Half-Life: A Crucial Metric for Mobile Game Success

In the fiercely competitive mobile gaming landscape, marketers are constantly seeking strategies to better understand their metrics and enhance their chances of success. A critical, yet often overlooked, aspect of this is the concept of duration. In an environment where ROI positive user acquisition is hard to achieve, understanding not only how long it takes for users to monetize, but also the profile of how they do so over time, can help UA and finance teams make better-informed budgeting and planning decisions.

Introducing Half-Life

The concept of half-life, borrowed from nuclear physics, offers a simple yet powerful lens to analyze user behavior and monetization patterns of user cohorts. In the context of mobile gaming or apps, half-life refers to the number of days it takes for a user to generate 50% of their predicted LTV (pLTV).

Whilst deceptively simple, this metric provides valuable insights into user behaviour about the pace of monetization, and in particular how best to finance user acquisition and growth. It helps everyone around the table understand better the investment opportunity offered by paid user acquisition.

Convexity of LTV Curve

In financial markets, convexity is normally a metric used to measure the sensitivity of bond prices to interest rates. In mobile marketing, convexity relates to the shape of the LTV curve or monetization profile is relevant to marketing as it shows the rate of monetization of a user. Typical monetization of a user can be fairly rapid before flattening off but is highly dependent on genre. Half-life as a duration metric factors in both the ultimate pLTV of the user cohort, but also the shape of the curve.

In the first example of a hybrid casual game, we see a convex LTV curve, meaning the initial monetization is fairly rapid then tails off quickly and progresses towards a terminal LTV of $2.00 in 180 days. Of course, the game will likely continue to monetize after 180 days, but extending LTV horizons longer, for a very small incremental LTV uplift can skew the numbers which can lead to bad buying decisions. In this case, the half-life is achieved after a very short period, just 30 days, out of a 180 day horizon.

In the second example of a puzzle game, the monetization scenario is over a much longer period. The monetization profile of the cohorts, while it exhibits some convexity to the shape of the curve, is more linear suggesting a different but more stable and predictable profile of users paying for content over time, which of course is great for the studio as they have created a product that monetizes well over time, but also creates a challenge over what period they should “call” the LTV horizon of the cohort. In terms of duration, the half-life of this cohort is achieved after 270 days with the terminal LTV projected to 720 days.

Why is half-life important?

The half-life metric gets both UA teams and finance teams thinking about time much more centrally, in a way that the clunky ROAS metric ignores time. Whilst many UA managers focus on achieving a positive ROAS and may measure where they are at on that journey at a given point in time, more emphasis should be given to the overall profitability of the UA — not just earning enough to pay for the ad that brought the user to the game/app in the first place.

Time value of money in particular is a huge consideration, particularly in a higher interest rate environment. Remembering that UA is an investment like any other investment, it requires a thoughtful approach to how it is both funded and measured. Half-life also offers an important comparison metric between UA investment opportunities across a portfolio of games or apps.

Risk/reward spectrum

Different financial instruments are appropriate for apps/games with different duration metrics. Any lender or revenue-based finance provider is concerned with where the opportunity lies on the risk/reward spectrum. CFOs and UA teams need to seek out a financial instrument

that is not only priced correctly, but also offers the right flexibility to continue to scale when metrics permit and not either run out of available funding, or force the studio into an early amortization scenario which caps the potential upside whilst the UA opportunity exists.

Short Half-Life

Debt-based financing is typically best suited to shorter-duration games (ie shorter half-life). Products include borrowing against AR (funds trapped up in the payment cycles of the platforms — up to 90 days), and against value trapped in residual cohorts (user cohorts who have been acquired and paid for but not fully monetized, whose behavior can be modeled and priced and used as collateral to borrow against. Higher certainty and lower duration makes debt financing a very cost-effective instrument to fund additional spend on UA, particularly where a credit facility is dynamically linked to the underlying metrics, making more capital available as the metrics permit. Pricing is typically on an interest rate basis, making the cost vs return easy to calculate, provided the calculation is done on an equivalent timescale, ie daily, monthly or annually.

Long Half-Life

Revenue-based financing (RBF) is better suited to longer duration games/apps (ie longer half life) where timescales are longer and certainty is lower. This model typically works where the RBF investor provides capital to fund new cohorts (typically 80% of the cost of UA, with 20% funded by the studio), taking a revenue share on the cohort until it has repaid with a fixed return (e.g 1.1x capital invested). The fixed-rate nature of the payback appears to offer a simple and transparent return profile on capital invested, but can mask very high effective interest rates when measured on a IRR basis.

Absolute Returns

Whilst of course duration is important, so of course is the absolute return. The investment into UA needs to have a high degree of certainty of achieving breakeven and delivering a profit. Short-duration cohorts do not mean that the cohorts will break even on their ad spend with any greater degree of certainty, just that the outcome will be clearer in a shorter timeframe, which may be helpful in the overall planning of media spend.

Key Takeaways

• Half-life is a useful metric for understanding user behavior, financial planning, and risk management in mobile gaming.
• It provides insights beyond traditional metrics like ROAS, taking into account the shape of the LTV curve
• Half-life can help understand the risk/reward profile of a UA investment and what instrument should be used to fund it
• Understanding and optimizing user half-life is crucial for sustainable growth and success in the competitive mobile gaming/apps market.

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