Customer Lifetime Value

A fast-growing neo bank providing digital banking services to retail customers across the U.S. expanded acquisition through a mix of digital and partner channels, but soon faced a pivotal strategic question:

"Which customers truly contribute to long-term profitability and which acquisition sources bring them in?"

While acquisition growth was strong, the leadership team observed that not all customers generated sustainable margins. Some cohorts had long payback periods or became inactive quickly, leading to inefficient marketing spend.

Business Challenge

The key business questions were:

Predict Profitability

How can we predict the future profitability of each customer from their early behavior?

Channel Optimization

Which marketing channels attract customers with high lifetime value (CLV) and faster break-even?

Real-time Integration

How can this intelligence be integrated into marketing platforms (Google, Meta) and CRM systems for real-time action?

Specific Problems Identified

  • - Fragmented view of customer economics across acquisition channels
  • - Heavy reliance on short-term revenue metrics (first-month or first-quarter ROI)
  • - Lack of data-driven retention targeting
  • - Marketing decisions driven by CAC (cost per acquisition) rather than true lifetime profitability

Our Approach

We developed a data-driven Customer Lifetime Value (CLV) prediction model combining transaction-level data, revenue components, and churn dynamics into a unified profitability framework.

Data Sources
  • - Transactional: deposits, payments, card spends
  • - Cost components: fixed / variable costs
  • - Churn indicators: inactivity days, balance decay
Engineered Features
  • - Early engagement patterns
  • - Transaction frequency and spend velocity
  • - ADB per active month
  • - Recency-Frequency-Monetary variables
  • - Channel acquisition behavior

Business Application

Application Area Description
Marketing Channel Evaluation CLV was aggregated at acquisition-channel level to compute "Predicted Profit per Customer". Channels with negative or slow-payback CLV were identified and optimized.
Budget Allocation Marketing spend was reallocated toward channels with early-profitable cohorts ( <18 months payback).
Audience Targeting CLV-based segmentation fed into Google Ads and Meta for lookalike audience creation, improving ad efficiency.
Retention Strategy Customers predicted to churn but with positive CLV potential were prioritized for reactivation campaigns.

Results and Impact

The implementation of the CLV framework transformed how marketing and finance collaborated to evaluate ROI.

Key Outcomes

60% Increase in ROAS

Increase in ROAS from CLV-based targeting compared to baseline campaigns

Smarter Acquisition Channels

Elimination of low-quality acquisition channels that previously drove negative value customers

Profitability Over CAC

From CAC-based to profitability-based decision-making

Executive Insight

"We no longer debate which channel brings the most customers, we focus on which channels bring the right customers."

— CEO

Conclusion

The CLV modeling initiative enabled the neo bank to evolve from a growth-at-all-costs mindset to profitable, data-driven growth. By quantifying the future value of every customer, the organization aligned its marketing, product, and retention teams around a single profitability metric. The result is smarter spending, stronger customer relationships, and a scalable model for long-term financial sustainability.