Google Play Keyword Ranking Algorithm

Google is always updating its algorithm. After the app is put on Google Play, what keywords will affect the google play ranking algorithm for your app? Here we discuss the keyword evaluation areas and optimization points involved in the search ranking algorithm in the app store.

Algorithm update about keyword search function and keyword correlation judgment in the search box area.

Previously, Google’s feature updates are aimed at improving relevance of apps returning for so-called "broad" searches, or non-app name searches like "horror games" or "selfie apps." Per Google's words, about 50% of play store searches are broad, and:

"Searching by topic requires not only simply indexing the application by query terms, but also understanding the topics related to the application. Machine learning methods have been applied to similar problems, but success largely depends on the number of training examples To learn about apps.

For some popular topics like "social networks", we have many tagged apps for learning, but most topics have only a few examples, and our challenge is from a limited number of The training examples learn and expand to millions of applications covering thousands of topics, forcing us to adapt to our machine learning technology."

Google’s article explains that when they first tried to build machine learning algorithms that could provide good results for these wide-ranging searches, they used deep neural networks, but the results were not as good as the new application discovery they wanted, but they were produced over time. The same application responds to widespread searches, not new applications.

Google’s new attempt is to make this process more like the way humans learn and understand language and word associations. This new attempt utilizes the Skip-gram model, which can predict related words given the input words.

Google's new model creates a so-called "classifier" for any given word to create a list of many classifier relations, and finally create {app, topic} associations. In the latest update, Google will also rely on non-machine learning efforts by allowing people to evaluate the quality of the results.

Skip parsing

According to the Tensor Flow document, on the left are some example relationships between words, which are determined by Skip-Gram analysis.

Google’s goal is to create an algorithm that can generate a reasonable relationship between keywords (such as {photo} and {share}), and by studying application metadata and user interactions, to generate the best results for a given keyword. Related applications, even if the application returned is brand new. In addition, Google's algorithm must be able to learn new words (for example, selfies, flick, etc.), and be able to establish new associations with these words and other words and applications.

It seems that despite some premature generalization issues, Google is still working to improve the wide range of search results for Play Store users. It is interesting how these changes play a role in the keyword ranking (and download) of all Android apps.

Bottom line: Since 50% of Play Store searches are classified as "broad" (e.g., selfie apps) rather than app names, Google uses machine learning plus manual input to improve the app keyword ranking algorithm when users use broad searches. The ability to return to related applications is used to discover new applications. This may mean that the Play Store keyword ranking is about to change significantly.

Google Play organic search keyword insights

Next, let’s explore more information about organic app marketing, Google Play search keywords and share our relevant insights.

1. Google Play organic keyword data analysis

When optimizing based on data, it is more prudent to first evaluate the data as another key point in the overall plan, and to understand the normal major decisions before making natural search keywords.

💡 Key Challenges:

• Hidden keyword data: A large portion of keyword traffic appears under “Other,” making it difficult to analyze long-tail keyword contributions.
• No country-specific breakdowns: Regional influences on keyword rankings remain unclear.

🔹 Updated Strategy: Use third-party ASO tools to extract hidden keyword trends from the “Other” category. Cross-reference data with Google Play Store listing experiments to refine regional keyword optimizations.

➡ Expert Tip: Track weekly keyword fluctuations and double down on keywords that consistently drive impressions and downloads.

2. The keyword grouping of apps and the location of related apps are of great significance in Google Play ASO

Google Play organizes apps in clusters, meaning that ranking success depends on your app’s association with related apps rather than just standalone keyword strength.

💡 Best Practices:

• Ensure your app’s description and metadata align with top-ranking competitors in your niche.
• Optimize your title and short description with semantic keywords closely related to high-ranking apps.

🔹 Updated Insight: Google has improved its contextual app grouping, meaning apps with similar user engagement patterns are now more likely to be suggested together.

➡ Optimization Tip: Monitor your app’s placement in “Similar Apps” and adjust positioning strategy accordingly.

3. Google Play random keyword insights should be evaluated next to keyword ranking

Ranking high is only half the battle — conversion rates (CVR) are equally pivotal.

Recent findings reveal that keyword rankings fluctuate based on engagement signals:

• If your app ranks high but has low conversions, Google may penalize it by lowering its ranking.
• If an app ranks lower but has strong user engagement, its rankings may improve over time.

🔹 Updated ASO Tip: Track both keyword rankings & conversion rates simultaneously to make informed decisions. A keyword with fewer installs may still be valuable if it shows high conversion potential.

👉 How To Enlarge Your App Store Searching Traffic With Keyword Research & Keyword Optimization?

4. For large applications, installing contribution margin tilt in Android is more than exploring on iOS

The last most interesting finding is that by randomly sampling apps, we found that the Play Store (organic) exploring the source of large apps usually generates higher install traffic than searches. In some cases, the installs generated by the exploit are 100-300% higher than the installs from the Play Store (organic) search source.

This is very different from the trend of the iOS App Store. In the trend of the iOS App Store (except for the "strange Today" application function), the "App Store browse" source type provides much fewer application units than the App Store search.

💡 “Organic discovery is no longer just about search ranking. Explore-driven installs are accelerating as Google surfaces apps contextually rather than through keywords alone.” 

There are four main points:

1) Both Apple and Google are interested in controlling the discoverability of applications that have been discovered to attract user interest (ie, high download speeds, high conversion rates, but also ratings/retention rates/revenues).

Neither Apple nor Google seem to care about smaller apps (unless they want to make money from UAC or Search Ads Basic).

2) Google proved that although Apple did its best to update iOS 11 (for example, editorials, "Today" tags, split games and applications, application categories, etc.), Google is better than Apple in applications (especially large applications). (Program) has more control over the discoverability. ).

At this point, Google is also more willing to play its role in the pursuit of control. For example, the Google Play Store includes powerful keyword grouping and programmatic suggestions for applications in the application/game view, and nearly endless scrolling, while Apple truncates its application/game functions to support more user-friendly Experience, and the "-y" design style.

3) Perhaps most importantly, as the larger budget releases more browsing/browsing returns, the success of ASO continues to follow the path of "spending money to make money", which accounts for an increasing share of new downloads and searches Big.

4) The last point may involve the whole macroeconomic issue (Eric Seufert?), but one of the reasons Google sees success here may be due to its experiment in redesigning Play Store UX.

For Apple and Google, over time, as the two companies continue to optimize their control of discoverability (and their own checkbook), the percentage of downloads from the browser/browser may increase.

5. The conversion rate of Google Play Organic Explore is much higher than that of iOS App Store browsing

The fourth point is the final discovery. This is the final discovery of the fourth point. This is that the conversion rate of browsing resources from Google Play is not much lower than that of Google Play search. In fact, in some cases, we found that Explore has a higher conversion rate than search. In the case we have seen, the retention rate and ARPU also seem to be strong.

The conclusion drawn from this discovery is that it turns out that Google’s Play Store app discovery algorithm is the same as identifying Google’s original innovation: keyword search, which can identify users who need certain applications, or is close to it.

In view of this, the combined advantages of UAC paid application discovery and Google Play Store browsing discovery may eventually become the turning point for the company to confront another opponent: Facebook.

Although forcing app advertisers to use UAC is a huge sum of money for Google in many ways, they worry that Facebook has achieved great success in attracting mobile marketing budgets, but this is a pre-emptive action and it gives Google more time (And data) to train it.

When Facebook’s mobile marketing prowess takes off on another “S-curve” with its deployment, value-based similarity and event-optimized campaign positioning in the industry, the algorithm will take off become better.

Google's machine learning algorithm has the unique advantage of learning from organic discovery and paid discovery, which Facebook does not have, and by imposing UAC on advertisers, Google's algorithm learning speed has doubled and the catch-up speed has increased.

Even surpassed Facebook. In addition, by training users in the Play Store to click on relevant/suggested apps (i.e. "explore"), Google has expanded the placement of UAC to more locations in the Play Store (i.e., "explore"), thereby increasing Lock in revenue-driven behavior.

Final Takeaways & Future Trends

Key Lessons from Google Play’s Algorithm Evolution

The next phase of ASO isn’t just about keyword optimization—understanding Google’s AI-driven app discovery paradigm will differentiate top-ranking apps from the rest.

1) Search Behavior is More Context-Driven

Google Play no longer just maps keywords to app metadata—it ranks apps based on user intent & relevance.

2) Human & Machine Learning Combination is Improving Ranking Accuracy

Google uses both AI-driven algorithms & manual evaluations to refine search results.

3)  Explore-Based Recommendations Are a Growing Traffic Source

Apps need to optimize for engagement, not just keywords, to succeed in Google Play’s “Explore” ecosystem.

What’s Next?

With Play Store optimizations increasingly AI-driven, successful ASO strategies will blend traditional keyword tactics with behavioral ASO—focusing on user engagement, app clustering, and metadata relevance.

🔹 Future-proof your ASO strategy by:

• Implementing semantic keyword variations
• Monitoring Explore-generated traffic
• Enhancing user engagement metrics
• Adapting to Google’s evolving machine learning models

Ready to scale your ASO strategy? 🚀 Leverage ASOWorld’s expert insights to optimize your app ranking today!