Churn Prediction Consultant · E-Commerce & SaaS

Stop Losing Customers Before They Even Leave

Most businesses discover churn after the revenue is gone. I build machine learning models that flag at-risk customers 30-60 days in advance — so you can intervene, recover revenue, and turn retention into a competitive advantage.

Book a Free Discovery Call See Retention Case Studies

Free first call — no obligation

Remote worldwide — timezone flexible

22% average churn reduction for clients

Is This Your Reality?

These are the churn problems I solve most often for e-commerce and subscription businesses.

"We don't know why customers leave"

You see the churn metric going up, but you can't pinpoint whether it's pricing, product fit, onboarding, or support driving the loss.

"We find out too late"

By the time a customer cancels or stops buying, it's already too late to save them. You need early warning signals.

"Our retention efforts are spray-and-pray"

You're sending the same discount emails to everyone instead of targeting the customers most likely to respond.

"Churn is silently killing our growth"

You're acquiring new customers, but hidden churn is eating 15-30% of your revenue every month — and you can't see it clearly.

What I Deliver for Retention

Every model is built around your specific business context — not a generic algorithm.

Churn Risk Scoring Model

A machine learning model that assigns each customer a churn probability score (0-100%) updated weekly — so you know exactly who to prioritize.

Features: purchase frequency, recency, support tickets, engagement metrics
Output: risk score + top 3 drivers per customer
Integration: CSV export, API endpoint, or direct dashboard connection
Accuracy: Typically 75-90% precision on at-risk identification

Segmented Retention Playbooks

Not all at-risk customers are the same. I help you design targeted interventions for each segment — win-back offers, product education, or support outreach.

High-value at-risk: personalized account manager outreach
Price-sensitive: targeted discount or bundle offers
Product-fit issues: onboarding reinforcement or feature tutorials
Automated triggers: email/SMS workflows based on risk score thresholds

Retention Dashboard (Power BI / Streamlit)

A live dashboard showing churn trends, at-risk customer lists, and intervention ROI — so your team can act on the model without needing data science skills.

Weekly churn rate by cohort and segment
Top at-risk customers with recommended actions
Intervention performance: which offers actually work
Revenue recovered vs. cost of retention campaigns

Production-Ready Code & Documentation

You own the model. I deliver clean, documented Python code with instructions for retraining, monitoring, and updating — no vendor lock-in.

Modular scikit-learn / XGBoost pipeline
Feature engineering scripts with data dictionary
Model monitoring: drift detection and retraining triggers
Handover session: your team can maintain it independently

How We Reduce Your Churn

A proven, transparent process from data audit to deployed model — no black boxes.

Discovery & Data Audit (Free)

We review your customer data sources (CRM, transaction logs, support tickets, app analytics) and define what "churn" means for your business.

Feature Engineering & Modeling

I build predictive features (recency, frequency, engagement decay) and train multiple models to find the best balance of accuracy and interpretability.

Validation & Business Review

We test the model on historical data, review which customers it would have flagged, and refine until the predictions align with your business intuition.

Deployment & Integration

I deliver the model in your preferred format (Python package, API, or dashboard) and integrate it with your existing tools (email, CRM, Slack).

Handover & Retention Strategy

You receive the model, documentation, and a tailored retention playbook. Optional: ongoing monitoring and model refreshes.

Technical Stack

Python: scikit-learn, XGBoost, pandas

Feature Engineering: RFM, behavioral decay

Model Explainability: SHAP, LIME

Deployment: FastAPI, Docker, cloud-ready

Monitoring: drift detection, retraining pipelines

Integration: Webhooks, Zapier, CRM APIs

Common Questions About Churn Prediction

How much historical data do I need?

Ideally 6-12 months of customer transaction and engagement data. However, I can often build a useful MVP with as little as 3 months if your data is clean and well-structured. We'll assess your data during the discovery call.

What if my definition of "churn" is complex?

That's actually ideal. Churn isn't always a binary cancel/no-cancel. I work with you to define meaningful churn events: 30 days inactive, subscription downgrade, reduced order frequency, etc. The model learns your business logic.

Can you integrate the model with our existing tools?

Yes. I can deliver the model as: a Python package you run on your schedule, a REST API endpoint, a scheduled CSV export to your data warehouse, or directly embedded in a Power BI/Streamlit dashboard. We choose what fits your workflow.

How do you measure model success?

Beyond technical metrics (precision, recall), we track business outcomes: % of at-risk customers successfully retained, revenue recovered vs. intervention cost, and reduction in overall churn rate. I help you set up tracking for these during deployment.

What does a churn prediction project cost?

Most projects range from $3,000-$8,000 depending on data complexity, model sophistication, and integration requirements. Simple scoring models start at $3K; end-to-end solutions with dashboard and automation are $6K-$8K. Fixed-price quote after discovery — no hourly surprises.

Ready to Stop the Leak?

Let's Build a Churn Model That Saves Revenue

Tell me about your customer data and churn challenges. First call is free — no pitch, no obligation. Just a conversation about saving your revenue.

Book Your Free Discovery Call

Remote worldwide · Typically respond within 24 hours · adeyemi@adediranadeyemi.com