CHURN & RETENTION ANALYTICS

Challenge

Many businesses lose a significant portion of their customers without realizing it.

Customers who stop purchasing or unsubscribe often do so silently.
Manual tracking is difficult and doesn’t provide early warning signs.
Loss of customers leads directly to revenue decline and higher acquisition costs.

Implement a Churn Prediction and Retention Analytics system using machine learning.

Implement a Churn Prediction and Retention Analytics system using machine learning.

Data Collection & Feature Engineering:
- Collected historical customer activity, purchase frequency, engagement metrics, demographics, and support interactions.
- Engineered features like average purchase gap, last engagement date, and product preferences.
Churn Prediction Model:
- Train ML models to classify customers at risk of leaving.
- Generate a churn probability score for every customer.
Retention Strategy Recommendations:
- Segment high-risk customers into actionable groups.
- Suggested Tailored interventions: targeted offers, loyalty rewards, personalized communication.
Dashboard & Alerts:
- An Interactive dashboard for marketing and sales teams to track churn risk in real time.
- Alerts notify teams when high-value customers show warning signs.

Identified high-risk customers early, enabling proactive engagement.
Improved customer retention by 15–20% within the first 6 months.
Reduced lost revenue from churn by thousands of dollars per month.
Provided marketing teams with data-driven insights, making retention campaigns more effective and measurable.

Predicting churn is not just about analytics, it’s about taking timely action to retain customers.
Businesses using ML-based churn analytics can save money on customer acquisition, increase loyalty, and improve lifetime value.

Let’s collaborate to transform your data, design, and business goals into impactful digital experiences.