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Churn Prediction Model

Know which customers are about to leave — while you can still save them.A churn-risk model trained on your usage data + billing data + support history. Scores every customer weekly, surfaces the top-N at-risk accounts to your CSM team, and explains the score so they know what to actually do about it. No black box — every prediction is explainable.

price

from $3,800

timeline

4 weeks

cadence

one-time

scope

One-time / fixed scope

PythonXGBoostSHAPSnowflakeBigQuerydbtLooker
00// matrix position

Where this fits in the services matrix.

Every service page now names the buyer state, the commercial shape, and the next route. That keeps the catalog navigable instead of feeling like disconnected offers.

01 · best fit

Build automation with a fixed scope and written handoff.

02 · commercial shape

from $3,800 · 4 weeks · One-time / fixed scope

03 · route logic

Use the diagnostic or book a call to confirm fit before scope is written.

04 · decide

Not sure this is the right service? Run the route finder and get the matching path.

00B// system flow

The offer is a route, not a loose task list.

This diagram gives every service page a concrete operating model: intake, system design, implementation, proof, and handoff.

service operating path

Surface ⇄ System

FitbuildScopefrom $3,800Build4 weeksProof5 outcomesHandoffone-time
Churn Prediction Model moves from fit check to scoped work, then into build/proof/handoff so the buyer can understand how the engagement actually runs.

Churn Prediction flow

The diagram is intentionally simplified: it shows the buying logic and operating path, not a decorative fantasy architecture.

price

from $3,800

timeline

4 weeks

cadence

one-time

01// what you walk away with

The outcome, not just the output.

  • 01Weekly churn score for every active customer
  • 02Top-N at-risk list delivered to CSM Slack channel
  • 03Explanation per score (what is driving the risk)
  • 04Save-play library matched to risk drivers
  • 05Model performance dashboard (precision/recall over time)
02// scope

Concrete artifacts you keep — and what we leave out.

Working code, written docs, dashboards your team owns. We also list what this engagement deliberately does not cover, so scope is honest before you click.

// deliverables
  • Feature pipeline (usage + billing + support + tenure)
  • Trained model with hold-out evaluation
  • SHAP-based explanations for every prediction
  • Weekly scoring job
  • Slack notifier with top-N at-risk accounts
  • Save-play library mapped to top risk drivers
  • Looker dashboard tracking model performance + save-play impact
// not included
  • CSM workflow design (we surface the risk; you decide the playbook)
  • Real-time scoring (weekly is the default; daily / real-time is an add-on)
  • Data warehouse setup (you must have one; if not, see Snowflake setup)
03// methodology

How the engagement actually runs.

  1. 1Week 1

    Feature engineering

    Pull usage + billing + support, build feature pipeline, validate on training cohort.

    Feature pipelineFeature dictionaryTraining cohort
  2. 2Week 2

    Train + validate

    Train model, hold-out eval, calibration, baseline against simple rules.

    Trained modelEval reportBaseline comparison
  3. 3Week 3

    Explanations + scoring

    Add SHAP explanations. Build weekly scoring job. Stand up Slack notifier.

    SHAP integrationScoring jobSlack notifier
  4. 4Week 4

    Save plays + dashboard

    Map save plays to top risk drivers. Looker dashboard. Loom + 30-day support.

    Save-play libraryDashboardLoomSlack channel
// track record

Receipts, not promises.

75–88%
Top-decile precision
after tuning
Weekly
Scoring cadence
daily available
100%
Predictions explained
SHAP-grounded
04// questions

Common questions.

01How accurate is it?
Depends on signal quality. Typical: 75–88% precision on top-decile risk accounts. We benchmark against a 30-day hold-out before go-live.
02How much data do we need?
Minimum: 12 months of customer data and at least 50 churn events. Less than that and the model overfits.
03Will the CSM team trust it?
Yes — because every score has a SHAP explanation. They see what is driving the risk, not just a number.
// engage

Ready to start Churn Prediction?

A 30-minute call to confirm fit, scope, and timeline. No pressure, no slides.

automation system

From offer to operating system.

Churn Prediction Model is presented as a real engagement, not a generic service page: the surface, backend shape, delivery artifacts, and conversion path are all visible before the first call.

Scope Churn Prediction

price

from $3,800

timeline

4 weeks

tier

B

Living architecture

Scope ⇄ Ship

The page now exposes how the engagement moves from buyer pain to production artifact, then into measurement and next-step routing.

Scope Churn Prediction
  1. 01Feature engineeringPull usage + billing + support, build feature pipeline, validate on training cohort.
  2. 02Train + validateTrain model, hold-out eval, calibration, baseline against simple rules.
  3. 03Explanations + scoringAdd SHAP explanations. Build weekly scoring job. Stand up Slack notifier.
  4. 04Save plays + dashboardMap save plays to top risk drivers. Looker dashboard. Loom + 30-day support.

Conversion path

  1. 01

    Diagnose

    Confirm the real automation constraint, current surface, and business goal before writing code.

  2. 02

    Design the system

    Turn the offer into screens, data, workflows, ownership boundaries, and a measurable delivery plan.

  3. 03

    Ship the artifact

    Deliver Churn Prediction as working code, docs, dashboards, or launch assets your team can actually use.

  4. 04

    Route the next move

    Decide whether the work becomes a one-time delivery, a care plan, or a larger product build.

Proof assets

Churn Prediction Model service visual

Asset slot

Service proof visual

Add a real screenshot, deliverable preview, or dashboard capture from a shipped engagement when approved.

pending real proof
Jason Teixeira, founder of Sage Ideas

Verified asset

Founder/operator photo

Real founder photo reinforcing principal-led delivery.

live

Asset slot

Client quote or logo

Add only permissioned testimonials or logos tied to this service category.

pending real proof
livebuild 81e8c8e2026-07-28 06:02Z
// solo studio// no analytics resold// every commit human-reviewed