How to Calculate Mobile Game LTV vs CAC

This article provides a practical overview of how mobile game studios measure profitability by calculating player Lifetime Value (LTV) against Customer Acquisition Cost (CAC). It covers standard industry formulas, predictive modeling techniques, and payback period frameworks essential for scaling user acquisition campaigns sustainably.

Defining the Core Metrics

Mobile game studios must understand the baseline metrics before evaluating marketing efficiency:

  • Customer Acquisition Cost (CAC): The direct cost of acquiring a single install.
    • Paid CAC: Total marketing spend on a specific channel divided by direct paid installs attributed to that channel.
    • Effective CAC (eCAC): Total marketing spend divided by total installs (including organic users generated by viral growth or store featuring).
  • Lifetime Value (LTV): The net projected revenue a player generates through in-app purchases (IAP), in-app advertisements (IAA), and subscriptions from their first session until they permanently churn.

Primary Methods for Calculating Mobile Game LTV

1. The ARPDAU and Retention Model

The most common structural method calculates LTV by combining Average Revenue Per Daily Active User (ARPDAU) with player retention curves over a specified horizon (\(t\) days).

\[\text{LTV}_t = \sum_{n=1}^{t} (\text{Retention}_n \times \text{ARPDAU}_n)\]

  • Retention (\(R_n\)): The percentage of acquired users who return on day \(n\) (Day 1, Day 7, Day 30, etc.).
  • ARPDAU: The total revenue earned on day \(n\) (IAP + IAA) divided by active users on that day.
  • Application: Studios model retention decay using power-law or logarithmic curves to forecast retention beyond observed days (e.g., extending D30 data to D180 or D365).

2. The Cumulative ARPU Method

This empirical approach aggregates the cumulative Average Revenue Per User (ARPU) over time for a specific cohort:

\[\text{LTV}_t = \text{Cumulative Revenue up to Day } t / \text{Total Cohort Installs}\]

Studios plot observed cumulative ARPU curves across historical cohorts and fit mathematical functions (such as logarithmic regression: \(y = a \cdot \ln(x) + b\)) to project where the curve flattens out, representing terminal LTV.

3. Predictive Machine Learning Models (pLTV)

Modern privacy changes (such as Apple’s SKAdNetwork and Google’s Privacy Sandbox) limit long-term user tracking. As a result, studios increasingly rely on machine learning models:

  • Data Inputs: Early gameplay metrics collected within the first 24 to 72 hours (levels completed, session length, ad views, tutorial completion, early IAP).
  • Algorithms: Gradient boosting algorithms (XGBoost, LightGBM) or neural networks map early behavior against historical cohorts to predict Day 180 or Day 360 LTV.

Calculating CAC

To accurately compare acquisition expenses to LTV, studios isolate acquisition costs by segmentation:

\[\text{CAC} = \frac{\text{Total Acquisition Spend}}{\text{Total Attributed Installs}}\]

When accounting for virality (the K-factor), studios calculate effective CAC (eCAC):

\[\text{eCAC} = \frac{\text{Direct Paid Spend}}{\text{Paid Installs} + \text{Organic Installs}}\]

Methods to Evaluate LTV Against CAC

1. Return on Ad Spend (ROAS) by Day Cohort

Rather than waiting for full lifetime value, studios track ROAS targets at fixed intervals to determine campaign viability:

\[\text{ROAS}_d = \frac{\text{Cumulative Revenue at Day } d}{\text{Campaign Spend}} \times 100\]

  • Day 7 ROAS: Provides early indicators for ad-heavy hyper-casual or hybrid-casual games.
  • Day 30 to Day 90 ROAS: Common break-even targets for mid-core and core games with deep IAP economies.

2. Payback Period (Time to Break-Even)

The payback period measures the exact number of days required for cumulative LTV to equal CAC (\(\text{LTV} = \text{CAC}\)).

  • A shorter payback period reduces working capital requirements and reinvestment risk.
  • If a game achieves break-even at Day 60, all cohort revenue generated from Day 61 onward represents gross profit.

3. The LTV:CAC Ratio

For mature titles with stable long-term retention, studios evaluate the overall ratio:

  • LTV:CAC < 1:1: The game loses money on user acquisition; spend must be halted or monetization improved.
  • LTV:CAC = 1:1 to 1.5:1: Marginal sustainability; vulnerable to platform fee increases or ad mediation volatility.
  • LTV:CAC \(\ge\) 3:1: Healthy margin; indicates the studio can aggressively scale ad spend to capture more market share.