Identify churn risk up to 12 months before it happens

    QuadSci's Growth AI predicts churn and contraction with 90% accuracy by analyzing the product telemetry that existing tools never see. Every account is classified monthly, your team gets specific plays to run through Q Chat, and all of it surfaces inside the tools where your team already works.

    90%

    Accuracy on churn and contraction predictions across the customer base

    80%

    Of customer signal lives in product telemetry, invisible to existing tools

    12 mo

    Advance warning on annual contracts, giving teams time to change the outcome

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    Your churn signals exist.Your tools can only see 20% of them.

    CRM records, health scores, survey results, support tickets. The systems most teams rely on capture roughly 20% of the signal that actually predicts customer behavior. The other 80% lives in product telemetry: how customers use your software, which features they engage with, where activity is declining, and what behavioral patterns precede disengagement.

    Without that signal, health scores lag reality. Risk gets identified after engagement has already dropped. And by the time a customer shows up in a churn report, the decision to leave was often made months earlier.

    • Health scores are built on lagging indicators that miss early behavioral shifts
    • Product usage data exists but never reaches the teams responsible for retention
    • At-risk accounts are flagged after the window for effective intervention has narrowed
    • CS prioritization is driven by intuition rather than statistically grounded risk ranking
    • Customer Success, Sales, and Product operate from different and incomplete views of the same accounts

    Most churn is not unpredictable. It is invisible to the tools teams depend on to see it.

    How it works

    From telemetry to predictionto action

    Predict churn from top-of-cycle signals, not at renewal

    Behavioral signals predict intent more reliably than sentiment ever will. Growth AI is trained on product telemetry at scale, analyzing billions of real customer events to identify the behavioral patterns that precede churn months before they surface in health scores or support volume. The longer the contract cycle, the earlier and more precisely risk can be detected.

    • Detect declining usage patterns, adoption gaps, and behavioral risk signals early
    • Surface accounts where product behavior contradicts a healthy health score
    • Monitor the full customer base continuously rather than at QBR or renewal time only

    Classify every account, every month

    A single risk flag is not enough. Every account needs a clear trajectory. Growth AI scores and classifies every account monthly into one of five categories: High Growth, Medium Growth, Stable, Partial Churn, or Full Churn. Your entire team gets a shared, statistically grounded view of the book of business, updated continuously rather than when someone pulls a report.

    • See each account's current classification and how it has trended over time
    • Identify accounts moving toward contraction before it shows up in ARR
    • Give CS, Sales, and leadership a common language for account health

    Prioritize intervention by the risk that matters most

    Not every at-risk account deserves the same response. QuadSci ranks churn risk by both likelihood and revenue impact so your team focuses time on the accounts where intervention will have the greatest effect, not just the ones generating the most noise.

    • Rank at-risk accounts by churn likelihood weighted by ARR at stake
    • Identify gaps between your team's risk assessment and what the data is showing
    • Allocate CS capacity where it changes outcomes rather than where alerts are loudest

    Turn signals into plays through Q Chat

    Knowing an account is at risk is only useful if your team knows what to do about it. Q Chat synthesizes Growth AI's predictions with your playbooks, product documentation, and internal best practices to generate specific, account-level plays for CS and Sales to run. It surfaces wherever your team's workflow lives including Salesforce, Gainsight, Clari and others.

    • Get specific intervention plays grounded in each account's actual usage patterns
    • Access recommendations from within your existing CRM, CS platform, or revenue tooling
    • Align CS and Sales around the same account strategy without manual coordination

    What this unlocks

    Churn risk identified months before it appears in health scores or renewal data

    Every account classified monthly with a clear, shared revenue trajectory

    Intervention prioritized by ARR at risk, not just signal volume

    Specific plays delivered inside the tools where your team already works

    Give your team time to make a difference

    Most churn is preventable. Customers who leave were not necessarily lost causes. They just did not get the right attention at the right time, because the signals that could have prompted action were never visible. QuadSci gives your team the advance warning and account-level clarity to change that, so retention becomes something your team drives rather than something that happens to them.

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