Churn Prediction Software for Retention Teams

Churn prediction software for the accounts your team can still influence.

A risk score is useful only when churn has a stable definition, signals arrive before renewal, and the retention team can act on a ranked list. We validate those conditions first, then deliver calibrated scores and reasons into the CRM workflow.

See our work

Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Does the team first recognize risk in a cancellation email or missed renewal?

02

Would a long risk list exceed the number of accounts customer success can review well?

Plain answer

Churn prediction software estimates which customers may cancel or fail to renew before the retention window closes. RaftLabs defines the churn event, tests product, billing, support, and CRM history, validates a useful ranking, and delivers scores and reasons into the team's workflow. A focused release starts at $20,000.

The model found 2,000 risky accounts. The team could call 40.

Technically, the list was correct. Operationally, it was useless. Nobody had chosen a threshold from the number of accounts customer success could review, so every account manager created a private rule and the model disappeared from the renewal meeting.

A churn score has to fit the retention queue.

Adjacent retention-data proof

LoyaltyPass product delivery
14 weeks
Recorded case-study duration
higher enrollment
60%
Case-reported result vs app-based programs
repeat-visit increase
3x
Case-reported outcome

The LoyaltyPass case study documents visit, redemption, and drifting-customer signals inside an operating loyalty product. It does not document a deployed churn model. RaftLabs does not yet publish a named churn-prediction outcome, so a new model must beat the buyer's own baseline before production use.

Build churn prediction when risk can be measured before a useful retention response.

A model cannot repair an undefined churn event, missing history, or a retention process with no owner.

A fit
01

Cancellation or non-renewal has a stable label and eligible population.

02

Customer signals exist before the last useful intervention date.

03

The retention team can review a ranked queue and record outcomes.

Not a fit
01

Most churn is driven by causes the business cannot observe or influence.

02

There are too few comparable churn events for a meaningful validation set.

03

No team owns outreach, threshold changes, or outcome recording.

Churn risk scoring vs uplift modeling

Risk modelUplift model
QuestionWho is likely to churn?Whose outcome may change after intervention?
DataHistorical signals and churn labelsComparable treated and untreated outcomes
UsePrioritize a review queuePrioritize persuadable accounts
Start withA validated risk ranking and recorded outreachA controlled intervention history

Scope

What the first churn release must carry

  • 01

    Churn and eligibility definition

    State the event, customer population, prediction horizon, exclusions, and the date on which each training label becomes known.
  • 02

    Feature and leakage review

    Build time-bounded usage, billing, support, contract, and engagement signals without letting post-outcome information enter training.
  • 03

    Calibrated risk ranking

    Compare models with a simple baseline and report precision, recall, calibration, and queue size at useful thresholds.
  • 04

    CRM delivery and explanation

    Write score, risk band, timestamp, and reason into Salesforce, HubSpot, or the existing retention workspace.
  • 05

    Intervention and drift monitoring

    Record outreach and renewal outcomes, watch the population change, and retrain only when the evidence supports a new version.

How it works

From churn definition to monitored scores

  1. Phase 1
    01

    Define churn and intervention

    Agree the event, horizon, eligible cohort, review capacity, current baseline, and retention response the score should inform.

  2. Phase 2
    02

    Test the available history

    Join product, billing, support, contract, and CRM records, then check label quality, leakage, rarity, and useful lead time.

  3. Phase 3
    03

    Validate the ranking

    Compare a simple baseline with candidate models and select a calibrated threshold that fits the team's real capacity.

  4. Phase 4
    04

    Deliver and learn

    Write scores and reasons into the CRM, record interventions and outcomes, and monitor drift before retraining or expansion.

Risk

Why churn models look better than they work

Rare churn inflates accuracy
Report ranking and calibration measures at the operating threshold instead of celebrating the majority class.
The label leaks into the features
Freeze every feature at the prediction date so cancellation evidence from the future cannot enter training.
Risk is confused with persuadability
Measure intervention outcomes separately before claiming the score causes retention.
The queue exceeds team capacity
Choose the threshold from the number of accounts the team can investigate and serve well.

Scope and price

A focused churn-prediction release starts at $20,000.

Begin with one churn definition, customer population, validation baseline, operating threshold, CRM delivery point, and monitoring path.

We require the model to beat a simple baseline on unseen history at a review volume the retention team can absorb.

Starting investment

Starts at $20,000

A focused first release usually takes eight to twelve weeks. Data repair, more populations, real-time scoring, and intervention experiments can extend the plan.

No accuracy promise before validation

The scope names the baseline and measures; production use depends on the result from unseen historical periods.

The CRM path is part of the model

Score delivery, reason, timestamp, intervention record, and outcome monitoring belong in the first production release.

Related predictive analytics services

Common questions

Churn prediction software estimates the probability that an eligible customer will cancel or fail to renew within a defined period. It combines historical customer signals, produces a ranked risk score, explains the main contributing factors, and delivers the result where a retention team can act.

Useful sources often include product activity, billing events, contract dates, support history, account attributes, engagement, and confirmed churn outcomes. The important test is not a universal month count. The history must contain enough comparable churn events and signals that appear before the intervention deadline.

Accuracy cannot be estimated responsibly before testing your labels, event frequency, and signal coverage. We compare the model with a simple baseline on unseen historical periods, then report precision, recall, calibration, and the expected review volume at candidate thresholds. Raw accuracy is misleading when churn is rare.

No. Risk predicts who may leave, not who an intervention can persuade. A save call has cost and can sometimes create friction. We separate risk ranking from intervention-effect measurement, record outreach and outcomes, and add uplift modeling only when the history can support it.

A focused first release starts at $20,000 and usually takes eight to twelve weeks. It covers one churn definition, customer group, validation baseline, threshold, CRM delivery path, and monitoring. Data repair, more populations, real-time scoring, and controlled intervention experiments increase scope.

Work with us

Bring the churn event and the last useful moment to respond.

Share the customer history, current retention process, and review capacity. We will tell you whether the data can support a useful risk ranking.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.