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How Canva Is Experiencing a Boost in Visibility Thanks to LLMs

February 27th, 2026

How Canva Is Experiencing a Boost in Visibility Thanks to LLMs
Anh Nguyen

by Anh Nguyen

COO & Co-Founder at Gummicube, Inc

Canva closed out 2025 with more than $4 billion in revenue and a 20 percent increase in monthly active users. Beyond the financial milestone, the company has pointed to a powerful growth driver: rising referral traffic from large language models. As integrations with platforms like ChatGPT and Claude deepen, LLM-driven discovery is becoming a meaningful acquisition channel.

For app developers and marketers, this is a clear signal that app visibility is changing. Discovery is no longer limited to the App Store search bar. AI systems are increasingly shaping how users find, evaluate, and select apps. Canva’s momentum offers a real-world case study in what this shift looks like and why it matters for modern App Store Optimization (ASO) strategy.

LLM APP REFERRAL TRAFFIC IS BECOMING A SERIOUS ACQUISITION CHANNEL

Historically, the app discovery journey was linear. A user typed a keyword into the App Store. An algorithm ranked the results. Creative assets influenced conversion. The funnel was structured and predictable.

Today, users often begin their discovery journey in AI-powered chat environments. Instead of searching “best design app for presentations” in an app marketplace, they ask a conversational question:

  • What is the best tool for creating professional slide decks quickly?
  • Which design app has built-in AI features?
  • What app should I use to collaborate on marketing graphics?

When an LLM responds with a curated recommendation and includes Canva by name, that recommendation carries authority. The user often arrives at the app listing with intent already formed. The evaluation stage is compressed. The AI platform becomes a pre-qualification engine.

Canva has indicated that LLM referrals now account for double-digit percentages of traffic growth and are becoming one of its top final acquisition platforms. That is a fundamental shift in how visibility translates to installs.

For ASO professionals, this means optimization must extend beyond marketplace algorithms. It must account for how AI systems interpret, categorize, and recommend apps.

Canva’s AI Adoption Strengthened Its App Discoverability

Canva’s 20% increase in monthly active users is closely tied to the adoption of its AI-powered tools. From AI-assisted design generation to automated editing and workflow enhancements, the company has embedded AI deeply into its product experience.

But feature innovation alone does not guarantee visibility. The critical factor is how those features are positioned and described.

AI systems evaluate entities. They assess relationships between:

  • Brand names
  • Product categories
  • Feature sets
  • Use cases
  • Target audiences

Canva has maintained consistent positioning across its website, app store listings, and public messaging. It is clearly associated with design creation, collaboration, ease of use, and increasingly, AI-powered productivity.

That clarity strengthens machine confidence. When a user asks an LLM for a design solution with AI capabilities, Canva is an obvious choice.

THE RISE OF ENTITY-DRIVEN APP DISCOVERY

Traditional ASO has long emphasized keyword optimization. While keywords remain essential, LLM-driven discovery introduces a broader framework built around entities and semantic understanding. LLMs do not rely solely on exact-match keywords. They interpret context, they understand relationships between concepts, and they synthesize answers based on authority and relevance.

For example, if a user asks:

“What is the best AI design platform for small businesses?”

The model is not simply scanning for the phrase “AI design platform.” It is evaluating:

  • Which apps are strongly associated with AI tools
  • Which brands are recognized as design platforms
  • Which products serve small business use cases
  • Which entities carry authority in the category

If your app’s metadata and external positioning consistently reinforce those associations, your probability of being surfaced increases.

WHAT THIS MEANS FOR ASO STRATEGY

Canva’s visibility boost underscores a critical evolution in App Store Optimization. Developers must think beyond keyword density and begin optimizing for semantic inclusion.

Below are best practices that align ASO fundamentals with the AI-era of app discovery.

1. App Developers Must Maintain Consistency in Brand Positioning

AI systems build confidence through repetition. If your app describes itself as a “content creation platform” in one location and a “digital publishing tool” in another, you dilute your entity strength.

Define your primary positioning and reinforce it across:

  • App title
  • Subtitle
  • Promotional text
  • Description headings
  • Website copy
  • Press coverage

Consistency strengthens both human trust and machine recognition.

2. Optimize App Listings for Natural Language Questions

Users engage with LLMs conversationally. They do not input fragmented keywords. They ask full questions. Your app metadata should reflect that reality.

Instead of focusing exclusively on “budget tracker app,” incorporate phrasing that mirrors user queries:

  • How can I track monthly expenses automatically?
  • What app helps couples manage shared finances?
  • Is there a simple AI-powered budgeting solution?

Incorporating question-based language into descriptions increases the likelihood that AI systems will recognize your app as a direct answer.

3. Use Structured Headings in App Descriptions

Structured content improves machine readability.

Organize your app description with clear sections that answer functional questions:

  • What does this app help you accomplish?
  • How do the AI features work?
  • Who is this app best for?
  • Why is this better than alternatives?

Clear formatting enhances both user comprehension and AI extraction. When LLMs pull structured explanations, your app becomes easier to recommend accurately.

4. Continue Regular Mobile App A/B Testing

Even as LLM referrals grow, performance optimization remains essential.

Mobile App A/B testing should continue to guide:

  • Creative asset refinement
  • Value proposition messaging
  • High-volume keyword prioritization
  • Conversion rate optimization

As traffic sources diversify, testing reveals which messaging resonates with users arriving from AI platforms versus traditional search.

ASO fundamentals still matter. They simply operate within a broader discovery ecosystem.

APP DISCOVERY IS EXPANDING BEYOND THE APP STORE

Canva’s revenue milestone and LLM referral growth validate a broader reality. AI platforms are becoming decision-making layers within the user journey.

Users are increasingly comfortable trusting conversational AI to shortlist tools and platforms. That trust compresses the evaluation funnel and elevates the importance of being included in AI-generated responses.

For app marketers, this means:

  • Discovery is multi-platform.
  • Entity clarity is critical.
  • Semantic optimization complements keyword strategy.
  • Consistent positioning influences machine interpretation.

Visibility is no longer confined to marketplace algorithms. It is shaped by AI systems synthesizing the digital ecosystem.

THE FUTURE OF ASO IN AN AI-DRIVEN LANDSCAPE

App Store Optimization has always evolved alongside technology shifts. The rise of LLM-driven discovery is simply the next chapter.

ASO now requires:

  • Precision in messaging
  • Alignment with natural language queries
  • Cross-channel consistency
  • Strong category authority
  • Regular experimentation and testing

Canva’s boost in visibility thanks to LLM referrals demonstrates that AI-driven discovery is not theoretical. It is measurable. It is impactful. And it is accelerating.

Developers who proactively align their metadata and positioning with how AI systems interpret information will be better positioned for sustained growth.

FINAL THOUGHTS

Canva’s $4 billion in revenue and 20 percent monthly active user growth reflect more than just product strength. They highlight the increasing role of LLMs as visibility engines and acquisition drivers.

AI platforms like ChatGPT, Google's Gemini, and Claude are shaping how users discover apps, narrowing decision cycles and elevating authoritative recommendations. For developers, this reinforces the need to treat entity clarity, semantic optimization, and consistent positioning as foundational pillars of modern ASO. Metadata and creative optimizations remain essential. But AI-driven discovery introduces a new layer that cannot be ignored.

LET’S CHAT!

If you are evaluating how AI-driven discovery is influencing your app’s performance, it may be time to revisit your positioning and metadata strategy.

Our ASO services are built to help developers strengthen entity clarity, optimize for both traditional search and LLM-driven recommendations, and implement data-backed testing frameworks that support long-term growth.

We approach ASO as an evolving discipline that demands precision, adaptability, and strategic insight. If you are ready to explore how your app can remain competitive in an AI-first ecosystem, let’s chat.

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