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Together with AirOps

You’re Still Fighting for Rank 1. That Fight Ended.

Buyers ask ChatGPT before they ever hit your site. If it can’t find you there, you don’t exist to them.

This isn’t a future-proofing project. It’s one live session that hands you a 30-day plan to run the next month, and Josh Grant (founder of StackedGTM, former VP of Growth at Webflow) walks through exactly how on August 5 at 2pm ET.

• Take home the exact changes that make your comparison pages the ones AI quotes

• Get a checklist for making your pricing page the one AI trusts enough to cite word for word

• Leave with a measurement framework that finally shows leadership what AI search is worth

Run your own pages through Josh’s AEO framework live, and leave knowing precisely why AI is or isn’t citing you.

Secure your free spot

Can’t make it live? Register anyway and get the recording in your inbox within 24 hours.

🪄 The Symptom, the Diagnosis, and Three Fixes for a Subscription Box’s Quiet Churn Problem

The symptom: a monthly subscription box brand’s churn rate has been creeping up for two quarters, and every exit survey response says something vague, “didn’t need it anymore,” Nothing actionable, nothing the retention team can build a campaign around.

The diagnosis: the billing reminder email links out to a subscription management portal, a real piece of friction between the reminder and any action smaller than canceling outright.

A customer who wanted to skip one box or swap a flavor faces more clicks to do that than to just hit cancel, which sits one click away inside the same email.

The portal isn’t causing churn through bad service. It’s causing churn through asymmetric friction, making the wrong outcome the easy one.

Fix 1: Move skip, pause, and swap into the email itself. At minimum, make those links as prominent and immediate as the cancel link, landing on a pre-filled single-step confirmation rather than a full portal login. One subscription brand that made this change alone cut cancellations by roughly half.

Fix 2: Capture the real reason at the moment of pause, not after the fact. A one-tap reason selector, too much product, financial, trying something else, attached to the pause confirmation feeds a genuinely targeted win-back sequence.

“Too much product” gets offered a smaller size or lower frequency before the standard win-back timer even starts. “Financial” gets a different, lighter-touch offer entirely. One vague exit survey response gets replaced with a specific, actionable signal captured at the exact moment it’s freshest.

Fix 3: Build a predictive pause offer that fires before the cancel click, not after. Customers showing the early behavioral pattern that historically precedes a skip, a drop in app engagement, or a change in browsing pattern receive a proactive frequency or size-down offer ahead of the decision, rather than a reactive win-back after they’ve already left.

Auditing the current flow for click asymmetry and building reason-capture into the pause flow are real work most retention teams know they should do and never quite get to.

Say “build it” with Omnisend connected to ChatGPT or Claude, and Omnisend can create the segment, restructure the email, and prepare a draft for your review without leaving the conversation. Strategy three’s early-warning model is a genuinely harder build, worth scoping with an analyst before treating it as something any single tool can deliver from one prompt. You can get started with ready-made prompts.

If cancel wins the click race and nothing captures why someone left, the flow is the churn driver, not the product, and no amount of better retention copy fixes friction sitting one click upstream of the message itself.

📊 Meta Is Strengthening Every Part Of Its Ad Ecosystem

Meta introduced a series of updates this week that reinforce its position across consumer reach, AI advertising, and campaign measurement.

Its audience remains unmatched: Facebook, Instagram, and WhatsApp now each have more than 3 billion monthly active users, while Threads has grown to 500 million users, giving advertisers access to the largest social ecosystem despite intensifying competition.

AI is taking on more of the creative work: Meta’s upgraded Advantage+ Creative can now rewrite headlines directly on existing ad images without changing the design, while improved AI-generated copy and brand controls help advertisers create and test more creative variations with less manual work.

Offline sales are becoming measurable: Through a new Lightspeed integration, brick-and-mortar retailers can automatically send point-of-sale transactions into Meta’s Conversions API, allowing campaigns to optimize using real in-store purchases instead of relying only on website conversions.

Why It Matters

Meta is making it easier to reach customers, generate creative assets, and measure real business outcomes from a single platform. Together, these updates strengthen its position as a full-funnel advertising platform for both online and physical retailers.

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💪 Eight to Ten. That’s It. Doesn’t Matter How Big the Account Is.

There are two camps on how many ads to launch each week. One says launch as much as possible, volume drives CAC down. The other says heavy volume resets learning and tanks performance. Tested against real account data, sized for significance, both are half right.

Same week, cramming in the most launches an account could handle was the worst outcome for CAC across the board. A moderate, steady cadence was the most efficient.

The number that kept appearing:

• 8 to 10 launches a week, regardless of account size

• A $10K-a-week account and a $150K-a-week account landed in the same range

• Bigger accounts didn’t launch more, they poured extra budget into proven winners instead

And the timing matters as much as the number. The payoff from a given week’s launches didn’t show up that week at all:

• ROAS improved at the two-week mark in 9 of 12 accounts tested

• Judged against tomorrow’s dashboard, a launch sprint looks like nothing happened

• Judged two weeks out, the picture is completely different

Pick a steady weekly number in the 8 to 10 range and hold it, regardless of how the account is spending that week. Resist the urge to flood it when things feel slow. Judge results on a two-week lag, not a same-week read.

Together with Omnisend

See What Your Email Account Needs in 30 Seconds

You probably know your open rate. The harder question is what needs fixing next, and finding that answer can take longer than the campaign itself.

Omnisend just made checking in a 30-second habit. It connects straight to Claude and ChatGPT, so you can ask your account what’s going on, and Omnisend AI does the fixes for you without leaving the chat.

You don’t even need to write the prompts yourself. There’s a full library already built, sorted from beginner to advanced.

• Beginner: Get an instant health snapshot, rank campaigns by revenue, or check deliverability.

• Intermediate: Compare weekly performance, analyze subject-line patterns, locate automation revenue leaks, and do the fixes.

• Advanced: Score the complete account, build a re-engagement campaign for you, or replace two hours of monthly reporting.

Built on feedback from 150,000+ brands, Omnisend users generate an average of $79 for every $1 spent.

Switching takes zero effort since Omnisend migrates you for free within 5 days, and your bill drops up to 35%.

Connect your AI and run your first prompt today.

🔍 Two New Studies Reveal How To Win AI Search

Two new reports show that AI visibility depends on far more than your website. They also suggest Google’s new AI controls could create unexpected trade-offs for publishers.

AI knows your brand but rarely recommends it: Victorious found AI correctly recognized 96% of brands, yet 89% never appeared in category recommendations. Instead, AI visibility was most closely tied to third-party mentions and referring domains, not just rankings or backlinks.

The buyer journey changes what AI trusts: For early-stage questions, AI mainly cited educational content, often without naming the brand. During product comparisons, it relied on directories and comparison sites, where brands were much more likely to be mentioned.

Google’s AI opt-out could reduce visibility: NewzDash found Google increasingly placing Top Stories inside AI Overviews. It believes publishers using Google’s AI opt-out may lose visibility in those carousels, although Google has not confirmed this interpretation.

Why It Matters

Success in AI search now depends on building authority beyond your own website. At the same time, publishers may soon have to balance AI opt-outs against the risk of losing valuable search visibility.

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Together with Omnisend

The AI prompt that writes your monthly email report for you

Want your monthly marketing report without spending an afternoon on it? Now that Omnisend works with Claude and ChatGPT, you can just ask for it.

Ask your AI: “Give me a monthly summary of my Omnisend account I can share with my team. What worked, what didn’t, how this month compared to last month across campaigns, automations, revenue, and subscribers, and what we should focus on next month.”

It reads your real account and hands back a report that’s ready to send, not a template you still have to fill in.

Not using Omnisend yet? Join more than 150,000 brands generating an average of $79 for every $1 spent.

Migration is free and takes up to five days, while pricing is up to 35% lower than Klaviyo’s.

Get started with ready-made prompts

📺 Don’t skip TV just because you cannot measure it like digital

The myth keeping TV out of most DTC media plans: it’s a brand-awareness play, unmeasurable at the level performance marketers actually operate, a bet you make on faith and check back on months later with a lagging brand-lift study that tells you what already happened.

The reality is that the measurement gap isn’t inherent to the channel, it’s inherent to how most brands still buy it, through legacy insertion orders that report reach and impressions and stop there. Impressions were never the number that mattered.

Whether a specific dollar of spend produced an actual site visit, signup, or purchase is the number that matters, and that’s a measurement architecture problem, not a limitation of television as a medium.

Saatva saw a 40% lift in branded search from their very first test on a platform built around this distinction, tying every dollar back to actual site visits, signups, and revenue rather than impressions that look good on a report and prove nothing about what they produced.

That’s the same rigor performance marketers already apply to Meta and Google, applied to a channel most of them assumed couldn’t support it.

The practical test before writing off TV entirely: ask whatever platform or vendor is being considered one direct question. Can spend be tied to site visits and revenue by specific flight, specific creative, specific daypart, the same granularity already expected and demanded from a Meta ad account before a single dollar gets approved.

If the answer is a brand-lift study landing in eight weeks, that’s the legacy version of the channel. If the answer is a dashboard showing exactly where every dollar ran and what it produced, that’s the version worth actually testing.

Tatari runs linear TV, streaming, and direct publisher buys from one platform built specifically around this question, showing exactly where every dollar ran and what it produced instead of a reach number and a hope. Aroma360 cut CPA by 80% and doubled ROAS from a $14K linear pilot using exactly this measurement approach.

Every week spent assuming TV can’t be measured the way digital can is a week a competitor in the same category spent actually testing whether it can, and coming back with a branded search lift or a CPA number that makes the assumption look expensive in hindsight. You can book a free demo and get a launch-ready TV plan built around your numbers.

📢 Google Simplifies Smart Bidding And AI Disclosures

Google is rolling out updates that make campaign setup simpler while adding new AI transparency tools for advertisers.

Smart Bidding Gets Clearer: Target CPA and ROAS now appear as standalone bidding strategies instead of being hidden within Maximize Conversions, making campaign setup more intuitive ahead of Google’s August bidding changes.

Built-In AI Labels Arrive: Advertisers can now add AI-generated content labels directly to image and video assets across Google Ads, DV360, Merchant Center, Campaign Manager 360, and Ads Editor.

Built For Compliance: The labels are designed to help advertisers meet growing AI transparency requirements, while Google may also automatically label creatives generated using its own AI tools.

Why It Matters
Google is making campaign management simpler while helping advertisers prepare for stricter AI disclosure rules and upcoming Smart Bidding updates.

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Together with Omnisend

How healthy is your email setup, really?

You probably know your open rate. But do you know how healthy your email marketing setup actually is?

Now that Omnisend works with Claude and ChatGPT, all it takes is a simple prompt.

Ask Omnisend AI to score your account out of 100 based on campaign cadence, automation coverage, and catalog completeness, then list the top three areas to improve.

In seconds, you’ll get a clear picture of how your account is performing and where to focus next.

Not yet an Omnisend user? Brands using Omnisend generate an average of $79 for every $1 spent. Migration is free and takes up to five days, while pricing is up to 35% lower than Klaviyo’s.

Get started with ready-made prompts.

📊 Amazon Is Already Running Your Diagnostic. It’s Just Not Sharing the Results.

The instinct when conversion is flat is to run a manual check: split CVR by traffic source, Sponsored Products against organic, and see which one is dragging the number down.

That’s a genuinely useful test. It’s also not the full picture, because A10 doesn’t wait for a seller to run it manually. The algorithm reads the gap between PPC conversion rate and organic conversion rate as a relevance signal on its own, continuously, whether or not anyone on the team has ever pulled that comparison.

Buyers arriving through PPC converting at a meaningfully lower rate than buyers arriving through organic search isn’t just a symptom a seller might eventually notice on their own schedule.

It’s an input A10 is already weighing when deciding organic placement, which means a persistent gap isn’t sitting quietly waiting to be discovered whenever it’s convenient. It’s actively suppressing organic rank in real time, every day the gap exists, independent of whatever conclusion the seller eventually reaches about it or how long that takes.

This changes the urgency of running the check, not just the value of running it. A seller who assumes the diagnostic is optional, something to get to once ACoS looks concerning, is missing that the algorithm has already drawn its own conclusion and started acting on it weeks or months earlier.

The manual split still matters, since it tells a seller which side of the gap to fix first. What most sellers miss is that the gap itself is being read and penalized continuously, in the background, long before anyone gets around to running the check that would have revealed it.

Closing the gap means bringing organic-quality intent to the listing from outside Amazon, buyers who convert well because they arrived already wanting the specific product, not because an ad happened to catch them mid-scroll.

Stack Influence sources exactly that: creators who purchase the ASIN with their own money and convert like genuinely interested buyers, not ad-driven traffic. You can book a free demo call here.

Run the split this week regardless, since it still tells a seller exactly where to point the fix once the gap is confirmed. But the number that should actually worry a seller isn’t what the split reveals when it finally gets run. It’s how long the gap had already been sitting open, quietly costing organic rank, while nobody on the team thought to check.

📈 ChatGPT Ads And Google Ads Get Major Marketing Upgrades

OpenAI and Google have announced new advertising platform updates aimed at giving marketers better campaign optimization, measurement, and AI-powered management tools.

ChatGPT Ads Expands Performance Features: ChatGPT Ads now supports conversion-optimized CPC campaigns, automatic budget pacing, average daily budgets, geographic exclusions, improved conversion attribution, and new integrations with AppsFlyer and Adjust for mobile app measurement.

Better Automation For Advertisers: The platform also adds bulk API support for managing campaigns at scale and refreshed product feed ads with pricing and star ratings, making campaign management more efficient.

Google Brings More AI To Ads Editor: Google Ads Editor 2.13 adds AI Max support for Shopping campaigns, Customer Retention Goals for Performance Max, Demand Gen Maps placements, Channel Performance reporting, and AI-generated content disclosure controls.

Why It Matters

Both updates strengthen their advertising ecosystems by giving marketers more automation, better measurement, and AI-powered optimization, making it easier to manage and scale performance campaigns across both platforms.

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🎯An aimless start hits the right spots

📢 Google Ads Editor Gets Major AI Upgrade

If you’re new to Buyology then a hearty welcome to you, You’ve reached the right place alongside 50k+ amazing people, Before you forget, if someone forwarded this newsletter to you, don't forget to subscribe to our newsletter so you never miss out!

🎯An aimless start hits the right spots

A woman rambling, unscripted, mid-thought, mid-tangent, no product, no pitch, pulled 136,000 views and nearly 700 comments purely because people wanted to attach something to it.

That’s the tell worth noticing: nobody could say exactly what the clip was about, and that’s precisely why so many people found a reason to engage with it.

A scripted ad commits to one meaning the moment it’s written.

The hook states a claim, the body supports it, the CTA closes it, and that specificity is the entire point of a finished ad. Until it’s time to make a second, the same footage can’t say anything else, because it already said the one thing it was built to say.

Raw, unscripted footage never made that commitment. The same clip of someone genuinely reacting, genuinely explaining, genuinely mid-thought can be recut with an entirely different caption, voiceover, or framing and become a different ad each time, because the raw material never closed off which meaning it was going to carry.

This is a reusability property, not just an authenticity one.

The value of ambiguous raw content isn’t only that it feels more real; it’s that its lack of narrative closure is what lets one piece of footage become the source for several genuinely distinct concepts, targeting different objections or angles, without a second shoot.

A finished, scripted ad is a single-use asset by design. An open, undirected clip is closer to raw material with several finished products still latent inside it.

The practical filter for judging raw footage before it’s edited: does this clip already resolve into one specific claim, or could three different captions turn it into three different ads.

Footage that only supports one obvious cut is closer to a finished ad already, valuable once. Footage flexible enough to carry several unrelated framings is the more valuable source material, valuable repeatedly.

Insense sources exactly this kind of raw, unscripted footage, 20-plus clips per creator with lifetime usage rights, material that hasn’t been pre-committed to one meaning, so it can be cut into several genuinely different concepts instead of one finished ad used once. You can book a free strategy call and get $200 toward your first campaign.

Together with Tatari

When Meta hits a ceiling, the smartest brands look beyond it.

Scaling digital used to be straightforward. Now brands hit a Meta Ceiling where reach stalls and CAC skyrockets.

The brands still compounding aren’t fixing it inside Meta. They’re diversifying.

Tatari is a TV buying platform that lets you run across linear TV, streaming, and direct publisher buys, all from one place.

You see exactly where every ad aired, so nothing is hidden, and nothing is wasted. Every dollar ties back to actual site visits, signups, and revenue not impressions that look good and prove nothing.

• Aroma360 cut CPA by 80% and doubled ROAS from a $14K linear pilot

• Fiverr retargeted Super Bowl viewers on streaming and slashed CAC by 75%

• Saatva saw a 40% lift in branded search from their very first Tatari test

Every week you wait is a week someone in your category is pulling customers you could have reached first.

Book a free demo and get a launch-ready TV plan built around your numbers.

📢 Google Ads Editor Gets Major AI Upgrade

Google has launched Ads Editor 2.13, bringing many of its newest AI-powered advertising features to its offline campaign management tool.

AI Max Comes To Shopping: Advertisers can now manage AI Max for Shopping campaigns directly in Ads Editor, including automated text generation, URL expansion controls, brand lists, and URL exclusions without returning to the web interface.

Performance Max Adds Retention Goals: The update introduces Customer Retention Goals for Performance Max campaigns, allowing advertisers to optimize specifically for re-engaging existing customers.

Demand Gen Gets More Capabilities: Demand Gen campaigns now support Google Maps placements, while responsive and non-skippable video ads gain new Business Name and Business Logo fields for stronger branding.

More AI And Reporting Tools: Ads Editor now includes Channel Performance reporting, AI-generated content disclosure controls, workflow improvements, and officially removes support for creating new Video Action campaigns as Google completes the shift to Demand Gen.

Why It Matters

Google is bringing nearly all of its latest AI advertising capabilities into Ads Editor, making it easier for advertisers managing campaigns at scale to adopt new features without leaving their offline workflow.

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In this newsletter, you’ll find:

😤The Myth: Everyone Gets 20% Off for BFCM. The Reality: You’re Overpaying Half Your List.

🤖 AI Ads Expand Across Platforms

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😤The Myth: Everyone Gets 20% Off for BFCM. The Reality: You’re Overpaying Half Your List.

The myth every DTC founder repeats going into Q4: blanket discounting is simple, fair, and easiest to execute under deadline pressure. One code, one banner, one email. Every customer gets the same 20% off.

The reality sitting in the order history: a real share of any list converts on free shipping alone with zero percentage discount needed, another share only moves with a meaningful percentage on the table, and a third segment buys regardless of any offer at all. Blanket discounting overpays the first and third groups and possibly still underpays the second.

Segment by historical offer response, not purchase recency. Export the last four promotional sends and tag which customers converted on shipping-only offers versus percentage offers versus no offer at all. Three genuinely different incentive needs, currently getting one identical email.

Build a time-decay discount ladder instead of one flat code for the whole window. Open the sale with free shipping only for the first two days, escalate to a modest percentage by day four, and reserve the full 20% for the final 24 hours only. High-intent early browsers convert without needing the deepest discount, and the price-sensitive segment still gets moved before the window closes, without every customer defaulting straight to the maximum incentive from hour one.

Match discount depth to inventory position, not to a blanket revenue target. Full-price hero SKUs get a gift-with-purchase or bundle instead of a percentage off, protecting margin on what’s already selling well. Slow-moving inventory gets the real percentage discount, since that’s the stock actually needing a push to clear before the next season’s drop.

Running all three by hand means cross-referencing order history against past offers and current inventory position manually, product line by product line, under exactly the deadline pressure that makes teams default to the easy flat code instead.

Connect Omnisend to Claude or ChatGPT and say “build it”: your AI creates each of these three segments directly from live revenue and campaign data, ready to review. You can get started with ready-made prompts

Founders defending margin during the highest-spend quarter of the year are the ones this actually protects. The flat 20% code is the easy plan. It’s also the expensive one, and the gap between the two only shows up in the P&L after the season’s already over.

Together with AirOps

Why are your top pages still missing AI’s pipeline

Traffic from AI search is growing, but most teams haven’t figured out how to turn it into a pipeline. Comparison pages, pricing pages, and feature pages either aren’t getting cited or aren’t converting when they are.

On August 5 at 2pm ET, Josh Grant, founder of StackedGTM and former VP of Growth at Webflow, joins AirOps’ Josh Spilker for a tactical session on the plays that turn AEO into revenue this quarter.

You’ll walk away with:

• Why comparison pages are one of the fastest ways to capture mid-funnel AI search demand

• What makes pricing and feature pages more extractable for models to cite and summarize accurately

• How third-party corroboration from sites like G2, Capterra, and Reddit strengthens on-site messaging

Audit your feature pages against Josh’s AEO framework live, and leave with a ranked fix-it list

Secure your free spot

Can’t make it live? Register anyway and get the recording in your inbox within 24 hours.

🤖 AI Ads Expand Across Platforms

AI assistants are evolving into advertising platforms, while new research suggests brands will need different strategies for paid placements, AI citations, and audience reach.

The Breakdown:

ChatGPT Opens More Ad Space - OpenAI is expanding ChatGPT’s advertising tests by showing two ads from different advertisers within a single response, signaling a broader rollout of ad inventory inside the chatbot.

Google Treats Ads And Citations Differently - A recent study found Google AI Mode displayed ads on 29% of commercial searches, but advertisers were cited by the AI only 11% of the time, meaning paid placements don’t automatically influence AI recommendations.

Expensive Keywords See More Ads - Google AI Mode was far more likely to show ads on high-CPC searches, with ad visibility climbing to 53.56% for keywords costing more than $10 per click, while search volume had little effect.

AI Users Are Spreading Out - ChatGPT’s share of AI users has slipped below 50% as Gemini and Claude continue gaining users, making it harder for brands to rely on a single AI platform for reach.

AI is becoming a multi-platform advertising ecosystem. As paid placements, AI citations, and user attention increasingly operate independently, marketers will need to optimize each instead of relying on a single visibility strategy.

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At Buyology, we care about our readers and want to provide the best possible experience. That's why we always look for ways to improve our content and connect with our audience. It would be amazing if you could hit us up with feedback about our content or absolutely anything, we are always up for a chat 🥰

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In this newsletter, you’ll find:

⏳It’s 11:47 pm on a 24-Hour Drop, and Her Phone Never Shows the Preview Text at All

📊 New Research Challenges Marketing Assumptions

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Together with Stack Influence

The ad channel where the creative buys your product first

Most UGC is actors holding products they were shipped for free. The audience can tell.

Stack Influence runs it differently: vetted micro-influencers purchase your product on Amazon, use it, then create the content.

The result is creative with receipts - and a side effect most channels cannot offer: those real purchases drive sales velocity that lifts your organic Amazon rank while the content earns external traffic.

You get full-rights image and video UGC to repurpose across paid social, paying per creator instead of per impression.

One brand activated 187 creators: BSR up 11X, recurring sales up 8X, $342K YoY. Another went from zero to $150K a year plus an Amazon’s Choice badge on 291 collaborations. Magic Spoon, Unilever, and Thrasio all run this engine.

Sign up in the next two weeks and take 10% off your first campaign.

Book a free demo call today

⏳It’s 11:47 pm on a 24-Hour Drop, and Her Phone Never Shows the Preview Text at All

A streetwear brand’s flash-drop email lands in a customer’s inbox. She’s on an iPhone, Apple Intelligence enabled, and the preview line under the subject isn’t the preheader the team wrote.

It’s been replaced entirely, before she ever opens anything, by a one-line summary Apple Mail generated itself from reading the email body. A small grey icon marks it as a summary, not the original text, and most recipients never notice the difference or go looking for a setting to turn it off.

This is Apple Mail specifically, not a blanket “every inbox now works this way” problem. Gmail’s AI summarization currently works mostly after the open, a summary card once the email’s already been read, which is a different problem, whether someone bothers to click through once the summary satisfies them, not whether they open at all.

Apple’s version is the one deciding the open itself, silently substituting whatever the drop email’s carefully written preheader said with its own read of the content.

That’s the part most DTC drop calendars haven’t adjusted for. A lookbook-style email, hero image, brand story, offer buried three scrolls down, gives Apple’s model almost nothing clean to summarize, and a vague AI-generated line kills the open before the subject line’s even registered.

The fix: front-load the exact mechanic in the first visible line of body content, not just the preheader field, since the preheader itself may never reach the recipient at all on an Apple Intelligence device. “40% off, ends midnight, limited restock” summarizes cleanly regardless of what field it technically lives in.

Checking this manually during a live drop means opening the campaign on an actual Apple Intelligence device right after sending and reading the summary that was generated. An AI connected to a live Omnisend account can draft the corrected opening block in the minutes before a drop goes out, written to summarize cleanly regardless of which field Apple decides to read from.

Omnisend runs on the same infrastructure that already serves 150,000+ brands; migration-free within five days. Pull the last flash-sale campaign and check it on an actual iPhone before the next drop goes live at midnight. You can get started with ready-made prompts

📊 New Research Challenges Marketing Assumptions

Two new reports suggest that collecting more data and publishing more content don’t automatically lead to better results. The real advantage comes from using insights and existing content more effectively.

📱Social Intelligence Isn’t Reaching The Business

Sprout Social found 93% of marketers see social intelligence as important for growth, but only 36% regularly use those insights outside the marketing team, leaving valuable customer feedback stuck in silos.

The impact is significant: 33% of organizations missed cultural shifts, 31% overlooked changing customer preferences, 26% escalated issues that could have been solved earlier, while 74% said social delivers insights faster than traditional research.

✍️ Publishing More Isn’t The Answer

New SEO analysis suggests more content doesn’t always mean better results. As Google leaves more pages unindexed, crawl budget, internal linking, and content quality are becoming more important than publishing volume.

The data backs it up. QuickBooks deleted 2,000+ articles and saw 20% more traffic with 70% more lead signups. Userpilot removed nearly 25% of its content and recorded its highest-traffic month.

Both reports point to the same shift: success is becoming less about doing more and more about making better use of what you already have. Whether it’s customer conversations or existing content, the teams that organize, share, and optimize their data are more likely to outperform those simply increasing output.

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🫢Your TV Attribution is wrong

📢 Platforms Reshape Digital Advertising

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🫢Your TV Attribution is wrong

View-through attribution credits a TV or streaming ad for a conversion whenever someone was exposed to it and later bought, regardless of whether the ad actually caused the purchase.

Someone sees a streaming ad Tuesday and buys Thursday because a promotional email landed that morning, and view-through still hands the credit to TV.

The problem compounds because heavy TV advertisers tend to reach high-intent audiences who were reasonably likely to convert anyway, regardless of whether they saw the ad at all.

That means view-through doesn’t just occasionally misattribute a single conversion here and there. It systematically inflates TV’s apparent performance in exactly the direction that makes a weak or genuinely non-incremental campaign look like it’s working when it may be doing nothing at all beyond capturing demand that already existed.

The industry’s actual standard is shifting hard toward holdout-based incrementality instead, splitting the target audience into an exposed group and a genuinely unexposed control group, then measuring the real difference in outcomes between the two groups directly.

Roughly half of US marketers already run incrementality testing today, with adoption climbing fast, because “show me attribution” is no longer treated as sufficient by advertisers who’ve already been burned once by inflated view-through numbers that fell apart the moment anyone actually checked them properly.

The practical shift for any brand running or considering TV: ask the measurement question before the creative question. What’s the actual incrementality methodology behind the number being reported, a real holdout comparison or view-through exposure overlap dressed up to look like proof.

If it’s the latter, the reported ROAS may be measuring correlation with an already-converting audience, not causation from the ad itself.

PATTERN Beauty’s results with Tatari were built on exactly this standard: revenue impact measured from day one against real comparison data, not a view-through number sitting around waiting to be challenged and debunked later once someone finally asks the harder methodology question.

Before committing budget to any TV or streaming test, ask what methodology will prove it worked. Build demand with TV measured the right way from day one, not a number that only survives if nobody checks it too closely. You can book a free demo and build demand with TV before Q4 gets crowded.

Together with Omnisend

Your AI Knows What to Send. Omnisend Sends It.

You’ve got an AI that can talk strategy all day; what it can’t do is open your email platform, pull live data, and build the campaign while you watch

Omnisend fixes that. Connect it to Claude or ChatGPT and your AI starts doing the actual work. Real segments, emails drafted and ready to review, zero copy-pasting

• See exactly what’s driving revenue in your account by campaign, by segment, by send

• Find the automations and sequences you’re missing and what they’d be worth if you turned them on

• Say “build it” and watch your AI create the segment, draft the email, and hand it back ready to send

150,000+ brands are already averaging $79 back for every $1 spent on this platform

Switching used to be the hard part. Omnisend migrates your lists, campaigns, and automations free in 5 days, so the only thing you’re really deciding is how long you’re okay leaving revenue on the table

Connect Omnisend and let your AI actually do something

📢 Platforms Reshape Digital Advertising

YouTube, Apple, and Google each introduced updates affecting how ads are shown, who can advertise, and how campaigns are built.

The Breakdown:

YouTube Tests More Ads - Users report 3 ads, breaks every 3–4 minutes, and longer unskippable blocks. More inventory could mean more ad slots, but heavier loads may quickly dilute attention.

Apple Maps Ads Near Launch - Apple Maps Ads are expected to launch soon in the U.S. and Canada with one sponsored pin per search, while excluding home services, bail bonds, and crypto ATMs. If you’re a restaurant, store, café, or other visit-based business, now is the time to claim and verify your listing to compete for that single placement.

Demand Gen Expands - Google now lets travel, real estate, and automotive advertisers use business data feeds in Demand Gen without Google Merchant Center. Structured data can automatically power more relevant dynamic ads while reducing manual creative updates.

Enhanced Brand Lift - Google introduced Enhanced Brand Lift Studies, measuring lifts as low as 1.2% instead of 2%. The option requires roughly 3× the budget but improves the chances of detecting a positive lift by 60%.

Platforms are evolving in different directions. More inventory, tighter local ad access, better automation, and stronger measurement mean advertisers will need to adapt their strategy for each ecosystem rather than treating them all the same.

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🪄The Four-Question Audit for Where Your Emails Actually Land

📱 New Research On Social Commerce

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🪄The Four-Question Audit for Where Your Emails Actually Land

Gmail now sorts incoming mail with more precision than a single Promotions tab ever allowed, splitting purchase-related content into its own dedicated space, separate from general marketing entirely. An email that reads as transactional lands somewhere a customer actively checks with intent, since they’re looking for exactly that kind of update when they open it.

An email that reads as promotional competes in a crowded tab most people skim once, if that, before moving on. Getting classified into the wrong one costs real attention that formatting alone can win back.

Run these four checks against the current post-purchase sequence before assuming the content itself is the problem.

• Does the subject line read as clearly transactional or promotional? “Your order shipped” reads one way, clean and expected. “Your order shipped, plus 20% off your next order” reads as both at once, and that kind of mixed signal in the subject line is often the first thing that risks miscategorization into the wrong tab entirely.

• Does the formatting match what a receipt actually looks like structurally? Order numbers, shipping details, and delivery windows presented cleanly signal transactional content on their own, independent of the copy. Heavy promotional styling layered onto a shipping update works against that signal even when the underlying content is genuinely just an update.

• Is a promotional offer riding inside an otherwise transactional email? A cross-sell block stapled to the bottom of a shipping notice turns one clean signal into a mixed one instantly. Splitting it into two separate sends, a clean transactional message and a separate promotional follow-up, protects both pieces rather than risking either.

• Would a customer searching for this specific email later actually find it useful on its own? An email built to be found and referenced afterward, an order number, a delivery date, functions differently than one built purely to be read once and clicked immediately.

Running this audit by hand means reviewing every post-purchase send individually against all four questions, product line by product line. Omnisend now works with Claude and ChatGPT, and can run the same four-question check against the live flow in a single prompt, flagging exactly which sends need splitting before the next order goes out.

Brands on Omnisend generate an average of $79 for every $1 spent, with free migration in five days and pricing up to 35% lower than Klaviyo’s.

You can get started with the ready-made prompts here and find out which sends are quietly landing in the wrong place right now.

Together with Billo

Most hooks fail in 2 seconds. These 3 types don’t.

Billo pulled 6 months of creator ad data and tagged every top performer by hook type. Most of what’s in your current brief barely showed up.

• Authentic Moment hooks made up over a quarter of top performers alone, and most had no script at all

• Audience Call-out hooks skipped the product completely in line one. Just named the viewer. “Husbands.” “Hockey mom.”

• List hooks, FOMO countdowns, and “forget X, try Y” setups were almost nowhere in the top tier

If your brief leans on that last one, you’re fighting for attention exactly where people tune out fastest.

The breakdown has every hook type plus the actual lines that made the top tier, so you can steal the structure instead of guessing at it.

Read it before your next round of creator ads.

📱 New Research On Social Commerce

Two new studies show that AI is becoming part of every marketer’s workflow, while social media continues to dominate product discovery without becoming the preferred place to buy.

The Breakdown:

AI Speeds Up Creation - The TikTok-WARC report found 90% of marketers say GenAI is now a key part of creative development and 72% use it regularly. It’s used most for visual, image, and video production (73%), while only 42% rely on it for copywriting and scripting.

Culture Still Wins - The report found 88% of marketers are creating more content with AI, but only 45% say quality has improved significantly. It also found 1 in 3 marketers believe richer audience and cultural signals would help AI produce better creative.

Discovery Beats Checkout - An ESW survey of 23,000 shoppers found 62% of consumers are influenced by social platforms, but only 6% prefer buying there, with many choosing trusted brand or retailer websites instead.

Trust Drives Purchases - The research shows shoppers increasingly discover products on social media, validate them through reviews or AI tools, and then complete purchases on websites they trust, making trust a bigger driver of conversions than discovery.

The research points to a clear split in digital marketing. AI is making content production faster, but relevance still depends on human and cultural insight. At the same time, social platforms are winning discovery, while trusted websites remain the preferred destination for completing purchases.

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🫠The Pages AI Refuses To Quote

🛍️ TikTok Expands Commerce Push

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🫠The Pages AI Refuses To Quote

Traffic from AI search keeps growing, and most teams still haven’t figured out how to turn it into a pipeline. Comparison pages, pricing pages, and feature pages either aren’t getting cited by AI models at all or they get cited and still don’t convert once someone actually clicks through. Three specific mechanisms explain why, and each one is fixable before the next content cycle starts.

Comparison pages fail extraction because they try to answer fifteen questions at once. A model pulling a comparison answer needs one clean, isolated claim, “X handles Y better because Z,” not a sprawling matrix covering every feature side by side.

The standard battle-card format that sells well to a human skimming a table is structurally the wrong shape for a model trying to extract a single defensible answer.

Split the page into individually isolated comparison claims, each one a complete, standalone answer to one specific question, and extraction rates improve because there’s no ambiguity about which row the model is even citing.

Pricing and feature pages lose extraction to their own hedging language. “Starting at,” footnoted caveats, and conditional pricing tiers are honest, and they’re also exactly the kind of ambiguity a model avoids citing outright, since an incorrect extracted price creates real risk for whoever’s answer engine got it wrong.

A plain, declarative sentence stating the fact once, unhedged, paired with structured data markup behind it, gets cited far more reliably than the same fact buried in three qualifying clauses.

Third-party corroboration needs to clear a real threshold, not just exist. A single G2 review or one Reddit mention rarely moves the needle alone.

Models weigh a claim more heavily once it appears independently across at least two separate external domains, G2 and a Reddit thread, or Capterra and a review blog, because that’s the pattern that reads as corroboration rather than coincidence. One mention is a data point. Two independent ones are a signal.

On August 5 at 2 pm ET, Josh Grant, founder of StackedGTM and former VP of Growth at Webflow, joins AirOps’ Josh Spilker to go deeper on all three live-auditing feature pages against Josh’s full framework and send every attendee home with a ranked fix-it list specific to their own pages.

You can secure your free spot. Can’t make it live? Register anyway, and the recording lands in your inbox within 24 hours.

Together with Tatari

Your BFCM CAC problem starts before November.

By November, every brand is fighting for the same shoppers in the same channels: Meta, search, email, affiliates, and discounts. That’s when CAC gets expensive.

The brands that outperform during BFCM don’t just wait to capture demand. They create it earlier, often with channels their competitors haven’t fully tapped yet, like TV.

PATTERN Beauty shows what that can look like. With Tatari, the brand ran a phased 12-week campaign across streaming and linear to build awareness, drive site traffic, and measure revenue impact from day one. No massive TV budget required.

The results:

• 58% growth in unique site visitors

• 3x revenue lift from month one to month three on similar budgets

• 50% higher brand consideration than social and digital alone

TV didn’t replace PATTERN Beauty’s digital stack. It gave digital more demand to capture before shoppers were already in-market.

Over 400 brands like Jones Road Beauty, Fabletics, and Calm built TV into their core growth stack with Tatari. It shows you exactly where every dollar ran and what it produced.

Book a free demo and build demand with TV before Q4 gets crowded.

🛍️ TikTok Expands Commerce Push

TikTok is tightening its control over e-commerce while reshaping how brands sell on the platform, introducing managed services, stricter livestream rules, and strengthening its position in the global advertising market.

The Breakdown:

Managed Services Launch - TikTok is piloting a service that manages sellers’ ads, creators, product listings, GMV Max campaigns, and hundreds of AI-generated videos for a $10,000 flat fee plus 10–20% commission.

AI Voice Ban - TikTok Shop now prohibits AI-generated voices, pre-recorded audio, and radio-style narration in shopping livestreams, requiring sellers to use live human hosts instead.

Account Health Rules - Violations now impact TikTok’s new Account Health Rating (AHR), where repeated offenses can limit campaign access, suspend seller programs, and ultimately deactivate accounts.

Ad Revenue Growth - WARC projects TikTok will overtake YouTube in global advertising revenue by 2030, as marketers continue shifting budgets toward social commerce and lower-funnel performance campaigns.

TikTok is taking greater control of its commerce ecosystem by managing seller operations, enforcing stricter livestream standards, and expanding its advertising business, signaling a broader push to become a full-service commerce platform.

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🎯Choose your target wisely

🤖 Google Brings More Visibility to Performance Max

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🎯Choose your target wisely

Amazon Marketing Cloud’s latest update looks like another targeting feature.

It isn’t.

For the first time, AMC custom audiences and lookalikes can be activated inside Sponsored Products, the campaign type where most Amazon brands spend the majority of their ad budget.

That changes one important question.

You’re no longer deciding who to target. You’re deciding which customers teach Amazon who to target next.

Every customer in your seed audience becomes training data. The quality of tomorrow’s targeting depends on the quality of the customers you let into that dataset.

Stop feeding the algorithm every purchase.

A purchase proves someone converted.

It doesn’t prove they’re the customer you want more of.

A discount hunter who never returns teaches Amazon something very different from someone who buys at full price, leaves a detailed review, reorders and never requests a refund.

Instead of building audiences from all purchasers, build them from customers who demonstrate conviction after the sale. Repeat purchases, thoughtful reviews and meaningful post-purchase engagement are stronger predictors of future high-value customers than the purchase itself.

Solve the real bottleneck.

The challenge isn’t building an AMC lookalike.

It’s creating a large enough pool of high-conviction customers to build one from.

Most brands wait months for enough detailed reviews and engaged buyers to accumulate naturally. That means their targeting model is learning from weak signals for far longer than necessary.

This is where Stack Influence changes the equation. Instead of hoping enough ideal customers appear over time, creators purchase your product on Amazon, genuinely use it, leave detailed experience-based reviews and create original content. You’re not just generating reviews.

You’re accelerating the exact behavioral signals AMC uses to identify and find more customers like them. Every campaign strengthens the training data behind your highest-spending Sponsored Products campaigns, creating value long after the creator campaign ends.

Before your next Sponsored Products budget cycle, build the audience Amazon actually deserves to learn from. You can sign up in the next two weeks and take 10% off your first campaign.

Keep raising the bar.

As your catalog grows, become more selective about who enters the seed.

Better algorithms don’t come from more customer data. They come from better customer examples.

The brands that win with AMC won’t have the biggest audiences. They’ll have the best teachers.

Together with Billo

Half of the top UGC ads share 3 hook types. Most marketers skip all of them.

Billo analyzed every top-performing creator ad on the platform and tagged each one by hook type. The hooks most marketers default to - list formats, FOMO framing, comparison setups, and insider angles barely showed up at the top.

The three that won are in the full breakdown, with the exact lines those ads opened with.

Read it before your next round of creator ads.

🤖 Google Brings More Visibility to Performance Max

Google is expanding both AI-powered advertising and campaign reporting, with AI Mode showing ads on more searches while Performance Max now reports product data across all eligible networks.

The Breakdown:

AI Mode Ad Growth - Google AI Mode displayed text ads on 29.45% of commercial queries in SE Ranking’s study, with keywords costing $10+ CPC showing ads on 53.56% of searches.

Ads Don’t Boost Visibility - Buying AI Mode ads did not improve AI citations or organic rankings. Only 11.53% of advertiser domains appeared as cited sources, while about 85% didn’t rank organically for the same queries.

PMax Reporting Expands - Google expanded Performance Max product reporting to include all eligible networks, including Search, Video, App, and Demand Gen campaigns, providing a broader view of product performance.

Reporting Metrics Shift - Advertisers may notice sudden increases in impressions, clicks, conversions, and other metrics, but Google says these reflect expanded reporting coverage rather than improved campaign performance.

Google is separating AI advertising, AI visibility, and organic search into distinct channels while giving advertisers more comprehensive measurement across its ad ecosystem. Brands should evaluate reporting changes carefully and avoid comparing new Performance Max data directly with historical results.

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👤They just left a channel, not the brand

🖼️ Google Expands AI Search & Shopping

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👤They just left a channel, not the brand

Every unsubscribe gets attributed to the channel where it happened. Almost none get attributed to the customer relationship.

That’s why many lifecycle dashboards quietly overstate churn. They measure where the conversation stopped, not whether the customer actually disappeared.

Imagine this customer:

• They unsubscribe from SMS on Monday

• Open two emails during the week

• Browse your site on Friday

• Buy again the following Tuesday

The SMS dashboard records a loss. The business retained a customer. Those are two completely different stories.

Before treating the next unsubscribe as churn, ask a different question:

Did the customer leave the brand, or only this channel?

Start by measuring what most brands never calculate.

Audit 1: Measure False Churn

Instead of counting everyone who unsubscribed from SMS or email, identify everyone who remained active on another owned channel within the following 30 days.

False Churn Rate: Customers active on another owned channel within 30 days ÷ Total single-channel unsubscribes

A surprisingly high number means you’re measuring channel attrition instead of customer attrition.

Audit 2: Find Your Migration Paths

Don’t assume customers disappear. Track where they go next.

This table isn’t a playbook, it’s a list of hypotheses and your own data decides which ones are true.

Audit 3: Validate Before You Automate

Take customers who unsubscribed from SMS but still engaged with email within the next month.

Split them into two equal groups:

One group receives nothing

The other receives one simple message: “Looks like text messages weren’t the right fit. We’ll keep updates in your inbox instead.”

Then compare the two groups over the next 30 days.

Measure:

• Email engagement

• Repeat purchase rate

• Additional unsubscribes

• Revenue per customer

If meaningful differences appear, you’ve validated channel migration instead of assuming it.

Replace Channel Reporting With Relationship Reporting

Every month, calculate these three metrics.

Those three numbers reveal whether customers are actually leaving your business or simply choosing a different way to hear from it.

Omnisend can automate this workflow by detecting unsubscribe events, checking activity across every remaining channel, and triggering the appropriate follow-up automatically instead of treating every opt-out as the end of the relationship.

Brands using Omnisend generate an average of $79 for every $1 spent, migration is free and takes up to five days, and AI can build the automation for you. You can get started with a ready-made prompt.

Good lifecycle marketing isn’t measured by how many subscribers you lose. It’s measured by how many relationships you preserve after preferences change.

Together with AirOps

What 400+ Sites Reveal About Surviving Google’s AI Overviews

Everyone’s bracing for Google Zero. Cyrus Shepard studied 400+ sites and found the ones still growing share five specific traits.

Zero-click search is real. The “SEO is dead” takes are not the full story.

Cyrus mapped what’s actually driving organic growth right now, plus the 17 content types most likely to survive Google Zero.

On July 23rd, AirOps is hosting Cyrus for a practical session on what every content team is trying to decide: what to keep investing in, what to rethink, and where AI actually helps.

You’ll walk away with:

• The five traits behind sites still growing in Google

• Which content formats still earn their keep, and which are getting harder to justify

• How to turn proprietary assets you already own into content

• Where AI fits now: what to stop producing faster, what to build instead

This webinar is built for everyone, from SEO and content leaders to content directors and growth teams.

Secure your free spot

Can’t make it live? Register anyway and get the recording in your inbox within 24 hours.

🖼️ Google Expands AI Search & Shopping

Google introduced new AI-powered search, shopping, and advertising features as it marks 25 years of Google Images, while giving merchants and advertisers more visibility into AI-driven discovery.

The Decode:

Google Images Turns 25 - Google launched a personalized Google Images homepage and is bringing Nano Banana-powered image generation into AI Overviews, allowing users to create custom images directly from Search.

Merchant Center Gets AI Insights - Merchant Center is piloting AI Performance Insights, showing brands their AI Mode and AI Overviews visibility, share of voice versus competitors, and shopping query trends. The report currently tracks organic AI traffic only and is available to select U.S. merchants.

YouTube Ad Controls Expand - Google Ads now lets advertisers manage reach and frequency across multiple YouTube video campaigns from a single campaign group while keeping individual budgets and creatives separate.

ROI-Based Optimization - Google said its Meridian study found that an average ad frequency of 2.7 impressions per week produced a 19% lift in ROI, with the new controls designed to help advertisers reach that target.

Google is expanding AI across the full search journey, from visual discovery and image creation to shopping analytics and advertising optimization, while giving businesses better tools to measure visibility and performance in AI-powered experiences.

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🤓 Amazon’s AI Doesn’t Rank Your Product. It Ranks Your Evidence.

🤖 Customers Prefer AI Over Brand Chatbots

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🤓 Amazon’s AI Doesn’t Rank Your Product. It Ranks Your Evidence.

For years, Amazon SEO was about making a product discoverable. AI shopping assistants change the game.

The new challenge isn’t getting found. It’s giving the assistant enough confidence to recommend your product when a shopper asks a specific question.

“Will this fit under my kitchen cabinet?”

“Does it survive the dishwasher?”

“Will this work for curly hair?”

Amazon states that its AI shopping assistant answers questions using information from product detail pages, customer reviews, customer Q&A, its product catalog, and other trusted sources. When information is incomplete or inconsistent, the quality of the answer suffers.

That means every listing now has something most sellers never measure:

Evidence coverage.

Here’s how to audit it.

1. Build an Answer Coverage Matrix. List the 15 highest-commercial-intent questions a customer asks before buying your category. Don’t guess. Pull them from Customer Q&A, negative reviews, support tickets, return reasons, competitor reviews, and Amazon’s suggested questions.

Now create a table with every question as a row and every evidence source as a column: attributes, title, bullets, images, A+ Content, video, Q&A, and reviews. Empty cells are answer gaps. If “Is this leakproof in a backpack?” isn’t answered anywhere, the assistant has nothing reliable to retrieve.

2. Eliminate Contradiction Debt. Open every source that answers the same question. If your bullets say “dishwasher safe,” a review says “top rack only,” and a customer answer says “hand wash recommended,” you’ve created competing evidence.

AI assistants don’t know which version to trust. Every contradiction reduces confidence before the customer even reaches your listing. Standardize factual claims first, then update older assets that no longer match the product.

3. Create an Evidence Priority Stack. Every source has a job. Structured attributes should carry measurable facts like dimensions and materials. Images should prove visible claims. Video should prove performance.

Reviews should prove lived experience. Q&A should resolve edge cases. Don’t duplicate the same message everywhere. Give every asset one responsibility so together they answer the complete buying decision instead of repeating the same benefit eight times.

4. Run an AI Retrieval Audit. Every month, ask your ASIN and your top five competitors the same ten buying questions through Amazon’s AI shopping experience. Save every answer.

Record which products appear, what evidence gets cited, what information is missing and where uncertainty appears. Then update the exact evidence source that failed. This turns AI visibility from guesswork into a repeatable optimization process.

Stack Influence strengthens the hardest layer to manufacture: lived experience. Because creators purchase through Amazon before creating content, they naturally generate reviews, photos and videos answering real buyer questions in ways brand copy rarely does.

Sign up in the next two weeks and take 10% off the first campaign to start filling those answer gaps before competitors do. You can book a free demo call today

Keywords help shoppers find products. Evidence helps AI recommend them.

Together with AirOps

The Content That’s Dying in Google Zero (and What’s Replacing It)

Google Zero is what happens when the answer shows up right in search results, and nobody clicks through to your site. Your rankings look fine. Your traffic doesn’t.

Cyrus Shepard studied 400+ sites to find out why some are still growing through it and mapped what’s actually driving organic growth right now

AirOps is hosting Cyrus live on July 23rd to break down exactly what those sites are doing differently:

• The exact 5 traits separating sites that grew from sites that stalled

• The 17 content types Cyrus flags as still worth producing, and likely to survive

• How to turn proprietary assets you already own into content

• Where AI fits now: what to stop producing faster, what to build instead

Score your content calendar against the 5 traits and 17 formats live, and leave the session knowing exactly which pieces to keep and which to kill.

Secure your free spot

Can’t make it live? Register anyway and get the recording in your inbox within 24 hours.

🤖 Customers Prefer AI Over Brand Chatbots

New research from Gartner and Numerator highlights changing consumer behavior, with third-party AI assistants becoming the preferred customer service channel while online shopping continues to grow alongside physical retail.

The Breakdown:

Third-Party AI Takes The Lead - Customers are three times more likely to use tools like ChatGPT or Claude than brand-owned chatbots, while adoption of company chatbots has remained flat since 2022.

Customers Want Action - More than 58% of consumers use generative AI to complete tasks, but many brand chatbots still only answer questions instead of helping users complete their journey.

Online Shopping Becomes Routine - Numerator found that two-thirds of U.S. households (86 million) now shop online weekly, with online purchasing expanding rapidly across grocery, restaurants, and household categories.

Stores Still Drive Discovery - Despite e-commerce growth, 55% of shoppers still discover products in physical stores, while 77% have used buy online, pick up in-store, showing digital and physical retail continue to work together.

Consumers are increasingly relying on third-party AI assistants instead of brand chatbots; brands here need to complete customer transactions within the same conversation, rather than redirecting users or answering questions.

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🧐One Revenue Number, Two Completely Different Businesses Hiding Inside It

🔎 ChatGPT Search, and Ads Continue To Expand

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Together with Omnisend

How healthy is your email setup, really?

You probably know your open rate. But do you know how healthy your email marketing setup actually is?

Now that Omnisend works with Claude and ChatGPT, all it takes is a simple prompt.

Ask Omnisend AI to score your account out of 100 based on campaign cadence, automation coverage, and catalog completeness, then list the top three areas to improve.

In seconds, you’ll get a clear picture of how your account is performing and where to focus next.

Not yet an Omnisend user? Brands using Omnisend generate an average of $79 for every $1 spent. Migration is free and takes up to five days, while pricing is up to 35% lower than Klaviyo’s.

Get started with ready-made prompts.

💔 Majority is not always right

One of the easiest ways to waste an entire product sprint is finding a pattern that isn’t actually there.

The mistake usually starts with good intentions.

Support hears the same complaint a few times. Sales mentions it on customer calls. Reviews begin repeating the same frustration. Before long, someone says, “Fifteen customers asked for this. We need to prioritize it.”

Maybe.

Or maybe you’ve just heard from the loudest corner of your customer base.

That’s becoming a bigger problem as AI makes it effortless to summarize thousands of tickets into neat themes. AI can cluster similar feedback in seconds. It still can’t tell you whether those customers represent your business. Teams have to do that part themselves.

Before moving any recurring request onto the roadmap, run these four checks.

1. Calculate the complaint rate, not the complaint count. Export last month’s support tickets and divide each recurring issue by total tickets received. Fifteen complaints sounds urgent until you realize they came from 15 out of 4,200 conversations, or 0.36% of support volume. Always compare trends month over month before deciding whether an issue is actually growing.

2. Map every complaint to a customer segment. Add three columns to your spreadsheet: acquisition source, plan type and customer tenure. If twelve of the fifteen complaints come from trial users acquired through one paid campaign, you’ve uncovered an onboarding problem for one audience, not a product problem affecting everyone.

3. Overlay support data with revenue. Pull the total annual revenue generated by customers reporting each issue. Ten complaints from enterprise accounts worth $250,000 ARR deserve a different response than fifty complaints from customers who bought a $15 accessory once. Prioritize revenue at risk, not just ticket volume.

4. Compare complaints with silent churn. Open your analytics platform and filter customers who cancelled or never made a second purchase without contacting support. Then compare their behavior with people who opened tickets. If hundreds abandon the same onboarding step without saying a word, while fifteen customers complain about a cosmetic feature, your roadmap just changed.

Support tickets are evidence.

They’re just not complete evidence.

The next time someone says, “Customers keep asking for this,” ask four questions instead: What percentage of customers reported it?

Which segments did they come from?

How much revenue do they represent?

And what are the customers who never contacted us doing instead?

Those answers are usually far more valuable than the complaint itself.

🔎 ChatGPT Search And Ads Continue To Expand

New research suggests wider ChatGPT Search adoption is changing search behavior, while OpenAI continues adding new advertising features as ChatGPT Ads expands into more markets.

The Breakdown:

Audience Targeting Arrives - OpenAI is also rolling out Audience Lists, allowing advertisers to upload raw or hashed email addresses and phone numbers to build custom audiences and improve campaign targeting within ChatGPT Ads.

ChatGPT Changes Search Patterns - A Bocconi University study found broader ChatGPT Search access was associated with a 9.4% decline in traditional search queries, with informational searches seeing the biggest drop. The research also found ChatGPT referred users to external websites in 5.2% of sessions, compared with 31.1% for Google.

ChatGPT Ads Reach More Markets - OpenAI expanded ChatGPT Ads to Japan and South Korea while introducing new advertiser tools, including an overview dashboard, suggested ad drafts based on website metadata, refreshed ad formats, and campaign performance insights.

ChatGPT is continuing to reshape both search and advertising. As search behavior evolves, OpenAI is simultaneously expanding ChatGPT Ads with new markets, targeting capabilities, and campaign management tools.

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💃Events

🔥 What 400+ Websites Reveal About Winning In Google Zero

July 23 | 60 Minutes | Virtual Event

Most SEO advice is based on opinion. This session is based on data. Learn what Cyrus Shepard discovered after studying 400+ websites, including the content formats still driving growth, the assets Google continues to reward, and where AI fits into a modern content strategy.

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In this newsletter, you’ll find:

💪Some Hooks Are Built to Die in a Week. Budget for That Before You Launch Them.

🔎 AI Discovery Keeps Changing Search

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💪Some Hooks Are Built to Die in a Week. Budget for That Before You Launch Them.

Hooks don’t fatigue at the same rate, and the rate is predictable before a single dollar is spent, because it’s tied to the mechanism the hook runs on, not the execution quality.

Novelty hooks decay fast.

A shocking visual, an unexpected sound, a pattern interrupt built on surprise. These work because the brain hasn’t seen the pattern yet. The tenth time an audience sees the format, even with different footage, the surprise mechanism is gone. Expect a real window of 5 to 10 days at meaningful spend before the hold rate starts dropping, regardless of how good the underlying creative is.

Structural hooks decay slowly.

A hook built on a genuine, ongoing tension, a specific identity being named, a real objection being pre-empted, keeps working because the tension it resolves doesn’t disappear after one viewing.

These can run 4 to 6 weeks at spend before fatigue sets in, sometimes longer if the underlying audience is large enough.

Most testing budgets get allocated as if every hook has the same shelf life, which means novelty hooks get judged too generously past their real window and structural hooks get pulled too early, right as they’re starting to compound.

The fix is tagging every hook by mechanism before it launches, not after the data comes in. A novelty hook earning strong early numbers should be scaled hard and fast, inside its short window, not nursed along at moderate spend hoping it holds.

A structural hook showing modest early numbers deserves a longer runway before judgment, because its real strength shows up in week three, not day three.

Billo breakdown’s top-performing creator ads tags each one by hook type, which is the fastest way to sort a new batch by expected decay rate before spend commits to a schedule that doesn’t match the hook it’s funding. Worth reading before the next production cycle.

Together with Stack Influence

This Channel Delivers 5X ROAS and Gets You to Page One in 60 Days

Ancient Nutrition had a real product. Strong formulation. Decent listing. What they didn’t have was sales velocity that the algorithm could work with. So they stopped throwing money at bids and started sending product to micro-influencers instead.

The result: 15x sales growth, a 7.2x rank boost, all from creators their customers actually trusted.

Stack Influence automates the motion. Their AI matches your brand with creators from a network of 11M+ micro-influencers, and you simply pay them only with your products, so every dollar maps to a real purchase and a real piece of content.

• Neutralyze hit $233K in revenue at a 3.8x ROI on product seeding alone

• Lenny & Larry’s activated 1,560 creators and hit 11x monthly sales

• Aunt Fannie’s gave free product to 189 creators, and 8x’d Amazon sales in 90 days

Now you can run the same motion and save 10% on your first campaign if you sign up this week.

Book your free strategy call today.

🔎 AI Discovery Keeps Changing Search

New research and product updates show AI search evolving quickly, with Google expanding its lead, OpenAI improving voice search, and brands increasingly adapting content for AI-driven discovery.

The Breakdown:

Voice Search Gets Smarter - OpenAI’s new GPT-Live lets ChatGPT Voice search the web, answer aloud, and display visual cards during conversations, with source attribution remaining an area to watch.

Google Leads AI Discovery - New research found Google’s AI Overviews and AI Mode now generate more AI-driven traffic than all standalone LLMs combined, while ChatGPT accounts for 92.4% of standalone AI referrals.

ChatGPT Doesn’t Always Search The Same Way - Two new analyses found ChatGPT routes searches through different hidden retrieval systems, meaning identical prompts can pull information from different sources behind the scenes.

Readable Content Matters More - Because retrieval methods can change, clear HTML pages, transparent pricing, structured information, and strong third-party mentions remain the most reliable way to earn AI citations.

Brands are increasingly treating AI mentions like trusted recommendations rather than traditional search rankings. At the same time, OpenAI is expanding ChatGPT Ads into France, Germany, Ireland, and Singapore, showing how AI discovery is becoming a larger part of both search and advertising.

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In this newsletter, you’ll find:

💪Your Reviews Are Training the Machine That Decides Your Visibility

📊 Meta And Google Expand AI Ad Transparency

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💪Your Reviews Are Training the Machine That Decides Your Visibility

Every review on your listing is now doing two jobs.

The first is the one it always did: convincing the next human shopper.

The second is new and mostly unnoticed: feeding the AI layer that now sits over Amazon search, which reads review language as primary evidence about what your product is, who it serves, and when to recommend it. The second job is quietly becoming the more valuable one, and almost nobody is writing their review strategy for it.

The AI assistant answering shopper questions does not evaluate your product the way a keyword index does. Asked which product suits a specific situation, it draws on the language real buyers used: the contexts they described, the problems they said it solved, the comparisons they made.

A listing whose reviews say “great product, fast shipping” five hundred times gives the system nothing to match against a specific question. A listing whose reviews describe morning workouts, sensitive skin, small apartments, or travel with toddlers hands the system exactly the retrieval material that conversational queries require.

This reframes what a review generation strategy is for. Volume and star average still matter for the human job. For the machine job, what matters is use-case density: the range and specificity of real situations described across your review base.

You cannot script this without violating policy, but you can influence who reviews. Buyers who purchased through authentic discovery, used the product in their real lives, and write from genuine experience produce exactly the language the retrieval layer values.

That is the structural logic behind micro-influencer seeding done properly. Stack Influence matches brands with creators from a network of 11M+ who purchase on Amazon, use the product, and share authentic experiences, generating verified purchases whose downstream reviews and content carry real use-case language into the exact systems deciding AI-era visibility. Average platform ROAS sits at 5X. You can sign up this week and save 10% on your first campaign.

Read your last fifty reviews this week and count how many describe a specific use context versus generic satisfaction. That ratio is your retrieval readiness, and it is now as much a ranking asset as your keyword strategy ever was.

Together with Omnisend

150,000 brands run on Omnisend. Now their AI does too.

You’re still exporting CSVs, switching tabs, and copy-pasting numbers just to answer “what worked this week?” That’s not a workflow.

Omnisend MCP connects your ChatGPT or Claude directly to your live Omnisend account. Ask it real questions, get real answers, and act without ever leaving the conversation.

• Connect the Omnisend MCP server to ChatGPT or Claude

• Paste any prompt into your conversation and watch it pull from live account data

• Say “now do it,” and your AI adjusts segments, builds campaigns, fixes what’s broken

Omnisend gives you a ready-made prompt library to hit the ground running. From a quick store snapshot to a full monthly executive summary, the prompts are already written for you.

150,000+ brands run on Omnisend, and they’re averaging $79 back for every $1 spent.

Switching takes zero effort since Omnisend migrates you for free within 5 days, and your bill drops up to 35%.

Connect your AI and run your first prompt today!

📊 Meta And Google Expand AI Ad Transparency

Meta and Google announced new updates for advertisers, adding better chatbot performance tracking and clearer disclosures for AI-generated advertising across their platforms.

The Breakdown:

Google Labels AI-Generated Ads - Google is introducing a new “How this ad was made” panel that tells users when generative AI was used, with disclosures added automatically for ads created using Google’s AI tools.

More Transparency For Advertisers - Google will detect AI-generated content using SynthID and C2PA metadata, while advertisers using external AI tools can also disclose AI usage through new controls.

Meta Adds Chatbot Performance Metrics - Meta Business Suite now tracks AI conversations, purchase intent, and containment rates, helping businesses measure how effectively their Messenger and WhatsApp AI agents handle customer interactions.

Better Insights For Businesses - The new reporting gives brands a clearer view of chatbot engagement and sales impact, making it easier to evaluate performance and identify where human support may still be needed.

AI is becoming a bigger part of digital advertising, but platforms are also adding ways to measure and explain its use.

The latest updates focus on helping businesses evaluate AI performance while giving users greater transparency around AI-generated content.

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Thanks for your support, We'll be back with more such content 🥳

In this newsletter, you’ll find:

🚀The 48-Hour Rule That Stops Screenshots From Running Your Account

🖼️ Meta Brings Muse Image To More Products

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Togther with Stack Influence

This Channel Turned a Sunscreen Brand’s #42K Amazon Ranking Into a Bestseller in 4 Months

Naked Sunday had a decent SPF product buried on page 4. So they stopped bidding and started seeding, sending free product to micro-influencers with no cash changing hands.

Four months later, monthly units went from 131 to over 1,300, rank jumped 2.4x, and 252 creators pushed 1.4M impressions, with a 22% testimonial rate still fueling the listing today.

Stack Influence runs this exact motion. Their AI matches your brand with creators from a network of 11M+ micro-influencers, and you simply pay them only with your products, so every dollar maps to a real purchase and a real piece of content.

• Lenny & Larry’s seeded 1,560 creators for 11x monthly sales

• Blueland grew Amazon sales 372% with a 6.3x rank lift

• Dreamland Baby turned one campaign into $150K from a standing start

Now you can run the same motion and save 10% on your first campaign if you sign up this week.

Book a free demo call and get started today.

🚀The 48-Hour Rule That Stops Screenshots From Running Your Account

Verification catches fake numbers. It does not catch the more common failure, which is reacting fast to a real number that was never comparable to your situation in the first place. The fix for that is not a sharper eye. It is a rule that removes your judgment from the moment it is most compromised.

The rule: no account change gets implemented within 48 hours of being prompted by something you saw online, full stop, regardless of whether the post checks out. Not a verification gate. A time gate.

The reason it works where verification alone does not is that verification answers whether a number is real, and the actual damage happens even when the number is completely real, because the moment you see an outlier result, your judgment about your own account is temporarily compromised by comparison.

You are not thinking clearly about your business. You are reacting to someone else’s, and no amount of checking their spend and date range fixes that you are still deciding a compromised state.

The mechanism is straightforward to install. The instant a post makes you want to change bid strategy, restructure a campaign, or copy an angle, write it down in a single running document: what you saw, what you want to change, and the date.

Do not touch the account. Forty-eight hours later, reread the entry with your own account’s data pulled up next to it.

Most entries, read again after two days without the adrenaline of the original post, resolve one of two ways: either the change was never going to apply to your specific price point and audience, or it survives the reread as genuinely worth testing, in which case you test it small, not structurally.

The rule costs you nothing when the impulse would have been wrong, and costs you two days when it would have been right, which is a trade every account should take. What it removes is not information. It removes the window where a stranger’s screenshot has more influence over your budget than your own performance data does.

Build the rule this week: one shared document, one column for the trigger, one for the proposed change, one for the date it is allowed to be actioned. Nothing moves before that date, no exceptions for how convincing the post was.

🖼️ Meta Brings Muse Image To More Products

Meta is rolling out its new Muse Image model across consumer apps and advertising, introducing AI-powered image creation, public post remixing, and smarter creative tools for brands.

The Breakdown:

Public Posts Become Remixable - Muse Image now lets users create AI versions of public Instagram posts, while creators who don’t want their content remixed can choose to opt out of the feature.

AI Creation Across Meta Apps - The model is also powering new image generation and editing features in Instagram Stories, WhatsApp, and Meta AI, with availability expanding to more regions over time.

New Creative Tools For Advertisers - Meta has upgraded its advertising platform with Muse Image, helping brands generate image variations, restyle existing creatives, and turn videos into still assets that stay on-brand.

Shopping Gets More Visual - The model can also place products inside a shopper’s own room using uploaded photos, creating interactive previews as Meta expands AI-powered shopping and advertising experiences.

Meta is extending one AI model across both consumer products and business tools. From content creation to advertising and shopping, Muse Image is becoming a core part of how users and brands interact across Meta’s apps.

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In this newsletter, you’ll find:

🤓 The Most Strategic File on Your Site Is 40 Lines of Plain Text

💰 Meta And LinkedIn Expand Their AI Offerings

👨‍💻 Tweet of the Day

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Together with Stack Influence

Why a stranger’s selfie outsells your best-performing ad

Your audience has learned to discount advertising. But a real person, holding a product they paid for, recommending it to people who follow them by choice?

That bypasses the skepticism filter entirely - it is social proof, authority, and the bandwagon effect firing at once.

Stack Influence turns that psychology into a repeatable channel.

Vetted micro-influencers buy your product on Amazon and post authentic content to their own audiences. The purchases drive sales velocity, the velocity lifts organic rank, and the external traffic compounds both.

The numbers downstream: one brand activated 187 creators and saw BSR improve 11X with recurring sales up 8X ($342K YoY). Another hit $150K a year and an Amazon’s Choice badge from a standing start. Magic Spoon, Unilever, and Thrasio run the same play.

Now you can run the same motion and save 10% on your first campaign if you sign up this week.

Book a free demo call and get started today.

🤓 The Most Strategic File on Your Site Is 40 Lines of Plain Text

robots.txt spent two decades as a technical afterthought. It is now a visibility strategy document, because which AI crawlers you allow or block directly sets where your brand can appear in AI answers, and companies are making that decision by default rather than by choice.

The tradeoff is real on both sides. Allowing AI crawlers means your content can be retrieved, cited, and surfaced when assistants answer questions in your category, which is increasingly where buying decisions form.

Blocking them means your content stays out of systems you may not want training on it, but it also means ceding every AI answer in your category to competitors who allowed access. For a publisher monetizing content directly, blocking can be rational. For a business whose content exists to create demand, blocking is usually self-inflicted invisibility.

The decision has more granularity than allow-all or block-all. Each crawler serves a different function and can be ruled on separately: retrieval bots that fetch content to answer live user queries deliver immediate citation visibility, while training bots collect data for future models, a slower and more diffuse return.

Many sites now allow retrieval agents while blocking pure training crawlers, a middle position that captures present visibility while limiting open-ended data collection. The user agent strings differ, and so should the rules.

The audit is short: open your robots.txt today and read what it actually says about AI crawlers. Common findings: blanket blocks inherited from a 2023 decision nobody revisited, template-copied rules, and blocks on a bot’s training agent mistaken for blocks on its retrieval agent.

Then check the enforcement reality in your server logs, because what you disallow and what actually gets crawled are not always the same thing.

Access is one half of the visibility equation. What you feed the crawlers you allow is the other, and the data on that half now exists.

On July 23, AirOps is hosting Cyrus Shepard for a session built on his study of 400+ sites: the five traits behind sites still growing through Google’s AI shift, which content formats still earn their keep, and where AI fits in production now. You can secure your free spot.

Read your robots.txt before the end of the day. It takes four minutes to learn what visibility policy you have been running by accident.

💰 Meta And LinkedIn Expand Their AI Offerings

Meta and LinkedIn introduced new AI updates this week, with Meta rolling out paid AI subscriptions while LinkedIn expanded its AI-powered advertising tools for marketers.

The Breakdown:

Meta Introduces Meta One Premium - Meta is rolling out Meta One Premium across Facebook, Instagram, WhatsApp, and its AI glasses, unlocking advanced AI features including unlimited Conversation Focus and premium device support.

Premium Features Come At A Price - Meta AI glasses owners will receive three hours of Conversation Focus each month for free, while unlimited access requires the reported $19.99 monthly Meta One Premium subscription.

LinkedIn Adds AI Creative Tools - LinkedIn launched AI-powered Brand Kits, landing page-based copy generation, personalized ads using professional profile data, and automated ad variants to simplify campaign creation.

AI Optimizes Campaign Performance - LinkedIn’s flexible ad creation combines images, videos, and copy into multiple creatives, then automatically shifts delivery toward the best-performing versions as campaign data accumulates.

Social platforms are moving beyond offering AI assistants. The latest updates show AI becoming a paid product for consumers while increasingly automating creative production and campaign management for advertisers.

🫠 Events

🔥 The Server Log Data Most SEO Tools Can’t See Is Exactly What’s Keeping Your Pages Out of AI Answers

Tomorrow | Virtual Event | Free

AI crawlers leave traces most teams never look for. Jairo Guerrero, founder of Organic Hackers, built his entire methodology around reading those traces, and tomorrow he’s showing you exactly how to do the same. You leave with a checklist you can run against your own site the same week.

✅ Reserve Your Free Spot

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At Buyology, we care about our readers and want to provide the best possible experience. That's why we always look for ways to improve our content and connect with our audience. It would be amazing if you could hit us up with feedback about our content or absolutely anything, we are always up for a chat 🥰

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In this newsletter, you’ll find:

🧐Three Questions About the Trust Check Happening Outside Your Funnel

🤖 AI Referral Traffic Has A Clear Winner

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🧐Three Questions About the Trust Check Happening Outside Your Funnel

Where do buyers go after the AI recommends you?

Almost half of shoppers now verify AI shopping recommendations on Reddit before purchasing.

The sequence is consistent: ask an AI assistant for a product recommendation, get an answer, then search the product name plus Reddit to see what real people say.

The AI provides the shortlist. Reddit provides the verdict. Your funnel sees neither step, only the shopper who arrives after passing both or the silence of one who did not.

What does Reddit actually say when they search your brand?

Run the search buyers run: your brand name plus Reddit, your product category plus the word “worth it,” your product versus your main competitor. What comes back is the verification layer your prospects are reading.

For many brands, the top results are years-old threads, a single unanswered complaint, or nothing at all. Nothing at all is its own problem, because a product with zero Reddit footprint fails the verification check by absence. The buyer, looking for confirmation and finding silence, buys the competitor with forty comments of discussion instead.

What builds a footprint that verifies rather than markets?

The wrong answer is seeding fake threads, which Reddit’s users detect fast and punish publicly, creating the exact negative record you were trying to avoid.

The durable approach runs through actual participation: founders and team members answering questions in category subreddits under identified accounts, genuinely useful responses in threads where your product category is discussed, and customer advocacy earned by asking happy buyers who mention Reddit usage to share their experience where it is relevant.

The bar is that every comment would hold up if the commenter’s identity were fully visible, because eventually it will be.

The compounding effect is what makes this worth starting now. Reddit threads rank in Google, feed AI training data, and get cited inside AI shopping answers themselves.

A healthy thread about your product works three jobs at once: verifying you for the Reddit-checking buyer, ranking for the search-checking buyer, and informing the next generation of AI recommendations. Spend one hour this week running the searches your buyers run, and let what you find set the priority.

Together with Dreem

The Wait Between Upload And Launch Just Disappeared

You upload a photo, and then you wait. A few days later it finally turns into something you can actually use, and that wait is the real cost here, not the shoot itself.

Every SKU needs assets across segments, channels, and formats. Most teams ship one shot and call it a launch, then stretch it for a year because nobody has six weeks to wait on samples and reshoots.

Dreem closes that wait. One upload and you get model shots, product shots, and video across 400+ virtual talents, for about $2 an image.

• One upload becomes every shot you need: model, product, and video, from a phone photo or flat lay, no studio required.

• Pick from 400+ talents across body type, age, and skin tone, so the shopper browsing actually sees someone who looks like them.

• A new SKU goes from flat lay to live page in a single day. No shoot scheduling or sample shipping.

CX teams use this to cut return tickets by showing real fit before the purchase. Growth teams ship 15+ audience-specific variants per launch.

Sign up before July 30 and you get 600 bonus credits, enough for 50 full per-segment kits on your top SKUs.

Claim your 600 free credits

🤖 AI Referral Traffic Has A Clear Winner

New research analyzing 6.77 million AI-driven sessions shows ChatGPT dominates referral traffic, while Claude is emerging as the fastest-growing challenger and AI discovery patterns continue evolving across industries.

The Breakdown:

ChatGPT Pulls Ahead - ChatGPT now accounts for 92.4% of measurable AI referral traffic, with overall AI-driven sessions growing nearly tenfold despite temporary traffic swings caused by platform updates.

Claude Overtakes Perplexity - Claude surpassed Perplexity as the second-largest AI referral source after rapid growth driven by enterprise adoption, while Perplexity and Copilot both lost significant referral share.

Where AI Sends Users - ChatGPT frequently directs visitors to internal search pages when it trusts a website but cannot identify the best destination, making on-site search an increasingly important acquisition channel.

Landing Pages Matter More - AI referrals land differently by industry, from ecommerce product pages to education course pages and publisher news articles, making page-level optimization more valuable than sitewide metrics.

AI visibility is no longer just about getting cited. As AI platforms become major discovery channels, businesses need to optimize the pages AI actually sends users to, not just focus on overall search rankings.

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In this newsletter, you’ll find:

🧐The Only Record of How AI Actually Reads Your Site Is One Nobody Opens

🎵 TikTok Is Expanding Beyond Short Videos

👨‍💻 Quick Hits

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🧐The Only Record of How AI Actually Reads Your Site Is One Nobody Opens

Somewhere on your server right now is a log line showing an AI crawler requesting a page you deleted eight months ago, another showing it skipped your highest-converting product page entirely, and a third showing it hit your pricing page forty times last week.

None of this appears in any SEO tool you run, because the tools were built to track Googlebot in a search paradigm, and the crawlers deciding your AI visibility behave nothing like Googlebot.

Server logs are the only ground truth for AI crawler behavior. Every request from GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and the rest leaves a line: which pages they requested, how often they return, what status codes they received, and what they never touched.

The gap between what you assume they crawl and what the logs show they crawl is where AI visibility problems hide, and it is routinely enormous. Pages you consider critical get ignored. Pages you forgot exist get crawled weekly. Redirect chains silently eat crawl requests that never reach the destination content.

The starter analysis takes one afternoon with log access and a filter. Pull 30 days of logs, isolate requests by AI crawler user agents, and build three views: most-crawled pages by bot, crawl frequency over time, and status code distribution.

The findings that matter most are the absences: your priority pages missing from the crawl record, and error codes served to bots on pages you believed were healthy. A 404 or a slow response served to an AI crawler is a citation opportunity that died before content quality ever entered the picture.

This is precisely the territory of the July 9 AirOps session with Jairo Guerrero, Founder of Organic Hackers, who has spent years inside server logs watching how AI crawlers actually behave, work that most SEO tools cannot see.

The session covers the first technical checks to run when pages go missing from AI answers and exactly where pages break in the crawl-to-citation pipeline. You can secure your free spot here.

Get log access this week if you do not have it. Every AI visibility conversation you have without it is a conversation about assumptions.

🎵 TikTok Is Expanding Beyond Short Videos

TikTok rolled out a broad set of updates covering AI advertising, branded storytelling, creator trends, and shopping policies, showing how the platform is evolving into a more complete business ecosystem.

The Breakdown:

AI Runs Your Campaigns - TikTok’s new Agentic Hub lets AI agents create, manage, optimize, and analyze ad campaigns through connected tools like HubSpot and Wix, reducing manual campaign work.

Stories Become Ads - Growth Max for Mini Dramas helps brands publish episodic content and amplify it into paid promotion, with TikTok reporting a 52% increase in incremental audience reach.

Wellness Keeps Growing - TikTok found wellness video views jumped 646.9% year over year during Q1 2026 despite overall platform views declining, highlighting how performance varies dramatically between content categories.

Stricter Shopping Rules - TikTok Shop now bans paid or incentivized reviews and review-management services, while introducing a rolling 60-day Negative Review Rate that can affect seller privileges.

TikTok is expanding well beyond entertainment. The platform is investing simultaneously in AI-powered advertising, new storytelling formats, category-specific insights, and stronger commerce policies to support long-term business growth.

🗝️ Tweet of the Day

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At Buyology, we care about our readers and want to provide the best possible experience. That's why we always look for ways to improve our content and connect with our audience. It would be amazing if you could hit us up with feedback about our content or absolutely anything, we are always up for a chat 🥰

Thanks for your support, We'll be back with more such content 🥳