

      Each customer is scored weekly, with the top reasons for the score surfaced alongside so the action is obvious, not opaque.

    5. 5

      A prioritised save queue is routed into the CRM, ranked by saveable lifetime value, not raw risk probability.

    6. 6

      Save plays are logged against outcomes so the model learns which interventions work, on which segments, with which reason codes.

    7. 7

      The leadership view reports cohort churn, model precision, and dollars saved each quarter against an agreed baseline.

    Capabilities

    The capabilities behind this

    Advanced Analytics & ML

    Forecasting, simulation and optimisation for consequential decisions, with visible uncertainty and quantified trade-offs.

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    AgileData Platform

    One governed data platform across your ERP, CRM, finance, and operational systems. We build it, we run it, and it is live in two weeks.

    Learn more

    Insight-to-Action Systems

    Connect evidence, scenarios and quantified trade-offs to human approval, approved action and outcome learning.

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    Common Questions

    Frequently asked

    Let's talk about Churn & Retention Intelligence.

    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.

    See how we connect insight to action

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