Analyze and Optimize Customer Lifetime Value

This skill provides expertise in Customer Lifetime Value (CLV) analysis, modeling, and optimization, with deep knowledge of calculation methods, predictive modeling techniques, segmentation strategies, and business implementation across industries. Historical (retrospective) CLV calculation covers a simple approach based on actual customer behavior - total revenue, average order value, purchase frequency, and customer lifespan in days - and cohort-based CLV analysis, which groups customers by their first-purchase month, computes revenue per cohort per period number, and divides by cohort size to get average revenue per customer at each stage of the relationship.

Predictive (prospective) CLV uses statistical models to forecast future customer value. RFM-based predictive CLV computes a probability-of-being-active estimate from recency and churn rate, derives expected future transactions, and sums discounted monthly value across future periods to a present-value CLV. Advanced CLV prediction uses the BG/NBD model (fit via the lifetimes library's BetaGeoFitter on frequency/recency/T to predict future purchase counts) combined with the Gamma-Gamma model (fit on frequency and monetary value for returning customers) to compute a full predicted CLV with a specified discount rate and prediction horizon.

Business-model-specific CLV approaches cover subscription businesses (a simple formula dividing monthly revenue by the sum of monthly churn rate and discount rate, and an advanced version accounting for plan upgrades/downgrades and expansion revenue with a separate expansion-CLV term) and e-commerce (computing average order value and purchase frequency from order history, with optional seasonality adjustment factors applied to the monthly purchase rate before projecting future value).

Use this skill when calculating or predicting customer lifetime value - whether a simple historical/cohort calculation or a statistically rigorous BG/NBD-based prediction - for subscription or e-commerce business models. It is well suited to businesses with enough transaction history to fit predictive models or build meaningful cohorts. It is not meant for businesses with too little transaction history to compute a meaningful CLV, or as a substitute for actually validating predicted CLV against realized revenue over time.

Input: customer transaction history (orders, revenue, dates) or subscription data (monthly revenue, churn rate, plan mix).

Output: historical or cohort-based CLV, and/or a statistically predicted CLV using RFM or BG/NBD-Gamma-Gamma modeling. Example subscription CLV formula:

def subscription_clv(monthly_revenue, churn_rate_monthly, discount_rate_monthly=0.01):
    return monthly_revenue / (churn_rate_monthly + discount_rate_monthly)

Works with Python's pandas/numpy for cohort analysis and the lifetimes library (BetaGeoFitter, GammaGammaFitter) for BG/NBD predictive CLV modeling.

Data scientists and growth/finance teams calculating or predicting CLV for subscription or e-commerce businesses, and teams that need statistically rigorous predictive CLV rather than a simple average-revenue estimate.