Comparing alternate LTV formulas
Customer Lifetime Value (LTV) is a core subscription metric that helps quantify the value a customer brings to your business over time. Different platforms compute LTV in different ways, and it's important to understand the tradeoffs behind each methodology.
Let’s compare OpenPay’s blended LTV approach against an alternate popular approach.
OpenPay’s blended LTV approach
This is how OpenPay calculates LTV:
You can find a full breakdown of the calculation here:
LTVOpenPay calculates Blended LTV (Blended Lifetime Value), giving you a single metric that reflects the overall value of your customer base, across all lifecycle stages.
Advantages
-
Based on actual customer payments, not just averages
-
Accounts for both churned and active customers
-
Adjusts expected value based on actual observed lifetimes
-
Stable across time and less prone to short-term churn swings
-
Granular, customer-level data powers the metric
Limitations
-
Requires more data infrastructure (MRR tracking, churn lifetimes, etc.)
-
Not available until at least 10 customers have churned
Alternate popular LTV approach
This is how many other platforms calculate LTV:
Where:
-
ARPA = Average Revenue Per Account during the interval
-
Churn Rate = % of customers who churned
For example if ARPA = $100 and churn rate = 5%, then:
Advantages
-
Simple to understand and calculate
-
Good for high-level benchmarking
-
Requires limited data (only ARPA and churn rate)
Limitations
-
Doesn't reflect actual customer payments
-
Sensitive to short-term churn volatility
-
Because churn is in the denominator, a spike in churn (e.g., seasonal downgrades or a one-time issue) can cause LTV to drop sharply, even if actual customer value hasn’t changed.
-
Likewise, a dip in churn (e.g., from a short-term promo) may inflate LTV temporarily.
-
-
Overly simplified - assumes revenue and churn stay constant over time
-
This method assumes that current ARPA and churn rate will continue indefinitely — no changes in customer upgrade/downgrade behavior, payment failures, or retention strategies.
-
So it's not tracking individual payment histories or predicting future behavior for real customers.
-
-
May mislead when customer behavior is non-linear (e.g., seasonal, tiered pricing, trial-to-paid conversions)
While ARPA/churn rate may be effective as a quick benchmark, this approach can distort reality when revenue and churn vary across customers. OpenPay's blended LTV, while requiring more data, gives a more accurate, stable, and actionable view by grounding predictions in actual MRR behavior.