Introduction

In the current economic scenario, characterized by rising Customer Acquisition Costs (CAC), the sustainability of an Ecommerce business depends increasingly on the ability to retain existing customers. Economic theory, supported by research from Frederick Reichheld (Bain & Company) and published in the Harvard Business Review, establishes that a 5% increase in retention can generate profit increases ranging from 25% to 95%. In this context, Email Marketing ceases to be a promotional channel and becomes the primary tool for increasing Customer Lifetime Value (CLV).

Acquisition Cost vs. Retention Cost

Acquiring a new customer costs 5 to 25 times more than retaining an existing one. Despite this, many e-commerce businesses continue to allocate the majority of their budget to acquisition. The use of Machine Learning models for CLV prediction allows for reversing this trend. Instead of calculating customer value “ex-post” (looking at the past), AI enables “forward-looking” prediction immediately after the first purchase, identifying high-potential segments for dedicated marketing investment.

From Static Measures to Dynamic Models

Traditionally, loyalty was measured via RFM models (Recency, Frequency, Monetary). However, recent studies show how Deep Learning models and causal inference outperform classical statistical models (such as Pareto/NBD) in predicting future behavior. AI analyzes non-linear variables, such as customer service interactions or email open frequency, to anticipate churn risk (Churn Rate) and activate preventive Email Marketing flows, personalizing the offer to maximize retention without wasting margin.

References

• [1] Reichheld, F., & Bain & Company. How to Multiply Customers While Lowering Customer Acquisition Costs. ResearchGate / Harvard Business Review data.
• [2] Madgicx (2025). How to Predict Customer Lifetime Value with Machine Learning.
• [3] Kumar, V., & Reinartz, W. (2022). Customer Lifetime Value Modeling for E-commerce Platforms Using Machine Learning. ResearchGate.