Cohort-Based LTV

1. What it means

• Cohort analysis = group customers by when they first joined (e.g., Jan 2025 cohort, Feb 2025 cohort).
• Cohort-based LTV = track how much revenue each cohort generates over time → average it → estimate lifetime value.

Instead of assuming churn is constant, we actually observe how cohorts behave month by month.

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2. Steps (simple method)

1. Define cohorts
   Example: all customers acquired in January 2025 = one cohort.
2. Track revenue (or gross profit) per cohort, over time
   For each month after signup, calculate average revenue per customer in that cohort. Example (per-customer revenue):
   • Month 0: \$100 (signup fee)
   • Month 1: \$40
   • Month 2: \$38
   • Month 3: \$35
     …
3. Calculate cumulative revenue
   Add up average revenue across months until the cohort stabilizes (or churns out). Example:
   • By Month 3: \$100 + 40 + 38 + 35 = $213
   • By Month 6: \$100 + 40 + 38 + 35 + 32 + 30 + 28 = $273
4. Estimate “lifetime”
   If after ~12 months the curve flattens (most churned), take the total.
   If not, fit a simple decay model (exponential/linear) to extend beyond observed months.

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3. Simple Formula (if you use average per customer over cohorts)

$\text{LTV} = \sum_{t=0}^{T} \frac{\text{Avg Revenue per Customer in Month } t}{(1+r)^t}$

• $T$ = number of months you track
• $r$ = discount rate (optional, for NPV adjustment; often ignored in simple calc).

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4. Numerical Example (no discounting)

• Cohort size = 100 new customers in Jan 2025.
• Revenue per customer over first 6 months (average):

Month	Avg Rev/Customer
0	$100
1	$40
2	$38
3	$35
4	$32
5	$30

LTV (6 months) = 100 + 40 + 38 + 35 + 32 + 30 = $275 per customer

If revenue keeps declining, you can project future months with a decay assumption.

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5. Advantages of cohort-based LTV

• Uses real observed behavior, not just churn assumptions.
• Highlights differences by acquisition month, channel, or segment.
• Helps see whether retention is improving or worsening across cohorts.

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Summary:
Simple cohort-based LTV = sum of average revenue per customer over time for a given cohort.
It’s more accurate than the churn-based shortcut ($1/\text{churn}$) because it uses actual cohort retention and spend data.