Which keywords drive downloads to your app? Branded or Generic?

How do you treat brand against generic keywords in your ASO routine? We all know that the whole purpose of ASO is to help increase your organic app visibility on high potential generic keywords. Therefore, we thought it would be insightful to separate brand from generic keywords in your app performance analysis. Meet our new keyword analysis tab: Brand vs. Generic.

Separating Brand and Generic keyword in your ASO performance analysis

When you optimize your app, your goal is to find the best potential keywords, that means the keywords that have high volume and on which your app can rank in top results. Most of these keywords will be generic, since both Apple and Google frown upon adding brand names in your app’s metadata.

How to choose the best keywords for your app in 3 steps

Therefore, in order to measure the real impact of your ASO work, you want to mostly look at your app’s visibility on generic keywords only. That doesn’t mean that visibility on other brands should be ignored, but it should be considered differently in your app performance analysis.

In AppTweak, we’ve added a new Brand vs. Generic tab to our keyword analysis tool. This tab automaticallyseparates the branded keywords from the generic keywords of your keyword list and then calculates the number of downloads your app gets from each set.

Comparing the number of downloads of food delivery apps across a set of generic food related keywords – US Apple App Store

How does this help in your ASO? First of all, you immediately see which competitor is getting the most downloads from generic keywords. That can help you identify the keywords that have the highest potential, and maybe adapt your strategy. Looking at the brand keywords that bring most downloads to your competitors, you can identify which brands are most interesting to focus on, this can guide your Search Ads strategy.

Comparing the number of downloads of food delivery apps from their brand keywords – US Apple App Store

We also added a special “Own Brand” filter, which will calculate the number of downloads an app gets from its own brand. This will give you an idea of which competitor has the strongest brand and whether you should invest more in your app’s brand or not.

Learn more about our Brand algorithm

Finally, we added a position map that maps out your app and your competitors according to two axes:

• Number of downloads an app gets from brand keywords
• Number of downloads an app gets from generic keywords

At just a glance, you can identify which app is driving most downloads from keywords overall, which apps are best positioned on generic keywords and which have the strongest brands.

Comparing the number of brand vs. generic keyword downloads of food delivery apps – US Apple App Store

To further share the type of insights you can get from this feature, we did two case studies on Education apps and Travel apps. Read more to find out what we learnt.

Case Study 1: Babbel, Duolingo and Rosetta Stone

To carry out this case study, we followed thousands of keywords related to education, we then removed all volume 5 keywords. This resulted into a list of 520 education related keywords, both brand and generic.

We then applied our Brand vs. Generic analysis to this list. These are the results (top 3 findings explained below):

Results of our Brand vs. Generic keyword analysis on three educational apps in the US Apple App Store

Our top 3 findings:

• Clearly, Duolingohas a higher presence in the US, with about 3 times more downloads coming from brand and generic keywords than its competitors.
• Rosetta Stone and Babbel are very close when it comes to performance on generic keywords. Rosetta Stone used to be ahead, but Babbel has been catching up.
• Babbel gets more downloads from its brand keywords than Rosetta Stone, suggesting a stronger brand in the US.

When looked closer into Babbel’s and Rosetta Stone’s downloads coming from brand vs. generic keywords we found very different results in distribution. We compared the number of downloads each app was getting from generic keywords, own brand keywords and other brands.

Comparing the distribution of downloads from generic, own brand and other brand keywords betweenRosetta StoneandBabbel.

As you can see, Babbel receives most downloads from its own brand keywords, however, Rosetta Stone receives more downloads from generic keywords than from its own brand!

Case Study 2: Booking.com, HotelTonight, Expedia, Kayak and Hotels.com

Here again, we took a list of thousands of keywords related to the travel industry from which we removed the keywords with a volume of 5. This resulted into a list of 900 travel-related keywords on which we applied the Brand vs. Generic analysis.

Results of our Brand vs. Generic keyword analysis on several travel apps in the US Apple App Store

Our top 3 findings:

• Expedia is by far the app with the highest performance on generic keywords.
• When it comes to branded words, Booking.com, and Hotels.com also have a strong presence. HotelTonight and Kayak lag behind.
• Although Kayak has a weaker brand, it performs very well on generic keywords compared to other apps.

We then pushed the analysis further and compared two apps: HotelTonight and Kayak.com.

Comparing the distribution of downloads from generic, own brand and other brand keywords between Kayak and HotelTonight.

This time, both apps receive most downloads from their own brand, however, the gap with the number of downloads from generic keywords is much more pronounced for HotelTonight than it is for Kayak.

We hope that these insights have inspired you and that you’re ready to perform the same analysis on your app and your competitors! Feel free to share your learnings with us!