A 3-tier framework for Shopify integrations that drive conversions

A 3-tier framework for Shopify integrations that drive conversions

Shopify powers more than 6 million live ecommerce websites, supported by a robust app ecosystem that can extend nearly every part of the customer journey. 

Anyone can develop an app to perform virtually any function. 

But with so many integrations to choose from, ecommerce teams often waste time testing add-ons that promise revenue gains but fail to deliver.

Having worked across a wide range of Shopify implementations, I’ve seen which tools consistently improve checkout completion, recover abandoned carts, and increase revenue. 

Based on that experience, I’ve organized the most effective integrations into three tiers by priority – so you can implement the essentials first, then move on to more advanced optimization.

Tier 1: Mobile-first, frictionless buying

With 54.5% of holiday purchases happening on mobile, the ecommerce experience must be seamless and flexible. 

As a result, every Shopify site should have two components integrated into its storefront: 

  • A digital wallet compatibility.
  • A buy now, pay later (BNPL) option. 

Without these in place, Shopify users introduce unnecessary friction into the purchase journey and risk sending customers to competitors. 

The good news is that both components integrate natively with Shopify, requiring no custom development.

Why you need digital wallets

Digital wallets, such as Apple Pay, Google Pay, and PayPal, autofill delivery and payment information with a single click, eliminating the friction of typing on a small screen. 

This ease of use can shorten the purchase journey to just a few clicks between a social ad and checkout.

Adoption is accelerating. Up to 64% of Americans use digital wallets at least as often as traditional payment methods, and 54% use them more often.

Eliminate price objections with BNPL

Beyond payment convenience, customers also expect flexibility. 

BNPL providers, including Klarna and Afterpay, allow buyers to spread payments over time, reducing price objections at checkout. 

These options contributed $18.2 billion to online spending during last year’s holiday season – an all-time high, according to Adobe.

Together, digital wallets and BNPL form the foundation of a modern, mobile-first checkout experience. 

With these essentials in place, Shopify users can focus on tools that re-engage customers and bring them back to complete their purchases.

Dig deeper: The ultimate Shopify SEO and AI readiness playbook

Tier 2: The re-engagement power players

The second tier focuses on re-engagement – tools designed to bring back customers who have already shown intent. 

These integrations improve abandoned-cart recovery, increase repeat purchases, and build trust through social proof.

Re-engage customers with email and SMS

Email remains one of the most effective channels for re-engaging customers at every stage of the journey. 

Klaviyo and Attentive are strong options for Shopify users because both offer deep platform integration with minimal setup.

Both platforms also support SMS, allowing Shopify sellers to send automated text messages directly to customers’ mobile devices. 

SMS consistently delivers higher open, click-through, and conversion rates than email, making it especially effective for re-engagement use cases such as abandoned-cart recovery.

Together, these tools enable targeted campaigns and sophisticated automated flows that drive incremental revenue. 

However, CAN-SPAM and TCPA regulations require explicit opt-in for email and SMS marketing, respectively. 

As a result, sellers can only use these channels to contact customers who have agreed to receive marketing messages.

Use human-centered SMS outreach

While Attentive and Klaviyo effectively reach customers who have opted in to marketing, CartConvert helps sellers engage the 50% to 60% of shoppers who have not. 

The platform uses real people to contact cart abandoners via SMS. Because the outreach is not automated, TCPA restrictions do not apply.

CartConvert agents have live conversations with potential customers about their shopping experience. 

They are familiar with the products and can guide buyers back toward a purchase by suggesting alternatives or offering discounts. 

Running CartConvert alongside Klaviyo or Attentive ensures both subscribers and non-subscribers are included in re-engagement efforts.

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Demonstrate social proof through reviews

Human-centered marketing also plays a role in building buyer confidence. 

Today’s online shoppers rely heavily on reviews when making purchasing decisions. 

When reviews are integrated directly into the shopping experience, they help establish trust and legitimacy, which in turn drive higher conversion rates. 

A product with five reviews is 270% more likely to be purchased than one with no reviews, research from the Spiegel Research Center at Northwestern University found.

Shopify users can choose from several review aggregators that pull Google reviews into product pages. 

Sellers should prioritize aggregators that also sync with Google Merchant Center, which powers Google Ads. 

Tools such as Okendo, Yotpo, and Shopper Approved integrate smoothly with both Shopify and Google’s ecosystem.

When reviews sync with Merchant Center, they can appear in Google Shopping ads, improving ad performance. 

While these tools add cost, they are also proven to generate incremental revenue that offsets the investment.

Dig deeper: How to make ecommerce product pages work in an AI-first world

Tier 3: Advanced optimization

The final tier includes more advanced integrations designed to help sellers optimize their sales funnel and performance at scale.

Attribution and analytics: Triple Whale

GA4’s changes to reporting, session logic, and interface have made attribution more difficult for many ecommerce teams. 

As a result, sellers are increasingly seeking clearer, independent performance insights.

Since 2023, Triple Whale has emerged as a leading alternative to Google Analytics, offering third-party attribution tools that integrate seamlessly with Shopify. 

The platform supports multiple attribution models – including first-click, last-click, and linear – along with cross-platform cost integration.

It also provides real-time data, which Google Analytics does not. 

This capability becomes especially valuable during high-pressure sales periods, such as Black Friday, when delayed reporting can lead to missed opportunities.

Although Triple Whale can cost up to $10,000 annually for mid-size brands, the improved data quality often justifies the investment for teams scaling paid acquisition.

Landing page customization: Replo

For sellers focused on improving conversion rates, landing page testing is essential. 

While Shopify is relatively easy to use, making changes to a live storefront for A/B testing carries the risk of breaking the site.

Replo allows Shopify users to build custom landing pages that can be tested at scale without coding. 

These pages typically provide a better user experience than default Shopify themes. 

It can also use site data to personalize landing pages based on a shopper’s browsing history. 

As a result, Replo-built pages often convert at higher rates than static site pages.

TikTok ads integration

TikTok continues to grow as a paid media channel, but it has traditionally presented a higher barrier to entry for advertisers. 

Previously, sellers needed an active TikTok account and could only purchase ads within the app, adding complexity and cost.

TikTok’s Shopify integration allows sellers to create ads that link directly to their websites, rather than keeping users inside the app. 

This change has lowered the barrier to entry and expanded access to the platform. 

Early testing shows promise for use cases such as cart abandonment, making the integration worth exploring despite its relative immaturity.

Dig deeper: Ecommerce SEO: Start where shoppers search

Prioritizing Shopify integrations for maximum impact

Shopify is a powerful platform for ecommerce, but maximizing results requires going beyond its default features. 

  • Start with essentials such as digital wallets and BNPL to reduce checkout friction. 
  • Then layer in email, SMS, and review integrations to re-engage interested shoppers. 
  • Finally, add analytics, attribution, and landing-page testing to optimize performance at scale.

Sellers do not need to implement every solution at once. 

Instead, conduct a quick audit of the existing stack against this framework, identify gaps, and prioritize the tools that improve conversion and re-engagement. 

Shopify’s flexibility is its greatest strength, and its app ecosystem enables sellers to turn more visitors into buyers.

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Google says doing optimization for AI search is “the same” as doing SEO for traditional search

Google’s Nick Fox, the SVP of Knowledge and Information at Google, said in a recent podcast that doing optimization for AI search is “the same” as doing optimization and SEO for traditional search. He added, you want to build great sites, with great content, for your users.

More details. This came up in the AI Inside podcast with Jason Howell and Jeff Jarvis interviewing Nick Fox. Here is the transcript from the 22 minute mark:

Jeff Jarvis ask, “And is is there are there is there guidance for enlightened publishers who want to be part of AI about how they should view, should they view their content in any say differently no?”

Nick Fox responded, “The short answer is no. The short answer is what you would have built and the way to optimize to do well in Google’s AI experiences is very similar, I would say the same, as how as as how to perform well in traditional search. And it really does come down to build a great site, build great content. The way we put it is build for users, build what you would want to read, what you would want to access.”

Here is the video embed, skip to 22 minutes and 5 seconds in:

Why we care. Many of you have been practicing SEO for many years, and now with this AI revolution in Search, you should know you are very well equipped to perform well in AI Search with many, if not all, of the skills you learned doing SEO.

So have at it.

Read more at Read More

Help us shape SMX Advanced 2026. You could win an All Access pass!

We celebrated a major milestone in June: the return of SMX Advanced as an in-person event. It was our first since 2019.

More than a conference, SMX Advanced 2025 was a reunion. Search marketers from around the world came together to connect, exchange ideas, and learn the most current and advanced insights in search.

But search never stands still. With rapid shifts in AI SEO, constant algorithm changes, and the challenge of balancing generative AI with a human touch, the need for truly advanced, actionable education has never been greater.

Help shape SMX Advanced 2026

We’re committed to making the SMX Advanced 2026 program our most relevant, advanced, and exciting deep-dive experience yet. And we can’t do it without you – the expert community that makes this event legendary.

We’re inviting you to directly shape the curriculum for 2026.

Help us build a program that tackles the biggest challenges and opportunities on your radar by completing our short survey. Tell us:

  • What advanced topics are most critical to your professional growth right now.
  • Which recent search changes or complexities are keeping you up at night.
  • Which search industry experts and innovators you need to hear from.
  • Which session formats – from deep-dive clinics to lightning talks and interactive panels – will help you learn more and retain what you learn.

Fill out the survey here.

Be entered to win an All Access pass

To thank you for your time and insights, everyone who completes the survey will have the opportunity to enter an exclusive drawing.

One lucky participant will win a coveted All Access pass to SMX Advanced 2026, taking place June 3-5 at the Westin Boston Seaport.

Submit a session pitch

Beyond shaping the agenda, we also invite you to submit a session pitch. If you have a breakthrough strategy, an innovative case study, or next-level insights, this is your chance to help lead the industry conversation.

Read our guide to speaking at SMX for more details on how to submit a session idea. When you’re ready, create your profile and send us your session pitch.

We look forward to your submissions and insights! If you have any questions, feel free to reach out to me at kathy.bushman@semrush.com.

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Google fixes weeks-long Search Console Performance report delay

Screenshot of Google Search Console

Google Search Console appears to have fixed the weeks-long delay in Performance reports. After several weeks of 50+ hour lag times, the reports now seem up to date as of the past few hours.

Now up-to-date. If you check the Search Performance report now, you should see a normal delay of about two to six hours. Over the past few weeks, that delay had stretched to more than 70 hours.

This is what I see:

The delays began a few weeks ago and took roughly three weeks to fully clear, including the backlog of data.

Page indexing report. Meanwhile, the Page Indexing report delay we reported weeks ago is still unresolved. The report is now almost a month behind, and Google has not fixed it yet. Google posted a notice at the top of the report that says:

  • “Due to internal issues, this report has not been updated to reflect recent data”

Why we care. If you rely on Search Console data for analytics and stakeholder or client reporting, this has been extremely frustrating. The Performance reports now appear to be updating normally, but the Page Indexing report remains heavily delayed and will continue to create reporting headaches.

Meanwhile, Google released a number of new features in the past few weeks, including:

Read more at Read More

How to boost ROAS like La Maison Simons by Channable

Managing large catalogs in Google Performance Max can feel like handing the algorithm your wallet and hoping for the best. 

La Maison Simons faced that exact challenge: too many products and not enough control. Then they rebuilt their segmentation with Channable Insights and turned a “black box” campaign into a revenue-generating machine.

Step 1: Stop segmenting by category

Simons originally split campaigns by product category. It sounded logical – until their best-selling sweater ate the budget and newer or overlooked products never had a chance to surface.

Static segmentation meant limited visibility and slow decisions.

Marketers stayed stuck making manual tweaks while Google kept auto-prioritizing only what was already working.

Step 2: Segment by performance

Enter Channable Insights. Product-level performance data (ROAS, clicks, visibility) now powers dynamic grouping:

Chart showing product segments: "Star Products" with a star, "Zombie Products" with a zombie face, "New Arrivals" with sparkles. Each has goals and strategies.

Products automatically move between these segments as performance shifts – no manual work needed. As Etienne Jacques, Digital Campaign Manager, Simons, put it:

“One super popular item no longer takes all the money.”

Step 3: Shorten your analysis window

Instead of waiting 30 days for signals, Simons switched to a rolling 14-day window.

The result: faster reactions, sharper accuracy, and less wasted spend in a fast-moving catalog.

Step 4: Push the strategy across channels

Why stop at Google? The same segmentation logic was automatically applied on:

  • Meta
  • Pinterest
  • TikTok
  • Criteo

Cross-channel consistency creates compounding optimization.

Step 5: Watch the metrics climb

Without raising ad spend, Simons unlocked:

  • ROAS growth: from ~800% to ~1500%
  • CPC decrease: $0.37 to $0.30
  • CTR lift: 1.45% to 1.86%
  • 14% increase in average order value
  • 1300% ROAS for New Arrivals campaigns
  • Faster workflows and fewer manual tweaks

Even the “invisibles” turned into surprise profit drivers once they finally got the spotlight.

Step 6: Treat automation as control, not chaos

Automation restored marketing control – it didn’t remove it.

Teams can finally learn from the data and influence which products grow, instead of letting PMax run everything on autopilot.

A table with a yellow header reading 'Quick Rules to Implement.' Two columns titled 'Principle' in pink and 'Why It Matters' in blue. Four empty rows beneath, with a colorful logo in the bottom left corner.

Your action plan

  • Classify products as Stars, Zombies, and New Arrivals.
  • Automate campaign reassignment based on real-time data.
  • Refresh product insights every 14 days.
  • Roll out segmentation logic to every paid channel.
  • Scale what wins – test what hasn’t yet.

Want Simons-style ROAS gains without extra ad spend? Start by testing the quality of your product data with a free feed and segmentation audit.

Read more at Read More

Google Ads adds VTC bidding for App campaigns

Google Local Services Ads vs. Search Ads- Which drives better local leads?

Google Ads launched VTC-optimized bidding for Android app campaigns, letting advertisers toggle bidding toward conversions that happen after an ad is viewed rather than clicked.

Previously, VTC worked as a hidden signal inside Google’s systems. Now, it’s a clear, explicit optimization option.

The shift. Google is shifting app advertising away from click-centric logic and toward incrementality and influence, especially for formats like YouTube and in-feed video. This update aligns bidding more closely with how users actually discover and install apps.

Why we care. You can now bid beyond clicks, improving measurement for video-led app campaigns and strengthening the case for upper-funnel activity.

Who benefits most. Video-first app advertisers and teams focused on awareness, engagement, and long-term growth – not just last-click installs.

What to watch

  • Increased reliance on Google’s attribution model.
  • Potential changes in CPA expectations.
  • Greater emphasis on creative quality over click-driving tactics.

First seen. This update was first spotted by Senior Performance Marketing Executive Rakshit Shetty when he posted on LinkedIn.

Read more at Read More

Sergey Brin: Google ‘messed up’ by underinvesting in AI

Sergey Brin at Stanford Dec. 2025

Sergey Brin, Google’s co-founder, admitted that Google “for sure messed up” by underinvesting in AI and failing to seriously pursue the opportunity after releasing the research that led to today’s generative AI era.

Google was scared. Google didn’t take it seriously enough and failed to scale fast enough after the Transformer paper, Brin said. Also:

  • Google was “too scared to bring it to people” because chatbots can “say dumb things.”
  • “OpenAI ran with it,” which was “a super smart insight.”

The full quote. Brin said:

  • “I guess I would say in some ways we for sure messed up in that we underinvested and sort of didn’t take it as seriously as we should have, say eight years ago when we published the transformer paper. We actually didn’t take it all that seriously and didn’t necessarily invest in scaling the compute. And also we were too scared to bring it to people because chatbots say dumb things. And you know, OpenAI ran with it, which good for them. It was a super smart insight and it was also our people like Ilya [Sutskever] who went there to do that. But I do think we still have benefited from that long history.”

Yes, but. Google still benefits from years of AI research and control over much of the technology that powers it, Brin said. That includes deep learning algorithms, years of neural network research and development, data-center capacity, and semiconductors.

Why we care. Brin’s comments help explain why Google’s AI-driven search changes have felt abrupt and inconsistent. After years of hesitation about shipping imperfect AI, Google is now moving fast (perhaps too fast?). The volatility we see in Google Search is collateral damage from that catch-up mode.

Where is AI going? Brin framed today’s AI race as hyper-competitive and fast-moving: “If you skip AI news for a month, you’re way behind.” When asked where AI is going, he said:

  • “I think we just don’t know. Is there a ceiling to intelligence? I guess in addition to the question that you raised, can it do anything a person can do? There’s the question, what things can it do that a person cannot do? That’s sort of a super intelligence question. And I think that’s just not known, how smart can a thing be?”

One more thing. Brin said he often uses Gemini Live in the car for back-and-forth conversations. The public version runs on an “ancient model,” Brin said, adding that a “way better version” is coming in a few weeks.

The video. Brin’s remarks came at a Stanford event marking the School of Engineering’s 100th anniversary. He discussed Google’s origins, its innovation culture, and the current AI landscape. Here’s the full video.

Read more at Read More

Google launches Data Manager API

GPT-4 or Google Cloud’s API library- What should you choose for SEO task automation

Google is rolling out a new Data Manager API that lets you plug first-party data into Google’s AI-powered ad tools with less friction. The goal: stronger measurement, smarter targeting, and better performance without the hassle of managing multiple systems.

Why we care. The Data Manager API helps you get more value from the data you already have by sending reliable first-party data into Google’s AI. This improves your targeting, measurement, and bidding. It also replaces several separate APIs with one easy connection, cutting down on engineering work and getting insights back into your campaigns faster.

About the Data Manager API. It will replace several separate Google platform APIs with one centralized integration point for advertisers, agencies, and developers. It builds on Google’s existing codeless Data Manager tool, which tens of thousands of advertisers already use to activate their first-party data.

You can use it to:

  • Upload and refresh audience lists.
  • Send offline conversions to improve measurement.
  • Improve bidding performance by giving Google AI richer signals.

Partnership push. To speed adoption, Google is launching with integrations from AdSwerve, Customerlabs, Data Hash, Fifty Five, Hightouch, Jellyfish, Lytics, Tealium, Treasure Data, Zapier, and others.

Available today. The API is available starting today across Google Ads, Google Analytics and Display & Video 360, with more product integrations on the way.

Google’s announcement. Data Manager API helps advertisers improve measurement and get better results from Google AI

Read more at Read More

Google AI cites retailers 4% vs. ChatGPT at 36%: Data

Google vs ChatGPT retail citations

Google cites retailers only 4% of the time, while ChatGPT does it 36% of the time. That 9x gap means shoppers on each platform get steered in very different ways, according to new BrightEdge data.

Why we care. Millions of shoppers now turn to AI for deals and gift ideas, but product discovery works differently on the two leading AI search platforms. Google leans on what people say, while ChatGPT focuses more on where you can buy it.

What each AI prioritizes. Google AI Overviews cite YouTube reviews, Reddit threads, and editorial sites, while ChatGPT cite retail giants like Amazon, Walmart, Target, and Best Buy.

Google AI Overviews prioritize:

  • YouTube reviewers and unboxings.
  • Reddit threads and community consensus.
  • Editorial reviews and category experts.

ChatGPT prioritizes:

  • Major retailer listings.
  • Brand and manufacturer product pages.
  • Editorial sources (secondary).

The citation divide. On Google, retailers appear only about 4% of the time. Its citations lean toward user-generated content and expert reviews. Google AI Overviews serve more as a research tool than a purchase assistant. Top sources included:

  • YouTube
  • Reddit
  • Quora
  • Editorial sites like CNET, The Spruce Eats, and Wirecutter

On ChatGPT, retailers appear about 36% of the time. ChatGPT acts as both the explainer and the shopping assistant, so retailer links show up far more often. Its top sources included:

  • Amazon
  • Target
  • Walmart
  • Home Depot
  • Best Buy

About the data. BrightEdge analyzed tens of thousands of ecommerce prompts across Google AI Overviews and ChatGPT during the 2025 holiday shopping season, then extracted and categorized citation sources. Domains were classified by type (retailer, UGC/social, editorial, brand) and compared across identical prompts.

The report. Who Does AI Trust When You Search for Deals? Google vs. ChatGPT Citation Patterns Reveal Different Shopping Philosophies

Read more at Read More

Mentions, citations, and clicks: Your 2026 content strategy

Mentions, citations, and clicks- Your 2026 content strategy

Generative systems like ChatGPT, Gemini, Claude, and Perplexity are quietly taking over the early parts of discovery – the “what should I know?” stage that once sent millions of people to your website. 

Visibility now isn’t just about who ranks. It’s about who gets referenced inside the models that guide those decisions.

The metrics we’ve lived by – impressions, sessions, CTR – still matter, but they no longer tell the full story. 

Mentions, citations, and structured visibility signals are becoming the new levers of trust and the path to revenue.

This article pulls together data from Siege Media’s two-year content performance study, Grow and Convert’s conversion findings, Seer Interactive’s AI Overview research, and what we’re seeing firsthand inside generative platforms. 

Together, they offer a clearer view of where visibility, engagement, and buying intent are actually moving as AI takes over more of the user journey – and has its eye on even more.

Content type popularity and engagement trends

In a robust study, the folks at Siege Media analyzed two years of performance across various industry blogs, covering more than 7.2 million sessions. It’s an impressive dataset, and kudos to them for sharing it publicly.

A disclaimer worth noting: the data focuses on blog content, so these trends may not map directly to other formats such as videos, documentation, or landing pages.

With that in mind, here’s a run-through of what they surfaced.

TL;DR of the Siege Media study

Pricing and cost content saw the strongest growth over the past two years, while top-of-funnel guides and “how-to” posts declined sharply.

They suggest that pricing pages gained ground at the expense of TOFU content. I interpret this differently. 

Pricing content didn’t simply replace TOFU because the relationship isn’t zero-sum. 

As user patterns evolve, buyers increasingly start with generative research, then move to high-intent queries like pricing or comparisons as they get closer to a decision.

That distinction – correlation vs. causation – matters a lot in understanding what’s really changing.

The data shows major growth in pricing pages, calculators, and comparison content. 

Meanwhile, guides and tutorials – the backbone of legacy SEO – took a sharp hit. 

Keep that drop in mind. We’ll circle back to it later.

Interestingly, every major content category saw an increase in engagement. That makes sense. 

As users complete more of their research inside generative engines, they reach your site later in the journey or for additional details, when they’re already motivated and ready to act.

If you’re a data-driven SEO, this might sound like a green light to focus exclusively on bottom-of-funnel content. 

Why bother with top-of-funnel “traffic” that doesn’t convert? 

Leave that for the suckers chasing GEO visibility metrics for vanity, right?

But of course, this is SEO, so I have to say it …

Did you expect me to say, “It depends?”

Here’s a question instead: when that high-intent user typed the query that surfaced a case study, pricing page, or comparison page, where did they first learn the brand existed?

Dig deeper: AI agents in SEO: What you need to know

Don’t forget the TOFU!

I can’t believe I’m saying this, but you’ll have to keep making TOFU content. 

You might need to make even more of it.

Let’s think about legacy SEO.

If we look back – waaaaay back – to 2023 and a study from Grow and Convert, we see that while there is far more TOFU traffic…

…it converts far worse.

Note: They only looked at one client, so take it with a grain of salt. However, the direction still aligns with other studies and our instincts.

This pattern also shows up across channels like PPC, which is why TOFU keywords are generally cheaper than BOFU.

The conversion rate is higher at the bottom of the funnel.

Now we’re seeing this shift carry over to generative engines, except that generative engines cover the TOFU journey almost entirely. 

Rather than clicking through a series of low-conversion content pieces as they move through the funnel, users stay inside the generative experience through TOFU and often MOFU, then click through or shift to another channel (search or direct) only when it’s time to convert.

For example, when I asked ChatGPT to help me plan a trip to the Outer Banks:

After a dozen back-and-forths planning a trip and deciding what to eat, I wanted to find out where to stay.

That journey took me through many steps and gave me multiple chances to encounter different brands and filtering or refinement options. 

I eventually landed on my BOFU prompt, “Some specific companies would be great.” 

From there, I might click the links or search for the company names on Google.

What matters about this journey – apart from the fact that my final query would be practically useless as insight in something like Search Console – is that throughout the TOFU and MOFU stages, I was seeing citations and encountering brands I would rely on later. 

Once I switched into conversion mode, I wanted help making decisions. That’s where I’m likely to click through to a few companies to find a rental.

So, when we read statistics like Pew’s finding that AI Overviews reduce CTR by upwards of 50%, and then consider what happens when AI Mode hits the browser, it’s easy to worry about where your traffic goes. Add to that ChatGPT’s 700 million weekly active users (and growing):

And according to their research on how users engage with it:

We can see a clear TOFU hit and very little BOFU usage.

So, on top of the ~50% hit you may be taking from AI Overviews, 700+ million people are going to ChatGPT and other generative platforms for their top-of-funnel needs. 

I did exactly that above with my trip planning to the OBX.

Dig deeper: 5 B2B content types AI search engines love

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But wait!

The good news is that while that vacation rental company or blue widget manufacturer might not see me on their site when I’m figuring out what to do – or what a blue widget even is – I’m still going to take the same number of holidays and buy the same number of products I would have without AI Overviews or ChatGPT, Claude, Perplexity, etc.

Unless you’re a publisher or make money off impressions, you’ll still have the same amount of money to be made. 

It just might take fewer website visits to do it.

More about TOFU

Traffic at the bottom of the funnel is holding steady for now (more on that below), but the top of the funnel is being replaced quickly by generative conversations rather than visits. 

The question is whether being included in those conversations affects your CTR further down the funnel.

The folks at Seer Interactive found that organic clicks rose from 0.6% to 1.08% when a site was cited in AI Overviews. 

And while the traffic was far lower, ChatGPT had a conversion rate of 16% compared with Google organic’s 1.8%.

If we look at the conversion rate for organic traffic at the bottom of the funnel – which we saw above – it was 4.78%. 

Users who engage with generative engines clearly get further into their decision-making than users who reach BOFU queries through organic search. 

But why?

While I can’t be certain, I agree with Seer’s conclusion that AI-driven users are pre-sold during the TOFU stage. 

They’ve already encountered your brand and trust the system to interpret their needs. When it’s time to convert, they’re almost ready with their credit card.

Why bottom-funnel stability won’t last much longer

Above, I noted that “traffic at the bottom of the funnel is holding steady for now.”

It’s only fair to warn you that through 2026 and 2027, we’ll likely see this erode. 

The same number of people will still travel and still buy blue widgets. 

They just won’t book or buy them themselves. And at best, attribution will be even worse than it is today.

I spoke at SMX Advanced last spring about the rise of AI agents. 

I won’t get into all the gory details here, but the Cliff Notes are this:

Agents are AI systems with some autonomy that complete tasks humans otherwise would. 

They’re rising quickly – it’s the dominant topic for those of us working in AI – and that growth isn’t slowing anytime soon. You need to be ready.

A few concepts to familiarize yourself with, if you want to understand what’s coming, are:

  • AP2 (Agent Payments Protocol): A standard that allows agents to securely execute payments on your behalf. Think of it as a digital letter of credit that ensures the agent can only buy the specific “blue widget” you approved within the price limit you set. Before you say, “But I’d never send a machine to do a human’s job,” let me tell you, you will. And if you somehow prove me wrong individually out of spite, your customers will.
  • Gemini Computer Use Model API: A model with reasoning and image understanding that can navigate and engage with user interfaces like websites. While many agentic systems access data via APIs, this model (OpenAI has one too, as do others) lets the agent interact with visual interfaces to access information it normally couldn’t – navigating filters, logins, and more if given the power.
  • MCP (Model Context Protocol): An emerging standard acting as a universal USB port for AI apps. It lets agents safely connect to your internal data (like checking your calendar or reading your emails) to make purchasing decisions with full context and to work interactively with other agents. Hat tip to Ahrefs for building an awesome MCP server.

Dig deeper: How Model Context Protocol is shaping the future of AI and search marketing

Why do these protocols matter to a content strategist?

Because once AP2 and Computer Use hit critical mass, the click – that sacred metric we’ve optimized for two decades – changes function. 

It stops being a navigation step for a human exploring a website and becomes a transactional step for a machine executing a task.

If an agent uses Computer Use to navigate your pricing page and AP2 to pay for the subscription, the human user never sees your bottom-of-the-funnel content. 

So in that world, who – or rather, what – are you optimizing for?

This brings us back to the Siege Media data. 

Right now, pricing pages and calculators are winning because humans are using AI to research (TOFU and MOFU) and then manually visiting sites to convert (BOFU). 

But as agents take over execution, that manual visit disappears. The “traffic” to your pricing page may be bots verifying costs, not humans persuaded by your copy.

The 2026 strategy

This reality pushes value back up the funnel. 

If the agent handles the purchase, the human decision – the “moment of truth” – happens entirely inside the chat interface or agentic system during the research phase.

In this world, you don’t win by having the flashiest pricing page. 

You win by being the brand the LLM recommends when the user asks, “Who should I trust?”

Your strategy for 2026 requires a two-pronged approach:

  • For the agent (the execution): Ensure your BOFU content is technically flawless. Use clean schema, accessible APIs, and clear data structures so that when an agent arrives via MCP or Computer Use to execute a transaction, it encounters no friction.
  • For the human (the selection): Double down on TOFU. Focus on mentions and citations. You need to be the entity referenced in the generative answer so that users – and agents – trust you.

As we move toward 2026 and then 2027 (it’ll be here sooner than you think), the “click” will become a commodity more often handled by machines. 

The mention, however, remains the domain of human trust. And in my opinion, that’s where your next battle for visibility will be fought.

Time to start – or hopefully keep – making the TOFU.

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