Why Lifetime IRR is better than ROAS for measuring marketing performance

• “The problem with ROAS is that it does not factor in the time value of money and that LTV cycles have to be financed”
• “Using IRR enables a much more granular analysis that takes into account real financial performance over time”

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Targeting users has become one of many challenges in the mobile games market. Games are competing in an over-saturated space whilst also having to contend with visibility on storefronts to ensure that their game gets noticed along with any marketing efforts. 

In this guest post, Martin Macmillan, founder of the mobile finance collective and CEO and founder of Pollen VC, explains how lifetime IRR improves analysis over static ROAS.

As user acquisition on mobile becomes more competitive, UA and finance leads are constantly looking for ever more sophisticated ways of analysing their performance marketing initiatives to ensure they really understand the value of users acquired using paid channels.

If you want to track a more financially relevant metric of your UA spend at scale, you should move on from ROAS and consider using an IRR-based approach.

The problem with ROAS is that it does not factor in the time value of money and that LTV cycles have to be financed. These financing costs are more material in a higher interest rate environment, yet they are often overlooked.

Using IRR enables a much more granular analysis that takes into account real financial performance over time rather than just a static ROAS number without the valuable context of time.

What is internal rate of return (IRR)?

“A metric used in financial analysis to estimate the profitability of investments”

IRR is used by investors to evaluate and rank potential investment opportunities. IRR is the discount rate applied to the cash flows of an opportunity to produce a net present value (NPV) equal to zero.

An IRR-based approach to tracking performance considers every financial event generated by your UA efforts, mapped out over time using individual cash flows tracked over time to offer the most accurate way to understand the true financial performance of marketing efforts.

Lifetime spend vs Lifetime return of UA performance

A lifetime IRR calculation may yield interesting results for games or apps that have been spending time on UA.

For this exercise let’s zoom out as far as possible to track your lifetime spend vs return since the launch of your game or app.

The objective here is to track an overall IRR, or “running yield,” a metric that shows total financial performance since launch and how it has evolved over time.

To arrive at this number, every single cash flow is mapped out and time tracked – everything spent on UA and all monetisation revenues across all channels – together with the date of each cash flow, to calculate the lifetime IRR.

This metric will indicate how capital-intensive the game/app has been to fund from launch, which depends on the ad spend break-even timeframes and the longer-term profitability of the game.

You’d rationally expect shorter break-even products to achieve a positive IRR faster than other genres, which have longer break-even periods and, therefore, require more capital to fund. The convexity of the LTV curve is also factored in, with more convex curves (products that monetise faster before gradually tailing off) improving the running yield due to faster cash generation. The greater the number of sets of cohort data you have, the smoother the curve becomes.

Of course, given the LTV profile of the app or game, there is likely to be value trapped in the existing cohorts that would continue to play out from the date you run the calculation. In a theoretical scenario, you’d stop investing in any more UA from today and wait until the existing cohorts have fully monetised, but in practical terms, this just isn’t possible. So, you may want to augment this calculation out into the future based on residual cohort information from your MMP.

Setting IRR targets rather than ROAS targets could provide a more meaningful approach for UA managers as it factors in time rather than just an arbitrary percentage target tied to a particular point in time.

Calculating lifetime IRR of a mobile game/app

Both Google Sheets and Excel have standard implementations of various IRR functions. We are going to use XIRR as it enables us to calculate the IRR based on an irregular series of cash flows.

XIRR (cashflow_amount, cashflow_date, [rate_guess])

To calculate the lifetime IRR of your UA efforts, we need to look at a more granular level of when the UA invoices were actually paid or cash was received from platforms. The [rate guess] is an optional field that can help cut down calculation time if you have a sense of what the rate might be, as calculating IRR is an iterative function.

In order to prepare the calculation, you’ll need to establish the following:

In calculating lifetime IRR, we are only concerned here about total outlay/return, so we do not distinguish between paid vs organic users. We are zooming out to look at just the totals to get the high level picture. Importantly to generate the most accurate IRR metric we are concerned about cash, not P&L as this actually reflects the cash outlay vs cash received.

You can download our Lifetime IRR Google Sheets template.

Financing user acquisition

Now you have a more rigorous financial framework to track UA returns, let’s turn our attention to how UA is financed.

Of course, many studios in the early days rely on their VC funding to start at least the early scaling phase of an app/game, but in a tougher market, there is an increasing focus on capital efficiency and the real costs vs returns of their spend. This concept applies all the way up to publicly listed companies. All capital has a cost associated with it, whether it is internal or external, and an opportunity cost.

In the longer term, any game or app should be able to fund its own continued growth based on performance. An interesting exercise is to include an assumed cost of capital alongside the running yield figure, which would compute an implied running yield on user acquisition net of the cost of capital to finance it (assuming a revolving credit facility where funds can be drawn/repaid as required).

This could be an internal cost, such as an opportunity cost of lost interest on a bank deposit if a company is cash-rich, tying up operating capital in financing UA cycles. Or it could be an external cost, such as financing with a revolving credit facility to externally finance the UA spend. Either way, it is important to measure the internal or external cost of capital here – from the smallest startup to the largest public company, the underlying concepts are still the same.

Any decision to invest in UA is a capital allocation decision at the expense of another opportunity. For example I may wish to deploy excess treasury cash on deposit in the bank earning 4% pa into a UA opportunity that earns me 20% pa.

Or I may wish to externally fund my UA spend with capital costing 20% pa into a UA opportunity, which is yielding 40% pa. The cost vs return argument always holds, even if the cost is an opportunity cost.

Summary

ROAS targets serve a useful purpose for day-to-day tracking of ad spending for UA managers, but for CFOs looking at the bigger picture, an IRR-based portfolio approach offers a potentially better approach to measuring performance at scale.

This applies across the board, from the smallest startups to the largest publicly quoted studios and offers both UA and finance leads a more financially relevant way to track and measure their aggregate paid marketing spend.

You can find out more about the Mobile Finance Collective and also reach out if you have any questions or want to discuss in more detail.

                                                                             Edited by Paige Cook