Filters are a key part of dashboards and reports. They help provide answers in an organized and structured way by enabling you to apply a set of values and narrow the results.
On our platform, you can use various filters to determine and analyze:
Our research has revealed that the average TTR (Tap-Through rate) is approximately 7%. As a rule, however, TTR is higher for Brand campaigns than for Competitors campaigns. 5% is a threshold level, and if TTR is below 5%, then the ad group performs worse than it could, and you need to find out why.
Low TTR indicates low relevance between a keyword and an ad variation. You can improve this score by optimizing your ads and thus get a higher TTR.
Here are the most effective ways to increase the relevance score (and TTR):
A/B testing of ad variations using CPP.
Using creative sets more relevant to user queries
Localize and adjust your app metadata to specific storefronts
To avoid search queries with low TTR, you can use:
Negative keywords
Exclude segments with poor performance, for example, disable non-working hours or weekends
Ad Groups with low Download Rates (Tap-Through) show the areas that require optimization. According to Apple, the average Download Rate (Tap-Through) is 50%.
Ad variations using custom product pages use the assets on your App Store product page (metadata), such as texts, screenshots, and icons. Thus, by optimizing metadata, you can improve the Tap-Through rate of an ad (TTR) and the number of downloads (Download Rate (Tap-Through)).
Optimization techniques are very similar to those in point 1) and are often used together. Some elements have a bigger impact on the TTR of a creative set (icon, name, rating), others on the Download Rate (Tap-Through) (screenshots, description).
Install Rate (Tap-Through) and discrepancies between Apple Search Ads and MMP
According to Apple, the discrepancy between the data provided by MMP and Apple Ads can reach 60% for some apps (read more on our blog article). Here are the key reasons:
Limited Ad Tracking (LAT)
Re-downloads
Different approaches to counting installs in MMP and Apple Ads
If the Install Rate (Tap-Through) is under 40%:
Make sure that enough time has passed and you have enough data available from MMP.
Check the MMP attribution window settings for Apple Ads; the window should be 30 days.
Search Match may cause such a discrepancy.
Keep in mind that installs and conversions have a delayed effect, meaning users do not always tap and buy on the same day. Analyze the data for the previous periods: they will typically show a higher rate
Apple Ads functions on an auction basis. For your bid to enter the auction, your app should be relevant to the keyword that matched a particular user query. It is technical keyword relevance. Apple’s algorithm determines this relevance mainly based on your app category and metadata: title, subtitle, and keywords.
How to count the share of keywords with low technical relevance?
1. Get the total number of Exact Match keywords (for example, 23,085)
2. Apply the filter to count keywords with few (for example, from 0 to 5) or no impressions (for example, 17,211)
3. Divide the number of keywords with few/no impressions by the total number of keywords: 17,211 / 23,085 = 74.6%
If this share exceeds 85%, then only 15% (or less) of keywords get impressions.
Here are some reasons for such poor performance and what can be done about it:
Problem | Solution |
Low technical relevance | Review and rethink your keyword list |
Low CPT bid (usually <1$) | Increase CPT bid (see our benchmarks in the blog) |
Few keywords (<50) | Expand the list of keywords (use our Keyword Discovery) |
Low volume keywords (popularity <10) | Use Keyword Discovery to find keywords with a higher popularity score |