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Services / build / automation
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.
service graph
Surface ⇄ System
- price
- from $3,800
- timeline
- 4 weeks
- cadence
- one-time
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
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.
- 1Week 1
Feature engineering
Pull usage + billing + support, build feature pipeline, validate on training cohort.
Feature pipelineFeature dictionaryTraining cohort - 2Week 2
Train + validate
Train model, hold-out eval, calibration, baseline against simple rules.
Trained modelEval reportBaseline comparison - 3Week 3
Explanations + scoring
Add SHAP explanations. Build weekly scoring job. Stand up Slack notifier.
SHAP integrationScoring jobSlack notifier - 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 Predictionautomation system
Surface ⇄ System
- price
- from $3,800
- timeline
- 4 weeks
- tier
- B
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- 01Feature engineeringPull usage + billing + support, build feature pipeline, validate on training cohort.
- 02Train + validateTrain model, hold-out eval, calibration, baseline against simple rules.
- 03Explanations + scoringAdd SHAP explanations. Build weekly scoring job. Stand up Slack notifier.
- 04Save plays + dashboardMap save plays to top risk drivers. Looker dashboard. Loom + 30-day support.
Conversion path
Surface ⇄ System
01
Diagnose
Confirm the real automation constraint, current surface, and business goal before writing code.
02
Design the system
Turn the offer into screens, data, workflows, ownership boundaries, and a measurable delivery plan.
03
Ship the artifact
Deliver Churn Prediction as working code, docs, dashboards, or launch assets your team can actually use.
04
Route the next move
Decide whether the work becomes a one-time delivery, a care plan, or a larger product build.
Proof assets
Real only
Asset slot
Service proof visual
Add a real screenshot, deliverable preview, or dashboard capture from a shipped engagement when approved.
Verified asset
Founder/operator photo
Real founder photo reinforcing principal-led delivery.
Asset slot
Client quote or logo
Add only permissioned testimonials or logos tied to this service category.