I can help you reduce your customer churn rate and suggest retention strategies

Why Hire a Customer Churn Consultant?

Connect on WhatsApp to Start Your Project

With over 7 years of experience in churn modeling and customer analytics, I specialize in turning raw data into retention results.

My expertise includes:

• Predictive analytics and early churn detection

• CRM data integration across eCommerce, SaaS, and service sectors

• Machine learning models (Python, R, SQL) for churn prediction

• Data-driven loyalty and re-engagement planning

Proven Outcomes:

• 15–25% reduction in churn within first three months of intervention

• Increased repeat purchase and subscription renewal rates

• Improved customer lifetime value (LTV) through retention optimization

My Data-Driven Process

1. Define What Churn Means for You
Understand whether churn means inactivity, cancellation, or non-renewal—each business needs a custom definition.

2. Gather and Clean Customer Data
Integrate purchase history, engagement logs, CRM exports, and feedback data for a holistic view.

3. Build a Predictive Churn Model
Use survival analysis, logistic regression, or XGBoost models to identify early churn signals.

4. Segment Customers by Behavior and Value
Group customers by churn risk and revenue impact to prioritize retention actions.

5. Design Targeted Retention Strategies
From personalized win-back emails to predictive loyalty programs—every insight translates into a measurable retention campaign.

How You Benefit

• Reduce churn and retain high-value customers

• Predict who’s likely to cancel before it happens

• Design measurable retention campaigns

• Improve ROI on acquisition spend

• Build dashboards to monitor retention KPIs

What I Need to Start Your Project

To build a precise churn model and retention strategy for your business, I will need the following details:

1) Business Overview & Customer Profile

• Summary of your product/service and customer lifecycle

• Typical buying journey and customer touchpoints

2) Customer Data Access

• Purchase history, subscription logs, engagement data, and CRM exports

• Details of past interactions (email opens, login activity, support tickets)

3) Churn Criteria Definition

• What you define as a churned customer (e.g., no login in 60 days, cancellation, non-renewal)

• The time window for calculating churn

4) Current Tools & Infrastructure

• What analytics platforms you use (e.g., Tableau, SQL, Google Analytics, CRM tools)

• Any existing models or scripts already in place

5) Historical Churn Patterns

• Past churn rates and any known peaks or anomalies

• Notes from customer exit surveys or feedback collection

6) Existing Retention Measures

• Loyalty programs, win-back campaigns, discounts, or onboarding processes

• Metrics used to monitor customer satisfaction (e.g., NPS, CSAT)

7) Key Metrics & Business Goals

• Customer acquisition cost (CAC), LTV, retention rate benchmarks

• Business KPIs that matter most to you

8) Timeline & Feedback Rounds

• Project deadline or any specific launch plans

• Preferred structure for review, feedback, and delivery of insights