Customer Lifetime Value
Customer lifetime value analysis calculates the total revenue each customer generates over their entire relationship with you, then identifies what firmographic and behavioral characteristics predict high-LTV outcomes. It tells you not just what a customer is worth, but what makes certain customers more valuable than others.
You know your average customer pays $24K per year. But 'average' hides the reality that some customers are worth $200K over five years while others churn after 6 months having paid $12K total. Without LTV analysis by segment, you can't optimize acquisition, retention, or expansion efforts.
How We Analyze LTV
- Revenue data mapping. We map your revenue data — initial deal, expansions, renewals, and churn — to build a complete LTV picture for each customer.
- Customer enrichment. We append firmographic and technographic data to customer records so LTV can be analyzed by company characteristics.
- Segment LTV calculation. We calculate average and median LTV by segment: industry, company size, product tier, sales channel, and other dimensions.
- LTV predictor identification. We identify which attributes at the time of acquisition most strongly predict whether a customer will be high-LTV or low-LTV.
- Payback period analysis. We calculate how long it takes to recover customer acquisition cost by segment, revealing which segments are most capital-efficient to acquire.
LTV Analysis Deliverables
- LTV calculations by customer segment showing which groups generate the most long-term revenue
- Acquisition cost payback periods by segment for capital allocation decisions
- Predictive attributes that identify high-LTV customers at the time of acquisition
- Revenue optimization recommendations: which segments to invest in, which to deprioritize
Common Questions
Do you need access to our billing system?
No. We work with exported data. A CSV with customer name, start date, monthly or annual revenue, expansion events, and churn date (if applicable) is enough. We don't need real-time billing access. Most teams can pull this from their CRM or billing platform in a few minutes.
How far back should revenue data go?
At least 2 years for meaningful LTV analysis. With less than 2 years, you're extrapolating heavily. With 3-5 years of data, the patterns are robust enough for confident decision-making. If your company is younger than 2 years, we can still do the analysis but will note the shorter observation window.
Can LTV analysis help with pricing decisions?
Indirectly. If LTV analysis shows that enterprise customers generate 5x more lifetime revenue than SMB customers, but your pricing only charges 2x more, that's a signal to evaluate your pricing tiers. We provide the data. Your team makes the pricing decisions based on what the LTV patterns reveal.
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