Identify the key drivers of CLV to close the gap between revenue and retention.
Analyzing Customer Lifetime Value (CLV) is never fast because not every customer is created equal. CLV predicts the amount a customer will spend on your product or service over an undetermined amount of time, making it one of the more difficult ones to assess. And before you can calculate CLV, you’ll also need to calculate the Average Order Value (AOV), purchase frequency, customer lifespan, and customer value.
Since CLV is also a forecasting metric, it’s not set in stone and needs to be continually monitored as customer behavior evolves to be actionable. And with varying contract lengths, differing acquisition channels, unique customer segments, discounts, refunds, and churn, diagnosing CLV has a high margin for error.
Calculating Customer Lifetime Value varies slightly between subscription, SaaS, and retail companies but no matter what industry you’re in, you’ll need a wide dataset to identify the key drivers behind CLV.
As you build your dataset, each row should represent a unique customer, with one record per subscription period. Start with the recommended fields below and add as many descriptive variables as you can to augment your analysis.
Customer First Order Date
Customer Last Order Date
Customer Acquisition Date
Customer Tenure (months)
Customer City, State, Zip