Customer Lifetime Economics

Numerous fintechs calculate LTV and CAC as rough averages that ignore critical factors like cohort retention curves, support costs over time, and payment failures. When investors ask for unit economics proof, founders present back-of-napkin numbers that don't hold up to scrutiny. Marketing budgets are allocated based on transaction volume or gut feel rather than actual customer profitability by channel. Without proper cohort economics, companies scale unprofitable segments while under-investing in their best acquisition channels.

This solution shows which acquisition channels and customer segments generate profitable growth over time, calculates sustainable customer acquisition costs, and delivers investor-grade cohort economics that prove your growth path leads to profitability.

WHAT DOES IT SOLVE?

LTV & Cohort Performance Analysis

• Answers: What's the true lifetime value of customers by cohort and segment, and how do retention and revenue patterns evolve?

• What it contains: LTV calculations by customer cohort, segment, and acquisition channel; month-by-month cohort retention curves; revenue build-up patterns over time; churn analysis by acquisition period; cohort maturity and performance comparison

Customer Acquisition Cost Analysis

• Answers: What's the fully-loaded cost to acquire customers by channel, and how has it changed over time?

• What it contains: CAC by marketing channel, campaign, and time period; all acquisition costs included (marketing spend, sales costs, onboarding expenses); CAC trends and efficiency patterns; cost per acquisition by customer segment

Channel Efficiency Dashboard

• Answers: Which acquisition channels deliver the best return, and where should we allocate budget?

• What it contains: Return on ad spend by channel and campaign; CAC comparison across channels; customer quality scores by acquisition source; payback period by channel; channel performance trends

LTV:CAC & Payback Analysis

• Answers: How do different customer cohorts retain and generate revenue over time?

• What it contains: Month-by-month cohort retention curves; revenue build-up by cohort; churn patterns by acquisition period and segment; cohort maturity analysis; retention rate benchmarks

Channel Attribution Modeling

• Answers: How do we attribute value across multi-touch customer journeys, not just last-click?

• What it contains: Multi-touch attribution model showing full customer journey; channel contribution beyond last-click; attribution weights by touchpoint; budget reallocation recommendations

CORE MODULES

Descriptive & Diagnostic

Predictive LTV Modeling

• Answers: What's the predicted lifetime value of newly acquired customers before they mature?

• What it contains: Machine learning model predicting LTV based on early behavioral signals; customer value forecasting; segment-specific prediction models; expected value calculations for acquisition decisions

Churn Prediction Model

• Answers: Which customers are at risk of churning, and what interventions could retain them?

• What it contains: ML-based churn risk scoring; identification of churn signals and triggers; predicted churn impact on LTV; retention intervention recommendations and ROI

Pricing Elasticity Model

• Answers: How would price changes affect customer acquisition, retention, and LTV by segment?

• What it contains: Price sensitivity analysis by segment; impact modeling of pricing changes on conversion, retention, and LTV; optimal pricing recommendations; revenue vs volume trade-off analysis

Customer Segment Value Optimization

• Answers: How would price changes affect customer acquisition, retention, and LTV by segment?

• What it contains: Price sensitivity analysis by segment; impact modeling of pricing changes on conversion, retention, and LTV; optimal pricing recommendations; revenue vs volume trade-off analysis

ADVANCED MODULES

Predictive & Prescriptive

DELIVERABLES

Built in PowerBI, Tableau, or Looker & adhering to client's brand book

Dedicated tab per analysis plus executive summary overview

AI-generated insights and recommended actions per analysis

SQL queries built in client's database system with controlled access

Python scripts for statistical and ML models (if applicable)

Added to client's GitHub repository, or delivered as standalone package

Technical Guide: Data sources, logic, formulas & maintenance procedures

Analysis Handbook: Metric definitions, interpretation, use cases & action framework

Dashboard

Code Base

Documentation

Knowledge Transfer

Live & Recorded walkthrough of dashboard functionality and insights

Q&A session covering methodology, use cases, and recommendations

30-day post-delivery support for questions and adjustments

MAIN REQUIREMENTS

• Transaction and operational data must be accessible in a relational database

• BI Platform Subscription with data base gateway for dashboard automation.

• Relevant APIs & ETL workflows should be functional and consistent.

Data Infrastructure*

• Transaction & Revenue Data - Customer-level revenue history with timestamps, transaction values, and product/service details

• Customer Acquisition Data - Acquisition source/channel, acquisition date, marketing attribution, and campaign details

• Marketing Spend Data - Marketing costs by channel, campaign, and time period; sales and onboarding costs

• Customer Activity & Engagement - Login frequency, feature usage, support tickets, and engagement metrics over customer lifetime

Data Sets

*Data infrastructure set up is out of scope. It can be provided as a separate engagement.