Stanislav Rudoi is the VP of growth & analytics at Pollen VC.
When mobile gaming companies start to scale their user acquisition, they often do not pay enough attention to the key metrics that drive profitable growth nor do they track these metrics in a systematic way.
While there are many metrics that can be tracked, and arguably each of them is important, the most important bottom line metric has always been ROAS (Return on Ad Spend).
We put together a UA metrics template to help gaming founders and UA managers simplify their reporting of campaign performance and help them better analyze the results of their user acquisition campaigns.
You can download the UA Metrics Template here. To save to your Google Drive, go to File and then Make a Copy.
In this template, you can simply track all your important user acquisition metrics to ensure that you are spending profitably.
This spreadsheet should be used daily by a mobile user acquisition manager to manually report on campaigns and record any changes made.
When you are just starting and not running hundreds of campaigns, it is beneficial for you to manually track every single campaign every day, so that you understand what's happening with each campaign.
At the beginning of any user acquisition activity, it is better to keep things simple rather than run complicated and automated dashboards.
UA Template Video Walkthrough
How to Calculate ROAS
The formula for return on ad spend is very simple:
ROAS = Cohort Revenues / Cohort Cost
ROAS tells you how much money you recouped after you spent it on app install ads. For example, when ROAS equals 1, it means you have recouped 100% of your money.
You can also track ROAS at different points of time. For example, D7 ROAS tells you how much you recoup in a week, while a D28 ROAS tells you how much you recoup after a month. This allows you to track your cohort performance closer.
Let's say you have D18 20% ROAS. Is that a good or bad ROAS?
In order to figure this out, you need to estimate your customer's Lifetime Value (LTV).
The example above shows a hypothetical app with $1.89 LTV over 90 days. Firstly, we need to normalize the curve to effectively transform the LTV curve into a ROAS curve where CPI equals LTV and we break-even at 100% LTV recovery.
Now we know that in order to "ride the LTV curve", by day 18 we are supposed to recover 27% of our LTV. However, in our example, we only have a 20% ROAS. It doesn't look so good.
An attentive reader will notice that I am comparing two curves which are likely to have different CPI assumptions. For example, for our normalized LTV curve, we assumed CPI of $1.89 (equals to LTV), and here we can have CPI of $2.50, which would mean our users are "riding the LTV curve", but we acquired them for more than we predict our LTV to be.
This warrants the question: should we impose a hard ceiling on CPI that equals to LTV to avoid this? I wouldn't do it right away.
Your LTV curve is an average performance of your users, and depending on all the aspects that a campaign can take into account, you may be able to acquire users for more than your LTV and still be profitable.
For example, you may use a narrow lookalike on your whales together with IAP optimization and acquire users for $4, and they are going to return you $5 over their lifetime. Thus, limiting your CPI bid in practice wouldn’t allow you to acquire such users.
How can we resolve this inconsistency?
There are a number of ways to resolve this, and I usually make an assumption about the shape of ROAS curve based on your normalized LTV curve. Then what matters is the percentage of ROAS recovery on a certain day, regardless of whether you acquired users at $1, $2, or $5.
If they deliver at least 27% of their acquisition cost by day 18, you are likely to breakeven by day 90 and get your money back. If they deliver more than that, you are going to speed up your payback window and earn some money.
To go even further on the ROAS calculation and learn more about active vs mature ROAS, read our how to calculate ROAS article.
About the Author
Stanislav Rudoi is the VP of Growth & Analytics at Pollen VC, the market leader in capital-efficient funding for mobile app developers and digital publishers. Before coming to Pollen VC, Stanislav used to manage large scale UA campaigns for various casual games at redBit games in Rome, Italy.
Pollen VC provides lines of credit to app publishers allowing them to unlock their unpaid revenues and eliminate payout delays of up to 60+ days by connecting to their app store and ad network platforms.