Finance
Customer lifetime value
Estimate customer spend over their lifetime with the company.
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Customer lifetime value vs Customer lifetime value analytics
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Marketing
Customer lifetime value analytics
Customer lifetime value analytics is the process of analysing how valuable the customer is to the business over the entire lifetime of the relationship.
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Use this when.
Use it to estimate the economic contribution of a customer over the relationship.
Review frequency depends on market change. Banking once enjoyed long relationships because switching accounts and direct debits was burdensome; easier switching has shortened that assumption. Insurance customers likewise compare alternatives more actively.
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Strengths, limits, and pitfalls.
Customer lifetime value
- Use contribution after all customer specific acquisition, service and retention costs—not revenue—as the economic input.
- Refresh the model whenever the offer, cost structure or customer behaviour changes.
Watch for
- Do not value future cash as if it arrived today. Discount it to net present value and expose the retention and spend assumptions driving the result.
Customer lifetime value analytics
- Choose the simplest formula that matches the relationship and decision. No CLV estimate is 100 per cent accurate, so expose assumptions and ranges instead of pursuing false precision.
- After estimating CLV, use Regression Analysis to explore factors associated with duration or contribution. Validate causal interventions through experiments before assuming that changing a correlated factor will increase value.
- Resolve customer identity across products so one person is not counted three or four times in separate systems. Modern storage and analytics can create a unified profile, but require purpose limitation, data quality and access governance.
Watch for
- Do not calculate product level fragments as though they were separate people. Without reliable identity resolution across products, total customer value will be understated or duplicated.
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