How often do you review your PPC ad copy? Not just analyzing the performance of each asset within the ad platform, but also reviewing your ads in the context of how they appear next to competitor ads?
Are you using the exact same messaging as your competitors? Does your offer stand out from theirs? Which ads are bland and generic, and which provide concrete calls to action and compelling selling points?
Let’s walk through several tips for writing paid search copy that stands out in search results and converts customers for your brand.
1. Think about how assets will appear together, not just individually
When you’re writing Responsive Search Ads, it’s easy to fall into the trap of simply filling in all 15 headline options and all four descriptions.
However, if each headline essentially says the same thing with slightly different wording, your ad copy will appear bland and repetitive in the SERP when two or three headlines are shown together.
For instance, if this example ad showed the following, it would be less helpful:
“Project Management Software – Trusted by 3 Million Users”
If you want to test multiple headlines with slightly different wording, pin them to the same position so the ad platform can rotate between them, but not show both at the same time. Zoho appears to be doing this by using both “Preferred by 3 Million Users” and “Trusted by 3 Million Users” as options.
The visibility of the ad strength rating looms over every Google Ads account. Don’t let chasing an Excellent score consume your focus.
Focus more on making sure each headline and description speaks accurately to your benefit points than on including the maximum number of each. Pinning may negatively impact ad strength, but as discussed above, it can help make your messaging cleaner.
3. Use AI as a partner, but don’t blindly outsource all your copy to AI
Google and Microsoft make ad writing easy, generating text for all your ad assets with a single click. Your LLM of choice can also spin out halfway acceptable copy with the right prompt.
These tools can provide a helpful starting point, but they shouldn’t be the final result you use without careful review. Don’t skip the human touch when reviewing the copy you get back.
Problems can range from copy that doesn’t reflect your brand voice to flat-out inaccuracies. In industries such as finance and healthcare, where legal guidelines matter, AI-generated copy may not be compliance-friendly.
It’s not enough to claim that you’re the “Best Local Contractor” in your area. Think of concrete ways to reinforce superlative statements like this.
For instance, “Voted Best Local Contractor by [News Outlet]” provides a tangible source for the claim. Mention awards or rankings from organizations your prospective customers are likely to recognize.
Incorporating numbers, where possible, also helps bring credibility to your messaging claims.
Years in business. If you’ve been around a long time, stating this positions you well against newer players in the market.
Number of customers served.
Number of locations for physical businesses.
Number of connectors for a software product.
Number of active users.
Number of trips booked.
Number of properties managed.
One word of caution: If you include numbers that are likely to change over time, such as how many customers you serve, revisit them periodically and update them for accuracy. Ranges are fine, too, for example, “Over 500 Locations.”
5. Highlight ease of effort
In today’s busy culture, saving time and hassle can be one of your biggest selling points. Think about where the product or service you’re promoting can reduce effort for your target audience.
Open an account in 10 minutes.
Complete your application online.
Schedule a same-day appointment.
Conduct your consultation remotely.
Repairs done while you wait.
Make sure you can back up what you promise here, and consider whether current customer reviews reflect the experience your claims describe.
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6. Offer a ‘free’ hook
Just like free samples at Trader Joe’s, mentions of “free” in ad copy immediately draw a user’s attention. What can you offer as a free entry point for potential customers?
Free demo.
Free trial.
Bonus for new customers.
Free college application.
Free quote.
Free content, such as ebooks, whitepapers, or webinars.
Whether it’s a trial of a software product or a free visit to your home to assess what’s needed for pest control, this type of offer can be what convinces prospects to fill out a form and enter your sales funnel.
For instance, Strayer University highlights, “Pass 3 Bachelor’s Courses, Earn 1 Tuition Free.” In an age of skyrocketing college costs, that’s an attractive reason to click and learn more.
7. Turn off automated assets
If you’re not careful with your account settings, Google and Microsoft can automatically generate assets, from ad copy to sitelinks, without your review. That can create concerns for compliance and for overall messaging accuracy.
Make sure you turn off this option at the account level to avoid issues with unwanted copy or unexpected links to irrelevant pages.
8. Highlight pricing where it makes sense for your brand
When people are comparison shopping, they usually want quick visibility into cost. Of course, providing pricing may be more or less straightforward depending on your business, and price isn’t always a primary selling point for every brand.
If you’re in an industry where showing a cost is simple, including it in your ad copy can help. When your pricing is competitive, mentioning it helps you stand out.
If your pricing is higher than most competitors, showing that cost may help filter out people you don’t want clicking your ads. For example, lower-priced competitors may cater to small businesses, while your company serves enterprise-level organizations that need more robust solutions.
If you offer multiple price tiers or clearly defined costs for different services, consider using price assets to highlight them. For example, you might break out cost by number of users for a SaaS product.
9. Mention locations in regional campaigns
If your business serves a particular region, mention locations in your ad copy to create a local connection.
For example, if you just opened a new store in Buckwheat County, including “Now Open in Buckwheat County” can help appeal to users in that area. Your ad will likely stand out against national brands running generic messaging.
You can set up ad groups based on regional keywords and tweak your headlines to reference those locations. Also consider using location insertion to dynamically include regions in your copy.
Now that we’ve covered ways to improve your paid search copy, take a moment to review your current ads.
Where can you better think through how assets combine?
What value propositions aren’t you mentioning yet?
How can you tailor your wording more directly to customers’ concerns, such as by highlighting pricing or regions?
Start creating new copy variants and testing them to improve your PPC performance.
Your ad doesn’t compete in isolation — it competes in the SERP
Paid search success isn’t about filling every field or chasing an Excellent ad strength score. It’s about how your messaging appears next to competitors in the SERP.
Review your ads in context. Look at how assets combine. Strengthen value propositions, highlight what makes you different, and test new variations.
If your ad sounds like everyone else’s, it won’t stand out. Make sure it does.
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Advertisers contacting Google Ads support may now need to grant explicit authorization before they can even submit a help request — giving a Google specialist permission to access and make changes directly inside their account.
Here’s what’s happening. Users are first routed to a beta AI chat. If they opt to submit a support form instead, they must tick an “Authorisation” box. The wording allows a Google Ads specialist, on behalf of the company, to reproduce and troubleshoot issues by making changes directly in the account.
The fine print is clear. Google doesn’t guarantee results. Any adjustments are made at the advertiser’s own risk. And the advertiser remains solely responsible for the impact on campaign performance and spending.
Why we care. The required checkbox shifts more responsibility onto advertisers at a time when automation and AI already limit hands-on control. If support makes changes, the performance and spend risk still sits with the advertiser.
Between the lines. This creates a trade-off between speed and control. Granting access could accelerate troubleshooting, but it also opens the door to account-level changes that may affect live campaigns — without any assurance of improved outcomes.
The bottom line. Getting support may now mean temporarily handing over the keys — while keeping full accountability for whatever happens next.
First seen. This new caveats to getting support was spotted by PPC specialist Arpan Banerjee who shared spotting the message on LinkedIn.
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Demand Gen marks a shift in Google Ads toward visual advertising beyond keywords and text. Relying on traditional strategies when testing it wastes budget, hurts performance, and limits opportunity. To succeed, you have to think more like a social advertiser than a search advertiser.
At SMX Next, Industrious Marketing owner Jack Hepp explained why many businesses struggle with demand gen campaigns — especially in B2B and lead generation — while also sharing insights relevant to ecommerce.
Understanding the Shift: From Intent to Interruption
Demand Gen reflects Google’s shift from intent-first search advertising to visual, discovery-based campaigns.
Instead of targeting users actively searching for your service, you reach them as they scroll through YouTube, Gmail, or Discovery feeds.
This changes your approach: visual creative becomes the new keyword, replacing traditional targeting.
Common misalignments in Demand Gen strategy
Applying outdated search strategies can lead to failure with Demand Gen. The four main mistakes:
Expecting bottom-of-funnel CPAs from mid-funnel traffic.
Using overly broad, “spray and pray” targeting.
Running bland, generic creative.
Not knowing how to optimize without negative keywords.
Success requires a social advertising mindset.
Campaign structure: Understanding the hierarchy
Demand Gen uses a two-level structure.
Campaign-level settings control broad parameters like bidding strategy, conversion goals, and device targeting.
Ad group–level settings control audiences, locations, and channels.
Each ad group learns independently—insights don’t transfer—allowing precise audience segmentation with tailored creative.
Creating interruption-based creative
You must stop their scroll within 3-4 seconds. Your creative must capture attention immediately, speak to a specific pain point, and present your solution.
Unlike search ads — where users are actively looking for you — Demand Gen interrupts browsing, so your message must be instantly compelling and problem-focused.
Aligning visuals to the customer journey
Match your offer to audience readiness.
Cold audiences need educational content like free guides or diagnostic tools.
Warm audiences respond to case studies, webinars, and comparison tools.
Hot audiences are ready for demos and direct purchase offers.
Misaligning them — like pushing demos to cold audiences — guarantees failure from the start.
The power of problem-focused creative
Generic ads with stock photos and basic headlines get scrolled past. Winning creative uses bold headlines, striking visuals, and problem-focused messaging.
For example, “43% of cyberattacks target small businesses” speaks to a specific pain point, making the ad stand out and prompting engagement instead of a scroll.
Bidding and budget strategies
Demand Gen uses campaign goals rather than traditional bidding strategies: conversion-focused, click-focused, or conversion–value–focused.
Aim for 50+ conversions per month and budget 10–15x your target CPA to build enough data.
For click-based bidding, set budget based on desired traffic volume and target CPC.
Demand Gen is highly data-reliant, so hitting these thresholds is critical to performance.
Can Demand Gen work with small budgets?
Yes, with strategic planning.
Focus on mid- or upper-funnel audiences and optimize for MQLs instead of bottom-funnel conversions. This helps you reach 50+ monthly conversions for data density, even with smaller budgets.
Align your goals, targeting, and budget to generate enough conversion data.
Building the right audience
Avoid two extremes:
Audiences that are too broad (billions of impressions) where Google can’t identify your target.
Audiences too narrow (a few thousand impressions) where you can’t build data density.
The sweet spot: start with custom segments based on search terms or competitor websites, then layer in lookalike segments and strategic first-party data. Avoid optimized targeting at first — it works best to expand already successful campaigns.
The role of creative in targeting
Your creative shapes who Google targets. The people who engage with your ads teach Google who to show them to next.
Performance peaks when your creative speaks to your ideal customer profile. Align messaging to the buyer’s stage — cold audiences need different messaging than hot prospects.
Strategic exclusions
Use exclusions surgically, not broadly. It’s tempting to exclude like negative keywords, but over-excluding shrinks your audience too much.
Focus only on clear non-converters (e.g., specific age groups, locations, or audiences you know won’t respond). Give Google room to find engaged users within your parameters, rather than narrowing to the point of ineffectiveness.
Optimization: Where to focus
Without negative keywords, optimize through three levers: creative, audience, and offer. Test multiple formats (video, image, carousel) and styles (UGC, testimonials, problem-focused messaging). Continuously refine what works with new hooks and data points.
Test offers to match audience readiness — cold audiences need educational content, while hot audiences need direct CTAs.
Prioritize post-click optimization: improve landing pages, strengthen tracking with CRM integration, and ensure clean data feeds Google’s learning.
Real-world case study
A telecommunications company targeting B2B managed IT services drove strong results by aligning all three elements.
Offer: An interactive quiz showing businesses how managed IT could reduce costs.
Targeting: Custom segments based on proven search terms and competitor website visitors.
Creative: Problem-focused messaging about cybersecurity threats to small businesses.
Results:
$10 cost per MQL.
3.8% conversion rate.
40% of quiz takers became SQLs.
20% increase in total SQLs.
Key takeaways
As you plan your next campaign:
Match your creative to your customer and their stage in the journey.
Target the right audience at the right point in that journey.
Test and optimize creative and offers to find what resonates and drives action.
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Most SEO professionals give Google too much credit. We assume Google understands content the way we do — that it reads our pages, grasps nuance, evaluates expertise, and rewards quality in some deeply intelligent way. The DOJ antitrust trial told a different story.
Under oath, Google VP of Search Pandu Nayak described a first-stage retrieval system built on inverted indexes and postings lists, traditional information retrieval methods that predate modern AI by decades. Court exhibits from the remedies phase reference “Okapi BM25,” the canonical lexical retrieval algorithm that Google’s system evolved from. The first gate your content has to pass through isn’t a neural network. It’s word matching.
Google does deploy more advanced AI further down the pipeline, including BERT-based models, dense vector embeddings, and entity understanding systems. But those operate only on the much smaller candidate set traditional retrieval produces. We’ll walk through where each technology enters the process.
This matters for content optimization tools like Surfer SEO, Clearscope, and MarketMuse. Their core methodology — a mix of TF-IDF analysis, topic modeling, and entity evaluation — maps directly to how that first retrieval stage scores documents. The tools are built on the right foundation. The problem is that most people use them incorrectly, and the studies backing them have real limitations.
Below, I’ll explain how first-stage retrieval works and why it still matters, what the research on content scoring tools actually shows — and doesn’t show — and most importantly, how to use these tools to produce content that earns its way into the candidate set without wasting time chasing a perfect score.
How first-stage retrieval works and why content tools map to it
Best Matching 25 (BM25) is the retrieval function most commonly associated with Google’s first-stage system.
Nayak’s testimony described the mechanics it formalizes: an inverted index that walks postings lists and scores topicality across hundreds of billions of indexed pages, narrowing the field to tens of thousands of candidates in milliseconds.
Here’s what matters for content creators:
Term frequency with saturation: The first mention of a relevant term captures roughly 45% of the maximum possible score for that term. Three mentions get you to about 71%. Going from three to thirty adds almost nothing. Repetition has steep diminishing returns.
Inverse document frequency: Rare, specific terms carry more scoring weight than common ones. “Pronation” is worth roughly 2.5 times more than “shoes” in a running shoe query because fewer pages contain it.
Document length normalization: Longer documents get penalized for the same raw term count. All of these scoring algorithms are essentially looking at some degree of density relative to word count, which is why every content tool measures it.
The zero-score cliff: If a term doesn’t appear in your document at all, your score for that term is exactly zero. Not low. Zero. You’re invisible for every query containing it.
That last point is the single most important reason content optimization tools have value. If you write a comprehensive rhinoplasty article but never mention “recovery time,” you score zero for that entire cluster of queries, regardless of how good the rest of your content is.
Google has systems like synonym expansion and Neural Matching — RankEmbed — that can supplement lexical retrieval and surface additional documents. But counting on those systems to rescue a page with vocabulary gaps is a risky strategy when you can simply cover the term.
After first-stage retrieval, the pipeline gets progressively more expensive and more sophisticated. RankEmbed adds candidates keyword matching missed. Mustang applies roughly 100+ signals, including topicality, quality scores, and NavBoost — accumulated click data over 13 months, described by Nayak as “one of the strongest” ranking signals.
DeepRank applies BERT-based language understanding to only the final 20 to 30 results because these models are too expensive to run at scale. The practical implication is clear: no amount of authority or engagement signals helps if your page never passes the first gate. Content optimization tools help you get through it. What happens after is a different problem.
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What the research on content tools actually shows
Three major studies have examined whether content tool scores correlate with rankings: Ahrefs (20 keywords, May 2025), Originality.ai (~100 keywords, October 2025), and Surfer SEO (10,000 queries, July 2025). All found weak positive correlations in the 0.10 to 0.32 range.
A 0.24 to 0.28 correlation is actually meaningful in this context. But these numbers need serious qualification. Every study was conducted by a vendor, and in every case, the vendor’s own tool performed best.
No study controlled for confounding variables like backlinks, domain authority, or accumulated click data. The methodology is fundamentally circular: the tools generate recommendations by analyzing pages that already rank in the top 10 to 20, then the studies test whether pages in the top 10 to 20 score well on those same tools.
The real question — whether following tool recommendations helps a new, unranked page climb — has never been rigorously tested. Clearscope’s Bernard Huang put it directly: “A 0.26 correlation is not the brag they think it is.”
He’s right. But a weak positive correlation is exactly what you’d expect if these tools solve the retrieval problem — getting into the candidate set — without solving the ranking problem — beating competitors once there. Understanding that distinction is what makes these tools useful rather than misleading.
Why not skip these tools altogether?
Expert writers are terrible at predicting how their audience actually searches. MIT Sloan’s Miro Kazakoff calls it the curse of knowledge. Once you know something, you forget what it was like before you knew it.
Clearscope’s case study with Algolia illustrates the problem precisely. Algolia’s writers were technical experts producing genuinely excellent content that sat on Page 9. The problem wasn’t quality. The team was using internal jargon instead of the language their audience actually typed into Google.
After adopting Clearscope, their SEO manager Vince Caruana said the tool helped the organization “start writing for our audience instead of ourselves” by breaking out of internal vocabulary. Blog posts moved from Page 9 to Page 1 within weeks. Not because the writing improved, but because the vocabulary finally matched search behavior.
Google’s own SEO Starter Guide acknowledges this dynamic, noting that users might search for “charcuterie” while others search for “cheese board.” Content optimization tools surface that gap by showing you the actual vocabulary of pages that have already demonstrated retrieval success.
You can do everything a tool does manually by reading top results and noting common themes, but the tools automate hours of SERP analysis into minutes. At $79 to $399 per month, the investment is justified when teams publish frequently in competitive niches or assign work to freelancers lacking domain expertise. For a solo blogger publishing once or twice a month, manual analysis works fine.
What about AI-powered retrieval?
Dense vector embeddings are the same core technology behind LLMs and AI-powered search features. They compress a document into a fixed-length numerical representation and can match semantically similar content even without shared keywords. Google uses them via RankEmbed, but they supplement lexical retrieval rather than replace it.
The reason is computational: A 768-dimensional embedding can preserve only so much information, and research from Google DeepMind’s 2025 LIMIT paper showed that single-vector models max out at roughly 1.7 million documents before relevance distinctions break down — a small fraction of Google’s index. Multiple studies, including findings on the BEIR benchmark, show hybrid approaches combining BM25 with dense retrieval outperform either method alone.
The bottom line for practitioners is clear: The AI layer matters, but it sits lower in the pipeline, and the traditional retrieval stage your content tools map to still does the heavy lifting at scale.
This is where most guidance on content tools falls short. The typical advice is “use Surfer/Clearscope, get a high score, rank better.”
That misses the point entirely. Here’s a framework built on how these tools actually intersect with Google’s retrieval mechanics.
Prioritize zero-usage terms over everything else
The highest-leverage action these tools identify is a term with zero mentions in your content. That’s a term where your retrieval score is literally zero, and you’re invisible for every query containing it. Going from zero to one mention is the single most impactful edit you can make. Going from four mentions to eight is nearly worthless because of the saturation curve.
When reviewing tool recommendations, filter for terms you haven’t used at all. Clearscope’s “Unused” filter does this explicitly.
Ask yourself: Does this missing term represent a subtopic my audience would expect me to cover? If yes, work it in naturally. If the tool suggests a term that doesn’t fit your angle — a beginner’s guide doesn’t need advanced technical terminology — skip it.
A high score achieved by forcing irrelevant terms into your content is worse than a moderate score with genuinely useful writing. As Ahrefs noted in its 2025 study, “you can literally copy-paste the entire keyword list, draft nothing else, and get a high score.” That tells you everything about the limits of chasing the number.
Be selective about which competitor pages you analyze
Default settings on most tools pull from the top 10 to 20 ranking pages, which frequently includes Wikipedia, major media outlets, and enterprise sites with overwhelming domain authority. These pages often rank despite their content, not because of it. Their term patterns reflect authority advantage, not content quality, and they’ll skew your recommendations.
A better approach: Look for pages that rank for a high number of organic keywords on mid-authority domains.
Ahrefs’ data shows the average page ranking No. 1 also ranks in the top 10 for nearly 1,000 other keywords. A page ranking for 500 keywords on a DR 35 site has demonstrated broad retrieval success through vocabulary and topical coverage, not just backlinks. Those pages contain term patterns proven effective across hundreds of separate retrieval events, not just one.
In most tools, you can manually exclude specific URLs from competitor analysis. Remove the Wikipedia pages, the Amazon listings, and any high-authority site where you know authority is doing the work. What’s left gives you a much cleaner picture of what content actually needs to include.
Use tools during research, not during writing
The worst workflow is writing with the scoring editor open, watching your number tick up in real time. That pulls your attention toward keyword insertion instead of communicating expertise. Practitioners reporting the worst experiences with these tools tend to be the ones writing to a live score.
The better workflow: Run the tool first. Review the term list. Identify gaps in your outline, especially terms with zero usage that represent subtopics you should cover. Then close the tool and write for your reader.
Run it again at the end as a sanity check. Did you miss any major subtopics? Add them. Is the score significantly lower than competitors? That’s information worth investigating. But your job is to build the best page on the internet for this topic, not to match a number.
Understand that content is one player in the game
NavBoost, RankEmbed, PageRank-derived quality scores, site authority, click data, and engagement signals all operate on the candidate set that first-stage retrieval produces. Content optimization gets you through the gate. It doesn’t win the race.
If you optimize a page, push the score to 90, and don’t see ranking improvements, that doesn’t mean the tool failed. It likely means the other ranking factors — backlinks, domain authority, and click signals — are doing more work for your competitors than content alone can overcome.
This is especially important when scoping on-page optimization projects. Be honest about what content changes can and can’t accomplish. If a page is on a DR 15 domain competing against DR 70+ sites, perfect content optimization is necessary but probably not sufficient.
When a client asks why they’re not ranking after you pushed their score to 95, the answer shouldn’t be “we need more content.” It should be a clear explanation of which part of the problem content solves — retrieval — which parts it doesn’t — authority, engagement, brand — and what the next strategic move actually is.
Focus on going beyond, not just matching
The philosophy behind these tools — structure your content after what top results cover — is sound. You need to demonstrate topical relevance to enter the candidate set. But the goal isn’t to produce another version of what already exists.
The pages that rank broadly, the ones that show up for hundreds or thousands of keywords, consistently do more than match the competitive baseline. They add original research, practitioner experience, specific examples, or angles the existing results don’t cover.
Surfer SEO’s December 2024 study supports this. It measured “facts coverage” across articles and found that top-performing content by keyword breadth had significantly higher coverage scores than bottom performers.
The content that ranks for the most queries doesn’t just include the right terms. It includes more information, more specifically. Use the tool to establish the floor of topical coverage. Then build the ceiling with value the tool can’t measure.
A note on entities
Google’s Knowledge Graph contains an estimated 54 billion entities. Entity understanding becomes most powerful in the later ranking stages where BERT and DeepRank process final candidates.
Some content tools are starting to incorporate entity analysis, but even the best versions present entities as flat keyword lists, missing the relationships between entities that Google’s systems actually evaluate.
Knowing that “Dr. Smith” and “rhinoplasty” appear on your page is different from understanding that Dr. Smith is a board-certified surgeon with published research at a specific institution. That relational depth is what Google processes, and no content scoring tool currently captures it.
Treat entity coverage as an additional layer beyond what keyword-focused tools measure, not a replacement for the fundamentals.
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Retrieval before ranking
Content optimization tools work because they’ve reverse-engineered the vocabulary of the retrieval stage. That’s a less exciting claim than “they’ve cracked Google’s algorithm,” but it’s the honest one, and it’s supported by what the DOJ trial revealed about Google’s infrastructure.
Use these tools to identify missing terms and subtopics. Be skeptical of exact frequency targets. Exclude high-authority outliers from your competitor analysis. Prioritize zero-usage terms over further optimization of terms you’ve already covered.
Understand that a perfect content score addresses one stage of a multi-stage pipeline and use the competitive baseline as your floor, not your ceiling. The content that ranks the broadest isn’t the content that best matches what already exists. It’s the content that covers what already exists and then goes further.
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SerpApi is asking a federal court to dismiss Google’s lawsuit, arguing the company is misusing copyright law to restrict access to public search results.
The motion was filed Feb. 20, according to a blog post by SerpApi CEO and founder Julien Khaleghy.
Google sued SerpApi in December, alleging it bypassed technical protections to scrape and resell content from Google Search.
The details: SerpApi argues Google is improperly invoking the Digital Millennium Copyright Act (DMCA). According to Khaleghy:
The DMCA protects copyrighted works, not websites or ad businesses.
Google doesn’t own the underlying content displayed in search results.
Accessing publicly visible pages isn’t “circumvention” under the statute.
Google’s complaint alleged SerpApi:
Circumvented bot-detection and crawling controls.
Used rotating bot identities and large bot networks.
Scraped licensed content from Search features, including images and real-time data.
SerpApi said it doesn’t decrypt systems, disable authentication, or access private data. Khaleghy said SerpApi retrieves the same information available to any user in a browser, without requiring a login.
Khaleghy also argued Google admitted its anti-bot systems protect its advertising business — not specific copyrighted works — which he said undermines the DMCA claim.
SerpApi cites the Ninth Circuit’s hiQ v. LinkedIn decision warning against “information monopolies” over public data. It also cites the Sixth Circuit’s Impression Products v. Lexmark ruling to argue that public-facing content can’t be shielded by technical measures alone.
Catch up quick: The lawsuit follows months of escalating legal fights over scraping and AI data use.
Oct. 22:Reddit sued SerpApi, Perplexity, Oxylabs, and AWMProxy in federal court, alleging they scraped Reddit content indirectly from Google Search and reused or resold it. Reddit claimed the companies hid their identities and scraped at “industrial scale.” Reddit said it set a “trap” post visible only to Google’s crawler that later appeared in Perplexity results. Reddit is seeking damages and a ban on further use of previously scraped data.
Dec. 19:Google sued SerpApi, alleging it bypassed security protections, ignored crawling directives, and scraped licensed Search content for resale. SerpApi responded that it operates lawfully and that accessing public search data is protected by the First Amendment.
By the numbers: SerpApi claims that, under Google’s interpretation of the DMCA, statutory damages could theoretically total $7.06 trillion — a figure it said exceeds U.S. GDP. The number reflects SerpApi’s calculation of potential per-violation penalties, not an actual damages demand.
What’s next. The case now moves to the court’s decision on whether Google’s claims can proceed.
Why we care: The outcome could reshape how SEO platforms, AI tools, and competitive intelligence software access SERP data. A win for Google could make third-party search data harder or riskier to obtain. A win for SerpApi could strengthen arguments that publicly accessible search results can be scraped and collected.
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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.
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.
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:
The exact moment or event described (quote the language they use)
The type of trigger (situational, temporal, emotional, or goal-driven)
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:
Most common words and phrases people use to describe positive experiences
Most common words and phrases that signal frustration or concerns
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 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:
The top 3-5 platforms where customers discovered us or researched solutions
Common pain points or information needs they mentioned
Preferred content formats for learning about solutions
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).
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.
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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.
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.
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:
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.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-20 17:50:472026-02-20 17:50:47How a 200-Person Company Competes with a $160B Giant in AI Search
