ROAS Measurement in Apple Search Ads

Through a carefully designed campaign, it is possible to grow your user base and increase your revenue. Check our article to learn ROAS measurement in Apple Search Ads.

Talip Sencan
December 14, 2020

Apple Search Ads is a powerful tool to increase your growth. Through a carefully designed campaign, it is possible to grow your user base and increase your revenue. However, you should be very careful when it comes to financial planning. This post will discuss some of the problems in measuring one of the key metrics, ROAS, and how you can overcome them.

The first goal in any business is to profit. This also applies to the app market. Advertising costs should not exceed the revenue that your campaign brings in. Thus key data such as Return on Advertisement Spent (ROAS) are critical for determining the success (or inefficiency) of your strategies. By realistically identifying your shortcomings, you can improve your tactics to maximize your profit.

For example, you might be conducting a high-budget, aggressive campaign. It can turn out to be very effective and bring in lots of new users. If what you spend on this campaign is a couple of times more than what you earn because of it, is it really an effective strategy? Or if your installs are not converting into in-app goals, you might be missing out on a lot of revenue.

Data Discrepancies

Before talking about improving the accuracy of ROAS, it will be reasonable to discuss the factors that cause distortion in our measurements.

apple search ads data

The main problem is the discrepancy between the data that Apple Search Ads provides and the data that you obtain from your Mobile Measurement Partner. Because these two parties have different measurement methodologies they usually have a mismatch in their data. The amount of this discrepancy can change according to context. The category of the app, demographics of users, or even location might increase or decrease the amount of discrepancy. By accounting for this difference, you can increase the accuracy of your other metrics which help you determine the success of your advertising efforts. Let’s briefly talk about some of the major factors that cause variance in measurements.

Definition of “Install”

What does install mean for you? Is merely downloading the app enough for qualifying as an install, or would you add some constraints to this definition? It turns out that MMP’s and Apple have different perspectives on this.

According to Apple Search Ads, if a user clicks on download after viewing an ad (referred to as App Store Verified Install), this action qualifies as an install. However for MMPs to classify a download as install, the app has to be opened by the user after clicking on the ad. It is possible for users to never open the app after they download it or there might be a gap between the install and open time. Different contexts might call for different perspectives, that is why either approach can be beneficial in certain conditions.

Attribution Windows

Attribution Windows are another cause of the discrepancy. The attribution window can be described as the maximum amount of time that can be between the impression and the install for the ad to receive credit. For Apple Search Ads, any install in the following 30 days after a tap on an ad is sufficient for that install to be attributed to your campaign. On the other hand, MMPs use different time intervals for attribution, usually between 7 to 30 days.


If a user deletes your app and then downloads it again, Apple Search Ads will report these downloads to you in your Apple Search Ads dashboard. However, MMPs may consider these users to be re-opening the app and not as new installs.

Limit Ad Tracking

Limit Ad Tracking gives users the opportunity to bypass being measured or tracked. This option is valuable to many consumers due to privacy reasons. Because these users can not be tracked, MMPs will not be able to locate their source thus excluding them from your downloads. These users will also be considered as organic downloads. Luckily Apple Search Ads provides a LAT Installs metric which is a key metric for ROAS calculation.

Calculating your ROAS

Running an advertising campaign is a costly effort. No one wants to throw their money away.

Cost and revenue are the key indicators of success in any business. To profit in Apple Search Ads, your costs of advertising should be a reasonable expense compared to the revenue that you gain (or hope to gain) from it. 

The first step in measuring your ROAS is to determine your costs. Apple Search Ads uses a Cost-per-Tap based system where you pay only if a user taps on your ad. A metric called Cost-per-Acquisition is also provided where you can see the estimated cost of acquiring one additional user for your app. The formula of ROAS can be described simply as;

roas apple search ads

Let us assume that you brought in 1000 new users into your app with a CPA of 3$. If you project to earn 5$ on average from one user for the duration that they will use your app:

Return on Ad Spend = 1000*5 / 1000 *3 = 1.66

The result of this equation will never be negative. Instead, you will be able to see your return for every dollar that you spent on advertising. In this case, you will be earning 1.66$ for every dollar you put in.

Effect of Data Discrepancies

However, as we discussed earlier, not every download is necessarily a user who opens the app. provides a Cost-per-Install metric which is a more realistic metric for UA managers. While CPA is based on installs, CPI is based on installs that lead to the opening of the app. To demonstrate our point, let’s assume that 10% of downloads never opened your app. You are left with 900 users. Because you would have spent the same amount of money on 900 users (even though Apple Search Ads reports 1000), your CPI would be approximately 1000*3 / 900 = 3.33. If we calculate the ROAS again;

Return on Ad Spend = 900*5 / 900 * 3.33 = 1.50

As you can see, now you are getting 1.50$ for every dollar you spent. We only took into account potential downloaders who did not open your app. If you consider other sources of discrepancies that we have discussed above (LAT, redownloads, etc.), the ratio of discrepancies in real-life applications is usually much higher than 10%. 

If you think your campaign is performing well, why would you bother to change it? This is correct, but if your ROAS is misleading you, it can very well cause your budget to drain.

How to Improve the Accuracy of your ROAS

roas accuracy calculation

So how can you actually improve the accuracy of your ROAS estimation? To be honest, the possibilities are endless. allows you to create custom columns to formulate your own performance metrics. By tailoring metrics according to your needs, you can draw more insights into the performance of your campaign.

One way to do a conservative calculation of your ROAS would be to divide the amount of MMP reported Installs by the cost that is seen in Apple Search Ads. This would be a “worst-case” calculation. However, the way to growth is through optimization. Consequently, an under-estimation of our ROAS is not necessarily better than over-estimating it from a strategy standpoint. It might be safer, but not better.

As discussed before, MMPs cannot determine the source of LAT on users or redownloads. These should be also factored in when calculating your ROAS. Or you can try to estimate your lat on base with some calculations. provides a solution with the LAT On Coefficient. With the use of the Lat On Coefficient, your key performance metrics are calculated again to find more accurate results. Let us go over a hypothetical scenario to see how you can employ these metrics in your ROAS calculation.

Quick Example

Our Apple Search Ads campaign for the app Hyphotetica seems to have accrued 500 installs according to Apple Search Ads with a CPA of 2$. Our ARPU is estimated to be 4$. On the other hand, our MMP reports 300 attributed installs. 

When we look at our dashboard, we can see that our LAT On user base is estimated at around 20%, our re-downloads are estimated at 5%. To calculate our cost, let’s use the cost seen by Apple, which is 500*2 = 1000$. 

For our revenue, let’s take the 300 MMP Installs, add 20% for LAT On users and 5% for redownloads. Now we estimate our actual installs to be around 375. If we multiply this number by our ARPU which is 4$, we will get an estimated revenue of 1500$. This would grant us a ROAS of 1500/1000 = 1.5.

What would happen if we calculated according to MMP reports? We would calculate our ROAS as 300*4 / 500*2 = 1.2. This could have made us think that our campaign is not effective enough while our ROAS is actually higher. By including other KPIs provided by, we were able to get a more accurate estimate of our earnings. 

To summarize this example, the amount of installs your app receives can be lower than what Apple reports. However it can also be higher than what your MMP reports. This caused our ROAS to look much lower than it actually was.

Key Takeaways

Even though there are certain challenges for accurately measuring the returns you are getting from your Apple Search Ads campaign, it is possible to overcome them. By using the data provided by Apple Search Ads and integrating it with your MMP data, can help your business decisions. Customizable and advanced metrics allow our users to develop their strategies in a way that is consistent with the actual performance of their campaign. If you are interested in improving your Apple Search Ads strategies, sign up for free here!

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