Let’s compare OpenPay’s blended LTV approach against an alternate popular approach.

OpenPay’s blended LTV approach Copied

This is how OpenPay calculates LTV:

$\text{Blended LTV} = \frac{\text{MRR}_{\text{churned}} + \text{Expected MRR}_{\text{active}}}{\#\text{total customers}}$

You can find a full breakdown of the calculation here:

LTV

OpenPay 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 Copied

• 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 Copied

• Requires more data infrastructure (MRR tracking, churn lifetimes, etc.)

• Not available until at least 10 customers have churned

Alternate popular LTV approach Copied

This is how many other platforms calculate LTV:

$\text{LTV} = \frac{\text{ARPA}}{\text{Churn rate}}$

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:

$\text{LTV} = \frac{100}{0.05}={\text{2,000}}$

Advantages Copied

• Simple to understand and calculate

• Good for high-level benchmarking

• Requires limited data (only ARPA and churn rate)

Limitations Copied

• 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.