Dual logistic regression models (conversion likelihood + churn risk) feeding an A–D customer tier score. Python + AWS pipelines write predictions back to HubSpot so campaigns target high-value prospects and reactivation triggers fire before churn.
Customer behavioral signals and CRM activity flow into an AWS pipeline, get scored by two logistic regression models, and write back to HubSpot as A–D tier tags — so every marketing decision downstream is made against a predicted profitability outcome, not a vanity metric.
HubSpot CRM, Stripe billing, on-site behavioral tracking, paid ad platforms, support tickets, and email/SMS engagement logs.
Python workers (pandas, scikit-learn) orchestrated by AWS Lambda. S3 for raw and feature stores, Glue jobs for ETL. Daily batch + hourly refresh for high-intent events.
Conversion model predicts purchase likelihood; churn model predicts 90-day attrition. Outputs feed a composite tier score (A–D) and an LTV Monte Carlo simulator.
Power BI dashboards for CAC by tier, retention curves, campaign ROI grid, and churn early warning. Tier tags written back to HubSpot for segmentation.
Marketing acts on tiers: scale Tier-A spend, nurture Tier-B, trigger Tier-C win-back sequences, kill Tier-D acquisition. Outcomes feed back into model retraining.
Two logistic regression models run in parallel. One predicts the probability someone becomes a customer. The other predicts the probability an existing customer churns. Combined, they drive the tier score that every downstream decision rides on.
Trained on 18 months of customer outcomes, retrained weekly. AUC 0.83 (conversion) / 0.79 (churn) on held-out data.
Each customer and lead gets one of four tier tags, written back to HubSpot as a property so marketing ops can build segments, triggers, and exclusions off a single predicted field.
Scale ad spend, priority sales routing, white-glove onboarding, reference program.
Email sequences, retargeting, webinars. Move to Tier-A with an additional activation event.
Trigger reactivation offers, CSM check-in, discount workflow. Silence general campaigns.
Remove from paid acquisition, exclude from high-cost channels. Re-score quarterly.
The models are only valuable if they change marketing behavior. These are the literal calls the scoring engine let the business make — each backed by a number, not a hunch.
Identified vertical/channel combos where Tier-A concentration was 4x average — redirected budget and cut blended CAC 60%.
Automated HubSpot workflow fires when a customer flips to Tier-C. Lifted 90-day retention ~20% on reached cohort.
Suppressed 7 ad campaigns spending against Tier-D lookalikes. Reallocated spend delivered 15% YoY profit contribution.
| Cadence | Job | Consumer | Owner |
|---|---|---|---|
| Hourly | Behavioral + CRM event capture → S3 raw zone | Feature store | Data eng |
| Daily 03:00 | Feature build + tier rescore (all contacts) | HubSpot write-back | Data eng |
| Weekly Mon | Model retrain + AUC report + drift check | Marketing Ops review | Analytics |
| Monthly | Campaign ROI roll-up + channel reallocation | Exec readout | Marketing lead |
| Quarterly | Vertical expansion analysis + Tier-D resurrection review | Growth strategy | Marketing lead |
Predictive scoring moved CAC, retention, and profit contribution together — by making every marketing decision downstream of a model, not a meeting.