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2026-04-14 20:09:18

A user asks Gemini: “Find me a task chair under $400 with lumbar support and free shipping. Order the best one.”

The AI doesn’t open a new tab. It doesn’t ask the user to click anything. Instead, it queries product databases, cross-references reviews, checks real-time inventory, compares shipping policies, and initiates a checkout — all without a human touching a single page.

These are all things the user would have done themselves, but now in a fraction of the time, with as much effort as it took to write the initial prompt.

Okay, we might not be quite at the stage where everyone is letting AI agents make all their purchases for them. But it’s no longer an unrealistic future.

What made that possible isn’t the AI models themselves. It’s the infrastructure we’re seeing become an increasingly important part of how modern websites are built. This infrastructure consists of a stack of protocols that tells AI agents how to find each retailer’s site, understand their catalog, verify their claims, and take action.

These protocols define how AI agents interact with your brand. And most SEOs have no idea they exist.

By the end of this article, you’ll understand what each protocol does, how they differ from one another, and why you need to pay attention to what’s going on underneath the hood of AI search if you want to stay visible going forward.

Why Protocols Matter for SEOs

Protocols determine whether an AI agent can interact with your brand programmatically, or whether it has to guess. Brands that can speak the agent’s language are more likely to not just be surfaced, but also recommended and, ultimately, interacted with to make purchases.

Think of how robots.txt and XML sitemaps became table stakes for search crawlers. Agentic protocols are shaping up to be that for AI agents.

Put simply: if you want agents to be able to take action on your site — whether that’s making a purchase, booking a table, or completing a form — you need to understand these protocols.

Note: We’re not suggesting that without these protocols AI agents and users will never access your site or buy from them. Agentic commerce is still pretty new, and even the protocols themselves are still evolving. But we believe that agents will increasingly act on behalf of users, and that the easier you make it for them to do that on your website, the better positioned you’ll be as agentic commerce becomes the norm.

The Protocol Stack: A Quick Map

These protocols aren’t competing standards fighting for dominance. They operate at different layers of the same stack, and most are designed to work together.

Here’s a quick breakdown of what these protocols do:

Note: As with everything AI, the agentic protocols we’ll give more details on below are constantly evolving. This means some platforms are yet to adopt some of the protocols, and the specifics of each protocol could also change over time.

MCP: Model Context Protocol

MCP is the universal connector between AI agents and external tools, data sources, and APIs.

How It Works

Before MCP, every AI tool needed a custom integration for every data source it wanted to access. If you wanted a chatbot to pull live pricing from your database and cross-reference it with your CMS, someone had to build a bespoke connection between those systems. Then rebuild it whenever either one changed.

MCP standardizes that connection. Think of it as USB-C for AI: one protocol that lets any agent plug into any tool, database, or website that supports it.

An agent using MCP can pull live pricing data, check inventory, read structured content from a site, or execute a workflow, all through the same interface.

The website or tool publishes an MCP server, and the agent connects to it. There’s much less need for custom integration work on either side.

Who’s Behind It

MCP was launched by Anthropic in November 2024. It has since been adopted by OpenAI, Google, and Microsoft. MCP is now governed by an open-source community under the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation.

As of early 2026, there are more than 10K MCP servers out there, making it the de facto standard for agent-to-tool connectivity.

What It Means for Your Brand

Structured data, clean APIs, and accessible HTML have always been good technical SEO. Now they’re also agent compatibility requirements. Brands with MCP-compatible data give agents something to work with. Brands without it force agents to scrape pages and infer meaning, which creates friction and can affect whether they recommend you.

Learn more about MCP here.

A2A: Agent-to-Agent Protocol

A2A is the standard that lets AI agents from different vendors communicate, delegate tasks, and hand off work to one another.

How It Works

MCP lets an agent talk to tools. A2A lets agents talk to each other.

When a task is complex enough to need multiple specialist agents — like one for research, one for comparison, and one for completing a transaction — A2A is the protocol that coordinates them.

Each A2A-compliant agent publishes an “Agent Card” at a standardized URL (that looks like “/.well-known/agent-card.json”). This card advertises what the agent can do, what inputs it accepts, and how to authenticate with it. Other agents discover these cards and route tasks accordingly.

The result: agents from entirely different companies, built on different frameworks, running on different servers, can collaborate on a single user request. No custom-built connections required.

Who’s Behind It

Google launched A2A in April 2025 with 50+ technology partners, including Salesforce, PayPal, SAP, Workday, and ServiceNow. The Linux Foundation now maintains it under the Apache 2.0 license.

What It Means for Your Brand

As multi-agent workflows become more common, agents may evaluate your brand across multiple checkpoints before a human sees the result.

That chain might look something like this:

• A research agent surfaces your product from a broad category query
• An evaluation agent reads your reviews and checks the sentiment
• A pricing agent verifies your costs against third-party sources
• A trust agent cross-references your claims for consistency

A2A orchestrates that entire chain. If your data is inconsistent across sources, like if your pricing page says one thing and your G2 profile says another, the AI agent might filter your brand out as a contender. All before the user even sees you as an option.

Learn more about A2A here.

NLWeb: Natural Language Web

NLWeb is Microsoft’s open protocol that turns any website into a natural language interface, queryable by both humans and AI agents.

How It Works

Right now, when an AI agent visits your website, it might have to make a lot of guesses. It scrapes your HTML, infers meaning from your content, and relies on your page being structured properly to be able to parse it effectively. There’s a lot of room for error.

Once a site implements NLWeb, any agent can send a natural language query to a standard “/ask” endpoint and receive a structured JSON response. Your site then answers the agent’s question directly, rather than the agent interpreting your HTML.

Every NLWeb instance is also an MCP server. A site implementing NLWeb automatically becomes discoverable within the broader MCP agent ecosystem without any additional configuration.

Who’s Behind It

NLWeb was created by R.V. Guha, the same person behind RSS, RDF, and Schema.org. (That’s no coincidence.) NLWeb deliberately builds on web standards that already exist, which means a lot of websites are close to NLWeb-ready right now.

Microsoft announced NLWeb at Build 2025 in May 2025. It’s open-source on GitHub. Early adopters include TripAdvisor, Shopify, Eventbrite, O’Reilly Media, and Hearst.

What It Means for Your Brand

For SEOs, NLWeb is a natural extension of work you may already be doing.

Schema markup, clean RSS feeds, and well-structured content are the foundation NLWeb builds on. Sites that have invested in structured data have a head start. Sites that haven’t are harder for agents to work with, but they can easily catch back up by implementing schema markup now.

Structured data already helps search engines, and it can make it easier for agents to understand and interact with your site too. That increases the value of technical SEO work you may have been putting off.

Learn more about NLWeb here.

WebMCP

WebMCP is a proposed W3C standard that lets websites declare their capabilities directly to AI agents through the browser.

How It Works

NLWeb makes your content queryable. WebMCP goes one step further: it lets websites declare what actions they support. These actions could include “add to cart,” “book a demo,” “check availability,” and “start a trial.”

These capabilities are declared in a structured, machine-readable format. Instead of an agent scraping your UI and guessing how your checkout works, WebMCP gives it an explicit map, straight from the source (you).

Who’s Behind It

Google and Microsoft proposed WebMCP, and the W3C Community Group is currently incubating it. Chrome’s early preview shipped in February 2026, with broader browser support expected by mid-to-late 2026.

What It Means for Your Brand

WebMCP is the clearest preview of where agent-website interaction is heading.

Imagine you have two brands with similar products, similar pricing, and similar reviews. The one whose site declares clear, structured capabilities is easier for an agent to act on. The other requires guesswork.

Agents are likely to take the path of least friction, and WebMCP helps you reduce friction to a minimum.

Learn more about WebMCP here.

ACP: Agentic Commerce Protocol

ACP is OpenAI and Stripe’s open standard for enabling AI agents to initiate purchases.

How It Works

ACP focuses specifically on the checkout moment. It creates a standardized way for an AI agent to complete a purchase on a merchant’s behalf, handling payment credentials, authorization, and security through the protocol itself.

Before ACP, an agent that wanted to complete a purchase had to navigate each merchant’s unique checkout flow. A different form, a different payment process, and a different confirmation step for every retailer. ACP standardizes this process.

Merchants integrate with ACP through their commerce platform, and once live, checkout becomes agent-executable. The user doesn’t have to do anything except approve.

ACP originally powered ChatGPT’s instant checkout functionality, but that has since been removed by OpenAI in favor of dedicated merchant apps. ACP may still power product discovery within ChatGPT, and may be used within these apps, but things are evolving fast.

Who’s Behind It

OpenAI and Stripe launched ACP in September 2025. It’s open-sourced under Apache 2.0, with platform support still expanding.

What It Means for Your Brand

If an agent has shortlisted your product and the user tells it to go ahead and pay, ACP is what allows the agent to complete the transaction. If your brand isn’t integrated with this workflow, you risk the AI agent getting stuck or being unable to complete that purchase.

The agent can recommend you, but it can’t buy from you. That gap will matter more as agentic commerce becomes the norm.

Learn more about ACP here.

UCP: Universal Commerce Protocol

UCP is Google and Shopify’s open standard for the full agentic commerce journey, from product discovery through checkout and post-purchase.

How It Works

ACP focuses on the checkout moment, while UCP covers the entire shopping lifecycle.

An agent using UCP can discover a merchant’s capabilities, understand what products are available, check real-time inventory, initiate a checkout with the appropriate payment method, and manage post-purchase events like order tracking and returns. All through a single protocol.

UCP is built to work alongside MCP, A2A, and AP2 (Agent Payments Protocol), meaning it plugs into the broader agent infrastructure rather than replacing it.

Merchants publish a machine-readable capability profile. Agents then discover it, negotiate which capabilities both sides support, and proceed.

Who’s Behind It

Google and Shopify co-developed UCP, with Google CEO Sundar Pichai announcing it at NRF 2026. More than 20 launch partners signed on, including Target, Walmart, Wayfair, Etsy, Mastercard, Visa, and Stripe.

What It Means for Your Brand

When a user asks Google AI Mode to find and buy something, UCP determines whether your brand is in the conversation, and whether the agent can actually complete the transaction.

The machine-readability of your product data, the consistency of your pricing across sources, the clarity of your inventory signals: all of it feeds directly into whether an agent can successfully transact with you.

Learn more about UCP here.

ACP vs. UCP: The Key Difference

ACP and UCP are often confused, and they do share some similarities, but here’s where they differ:

ACP and UCP are complementary, not competing. A brand may eventually support both — one for ChatGPT’s ecosystem, one for Google’s.

For now, the practical question is: which platforms matter most to your customers, and where does your commerce infrastructure make integration easiest? Choose the protocol that aligns with your answer, or use both.

Example of Agentic Search Protocols in Action

These protocols don’t operate in isolation. Here’s what they might look like working together (note that this isn’t necessarily exactly what’s going on at each stage, and is just for illustrative purposes):

Scenario: A user asks Gemini: “Find me a comfortable task chair under $400 with lumbar support and free shipping. Order the best option.”

Step 1: MCP Activates

The agent uses MCP to connect to external tools: product databases, review platforms, retailer inventory feeds. It can query live data rather than relying on cached or trained knowledge.

Step 2: A2A Coordinates

The agent then coordinates with specialist agents published by brands and review platforms via A2A. One evaluates ergonomics reviews. One checks pricing consistency across sources. One verifies free shipping claims against each retailer’s actual policy page.

Step 3: NLWeb Answers Queries Directly

The agents query each retailer’s site. Brands with NLWeb implemented respond to the agent’s /ask query with structured data. This includes things like accurate inventory, real-time pricing, and product attributes. Brands without it force the agent to scrape and infer, slowing it down and potentially leading to them being skipped altogether.

Step 4: WebMCP Declares Available Actions

The “winning” retailer’s site has declared its checkout capabilities via WebMCP. The agent knows exactly what actions are available and how to initiate them without any guesswork.

Step 5: UCP Completes the Transaction

The purchase is executed via UCP, entirely within Google’s AI experience. The merchant’s backend communicates through the standardized API. The user gets an order confirmation, and they never visited a single product page.

Obviously this is the fully agentic scenario. In reality, not every purchase is going to be left entirely to an AI agent.

But even when a human wants to evaluate options before clicking buy, making it as easy as possible for the agent to make recommendations is still good practice. That’s why these protocols are worth paying attention to.

What SEOs Should Do Now

Understanding the protocol layer is step one. Here’s where to focus next:

1. Prioritize Machine-Readable Content Over Volume

Before adding more pages, make sure your existing pages can be parsed cleanly by an agent. That means:

• Having your pricing in plain text, not locked behind JavaScript drop-downs
• Using feature lists that don’t require interaction to reveal
• Including FAQ content that renders server-side
• Using schema markup on product and organization pages

An agent that can’t read your page can’t recommend or buy your products.

2. Audit Your Structured Data

NLWeb builds on Schema.org, RSS, and structured content that sites already publish. If you’ve invested in schema markup, you have a head start on NLWeb compatibility.

If you haven’t, this is now a double reason to prioritize it: it improves your search visibility and makes your site more easily queryable by agents.

3. Check Your Consistency Across Sources

Agents verify claims by cross-referencing your site, review platforms, and third-party content. If your pricing page says one thing and your Capterra profile says another, agents can flag the discrepancy and lose confidence in your brand, making the recommendation or purchase less likely.

Audit for cross-source consistency the same way you’d audit NAP consistency in local SEO. It’s the same underlying principle, just for a different kind of crawler.

4. Get on the ACP and UCP Waitlists Now

These protocols are in active rollout. Early adopters benefit from lower competition in agent-mediated commerce while the rest of the ecosystem catches up. Join Stripe’s waitlist for ACP access. And join Google’s UCP waitlist too.

For other protocols like MCP, talk to your dev team about making sure your site supports them.

5. Monitor Your AI Footprint as a Regular Practice

Search your brand in ChatGPT, Perplexity, and Google AI Mode. Are agents describing your product accurately? Is your pricing consistent with what they’re surfacing? Are competitors appearing where you aren’t?

This is the new version of checking your SERP presence, and it needs to become a recurring part of your workflow, not a one-time audit.

Understand how your brand is appearing in AI search right now with Semrush’s AI Visibility Toolkit. It shows you where you’re showing up, where you’re behind your rivals, and exactly what AI tools are saying about your brand.

What’s Next for Agentic Search Protocols?

The protocols we’ve discussed here are already live, but they’re still evolving.

WebMCP is still in early preview. ACP and UCP are mid-rollout. New protocols — for agent payments, agent identity, agent-to-user interaction — are still being drafted and debated.

But the SEOs that understand and implement these protocols correctly are the ones most likely to see success.

Find out where your brand stands right now with our free AI brand visibility checker.

The post The 6 Agentic AI Protocols Every SEO Needs to Know appeared first on Backlinko.

2026-03-31 08:00:00

The marketing tech stack is always evolving. Marketing automation software enables improved efficiency with various features, from customer segmentation to campaign management.

What’s the marketing automation industry market size? What are the adoption rates of marketing automation, and what benefits does it bring to businesses? Continue reading as we’ll cover answers to these questions with these recent marketing automation statistics.

Here’s what you’ll find on this page:

• Marketing Automation Industry Revenue
• Leading Players in the Marketing Automation Software Industry
• Marketing Automation Budget Changes
• Top Channels Where Marketing Automation is Used
• Top Benefits of Marketing Automation
• Marketing Automation and Customer Data Platform Integration

Marketing Automation Industry Statistics

In 2026, marketing automation is steadily growing, with spending reaching billions every year.

This section presents key statistics to show market automation revenues, top players in the industry, and expected budget changes on automation among marketers.

• Between 2026 and 2032, the worldwide marketing automation industry revenue is forecasted to grow by 62.4% from $8.44 billion to $21.7 billion – Statista

• Revenue of marketing automation solution vendors is forecast to reach $6.6 billion in 2026 (up from $2.9 billion in 2020) – Frost & Sullivan
• Around 68% of surveyed marketers expect an increase in their budget for marketing automation for the upcoming year – Ascend2

• HubSpot dominates the marketing automation software market, holding a market share of 29.58%. Other commonly used marketing automation tools include RD Station (9.25%), Welcome (7.38%), All In Marketing Cloud (6.87%), and Cheetah Digital (6.86%) – Datanyze

• As of March 2026,  at least 454 companies provide marketing automation software solutions – G2

Marketing Automation Usage and Performance Statistics

Marketing automation software is widely used as a part of an effective marketing tech stack.

This section highlights key insights into current adoption and planned usage of marketing automation and its impact on helping achieve business objectives.

• Email (58%), social media management (49%), and content management (33%) are the most reported areas for currently using marketing automation – Ascend2

• 29% of surveyed marketers are planning to implement marketing automation for social media management and paid ads. Another 28% claim they will be adding marketing automation to email marketing programs – Ascend2

• Optimizing overall strategy and improving data quality are top goals for improving marketing automation, according to 37% and 34% of B2B and B2C marketers surveyed, respectively – Ascend2

• Around 41% of marketers report that their customer journeys are “mostly automated” or “fully automated” – Ascend2

• 30% of surveyed marketers strongly agree with the statement that their “marketing automation platform makes it easy to build effective customer journeys” – Ascend2

• About 1 in 4 (26%) of marketers say their multi-channel marketing strategy is fully or mostly automated. Another 22% claim it’s not automated at all – Ascend2

• Pricing is considered a key factor by 53% of marketers when deciding on a marketing automation tool. Ease of use (54%) and customer service (27%) are regarded as the other top factors driving automation tool purchase – Ascend2

• Only 18% of B2B marketers state they use marketing automation that’s integrated with a customer data platform (CDP). Another 42% say they use B2B marketing automation but don’t have CDP in their current tech stack. Other 40% have both B2B marketing automation and CDP, but they aren’t integrated – Adobe

Marketing Automation Benefits Statistics

Marketing teams can greatly improve their effectiveness through the use of automation software, which offers a number of benefits, from improving customer experience to enabling better use of marketing budgets.

• Improving customer experience (43%), enabling better use of working hours (38%), and better decision making (35%) are the most commonly reported advantages of using marketing automation among surveyed marketers – Ascend2

• Around 2 in 3 (66%) surveyed marketing professionals state that their current marketing automation is “somewhat successful” in helping to achieve marketing objectives. Another 25% of respondents say it’s “very successful”. Only 9% of marketers report no success from their marketing automation efforts – Ascend2

Conclusion

We hope you enjoyed this list of marketing automation statistics.

We frequently update this list of statistics. So feel free to check this stats page later for new insights.

The post 15 Key Marketing Automation Statistics appeared first on Backlinko.

2026-03-06 06:00:50

Fintech in AI search plays by much stricter rules.

Because it’s a Your Money or Your Life category, products must clear higher verification thresholds before AI mentions you:

• Is your product legitimate?
• Are your fees and protections explicit?
• Do other trusted sources back up your claims?

To find these answers, AI draws from your website and the wider web — including sources you don’t control.

That risks misrepresentation of your brand.

What matters, then, is whether those sources tell an accurate story.

In this article, I’ll explain how to influence that narrative.

Because the real goal is for your fintech brand to show up in AI search AND be represented accurately.

3 Types of AI Visibility in Fintech

Your fintech brand can appear in AI search in three ways.

Your goal is to show up in all three.

• Mentioned when AI explains topics in your category
• Cited and linked within the answers
• Recommended as part of a shortlist of products

Brand Mentions

Brand mentions are when AI systems include your name in an answer.

They’re great for brand awareness.

These references put your name in front of buyers even when they’re not seeking you out.

For example, I asked ChatGPT:

“Are buy now, pay later providers ideal for my business?”

It mentioned several BNPL platforms in its response.

This suggests the AI recognizes those brands as part of the category and relevant in the space.

You’ll often see mentions as:

• Lists embedded inside explanations: “Popular BNPL providers include X, Y, Z…”
• Examples supporting a point: “Some neobanks, like X and Y, offer…”
• Context for user stories: “Many users switch from traditional banks to apps like X…”

A mention isn’t a recommendation, but it still matters.

Mentions often appear in non-brand queries. That’s when users begin exploring their options.

And if they see your brand mentioned often, it builds familiarity.

(Also known as the mere exposure effect.)

So, by the time a user reaches the decision stage, they’re more likely to recognize your name.

But sometimes that isn’t enough.

That’s why having a positive brand sentiment is vital.

The way AI mentions your brand can shape buyer perception.

If it often frames your product as “known for strong security,” that idea sticks.

But if the AI always pairs your name with warnings like “high fees” or “frequent outages,” it can raise doubts.

Citations

Citations are when the AI uses your pages to support its answer.

They’re valuable for boosting credibility and consumer trust.

When AI uses your content as a source, there’s an implied endorsement. It references you because you’re trustworthy.

And when you’re consistently cited, your brand becomes associated with expertise in the topic.

Citations may appear differently across platforms and prompts.

Sometimes they appear as footnotes and/or as inline links.

You might also see a sidebar or expandable panel with grouped sources.

Other times, citations appear as thumbnails somewhere in the AI’s response.

Regardless of format, the principle is the same:

When the AI cites your documentation, it signals that your content is being treated as a reliable source.

It’s pulling information from your pages to build its answer.

And this allows you to influence how the AI explains your product.

For example, I asked ChatGPT:

“What reporting and analytics does Klarna offer brands after implementation?”

Many of the citations came from Klarna’s documentation.

This implies that Klarna has some level of influence over the AI’s answer.

There’s a caveat with citations, however.

LLMs might link to your site, but that doesn’t always mean more traffic.

Citations are less visible than brand mentions or recommendations.

I rarely click them myself.

If I need more detail, I’ll usually continue the conversation within the platform. Or switch to Google search.

That’s likely true for many users.

Still, citations signal that AI systems trust your documentation.

And that trust enables product recommendations, which we’ll cover next.

Further reading: E-E-A-T in the AI Era: Complete Guide + Free Audit

Product Recommendations

Product recommendations are when AI includes your brand or product in a shortlist.

They’re the most impactful type of AI visibility because they influence which brands users consider.

And ultimately, which product they choose.

Here’s what recommendations look like in ChatGPT and Google AI Mode.

I asked:

“Which BNPL platform is good for mid-size ecommerce brands?”

Both listed Klarna as one of the top options.

This places Klarna front and center as buyers narrow their options.

Showing up in high-intent queries is vital for recommendations.

These are prompts that include “top,” “best,” “compare,” or “alternative.”

Such as:

• What are good alternatives to X for a small business
• What are the best budgeting apps for freelancers
• List the best neobanks with high-yield savings

But showing up in these queries isn’t automatic.

AI systems use specific signals to decide which brands to recommend.

How LLMs Choose Which Fintech Brands to Feature

AI acts as a filter between buyers and brands.

So how do these systems decide which brands and products to recommend?

From what we can observe, comes down to two signals: consensus and consistency.

Consensus

Consensus is when multiple reputable sources mention your brand and product.

AI surfaces brands that have this kind of social proof — it suggests that you’re real, trustworthy, and worth recommending.

The stronger the consensus, the more confident AI is in featuring you.

But this cuts both ways.

If sources consistently highlight negatives, AI may repeat those warnings instead.

In fintech, AI systems likely assess consensus from several sources, including:

• Partner-bank and infrastructure disclosures
• Regulatory databases
• Personal finance publishers
• Finance communities and review platforms
• Partner sites
• Technical and investor communities

So, a big part of AI optimization is showing up in the sources LLMs use to form consensus.

The easiest way to identify those sources is to run brand-related prompts.

Example: “Best banks for international transfers.”

Then, check which sites appear in the citations.

Those are the sources the AI model trusts.

When these sites and reviews talk about your brand, it increases your chances of being mentioned by AI.

Further reading: Read our search everywhere optimization guide for tips on building a positive brand reputation across platforms.

Consistency

It’s not enough for your brand to be mentioned everywhere. The sources also need to agree on the facts they’re sharing about you.

That means the core details of your product align all over the internet, including your:

• Category
• Pricing and fees
• Product features
• Protections

For example, I asked ChatGPT and Google AI Mode for “best budgeting apps.”

Both recommended YNAB (You Need A Budget).

That’s no surprise.

YNAB appears in dozens of reputable sources, including Money, CNBC, NerdWallet, and Wirecutter.

It’s also frequently mentioned in finance communities, such as myFICO Forum.

These sources also highlight specific use cases: college students, goal-setting, and overall budgeting.

These consistency signals help AI confidently recommend YNAB for those exact scenarios.

Building consistency across platforms comes down to good ole PR and reputation management.

Ensure your key details align across your site and third-party coverage.

Working with publishers and affiliates will help you shape how your brand is described.

Ultimately, consistency starts with content: what you publish and what others publish about you.

3 Types of Content That Dominate Fintech in AI Search

LLMs will reference any public content they can access.

In fintech, three types carry the most weight.

1. Owned Content

Owned content is anything you publish and control on your own properties.

This includes your website, documentation, and any branded platforms.

AI analyzes these places for your version of the facts.

That’s why content like “What does this product do?” or “How does it work?” is so essential.

For example, I asked ChatGPT:

“Compare ATM withdrawal limits, card spending caps, and international FX fees for Wise, Revolut, and Monzo.”

Its answer cited many of the three brands’ pricing and product pages to build the comparison.

This indicates the AI uses these pages to answer this query.

For you, this means your website plays a big role in what AI says about your product.

Treat your site as both a marketing and educational channel.

Publish the product details that matter to buyers.

Look at your sales conversations, support tickets, and comparison research to identify questions, concerns, and pain points.

For example, Intuit’s TurboTax landing page includes extensive product details.

It covers everything from security and guarantees to key tax filing information.

This helps the AI (and users) understand what the product includes, how it works, and who it’s for.

2. Earned Media and Reviews

Earned media and reviews are third-party perspectives on your product.

This includes everything from editorial coverage to user feedback.

LLMs use these sources to fact-check your claims. It’s also how they understand what it’s like to use your product.

In fintech, third-party sources often include:

• Editorial guides and roundups by established finance sites such as Kiplinger and MarketWatch
• Affiliate and review platforms, including sites like the Better Business Bureau (BBB)
• Community discussions on platforms such as Quora and finance forums like MoneySavingExpert

For example, I asked ChatGPT:

“What reporting and analytics does Klarna offer brands after implementation?”

The citations included Klarna’s own documentation. Plus, sites such as print-on-demand platform Gelato, Forbes, and G2.

That mix is worth noting.

It shows the AI isn’t taking Klarna’s claims at face value.

It’s cross-checking them against third-party evaluations.

The takeaway here is to treat reputable third-party coverage as a core growth channel.

One proven strategy: publish original research that journalists can cite.

Take KPMG’s Pulse of Fintech H1 2025 Report, for example.

Each edition generates media coverage across major sites like Bloomberg and Trinetix.

This works because reporters are constantly hunting for newsworthy statistics.

Other things you can do to increase earned mentions include:

• Fill out or update third-party listings you control, like app store profiles
• Co-author articles to earn mentions in trusted sources
• Synchronize PR, product, legal, and marketing so your brand story stays unified everywhere

Further reading: LLM Seeding: A New SEO Strategy to Get Mentioned by LLMs

3. Official Records

Official records are documents that confirm your legal authorization to operate.

LLMs treat them as proof and confirmation of compliance and regulatory standing.

The types of official records LLMs cite include:

• Regulatory registries and licenses
• Regulatory disclosures and notices
• Partner bank disclosures
• Corporate records

These sources allow the AI to answer questions on legitimacy and protection.

For example, I asked Perplexity:

“Is Wise licensed to operate in the U.S., and what protections apply to Wise balances?”

The citations included:

• A PDF consent order from six state financial regulators
• Wise’s National Trust application filed with the OCC
• The California DFPI’s regulated-entity page for Wise US, Inc.

Along with Wise’s documentation, these give AI enough evidence to answer confidently.

They confirmed that Wise:

• Operates in the U.S.
• Under specific entities
• And with the appropriate approvals and protections

In fintech AI search, this kind of regulatory confirmation is a strong trust signal.

It tells AI systems that your product is legitimate and safe to mention.

This creates a real opportunity for you.

AI systems can only cite what they can find, parse, and verify.

Your job is to make your regulatory standing explicit, structured, and easy to retrieve.

Start by naming your partner banks, custodians, and key infrastructure providers on your site.

And keep those details up to date across your site.

Publish key pages that AI systems can pull from, including:

• Regulatory and licensing: Clearly list your licenses, registration numbers, regulatory bodies, and jurisdictions where you operate
• Protection: Explain in plain language how funds are safeguarded, what insurance applies, and which entities custody assets

Link to these pages from your footer and trust pages so AI bots can easily find them.

How Fintech Brands Can Improve AI Search Visibility and Accuracy

54% of Americans now turn to ChatGPT for financial research, according to a Motley Fool Money study.

That means buyers often get the “AI version” of your brand before they see your website.

That’s actually good news.

A Microsoft study found that AI traffic converts at 3x the rate of other channels. This includes search, direct, and social media.

The catch? This only works in your favor if the model accurately describes your product.

Here’s how to help it do that.

Provide Proof That Your Brand Is Real and Trustworthy

LLMs need proof they can validate before they include you in answers.

So your trust details need to be public and clear across your owned platforms.

One effective way to do this is with a dedicated section on your site.

This can serve as your primary source of truth.

Many fintech brands, like SoFi, do this with a “Trust & Security Center.”

But a well-structured “Help Center” like Venmo’s works, too:

Overall, make it easy for LLMs (and users) to find the facts that reduce perceived risk:

• Who holds the funds
• Who powers the product
• How the product works

Reiterate the same trust details in related pages and sections of your site.

Add them to your homepage, About page, and FAQ sections on service pages.

Many fintech brands also include disclosures, like Member FDIC or partner bank language, in the footer.

A keyword tool like Semrush’s Keyword Magic can help you find safety and trust concerns people have about your company.

If they’re asking these questions on Google, you can bet they’re asking them in AI tools, too.

How you format your content is crucial. Ensure it’s easy for AI to extract and cite:

• Use question-and-answer structures for common concerns
• Answer each question with a clear, direct, and quotable response
• Include facts and statistics when applicable

Finally, treat data hygiene as a required part of your process.

When a partner, protection, or operational flow changes, update the documentation immediately.

Then clean up anything outdated.

Redirect or remove old PDFs and help docs so AI only finds the current version.

Further reading: Content Writing 101: How to Create High Quality Content

Reduce Mixed Messages About Your Product Online

Contradictions undermine AI’s trust in your brand.

They break the consistency signal, making AI systems cautious about recommending you.

But inconsistencies can easily happen over time.

As your company evolves, public-facing information can become outdated.

Older pages, screenshots, or explanations remain discoverable online. But AI systems can’t always determine which version is current.

The good news is that you can fix this with a few focused actions.

First, start where you have complete control: your own site.

Ensure your core narrative, product details, and trust documentation are fully synchronized on all landing pages and trust hubs.

SoFi does this well.

Their “all-in-one” app positioning is reinforced throughout their site.

As you update your site, have marketing, product, and compliance teams work together.

This ensures consistency in promotional materials, regulatory disclosures, and product specs.

Next, make sure that affiliate and “best-of” publishers accurately describe you.

Affiliate sites and finance publishers are the most-cited sources in AI answers, according to the Semrush AI Visibility Index (December 2025).

So, it’s worth checking what these sites are currently saying about you.

(Especially on “best of” listicles, comparisons, and reviews.)

To do this, research the questions people ask when evaluating your product.

They’re usually formatted like this:

• Is [Brand X] legit
• [Brand Y] fees
• Can I trust [Brand Z]
• [Brand X] vs [Brand Y]
• Is [Brand X] safe

Google’s People Also Ask and keyword tools let you find these questions.

You can also use Semrush’s AI Visibility Toolkit to see what questions users ask LLMs about your industry.

It tells you the exact prompts they use:

Then, look at which pages and websites are often cited.

If you’re using the AI Visibility Toolkit, it will pull these for you:

Otherwise, manually search the questions you found in different generative engines.

Click each source and scan for inconsistencies.

If you find something wrong, reach out to the publisher for a correction or update.

Make it easy for them to make changes by providing clear, publish-ready facts.

Another vital step is monitoring (and participating in) online finance conversations.

Forum and social media posts have a long shelf life.

This can pose consistency problems for you as your company grows and your products change.

Reddit, for example, rarely deletes old posts.

So outdated answers can stay discoverable for years.

Reduce the impact of outdated information by:

• Replying with a simple correction, especially in threads you see cited in AI engines
• Making sure your social media accounts repeat the correct version
• Announcing updates where people discuss your category

It’s also worth being more present in communities that AI often cites.

For example, Fidelity’s subreddit often shows up in Fidelity-related questions.

If you manage or participate in spaces like this, you can influence the public record directly.

Use our brand subreddit guide for tips on setting one up and growing your visibility.

Manage Brand Perception and Sentiment

AI systems assess how other sites talk about you. That public sentiment shapes the answers users get.

For example, I asked ChatGPT: “Is PayPal safe?”

It didn’t give a definitive “yes.”

Instead, it used qualifying language like “generally considered” and “not perfect.”

It also added important caveats and security considerations.

Looking at the citations, you can see the sources that contributed to those caveats:

• Investopedia, comparing PayPal’s safety measures to credit cards
• Community discussions, such as r/privacy, where users debate PayPal’s risk profile
• Editorial sites and even some competitors like Wise outlining protections and limitations

This means:

How other sites describe you affects how AI describes you.

That makes sentiment tracking vital.

Set up regular AI search visibility audits for your brand. You can do this manually by monitoring different AI platforms.

Start with the top two most used generative AI tools:

ChatGPT and Google AI Overviews.

Each month, run a consistent set of high-intent prompts related to your brand and category.

Note the sentiment, including any differences between AI models.

Look for patterns to assess whether your sentiment is positive, neutral, or negative:

• Regular positive framing
• Repeated warnings
• Recurring pros and cons

Yes, doing this manually takes time.

If you’d rather automate the process, use the AI Visibility Toolkit.

For example, it provides your brand’s overall sentiment and share of voice.

It breaks this down by platforms, including:

• Google AI Mode
• ChatGPT
• Perplexity
• Gemini

You can also see how you stack up against your biggest fintech competitors.

The Narrative Drivers tool is especially useful.

It shows the exact questions people ask about your brand. And the percentage of favorable sentiment towards you in each answer.

This makes it easy to see where you’re perceived positively or negatively at scale.

Really cool.

Make Your Fintech Brand Easy for AI to Trust

AI is changing fintech in a number of ways.

Most notably, a buyer’s first touchpoint is now often an AI-generated answer.

If you’re not in those answers, you’re not in the decision.

The fix: build consistency and consensus signals for your fintech brand.

You already have a strong idea of how to do that. Now, dive deeper into the topic with our AI optimization guide.

The post Fintech in AI Search: How to Be the Trusted & Featured Brand appeared first on Backlinko.

2026-03-06 03:55:37

AI has changed the way people shop.

58% of consumers now use GenAI tools instead of traditional search to find products.

Imagine your customer runs a simple query in Google’s AI Mode: “Winter jackets for women.”

Instead of a long list of links, they get direct product recommendations — alongside:

• Descriptions of features and best use cases
• Ratings and reviews
• Editorial sites that mention the product
• Direct comparisons with top competitors

All in one response.

Which raises an obvious question:

Why do some products show up, while others are ignored entirely?

Many factors influence AI recommendations.

But one of the most important — and most controllable — is your product pages.

In basic terms, AI needs to understand what your product is and who it’s for.

When that information is clear, structured, and specific, your products have a much better chance of appearing in AI results.

In this guide, we’ll break down how AI evaluates product pages, and which elements matter most.

Plus, we’ll see how leading ecommerce brands structure their pages to get recommended.

Free checklist: To get a head start, download our Product Page AI Optimization Checklist. It includes everything you need to get more product mentions in AI platforms.

How AI Models “Think” About Product Pages

Ever wondered how large language models (LLMs) choose which products to surface in answers?

While there’s a lot at play, you can basically narrow it down to two factors:

• Consistency: Information about your brand and products matches across your website and third-party sites
• Consensus: Multiple reputable sources validate your product’s quality, use cases, and performance. This includes reviews on your product pages and third-party sites.

For LLMs to confidently cite a product page, they need consistent, up-to-date information.

AI models analyze product pages to pull details that help them answer user queries.

Remember, AI queries don’t look like a regular search.

Prompts are often highly specific requests for products that fit a clear use case or situation.

Example: What are the best women’s road racing shoes for a 10K in Ireland?

AI looks for product pages that clearly communicate:

• What the product is
• What it’s used for
• Who uses it
• In what situations it can be used

This helps the system understand your product in the context of user queries.

Take this Nike road racing shoe product page, for example.

AI systems understand when and how to recommend this product because it contains details like:

• What the product is: “Women’s Road Racing Shoes”
• Who should use it and when: Racing-related language like “marathon” and “race day shoe” makes it clear this product is for racing

When I searched “best road racing shoes for women” in AI Mode, it recommended Nike’s Alphafly.

And where did the information it quoted come from?

Nike’s own product page.

AI models also look for consensus signals on product pages.

This includes customer reviews and ratings.

When AI analyzes reviews, it looks for patterns. This includes repeated mentions of specific use cases, features, or product benefits.

For example, the Nike Alphafly is highly rated with plenty of reviews on the Nike website.

Among other benefits, this improves its chances of being recommended by AI platforms.

But AI doesn’t rely solely on product pages.

It cross-references independent sources to back up claims about your products.

In a similar search for racing shoes, I found that AI Mode cites various third-party sources to support its recommendations.

Like this one, that includes a review of Nike shoes, complete with product details.

Product pages are one piece of the AI visibility puzzle.

But they create the foundation AI systems need to confidently recommend your products.

Further reading: Learn how LLMs recommend brands in Semrush’s AI Visibility Index.

6 Essential Elements of a Product Page for AI Visibility

You likely already have some (or all) of the elements below on your product pages.

But for AI visibility, having them isn’t enough.

What matters is clarity, specificity, and structure.

Note: These elements aren’t in any particular order: all are important for AI visibility.

1. Clear Product Descriptions with Semantic Language

A clear product description explains more than what your product is. It spells out what it does, who it’s for, and why someone would choose it.

This matters for AI visibility because LLMs rely heavily on semantic retrieval.

In other words, AI understands the intent and meaning behind queries. Not just exact-match keywords.

For example, when someone searches for “vacuum for pet hair,” AI doesn’t just look for that phrase.

It also looks for semantically related terms. Things like “stubborn hair,” “carpets,” “pet odors,” and “allergens.”

These terms help AI infer use cases, surface the right features, and decide when your product is a good fit.

Including them on product pages improves your chances of appearing in AI-generated answers.

So, how do you find these terms?

First, read forums, reviews, and social media conversations.

Learn how people talk about the problems they’re facing and the products they’re using.

Using our vacuum example, I dove into r/VacuumCleaners. There, I found recurring phrases around weight, clogging, tangles, and flooring-specific concerns.

Next, conduct keyword research on related terms.

This shows you how people actually phrase their searches.

A tool like Semrush’s Keyword Magic Tool is great for this task.

Note: A free Semrush account gives you 10 searches in the Keyword Magic Tool per day. Or you can use this link to access a 14-day trial on a Semrush Pro subscription.

Enter a keyword, such as “pet hair vacuum.”

The tool will return a list of “Broad Match” queries, which contain variations of your keyword.

Review the “All Keywords” list on the left to find common themes.

Then, check the monthly search volume for each term.

In our example, we might use “handheld,” “carpet,” and “hardwood” as semantic keywords.

Collect a few key terms, and use them in product descriptions to explain what your product does.

You can still be creative. Just don’t sacrifice clarity.

Here’s what this looks like in practice.

I asked AI Mode for the best lightweight vacuum for pet hair. One of the top recommendations was a Shark vacuum.

User preferences and personal context aside, AI Mode recommended this product for a few reasons:

For one, it has strong consensus signals from third-party reviews and editorial sites.

(Which you can see from the sources on the right side.)

But let’s also take a closer look at the product page.

The product name alone — Shark UltraLight PetPro Corded Stick Vacuum — gives a core use case.

It’s meant for lightweight, pet-focused cleaning.

The product description reinforces that message with simple, specific language:

• Captures stubborn hair
• Works on carpets and floors
• Hand vac option
• Weighs less than three pounds

That same phrasing shows up in the AI response.

This strongly suggests AI Mode is pulling this information directly from Shark’s product description for this vacuum.

Bottom line: Customer-focused, use-case-driven language helps AI understand when to recommend your product.

This increases your chances of appearing in AI search results.

Further reading: Need inspiration? Check out some of our favorite ecommerce website examples.

2. Pricing and Availability in Real-Time Feeds

LLMs read product data from two places: your product pages and merchant feeds.

If your site has accurate structured data, AI can use that. But crawlers don’t run every minute. That means prices and stock can be stale.

That’s where a live product feed or API comes in.

This includes Shopify’s Catalog API, OpenAI’s Product Feed Spec, and feeds submitted through Google’s Merchant Center.

Pro tip: OpenAI product feed submission is currently available only to approved partners. Fill out the Merchant Application form for consideration.

When you use these, AI search engines can fetch current prices and inventory on demand.

That’s the tech that powers real-time recommendations and in-chat shopping in ChatGPT and other AI platforms.

More platforms are also adding this capability.

Google is rolling out a Universal Commerce Protocol.

This feature brings buy-in-chat functionality to eligible product recommendations in AI Mode and Gemini.

But what if you don’t use a product feed or API?

LLMs can still find product information on public webpages. But it may be outdated.

And that’s a problem.

AI platforms evaluate recency and consistency.

Mismatched prices or outdated stock can hurt your AI visibility. In part, because it leads to a poor customer experience.

​​To see how this plays out in practice, I tested ChatGPT’s “Shopping research” mode.

The AI asks questions to narrow results, including how much you want to spend.

I told ChatGPT I was looking for a new couch. I specified both my budget and need for delivery to Massachusetts.

ChatGPT returned five options, all of which fit my budget and availability requirements.

The “Best overall” option even highlighted that it was “in stock for fast delivery” to my state.

To further test how price affects results, I asked if any of the recommended couches were on sale.

It narrowed down my options and provided sale pricing.

ChatGPT only mentioned one couch as being on sale.

To find out why, I reviewed the product pages for each recommendation. But only one clearly highlighted both the original and sale price.

Walmart’s product pages boldly showcase the previous price versus the discount.

In its response, ChatGPT specifically mentioned that Walmart displays this info on its product page.

Walmart also submits its product feeds to platforms like Google Merchant Center.

So its pricing (both sale and original) is clear and current across platforms.

Product feeds and APIs keep your price and inventory fresh.

When AI systems have access to this data, they can recommend your products when users narrow options by price, availability, or discounts.

3. Ratings and Reviews

Many AI systems display ratings and reviews in product recommendations.

In AI Mode, you can click a product recommendation and see reviews directly in the sidebar.

ChatGPT also includes information from reviews.

It often surfaces them as part of the response:

But LLMs do more than show you reviews. They also weigh reviews and ratings when choosing recommendations.

ChatGPT often includes labels like “Budget-friendly” or “Most popular” based on reviews.

OpenAI has confirmed that answers may include summaries of the themes most commonly mentioned in reviews.

That could mean pros, cons, and use cases pulled directly from reviews.

Here’s how that looks in practice when I search for warm winter hiking boots:

Ultimately, reviews on your product page don’t just affect whether your product appears in AI search.

They can also influence how it’s positioned.

When AI systems analyze reviews, they look for consistency:

• Repeated mentions of specific use cases
• Commonly praised features
• Patterns in star ratings
• Shared language around benefits or problems

The more clearly those patterns emerge, the easier it is for AI to confidently recommend — and describe — your product.

This applies to reviews on your own product pages and on third-party sites.

When I asked AI Mode for a hydrating cleanser for sensitive skin, the first recommendation was a product from CeraVe.

Interestingly, the product description itself doesn’t explicitly emphasize “sensitive skin.”

But the reviews on CeraVe’s product page do.

Here’s what I noticed:

• Reviews are tagged with commonly mentioned phrases
• One of the most prominent tags is “sensitive skin”
• There are over 100 reviews referencing sensitive skin — most of them positive

Having reviews on every product page is a best practice that increases trust and authority.

Encourage customers to leave detailed feedback by:

• Prompting for use cases in review forms
• Asking follow-up questions after purchase
• Offering light incentives (like a coupon) in exchange for honest reviews

Note: The most important thing is that these reviews are real. Fake or AI-generated reviews may temporarily improve your brand’s visibility in AI search. But they are never worth the long-term risk to your reputation.

4. Contextual Use Cases

AI search looks for explicit connections between what a product is and why someone needs it.

So, your entire product page should explain when, why, and in what situations a product makes sense.

This requires a shift in how you think about product marketing.

Instead of asking, “What can this product do?”

Ask, “In what specific scenario would someone actively look for this?”

Start by identifying who buys your product and what triggers that purchase. If you don’t already have this insight, customer interviews are your fastest path.

Look for:

• The situation that prompted the search
• The alternatives they considered
• The constraint that mattered most (travel, space, safety, performance, etc.)

Once you have this, choose one or two clear, specific use cases to feature on each product page.

Don’t just list all the possible ways your product can be used.

AI isn’t great at matching vague versatility.

Instead, focus on the use cases that come up repeatedly in customer conversations. That way, AI can match your product to a specific intent.

Let’s look at an example for an electronics brand.

This product page for Anker’s 3-in-1 mobile charger states it’s “ultra compact and travel friendly.”

When I search for travel-friendly chargers on ChatGPT, Anker’s 3-in-1 device is the top recommended product.

Obviously, this little charger is a great option for more than just travel.

But by calling out that use case on the product page, it makes it easier for LLMs to recommend it in related queries.

5. Awards and Certifications

LLMs prioritize trustworthy, verifiable information when recommending products.

One of the strongest ways to demonstrate that trust is to feature third-party validation on your product pages.

This includes:

• Industry awards and “best of” recognitions
• Third-party testing results
• Safety and quality certifications
• Sustainability or ethical production badges

To see how much awards affect AI visibility, I analyzed 50 ecommerce brands in Semrush’s AI Visibility Overview tool.

This included Samsung, Patagonia, Everlane, Caraway, and others.

First, I identified brands with high AI Visibility scores.

This is a Semrush metric that measures how often brands appear in AI-generated answers.

I focused on brands scoring above their industry average. (This varies by industry, but is generally between 60 to 90.)

Next, I looked at how many of the top-ranking brands feature awards and certifications on their product pages.

And I found something very interesting:

82% of the brands with medium to high AI visibility prominently feature awards and certifications on their product pages.

For example, Samsung has an AI Visibility score of 90.

And its product pages feature multiple awards.

Like being “rated #1 in camera quality” by the American Customer Satisfaction Index.

And winning “Best Phone Camera” by Consumer Reports:

When I asked Claude which phone has the best camera quality, the Samsung Galaxy was one of its top recommendations:

BabyBjorn has an AI Visibility score of 67.

A quick look at its product pages reveals certificates and awards on every product page.

Like this one that references a “Best Bouncer” award from Parents Magazine:

When I asked ChatGPT to recommend the “best and safest baby bouncer,” BabyBjorn was the #1 pick:

Now, this is correlation, not necessarily causation. And awards and certifications are not the only factor.

But they can make a difference for product page visibility in LLM search.

If you already have awards and certifications, showcase them prominently on your product pages.

If you don’t, create a strategy to earn them.

Target industry-specific certifications (safety, quality, sustainability) and awards from reputable organizations.

This includes relevant certifications and “best” awards through PR outreach.

6. Structured Attributes and Schema Markup

Structured attributes are pieces of product information that machines can easily understand.

This includes things like:

• Price
• Dimensions
• Materials
• Ratings
• Availability
• Color
• Size
• Warranty details

These attributes are vital components of a product page.

Use tables, bullet lists, or specification sections to clearly structure them for machines and customers.

They should also be in your structured data and product feeds.

For example, health company Vitamix features a “Specifications” section on its product pages:

We can’t say definitively that schema affects LLM visibility (yet).

But major AI search engines confirm they rely on structured attributes to understand and recommend products.

It’s also still a best practice for traditional SEO.

Plus, it’s no secret that structured data helps products appear on Google’s main page and Shopping tab.

It’s what allows users to refine results, see ratings, and check prices right on the first page of Google.

But here’s where it gets interesting.

When I conducted a search in AI Mode, Google’s own shopping cards were the main sources.

Clicking into one of those sources, I saw even more of that search-friendly structured data.

And where does all this information come from?

You guessed it: the original product page.

That same structure is what enables Google’s AI responses to display live pricing, availability, sales, and comparisons.

Clear, consistent schema simply gives search engines and LLMs more to work with.

That context helps AI more confidently recommend your product in related queries.

AI Visibility Essentials for Product Pages (By Industry)

The elements above matter on every product page.

But AI evaluates product pages differently depending on the category.

In this section, we’ll break down the category-specific product page details that AI looks for across six common ecommerce industries.

Fashion Brands

Ask any AI engine for clothing recommendations, and you’ll notice something consistent: the results highlight fit, materials, and comfort.

Clearly, the most important product page elements for fashion brands are:

• Clear sizing and conversion charts
• Material and care information
• Customer fit data
• Sustainability certifications and ethical production badges

Fashion queries are also highly specific to the individual shopper.

To see how AI handles these searches, I used Semrush’s AI Visibility Toolkit.

I analyzed the topic “jeans for women” using Semrush’s Prompt Research tool.

What’s revealing is the variety of queries under this topic.

Take “Plus size and curvy women’s jeans” for example.

Even within this niche, searches vary widely:

• “Best plus size jeans for big thighs”
• “Best curvy fit jeans”
• Most comfortable jeans for curvy women”

Across all these queries, the AI responses consistently emphasize the same details:

• High-rise styles
• Stretch denim
• Tummy control
• Specific silhouettes like bootcut

These details are pulled directly from product pages and customer reviews:

For AI to match products to these specific queries, it needs structured details on your pages.

This is something Abercrombie & Fitch does well.

They display clear fit guidance and aggregated customer fit feedback prominently on product pages.

Health and Wellness Products

Nothing is more important to health and wellness brands than trust and safety.

That’s why non-negotiables for product pages in this industry include:

• Full ingredient composition
• Clear dosage and instructions
• Contraindications and allergen warnings
• Source transparency
• Clinical studies or certifications

Searches for health products are often deeply personal and complex.

Many start with a product type and the demographic it’s best for.

For example, the topic of “infant multivitamins” includes these common searches:

• “Where can I buy reliable infant multivitamins?”
• “How do I choose the best multivitamin for my baby?”

In their responses, AI models pull from ingredient lists, dosage information, and certifications.

Brands that perform well for wellness-related AI queries follow the same pattern.

They provide detailed information about ingredients, sourcing, and production on their product pages.

This is what helps popular health company Thorne get recommended often in AI search results:

Their product pages list ingredients in detail:

They also include dosage instructions and verifications of the product quality.

All in a clear, machine-readable format.

Electronics

When it comes to electronics, AI loves to quote specs.

Battery life, screen resolution, charging speed, refresh rates, and more are all pulled into responses.

So every electronics product page should include the essentials:

• Full technical specs
• Compatibility information
• Setup or installation guides
• Safety and efficiency certifications

For example, even a simple search — “best cameras for night photography” — returns spec-heavy recommendations.

Structured specs give AI systems what they need to compare products.

This is important on your own site and third parties.

Brands like Sony excel here.

They ensure their product and retailer pages feature technical details that are consistent and in-depth across platforms.

Home and Furniture Brands

Furniture shopping comes with one big question: Will it fit?

AI knows this, which is why technical details dominate recommendations.

Your home and furniture product pages need:

• Clear dimensions and room size recommendations
• Assembly requirements (tools, time, difficulty)
• Materials and care details
• Quality and sustainability certifications

For example, in a search for modular sofas for small apartments, ChatGPT mentions configurations in its answer:

One of its top recommendations is a couch by home brand Burrow.

While many factors go into this, its product page is definitely one of them.

It features different configurations of their modular sofas. Plus, the dimensions of each.

It also contains other vital information that users might ask AI systems, such as detailed materials and fabric care.

Outdoor and Sports Equipment

Customers need to know whether your products will survive their outdoor adventures.

Which is why AI takes these elements into account:

• Weather ratings and technical materials
• Performance specs (capacity, weight, range)
• Use-case scenarios
• Safety certifications or features

Let’s say your customers ask about hiking backpacks. They’ll see AI models highlight key features, max load, and materials.

Osprey’s backpacks are regularly recommended by AI.

This is because they clearly state use cases like “week-long backpacking trips”:

They also include features that make it ideal for common use cases: materials, weight, volume, dimensions, and load range.

Baby Products

Baby products trigger some of the most safety-sensitive AI recommendations.

AI models look for structured, verifiable details when recommending anything for infants.

If you sell baby products, here’s what your product page should include:

• Age and weight suitability
• Safety certifications (like OEKO-TEX, GREENGUARD)
• Ergonomic or developmental benefits
• Material and care instructions

For example, BabyBjorn includes safety certifications on its product pages.

And goes deep into safety information.

This includes how the fabrics are developed, and the appropriate age and weight for safe use.

When I asked Perplexity for the safest baby carrier on the market for newborns, BabyBjorn was among its top recommendations.

It also specifically mentioned the “hip healthy” certification featured on BabyBjorn’s product page.

Increase Your Product Page Visibility in AI Search

If you want AI to recommend your products, the best place to start is your product pages.

Small improvements compound quickly.

Clear descriptions. Structured data. Real reviews. Verifiable trust signals.

Together, they shape how AI understands — and surfaces — your products.

But product pages are just the start.

First, download the Product Page AI Optimization Checklist. It tells you exactly what to review, update, and add to make your product pages AI-friendly.

Then, learn how to build an AI ecommerce SEO strategy that improves your visibility across the entire buyer journey.

AI visibility is possible for your products. Keep testing, keep tracking, and keep growing.

The post How to Optimize Your Product Pages for AI Visibility appeared first on Backlinko.

2026-02-21 01:58:51

Search has changed, and so should your audience personas.

Your audience searches across Google, ChatGPT, Reddit, YouTube, and many other channels.

Knowing who they are isn’t enough anymore. You need to know how they search.

Search-focused audience personas fill gaps that traditional personas miss.

Think insights like:

• Where this person actually goes for answers
• What triggers them to look for solutions right now
• Which proof points win their trust

And you don’t need months of research or expensive tools to build them.

An audience persona is a profile of who you’re creating for — what they need, how they search, and what makes them trust (or tune out). Done well, it aligns your team around a shared understanding of who you’re serving.

In this guide, I’ll walk you through nine strategic questions that dig deep into your persona’s search behavior. I’ve also included AI prompts to speed up your analysis.

They’ll help you spot patterns and synthesize findings without the manual work.

By the end, you’ll have a complete audience persona to guide your content strategy.

Free template: Download our audience persona template to document your insights. It includes a persona example for a fictional SaaS brand to guide you through the process.

1. Where Is Your Audience Asking Questions?

Answer this question to find out:

• Where you need to build authority and presence
• Which platforms to target for every persona
• Which formats work well for each persona

Knowing where your persona hangs out tells you which channels influence their decisions.

So, you can show up in places they already trust.

It also reveals how they think and what will resonate with them.

For example, someone posting on Reddit wants honest advice based on lived experiences. But someone searching on TikTok wants visual content like tutorials or unboxing videos.

How to Answer This Question

Start with an audience intelligence tool that lets you identify your persona’s preferred platforms and communities.

I’ll be using SparkToro.

Note: Throughout this guide, I’ll walk you through this persona-building process using the example of Podlinko, a fictional podcasting software. You’ll see every step of the research in action, so you can replicate it for your own business.

For this example, we’re building out one of Podlinko’s core personas: Marcus, a marketing professional on a one-person or small team team, so he’s scrappy and in-the-weeds.

Pro tip: Start with one primary persona and build it completely before adding others. Focus on your most valuable customer segment (the one driving the highest revenue for your business).

In SparkToro, enter a relevant keyword that describes your persona’s professional identity or core interests.

This could be their job title, industry, or a topic they care deeply about.

I went with “how to start a podcast.” Marcus would likely search for this early in his journey.

The report gives a pretty solid overview of Marcus’s online behavior.

For example, Google, ChatGPT, YouTube, and Facebook are his primary research channels.

But it could be worth testing a few other platforms too.

Compared to the average user, he’s 24.66% more likely to use X and 12.92% more likely to use TikTok.

The report also tells me the specific YouTube channels where he spends time.

He’s watching automation, editing, and business tutorials.

He’s also active in multiple industry-related Reddit communities.

Maybe he’s posting, commenting, or even just lurking to read advice.

Since Marcus uses ChatGPT, I also did a quick search on this platform to see which sources the platform frequently cites.

I searched for some prompts he might ask, like “Which podcast hosting platforms should I use for marketing?”

If you see large language models (LLMs) repeatedly mention the same sources, they likely carry authority for the topic.

And by extension, they influence your persona’s research as well.

Compare these sources to the ones you identified earlier. If they match, you have validation.

If they’re different, assess which ones to add to your persona document.

Here’s how I filled out the persona template with Marcus’s search behavior:

2. What Exact Questions Are They Asking?

Answer this question to find out:

• What language to mirror in your content
• How to structure content for AI visibility
• What content gaps exist in your market

Your buyer persona’s language rarely matches marketing jargon.

Companies might talk about “podcast production tools” and “integrated workflows.”

But personas use more personal and specific language:

• What’s the cheapest way to record remote podcasts?
• How long does it take to edit a 30-minute podcast?

Knowing your audience’s actual questions reveals the gap between how you describe your solution and how they experience the problem.

And shows you exactly how to bridge it.

How to Answer This Question

Start by going to the platforms and communities you identified in Question 1.

Search 3-5 topics related to your persona.

Review the context around headlines, posts, and comments:

• How they phrase questions (exact words matter)
• What emotions do they express
• What outcomes they’re trying to achieve

Pro tip: As you research, save persona comments, discussions, and reviews in full — not just snippets. You’ll analyze the same sources in Questions 3-5. But through different lenses (challenges, triggers, language patterns). Having everything saved means you won’t need to revisit platforms multiple times.

For example, I searched “how to start a podcast for a business” on Google.

Then, I checked People Also Ask for related questions Marcus might have:

On YouTube, I searched “how to edit a podcast” and reviewed video comments.

Users asked follow-up questions about mic issues and screen sharing.

This gave me insight into language and questions beyond the video’s main topic.

In Facebook Groups, I found users asking questions related to their goals, constraints, and challenges.

It also provided the unfiltered language Marcus uses when he’s stuck.

Now, use a keyword research tool to visualize how your persona’s questions connect throughout their journey.

I used AlsoAsked for this task. But AnswerThePublic and Semrush’s Topic Research tool would also work.

For Marcus, I searched “Best AI podcasting editing software,” which revealed this path:

Which AI tool is best for audio editing? → Can I use AI to edit audio? → Which software do professionals use for audio editing? → How much does AI audio editor cost?

It’s helpful to visualize how Marcus’s questions change as he progresses through his search.

Next, learn the questions your persona asks in AI search.

You’ll need a specialized tool like Semrush’s AI Visibility Toolkit for this task.

It tells you the exact prompts people use when searching topics related to your brand.

(And if your brand appears in the answers.)

If you don’t have a subscription, sign up for a free trial of Semrush One, which includes the AI Visibility Toolkit and Semrush Pro.

Since Podlinko is fictional, I used a real podcasting platform (Zencastr.com) for this example.

This brand appears often in AI answers for user questions like:

• What equipment do I need to create a professional podcast setup?
• Can you recommend popular tools for managing and promoting online radio or podcasts?

You’ll also see citation gaps — questions where your brand isn’t mentioned. These reveal content opportunities.

For this brand, one gap includes:

“Which AI tools are best for recording, editing, and distributing an AI-focused podcast?”

After reviewing all the questions I gathered, I narrowed them down to the top 5 for the template:

3. What Challenges Influence Their Search Behavior?

Answer this question to find out:

• What constraints influence their decision-making process
• How to anticipate objections before they arise
• What kind of solutions does your persona need

Challenges are the ongoing issues driving your persona’s search behavior. These overarching problems shape their decisions to find a solution.

Understanding these challenges can help you:

• Position your solution in the context of these pain points
• Anticipate and address objections before they come up
• Structure your campaigns to speak directly to their limitations

How to Answer This Question

Review the questions you collected in Question 2 to identify underlying pain points.

For example, this Facebook Group post contains some telling language for Marcus’s persona:

Specific phrases highlight ongoing challenges:

• “Tech support is no help”
• Can’t find an editing software that consistently works”

Now, visit industry-specific review platforms.

Check G2, Capterra, Trustpilot, Amazon, Yelp, or another site, depending on your niche.

Look for reviews where people describe recurring frustrations.

Positive reviews may mention what drove a user to seek a new solution. For example, this one references poor audio and video quality:

Negative reviews reveal what users constantly struggle with.

Unresolved pain points often push people to find workarounds or alternatives.

This user noted issues with a podcasting tool, including loss of backups, unreliable tech, and more.

Pay close attention to the language people use. Word choice can signal underlying feelings and constraints.

When someone asks for the “easiest” and “most cost-effective” solution, they’re signaling:

• Limited resources
• Low confidence
• Risk aversion

After reviewing conversations and communities, you’ll likely have dozens of data points.

Copy the reviews, questions, and phrases into an AI tool to identify your persona’s top challenges.

Use this prompt:

Based on these reviews and discussions, identify the five biggest challenges for this persona.

For each challenge, show:

(1) exact phrases they use to describe it

(2) what constraints make it harder (budget, time, skills)

(3) how it influences where and when they search.

Format as a table.

This analysis helped me identify Marcus’s recurring challenges:

4. What Triggers Them to Search Right Now?

Answer this question to find out:

• What emotional and situational context should you address in your content
• How to structure content for different urgency levels
• Which pain points to lead with

Search triggers explain why your audience is ready to take action.

But they’re not the same as challenges.

Challenges are ongoing constraints your persona faces. This could be a limited budget, small team, or skill gap.

Triggers are the specific events or goals that push them to act right now. Like a looming deadline or a competitor launching a podcast.

Understanding triggers helps you reach your persona when they’re most receptive.

How to Answer This Question

If you have access to internal data, start there.

Your sales and customer support teams can spot patterns that push prospects from browsing to buying.

For example, your sales conversations might reveal that one of Marcus’s triggers is urgency. His manager might ask him to improve the sound quality by the next episode, prompting his search.

If you don’t have internal intel, use tools like AnswerThePublic, AlsoAsked, or Semrush’s Keyword Magic Tool.

This will help you identify the language people use when they’re ready to act.

For Marcus, my AlsoAsked research led to questions like:

• “Can I record a podcast with just my phone?” This may suggest a desire to start immediately, without professional equipment.
• “How to make a podcast with someone far away” could suggest the trigger of a sudden need to work with a remote guest/host

You can also refer back to your research on community spaces.

(Or conduct additional audience research, if needed.)

These spaces are where people describe the exact moments they decide to take action. Aka plateaus, milestones, and failed attempts.

When I searched “podcast marketing” on Reddit, I found a post from someone experiencing clear triggers:

This user has been unable to get a consistent flow of organic listeners despite high-quality content.

Trigger: A growth plateau that pushed him to ask for help.

He’s also trying to hit his first 1,000 listeners.

Trigger: A goal that pushed him to look for solutions.

If you collected a lot of content, upload it to an AI tool to quickly identify triggers.

Use this prompt:

Analyze these community posts and discussions. Identify the specific trigger moments that pushed people to actively search for solutions.

For each trigger, show:

1. The exact moment or event described (quote the language they use)
2. The type of trigger (situational, temporal, emotional, or goal-driven)
3. What action did they take as a result

Format as a table.

After analyzing the content I gathered, I identified the key triggers pushing Marcus to search:

5. What Language Resonates (and What Turns Them Off)?

Answer this question to find out:

• Which messaging angles resonate
• What tones build trust with your audience
• Which phrases trigger objections or skepticism

The words you use can affect whether your persona trusts you or tunes out.

The right language makes people feel understood. The wrong language creates friction and drives them away.

When you know what resonates, you can create messaging that builds trust and motivates your personas to act.

How to Answer This Question

Refer back to your research from Questions 3 and 4.

This time, focus specifically on language patterns in reviews and community discussions.

Look at:

• Exact phrases people use to describe success, relief, or satisfaction
• Words highlighting frustration, disappointment, and concerns

For example, on Capterra, users praised podcasting platforms that “do a lot” and let them “distribute with ease.”

This language signals Marcus’s preference for all-in-one platforms.

He would likely connect with messaging that emphasizes functionality without complexity.

Next, review the content you previously gathered from community spaces.

In r/podcasting, users like Marcus write with direct, benefit-focused language:

Notice what he values: simplicity and concrete outcomes (“automatic transcripts”).

He’s not mentioning jargon like “AI-powered transcription engine” or “enterprise-grade recording infrastructure.”

Plain language that emphasizes quick results over technical capabilities works best with this persona.

Once you have enough data, use this LLM prompt to identify language patterns:

Analyze these customer reviews and community discussions I’ve shared. Identify:

1. Most common words and phrases people use to describe positive experiences
2. Most common words and phrases that signal frustration or concerns
3. Emotional undertones in how they describe problems and solutions

Create a table organizing these insights.

This analysis revealed the specific language that Marcus reacts to positively (and negatively).

6. What Content Types Do They Engage With Most?

Answer this question to find out:

• Content types to prioritize in your content strategy
• How to structure content for maximum engagement
• What length and style work best for each format

Knowing the content types your audience prefers has multiple benefits.

It lets you create content that captures your persona’s attention and keeps them engaged.

Think about it: You could write the most comprehensive guide on podcast equipment.

But if your ideal customer prefers video reviews, they’ll scroll right past it.

How to Answer This Question

You identified your persona’s most-used platforms in Question 1. Now analyze which content formats perform best on each.

Conduct a few Google Searches to identify popular content types.

You’ll learn what users (and search engines) prefer for specific queries. Look at videos, written guides, infographics, carousels, podcasts, and more.

For example, when I search “how to set up podcast equipment,” the top results are a mix: long-form articles, video tutorials, and community discussions.

But organic search rankings don’t tell the full story.

Analyze content directly on your persona’s preferred platforms, too.

I searched “How to distribute a podcast” on YouTube and assessed the top 20 videos and Shorts for:

• Video length
• Views
• Comments
• Engagement patterns

Look at the creators your persona follows on each platform. (From the SparkToro report in Question 1).

Pay attention to:

• Content types drive the most engagement (videos vs. carousels vs. threads)
• How these creators structure content (length, style, tone)
• Which topics resonate most with their audience

Once you’ve collected this data, look for patterns.

Or drop your data into an LLM and ask it to find the patterns for you:

Analyze this engagement data I’ve collected for my audience persona.

Identify:

1. Which video lengths perform best (views, comments, engagement rate) and why
2. Which content styles generate the most engagement (tutorials, vlogs, behind-the-scenes, etc.)
3. Any patterns in thumbnails, titles, or formats that consistently perform well

Summarize my persona’s content preferences by video type and rank them as low, medium, or high

For Marcus, I learned that 5- to 15-minute video tutorials generated the highest engagement.

Shorts consistently underperformed for how-to queries, showing his preference for in-depth tutorials.

I documented my findings and ranked each content type by engagement level: high, medium, or low.

7. What Proof Points and Signals Matter?

Answer this question to find out:

• What proof points influence buyers
• How to structure case studies and testimonials
• Where to place proof points to win people’s trust

Proof points can influence whether someone acts on your content or bounces.

They’re also a ranking factor.

Search engines and LLMs reward content that demonstrates Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T).

But different personas might value different proof points.

Understanding what matters to each persona is crucial to building trust and visibility.

How to Answer This Question

Identify the most common trust markers on your persona’s preferred sites.

Look for:

• Author credentials: Bylines with relevant expertise
• Methods: Transparency about the method for creating this content
• Citations: Links to studies, expert quotes, industry reports, original research
• Recency signals: Publication and last updated dates
• Visual proof: Screenshots, before/after comparisons, annotated walkthroughs
• Social validation: Comment sections, user discussions, engagement metrics

Use Semrush’s Keyword Overview tool to find this information.

Note: A free Semrush account gives you 10 searches in this tool per day. Or you can use this link to access a free Semrush One trial.

Enter your keyword (I used “how to start a podcast”).

Scroll to the SERP Analysis report to view the ranking domains.

Aim to review 20 to 50 pages for the best results. (Create a spreadsheet to organize the information.)

Identify which proof points they use and how prominently they’re displayed.

Here’s how I did this for one of the articles I assessed:

• Quantified track record: “Since 2009, Buzzsprout has helped over 400,000 podcasters”
• First-person experience: “I’ve drawn on lessons from my own podcasts and thousands of conversations with creators”
• Third-party sources: Expert advice cited from Apple Podcasts on naming conventions
• Visual demonstrations: Embedded tutorials showing recommendations in action

Then, use an LLM to quickly spot patterns:

I’ve analyzed top-ranking pages for my persona and uploaded my findings.

Identify:

1. Which proof points appear most frequently (e.g., “8 out of 10 pages include X”)
2. How these proof points are displayed (above the fold, in sidebar, throughout content)
3. Which combinations of proof points appear together most often

Format as a summary with the top 5 most common patterns.

Ultimately, you’ll want to infuse your content with these same trust markers to attract and convert your persona.

After identifying Marcus’s top proof points, I ranked them from medium to high in the template:

8. Where (and How) Should You Distribute Content to Reach This Persona?

Answer this question to find out:

• Which platforms deserve your investment
• What content formats work best on each platform
• How to maximize organic reach through distribution

Where you distribute content determines whether it reaches your audience.

If you only publish content on your website but buyers find solutions on LinkedIn, you’re overlooking key touchpoints.

Even worse, you’re invisible on major platforms that LLMs scan for answers, recommendations, and citations.

How to Answer This Question

By now, you know your audience persona’s top platforms.

These are your initial distribution targets.

But you’ll ideally be able to validate them against real behavioral data.

If possible, survey recent customers to find concrete patterns about their search behavior.

Send a short survey to customers who converted in the last 90 days:

• Where did you first hear about us?
• Where do you go for advice about [primary pain points]?
• What platforms do you use when researching [your product category]?
• How do you prefer to learn about new solutions in your workflow?

Once responses come in, look for patterns in how each segment discovers, researches, and evaluates solutions.

Here’s a prompt you can use in an AI tool for faster analysis:

I surveyed recent customers about their search and discovery behavior.

Analyze this data and identify:

1. The top 3-5 platforms where customers discovered us or researched solutions
2. Common pain points or information needs they mentioned
3. Preferred content formats for learning about solutions
4. Any patterns in how different customer segments discover and evaluate us

Highlight the platforms and channels that appear most frequently, and flag any gaps between where customers search and where we currently have a presence.

Next, cross-reference your research against existing data in Google Analytics.

Open Google Analytics and navigate to Reports > Lifecycle > Acquisition > Traffic acquisition.

Sort by engagement rate or average session duration to see which channels drive genuinely engaged visitors.

Look for high time on site (2+ minutes) and multiple pages per session (3+).

Then, map each platform to the content format that performs best there.

Combine insights from Question 1 (preferred platforms) and Question 6 (preferred formats) to build your distribution strategy.

Here’s what this looks like for Marcus:

9. What Keeps This Persona Coming Back?

Answer this question to find out:

• What product features or experiences to double down on
• How to position your solution beyond initial use cases
• What content to create for existing customers

Winning your audience’s attention once is easy. Earning it repeatedly is the real challenge.

Understanding what keeps your persona engaged is the key to getting them to return.

How to Answer This Question

Review all the audience persona insights you’ve gathered so far to identify recurring needs.

Look at triggers, pain points, content preferences, and community discussions.

Pinpoints problems that can’t be solved with a single article or resource.

This could include:

• Tasks they do every week (editing, distribution, promotion)
• Decisions they face with each piece of content (format, platform, messaging)
• Skills they’re continuously learning (new tools, changing algorithms)
• Friction points that slow them down every time

Then, outline the content types that repeatedly solve these problems.

Think tools, templates, checklists, and guides they’ll use repeatedly.

If you don’t want to do this manually, drop this prompt into an AI tool to synthesize your findings:

Based on my audience persona research, here’s what I’ve learned:

Questions they ask: [Paste top questions from Q2]

Challenges they face: [Paste challenges from Q3]

Triggers that push them to act: [Paste triggers from Q4]

Their preferred content types: [Paste formats from Q6]

Identify recurring problems they face repeatedly (not one-time issues).

For each recurring problem:

1. Describe the problem in their own words
2. Explain why it’s recurring (weekly task, ongoing decision, changing landscape, etc.)
3. Suggest 2-3 content types that would provide repeatable value each time they face this problem

Format as a table with columns: Problem | Why It’s Recurring | Content Solutions

For Marcus, this could look something like this:

Every time Marcus faces these challenges, he can turn to them for a reliable solution.

These are the content types that have repeatable value for him:

Build Audience Personas That Win AI Visibility

Forget surface-level demographics.

These nine audience persona questions give you actionable, in-depth search intelligence.

You now know a lot about your persona.

You’ve uncovered where they search, what language resonates, and which proof points earn trust.

This is everything you need to show up in the right places with the right message.

If you haven’t already, download our audience persona template to organize your research.

Use it to guide your content creation, search strategy, and distribution efforts.

Your next move: Expand your visibility further with our guide to ranking in AI search. Our Seen & Trusted Framework will help you increase mentions, citations, and recommendations for your brand.

The post How to Build Audience Personas for Modern Search + Template appeared first on Backlinko.

2026-02-21 01:50:47

At just under 200 employees, Descript is not the biggest name in video editing software.

It’s not the most robust or the most popular, either.

But it’s punching way above its weight, competing with much bigger companies (like Adobe, and CapCut) in LLM search.

Using Semrush’s AI Visibility score, you can see that Descript is competing closely with giant brands like Adobe.

Descript found the way in.

And so can you.

In this SaaS LLM visibility case study, we’ll break down exactly how Descript is getting seen.

And more importantly, what you can copy to improve visibility for your own product.

Choosing Clear Niche Messaging

For years, Descript has been known as a podcast editing tool.

That matters.

Because when people talk about podcast editing, Descript comes up naturally.

In blog posts.

In forums.

And now, in AI answers.

This isn’t accidental. Descript is clear about who it’s for, and their content reflects that focus.

Their product pages and blog posts consistently speak to one core audience: people who want to edit podcasts easily.

Here’s why this matters:

When I asked Google’s AI Mode for the best software to edit podcasts — specifically as someone with no video editing skills — Descript was one of the first tools mentioned.

And what shows up second in the list of sources?

One of Descript’s own blog posts about podcast editing.

Across Descript’s own website and other third-party sources, this tool is regularly mentioned as ideal for podcasters.

This matters because of a key difference between AI search and traditional SEO.

LLMs don’t just surface pages. They based their answers on query fan-outs.

Here’s what that means: AI creates multiple searches after the original query, and tries to find an answer that is most directly matched to what was asked.

That’s why even articles and websites that aren’t ranking well in Google can still get cited by AI when they provide the most relevant, specific answer to what users are asking.

Because Descript’s content is tightly focused on one audience, one use case, one problem, it maps cleanly to those AI queries.

That doesn’t necessarily correlate to higher ranking in traditional search. In fact, Descript’s traffic from traditional SEO has been steadily decreasing since its peak in 2024:

But at the same time, branded traffic has increased.

So even while the brand isn’t succeeding in traditional search, more people are becoming aware of Descript and searching for the brand name specifically.

Why? In part, because the brand is known for exactly what it does: podcast editing.

AI knows that too. And I would bet that a higher amount of mentions in AI search is helping with brand recognition and influencing that increase in branded search traffic.

Here’s the point: Descript isn’t just checking off boxes of what to talk about.

The way they write — and the way they present their product — shows exactly who they’re speaking to. They match the way their audience talks.

Take the blog article on podcast editing that we mentioned above as an example.

The copy flows naturally, includes quotes from an internal expert in the way she describes the problem and solution, and speaks in an easy way that matches the tone of the audience.

As a byproduct of this natural way of writing and clear product position, their copy and content semantically matches what their audience is searching for.

And their AI mentions keep increasing.

Action Item: Identify and Focus on Your Niche Market

Effort vs. Impact: Medium effort. High impact.

If you’re trying to be all things to everyone, AI is less likely to recommend you for anything specific.

Instead, narrow your focus like Descript does:

Of course, you also want to find balance.

For example, “Podcast editing software for true crime hosts who only record on Thursdays,” may be a bit too niche.

To get the narrowest viable version of your core audience, look at your most successful customers.

Ask:

• Who gets the most ROI from our product?
• Who uses it weekly — or daily?
• Which customers have become vocal advocates?
• What do those users have in common? (Role, company size, industry, workflow)

That overlap is your niche.

Once that’s clear, your messaging gets easier.

You stop being an “All-in-one AI-powered platform for creators and teams.”

And start anchoring your product to a specific job: “Edit podcasts and spoken audio, without technical complexity.”

Then, your product becomes easier for AI systems to understand — and recommend — for specific use cases.

Further reading: Learn how to do deep audience research, along with a free audience research tracker template.

Developing Seriously Helpful Content

Once you know who you’re talking to, the next step is obvious: Help them.

That idea isn’t new.

Helpful content has long been a ranking factor in traditional search.

And in 2024, Google confirmed that their algorithm changes had reduced the appearance of low-quality content in search results by 45%.

But Descript’s example (and plenty of others) shows how this also applies to AI search.

Because clear, useful, unique content also drives LLM visibility.

Descript doesn’t rely on shallow blog posts or surface-level explanations.

They create:

• Instructional blog content that answers real questions
• Help Center pages that actually solve problems
• Product pages that clearly explain what features do — and who they’re for

They also publish content that isn’t strictly about their product, but is highly relevant to their audience.

For example:

When I asked Google’s AI Mode how much YouTubers actually make, one of the cited sources was a Descript blog post on the topic.

That article includes:

• Data from recent studies
• Real-world examples
• A YouTube earnings calculator

It’s comprehensive. And it’s written from an expert perspective.

Here’s another example: When I asked how much it costs to start a YouTube channel, I was again directed to an article from Descript.

That page includes a detailed FAQ and embedded video content from Descript’s own YouTube channel.

The pattern is clear.

Depth gets cited. Surface-level content gets ignored.

Action Item: Focus Your Content on Being Helpful

Effort vs. Impact: High effort. Medium impact.

Once you’ve defined your niche, focus your content on what actually helps them.

Descript doesn’t target video editing professionals. So, they don’t show up in those searches.

They focus on content creators and podcasters. And their content reflects that.

To do the same:

• Talk to people in your niche industry
• Ask about their workflows, goals, and sticking points
• Learn what slows them down

Pro tip: If you can’t speak directly to people in your audience or customer base, talk to your customer-facing teams. Customer success and sales teams have daily contact with your core audience. So, they’re in a better position to give you insights into what this audience cares about.

Online research also helps.

Find relevant subreddits to see what people are talking about. Check the comments section of relevant YouTube videos.

Look for recurring questions and complaints.

For example, the Descript team might peruse the r/podcasting subreddit to learn about their audience’s questions and opinions.

The goal: understanding.

When you deeply understand your audience’s day-to-day reality, creating helpful content becomes much easier.

And your content can become the source for AI answers.

Of course, getting citations back to your website isn’t the same as getting direct brand mentions. However, it’s still an opportunity to build awareness and authority.

Plus, building content around relevant core topics helps reinforce your niche messaging.

Further reading: Read the full guide on how to create helpful content.

Showcasing Images and Videos of Their Product

LLMs don’t just read text anymore.

They interpret visuals too.

With image-processing models like contrastive language–image pre-training (CLIP,) AI systems can understand what’s happening inside screenshots and videos — not just the words around them.

And those visuals now show up directly in AI answers. Especially for SaaS product queries in tools like ChatGPT.

For example, when I search for “best CRM software for a small business,” the top AI result includes images of the actual product interface.

That’s a shift.

Highly polished mockups matter less. Real, in-product visuals matter more.

Which is why Descript shows up like this in ChatGPT:

Descript consistently shows real product images and videos across product pages, Help Center articles, and blog content.

These aren’t decorative.

They show:

• What the product looks like
• How features work
• What users should expect when they log in

As a result, those same images and videos get pulled into AI answers — often with a link back to Descript’s site.

In this case, the link goes back to a very in-depth Help Center guide to getting started with podcast editing.

And most Interestingly, that’s a near-perfect semantic match to the original query.

Action Item: Include In-Product Images in Your Marketing Content

Effort vs. Impact: Low effort. Medium impact.

Start with the basics.

For every feature you highlight, ask one question: Can someone see this working?

Then act on it. Add real screenshots of your core product screens to key product pages. Replace abstract diagrams with in-product visuals where possible.

Next, expand beyond product pages.

Mention a feature in a blog post? Include a screenshot of it in use.

Explaining a workflow in a Help Center article? Show each step visually.

Teaching a process? Record a short screen capture instead of relying on text alone.

The goal is clarity.

Clear visuals help users understand your product faster. And they give AI systems concrete material to reuse in answers.

Which makes your product easier to recommend — and easier to recognize — inside AI search.

Creating Detailed MoFu/BoFu Content

Content mapped to different awareness levels performs especially well in AI search.

Descript understands this.

They don’t just publish top-of-funnel guides. They create content for product-aware and solution-aware searches, too.

When you search in ChatGPT for video creation or editing tools, Descript often appears in the results.

But more importantly, their own content is cited as a source.

In this example, the cited source is a Descript-owned “best of” article comparing video tools.

Instead of generic recommendations, the page:

• Breaks tools down by specific use cases
• Includes clear pros and cons
• Explains who each option is best for

Descript follows this same pattern with multiple “best of” lists and comparison pages against their main competitors.

The payoff?

When I asked AI to compare podcast video editing tools, Descript appeared with clear labels explaining:

• Who it’s best for
• Key features
• When it makes sense to choose it

That context helps AI recommend Descript to the right people (not everyone).

Action Item: Create Citable MoFu and BoFu Content

Effort vs. Impact: High effort. High impact.

Different awareness levels need different content.

To increase product-level AI visibility, focus on Product Aware and Solution Aware queries.

For Product Aware audiences, create:

• Comparison pages
• “Best alternative” posts
• Owned “best of” lists

Want more ideas?

Talk to your sales team.

Ask them: What features are convincing people to buy? Which competitors are commonly brought up in sales conversations?

Those answers map directly to comparison content AI likes to cite.

For Solution Aware audiences, focus on how-to content that naturally features your product.

For example, when I asked Google’s AI Mode how to reduce background noise from a microphone, it referenced a Descript how-to article.

This same pattern repeats itself across many of Descript’s blog posts: Find a clear problem, give a clear solution, add product mentions naturally.

It’s all about finding the right questions to answer.

To find these opportunities faster, use Semrush’s AI Visibility Toolkit. This data is powered by Semrush’s AI prompt database and clickstream data, organized into meaningful topics.

Head to “Competitor Research” and review:

• Shared topics where competitors appear
• Prompts where they earn more AI visibility than you

Then, dig into the specific questions behind those prompts.

The goal isn’t simply “more content”.

It’s answering the right questions — at the right stage — with content AI can confidently cite.

Building Positive Sentiment With Digital PR and Affiliate Marketing

AI visibility isn’t earned on your website alone.

LLMs look for signals across the web.

This is what we call consensus. And it means that positive sentiment has to exist outside your owned channels.

Descript is doing this in two ways:

• Digital PR on sites AI already trusts
• A creator-friendly affiliate program that drives third-party mentions

Here’s how it works: Google’s AI Mode tends to favor certain websites to source when answering queries about software.

Semrush’s visibility research for AI in SaaS from December 2025 shows these sites dominate citations:

• Zapier
• PCMag
• Gartner
• LinkedIn
• G2

Here’s what’s interesting.

Descript is mentioned in articles across nearly all of these top sources.

For example, in software listicles like this one on Zapier:

Or in real-world experience articles like this one on Medium:

Or in their clear listings on reviews sites like Gartner and G2:

When AI systems cite those favored sources, Descript comes along for the ride.

Not because it’s the biggest brand.

But because it’s present where AI is already looking.

The second lever is Descript’s affiliate program.

It’s simple:

• $25 per new subscriber
• 30-day attribution window
• Monthly payouts
• No minimums

Those are solid incentives.

And they lead to more creator-driven content across the web.

For example, a YouTube walkthrough from VP Land explains how to use Descript and includes an affiliate link in the description.

When I later asked Google’s AI Mode how to use Descript, that exact video was cited as a source.

That’s the pattern.

Affiliate content creates citable, trusted references that AI systems reuse.

Action Item: Build a Strategy to Get More Mentions Online

Effort vs. Impact: High effort. High impact.

Getting third party mentions is all about building relationships.

First, build relationships with publishers, starting with the ones AI already trusts.

Even if you’re not an enterprise SaaS company with a full-sized PR team, this is still possible.

Granted, it’s not the easy route — but when you find the right websites and perform regular outreach to those teams, you can get your brand on these sites.

Before you start outreach, get your bearings.

Start by going back to Semrush’s AI Visibility Toolkit. Head to the “Competitor Research” tab and select “Sources.”

This shows you:

• Which sites LLMs cite for your category
• Where competitors are already getting mentioned
• Gaps where your brand doesn’t show up (yet)

Those sites become your shortlist.

Outreach works better when you’re aiming at sources AI already relies on.

Second, build relationships with creators.

Affiliate programs work when creators want to talk about you.

So, build an affiliate program people actually want to be part of.

This means the program has to be easy to join, with clear terms that make it worth their time.

At a minimum, make sure you have:

• A simple signup
• Transparent tracking
• Reliable payouts

Pro tip: Use a tool like PartnerStack to handle all of the details automatically. Better signups, better tracking, and automated payouts build trust with your affiliates.

If you need inspiration, research top affiliate programs to learn more about the conditions creators expect.

But most importantly: Treat affiliates as distribution partners, not just a side channel.

This means enabling them with clear positioning on your product, example use cases, demo workflows, screenshots they can reuse, and other resources.

The better you equip them, the stronger their recommendations will be.

Once you have this set up, track the results.

Use AI visibility data to see:

• Which publisher relationships are turning into citations in AI search
• Which creators show up in AI answers
• Which formats perform best

Then, double down.

Now that we’ve discussed what Descript is doing well, let’s look at where there’s room for improvement.

Where Descript Could Improve: Reddit Marketing

Descript is doing a great job in many areas that are important for AI search visibility.

That said, there’s one area they’re missing out on: Reddit.

And yes, Reddit matters. A lot.

It’s still one of the most-cited sources in Google’s AI Mode.

And in almost all of the searches I tested above, Reddit was cited as a source (especially conversations in the r/podcasting subreddit).

Here’s the problem: right now, Reddit is not doing Descript any favors.

Here are a few thread titles I found just by searching for Descript in a podcasting subreddit:

And yes, there are positive mentions of Descript. But they’re buried under a wave of negative sentiment.

When LLMs scan Reddit for sentiment, that unbalance matters.

AI wants to see consensus. So when Reddit skews negative, recommendations may weaken, and alternatives get surfaced instead.

Even when the product is strong.

That’s why, while Descript’s AI visibility is good, it’s still not as good as it could be. And that vulnerability could hurt them in the long run, even if they’re still doing everything else right.

Here are some ways that Descript (and you) could turn the tides on Reddit:

• Avoid promoting and start participating: Reddit punishes marketing language. Helpful, honest comments perform better than posts.
• Respond to criticism directly (when appropriate): Not defensively, but with clear explanations and fixes
• Be present before there’s a problem: Accounts that only show up during damage control don’t build trust
• Focus on comments, not posts: High-value comments in active threads outperform standalone branded posts
• Monitor brand mention weekly: Focus especially on high-intent subreddits. In Descript’s case, that could be r/podcasting.

To be fair, it seems like Descript is taking steps in the right direction.

As of December 2025, the Descript team has taken control of a dedicated brand subreddit, with PMM Gabe at the helm.

And the team’s responses feel very Reddit-friendly, not using marketing jargon or being pushy.

But popular threads here still have very little interaction with the Descript team. And there seems to be very few (if any) comments from the Descript team outside of this branded subreddit.

It’s a step in the right direction, but there’s still a lot to work on.

Done right, Reddit becomes a sentiment stabilizer and a stronger input source for AI answers.

Ignore it, and Reddit can become a liability.

Remember: for AI visibility, silence isn’t neutral.

Further reading: If Reddit feels like a whole other world, we’ve got you covered. Read our full guide to Reddit Marketing.

What You Can Take Away from This SaaS LLM Visibility Case Study

Descript isn’t winning AI visibility because it’s the biggest brand.

It’s winning because it’s clear, focused, and consistently helpful.

None of that is accidental.

And none of it requires massive scale.

You can get started on this today by choosing one key action to work on.

Use the effort vs. impact lens from this article to choose where to start.

• Add in-product screenshots and videos: Low effort, medium impact
• Tighten your niche messaging: Medium effort, high impact
• Build citable MoFu/BoFu content: High effort, medium impact
• Invest in digital PR, affiliates, and community participation: High effort, high impact
• Create seriously helpful content: High effort, high impact

Pick one, start there. AI search visibility tools for SaaS companies — like Semrush’s AI Visibility Toolkit — can help you see exactly where you stand today, and where you can improve.

Remember: LLM visibility isn’t about chasing algorithms.

It’s about making your product easier to understand, easier to trust, and easier to recommend.

Do that consistently — and AI search will follow.

Want to learn how it all works on a deeper level? Read our LLM visibility guide to discover even more ways to increase your brand mentions and citations in AI search.

The post How a 200-Person Company Competes with a $160B Giant in AI Search appeared first on Backlinko.