App Store Optimisation (ASO) has long been rooted in keyword targeting and density, but the landscape is shifting, and fast. With Apple and Google continuing to evolve their algorithms through advances in natural language processing (NLP) and AI, the rules of the game are being quietly rewritten. No longer is it just about ticking off a list of high-volume keywords; app stores are now placing greater emphasis on semantic relevance, contextual understanding, and user intent.
For marketers, this means it’s time to rethink how we approach ASO, from the way we cluster keywords and shape metadata, to how we ensure our content aligns with what these evolving algorithms prioritise.
In this article, we’ll unpack the role of natural language in modern ASO, explore how strategies are changing as a result, and highlight how tools like Google’s Natural Language API (GNL) can offer valuable insight to stay ahead of the curve.
Why Natural Language is the new keyword strategy
Both Google and Apple are steadily moving away from old-school tactics like keyword density and exact-match targeting, instead leaning into more sophisticated, context-driven approaches.
Their app store algorithms are getting better at understanding what content is really about, which means it’s no longer enough to cram keywords into your descriptions and hope for the best. Now, it’s about how your content reads holistically: does it align with what users are searching for? Does it speak to intent, relevance, and real-world use? This is where strategies shaped by natural language processing start to take centre stage.
From Keywords to Clusters: The power of intent-led targeting
Rather than optimising for individual keywords, we now look at keyword clustering – grouping keywords into logical themes that align with:
- Functional intent (e.g. “habit tracker,” “fitness planner”)
- Situational use (e.g. “apps for busy mornings,” “study time helper”)
- Motivational triggers (e.g. “reduce stress,” “improve focus”)
This thematic approach doesn’t just improve visibility; it improves conversion too. When users see copy that mirrors their intent, they’re more likely to click and download.
Since app stores increasingly prioritise semantic relevance and user intent over exact match keywords, clustering helps apps rank for a broader range of related keywords and improve their discoverability. How? Users search for apps with varied phrases, including long-tail keywords (for example, “guitar lessons for beginners”). Clusters help to capture these variations and allow targeting niche, less competitive terms, while still ranking for high-volume keywords, balancing traffic and ranking difficulty.
Why this matters now: The AI surge at Google
Over the past year, we’ve seen Google accelerate its machine learning efforts, from rebranding Bard to Gemini, to quietly rolling out beta features like auto-generated Play Store descriptions and built-in translation tools. While these features might help streamline internal workflows, they also raise a red flag: if everyone’s using the same tools to generate copy, we’re heading for a wall of sameness.
To illustrate this, we asked ChatGPT to create app descriptions for music and language learning apps.
The descriptions may initially appear engaging and persuasive, but when placed side by side, their similar format and language reveal a lack of originality and make them feel repetitive and indistinct.
As tempting as it may be, solely using AI to craft your app’s metadata can harm your ASO. AI-generated text often provides overly broad descriptions that can miss nuanced phrasing that resonates with specific audiences. It may also lack the emotional and persuasive tone, making your descriptions sound too robotic to connect with your users. And if your AI descriptions lead to lower click-through or install rates due to poor salience, your app’s visibility will suffer.
That’s where bespoke, intent-rich metadata comes into play. When you take the time to craft content that’s well-written, unique, user-centric, culturally aware and rooted in real search behaviour, you not only differentiate your app but also give Google stronger signals that your product is both relevant and high quality.
How to validate your strategy with Google’s Natural Language API
Google’s Natural Language API (GNL) is one of the most underrated tools available to app marketers. But it’s fast becoming a key weapon in navigating the shift toward semantic search. At its core, the API allows you to analyse your metadata through Google’s lens, helping you understand how your copy is interpreted by machine learning models.
Whether you’re optimising your Play Store listing or stress-testing your content strategy, GNL offers a window into how well your messaging aligns with Google’s expectations around relevance, tone and category fit.
Although Apple’s App Store algorithm does not use the app description for ranking, a well-written description influences user conversion rates. The GNL API’s sentiment and syntax analysis can help you craft App Store descriptions that evoke positive emotions or align with user language patterns, as well as support your competitor analysis.
Key GNL API features to leverage:
Entity recognition & salience:
This feature identifies key themes, objects or concepts in your app descriptions, such as “yoga”, “symptoms tracker”, etc. and assigns a salience score for each (0-1). The higher your salience score, the more relevant your description is for your target theme.
To leverage this feature for your ASO, run your app description through your API and focus on high-salience terms as your primary keywords in the most prominent areas like app title, subtitle or short description.
You can also analyse competitors’ descriptions to compare if similar terms achieve higher salience scores than yours. If that’s the case, adjust your content to better compete for those terms.
Category confidence Scores:
The API classifies your metadata into predefined categories, such as Health & Fitness or Productivity, with a confidence score (0-1) indicating how strongly the text aligns with each category. Quite simply, the higher the score, the better the category fit.
There are a few ways you can maximise this feature for your ASO.
If the score is below 0.7, revise your description to include more category-specific keywords and strengthen alignment. Sometimes, the API can assign a high relevance score to a secondary category that you haven’t previously considered. You can then incorporate the related keywords to capture a broader audience.
As with the entity recognition and salience feature, you can use the category confidence scores to monitor your competitors. It can help you to better analyse their descriptions and incorporate similar terms to compete for category rankings.
Sentiment Analysis
Sentiment Analysis evaluates the tone of your description, assigning the sentiment score (-1 to 1) for overall positivity/negativity of your copy and a magnitude score for emotional intensity. This is particularly useful for apps within emotional or wellness-driven categories (mental health, fitness, education, etc.).
To make the most of this feature, aim for a positive sentiment score (0.5 and above). An uplifting, inviting and engaging tone of your descriptions will better resonate with your users and encourage downloads. However, don’t overdo it and ensure that the language you use sounds authentic. If the API flags an excessively high sentiment score with low magnitude, revise your copy to include specific benefits for credibility and balance.
Real-world impact: What we’ve seen
We’ve used NLP strategies and GNL analysis with numerous clients over the past year. In one case, we saw a 500% increase in organic Explore installs after refining their metadata to increase confidence scores across multiple relevant categories.
Another example is a client who maintained keyword ranking stability (and even gains) across multiple algorithm updates, a result of aligning their copy with how Google categorises and understands relevance.
How to make NLP-driven ASO work for you
Use clear, natural language throughout your long description
Your descriptions should be crafted as if you were explaining the app’s value to a friend. Algorithms prioritise readable and conversational texts that mirror how users speak, so avoid jargon, overly technical vocabulary or awkward phrases that feel unnatural.
Avoid keyword stuffing
Piling keywords into your description will disrupt readability and make your app seem spammy to both users and algorithms. For example, repeating “yoga tutorials” 10 times in one paragraph won’t boost your ranking if the context is unclear.
Write for humans first, but validate for algorithms
The audience you’re trying to attract is human, so your description should be engaging, persuasive and easy to read. But at the same time, it must be optimised for algorithms to help you achieve maximum visibility. To strike a balance between the two, first craft the copy, then refine it using GNL and ASO best practices.
Focus on user pain points, features, and benefits in a way that aligns with category expectations
Crafting your descriptions with a:
- pain point (“Struggling to find time for language lessons”?)
- feature (“Our 5min lessons fit any schedule”)
- benefit (”Speak confidently in just a few weeks”)
structure in mind will help potential users resonate with your app and see its value from the get-go. To ensure your language aligns with the category you’re aiming for, research top apps in your category to understand tone and expectations.
Final thoughts
As ASO evolves, adapting your strategy to reflect how platforms read content is crucial. Natural language isn’t just a trend, it’s a fundamental shift in how apps are discovered and evaluated.
By incorporating tools like Google’s Natural Language API and embracing intent-led keyword clustering, app marketers can build stronger visibility, better conversion, and long-term resilience in an increasingly AI-driven ecosystem.
Want help applying NLP to your ASO strategy?
Reach out to our team at Yodel Mobile and let’s chat about how we can optimise your growth strategy from every angle.
