Author: Pinaki Kotecha

  • How Google AI Overviews are going to affect your SEO traffic

    How Google AI Overviews are going to affect your SEO traffic

    Google AI Overviews answer many queries directly on the results page, so fewer people click through to websites. Pew Research Center (July 2025) found users clicked a link on 8% of searches that showed an AI Overview, against 15% without one. Informational and top-of-funnel pages lose the most traffic; high-intent and cited pages hold up.

    Most B2B SEO strategies were built for a results page that no longer exists. For 20 years the deal was simple: rank in the top three, earn the click, capture the visit. AI Overviews break that deal for a large share of informational searches. Google writes the answer at the top of the page and the user often gets what they came for without leaving. The loss isn’t evenly spread. Ranking still matters, and being quoted inside the AI Overview now matters as much.

    Key takeaways

    • AI Overviews cut click-through on informational and top-of-funnel queries. Pew Research Center (July 2025) measured an 8% link-click rate on searches with an AI Overview, against 15% without.
    • The traffic hit is uneven. “What is” and “how to” pages are most exposed. Comparison, pricing, product, and bottom-of-funnel pages hold up.
    • Getting cited inside an AI Overview is the new visibility. It rewards a clear answer near the top of the page, clean markup, existing top-5 rankings, and mentions elsewhere on the web.
    • Keyword volume is now a weak proxy for value. A query with an AI Overview can have high search volume and almost no clicks.
    • The first move is an audit of your top traffic pages, not a rebuild. Most sites need answer-structure fixes on 10–15 pages.

    What actually changes when an AI Overview appears

    When an AI Overview appears, Google generates a written answer above the organic results and cites a few sources inside it. Your organic listing gets pushed down the page, and a share of users get their answer without clicking anything.

    Google launched AI Overviews at I/O in May 2024 and expanded them through 2025. The mechanism has two effects. First, position: even at #1, the Overview sits above you, so your listing drops below the fold on most mobile layouts. Second, substitution: when the Overview answers the question fully, the click never happens. That’s what Pew Research Center measured in July 2025: an 8% link-click rate with an Overview present, against 15% without. [DATA — VERIFY: confirm Pew July 2025 figures before publishing.] Pew also found users clicked a source link inside the Overview about 1% of the time, so “we’ll win the citation referral” is weaker consolation than it sounds.

    Not every query triggers an Overview. Google shows them most on informational, research-style searches and rarely on transactional or brand queries. [DATA — VERIFY: current AI Overview trigger rate on B2B queries.] That split decides where your traffic is safe. For the wider picture, see our roundup of B2B digital marketing trends.

    Which pages lose traffic, and which hold up

    Exposure to AI Overviews tracks intent. The more generic and informational the query, the more likely Google answers it in the Overview and the more traffic you lose. The more specific or transactional the query, the safer the page.

    Page / query typeExposure to AI OverviewsWhy
    “What is” / definition pagesHighGoogle answers in two sentences. No reason to click.
    “How to” / basic guidesHighThe Overview summarises the steps inline.
    Listicles / “best X” roundupsMedium–HighOften synthesised into the answer, sometimes cited.
    Comparison (“X vs Y”) pagesMediumBuyers want detail and proof, so many still click.
    Original data / researchLow–MediumFrequently the cited source, which sends branded referral.
    Product / solution pagesLowBottom-of-funnel intent, not informational.
    Pricing pagesLowSpecific, transactional, and brand-owned.

    This is uncomfortable for a lot of B2B content plans, because the high-volume informational keywords those plans are built around sit in the top rows. A page targeting “what is marketing automation” is far more exposed than one targeting “marketing automation setup for [specific use case]”. That is why keyword volume has become a poor way to prioritise. A keyword with 12,000 monthly searches and a permanent Overview can send fewer real visits than one with 300 searches and none. The number that matters now is clicks you can win, not impressions Google might show. Pressure-test which target keywords still return a clickable results page before commissioning more posts.

    Getting cited is the new ranking

    Being cited inside an AI Overview is a distinct goal from ranking, and it’s earned differently. Google’s model picks sources that answer the specific question clearly, come from a trusted domain, and are structured so the answer can be lifted cleanly.

    From what’s visible so far, a few things correlate with citation. [DATA — VERIFY: Google has not published official citation criteria; treat these as observed patterns.]

    • A direct answer to the query in the first 100 words, written as a standalone statement a model can quote.
    • Pages already ranking in positions 1–5, since Overviews pull from sources Google trusts.
    • FAQ and how-to schema, and clean semantic markup.
    • Third-party mentions, so the model sees corroboration beyond your own site.

    None of that is new discipline. It’s the same relevance-and-authority work, aimed at extraction instead of the blue link. The practical shift is structural: stop burying the answer under 400 words of preamble. Say it in the first two sentences, then earn the rest of the read. This is where SEO and AEO overlap. AEO (Answer Engine Optimisation) applies the same idea to ChatGPT, Perplexity, and Google’s AI Mode, where buyers now research vendors before they reach your website. We treat this as one connected job rather than a separate line item, which is how we’ve built our services.

    What this means for B2B specifically

    B2B feels the shift harder than most sectors, because so much of the B2B funnel starts with informational research. The “how does X work” reading that used to land on your blog now often resolves inside an Overview or an AI chat tool, before a prospect knows your name.

    The consequence is specific. Awareness-stage traffic that fed retargeting pools and email lists thins out, so you have fewer people to nurture toward a demo. For any company that built its pipeline on top-of-funnel content, that’s a real gap. [DATA — VERIFY: your informational vs commercial organic split from Search Console.] The offset is that bottom-of-funnel intent is largely untouched. Someone searching “[your category] pricing” wants to act, and Google mostly leaves those results alone. So the sensible response is to rebalance: protect and expand the high-intent commercial pages that convert, and stop grading content on informational volume that AI now absorbs. There’s a credibility angle too. When a model chooses who to cite, it favours brands with third-party coverage and review-site presence, so off-site work (guest content, PR, getting quoted, G2 or Capterra listings) now feeds both classic search and AI answers.

    Should you stop investing in SEO?

    No. SEO still works for B2B, but the return has moved. Value is draining out of high-volume informational rankings and concentrating in high-intent commercial pages and in getting cited by AI. Cut the budget entirely and you lose the bottom-of-funnel demand capture that Overviews barely touch.

    The converting queries are the specific, commercial ones that were always your best traffic. Those buyers still search, compare, and click, because an AI summary doesn’t settle who to trust with a contract. What should change is the ratio. If your plan is 80% top-of-funnel pillar content and 20% commercial pages, that split now works against you. Weighting it toward the pages that either convert or get cited is a better use of the same budget. SEO isn’t the problem. A content strategy built for the 2020 results page is.

    What this looks like in practice

    For a UAE trade finance advisory, Express Trade Finance, we built the pipeline entirely on high-intent search. The programme ran on Google Ads and produced 100 high-intent leads a month, converting to high-value deals. [DATA — VERIFY: budget scale and campaign timeframe before publishing.]

    The relevant part here is why that structure holds up under AI Overviews. The queries we targeted were bottom-of-funnel and commercial: people looking for a specific financial service, ready to enquire. Those are the searches Google is least likely to answer inside an Overview, because a generated summary doesn’t help someone choose who to trust with a trade finance deal. The demand sat in the part of search AI isn’t absorbing.

    What we wouldn’t have recommended is chasing top-of-funnel informational keywords to “build awareness” first. It would have been slower, harder to attribute, and most exposed to substitution. Full write-up in the Express Trade Finance case study.

    Worth saying plainly: this was a paid-search programme, not an organic one. The principle transfers, since it’s about targeting buying intent, but we’re not claiming an organic ranking result we didn’t run.

    What most people get wrong

    The common reaction is to keep publishing informational content at the same rate and hope citation makes up for the lost clicks. It looks reasonable, because the pages still get impressions and the rankings still show. It costs you twice: you keep paying to produce pages that convert less, and you starve the commercial pages that would.

    The other error is treating AEO as a new service to buy later, separate from SEO. It isn’t a separate discipline. The work that gets you cited in an Overview (clear answers, clean structure, topical authority, off-site mentions) is the same work that ranks a page. Splitting them into two projects means paying twice for one job.

    For most B2B sites the fix is boring. Audit the top 10–15 traffic pages, find the ones targeting informational queries that now trigger Overviews, and either rebuild them around a direct answer or move that effort to commercial intent. No rebuild. No new retainer line. Just a harder look at what each page earns.

    Frequently asked questions

    Will AI Overviews kill my organic traffic?

    No, but they will reduce it on informational queries. Pew Research Center (July 2025) found link clicks roughly halved when an AI Overview was present (8% versus 15%). Commercial and bottom-of-funnel pages are largely unaffected, so your impact depends on how much traffic comes from “what is” and “how to” searches.

    Which of my pages are most at risk?

    Definition pages, basic how-to guides, and generic informational posts, because Google can answer those queries in a sentence or two. Comparison pages, pricing pages, product pages, and original research hold up, because they serve intent an AI summary can’t satisfy. Check your top traffic pages in Search Console against that pattern.

    How do I get my content cited in an AI Overview?

    Answer the query directly in the first 100 words, in a self-contained statement a model can lift. Add FAQ and how-to schema, keep the page technically clean, and build the topical authority that gets you into positions 1–5, since Overviews tend to cite pages that already rank. Third-party mentions help.

    Is AEO different from SEO?

    Not really. Answer Engine Optimisation aims the same relevance and authority work at AI answers instead of blue links. The techniques overlap almost completely: clear answers, structured markup, trusted domains, off-site citations. Treating them as one job is cheaper and more coherent than buying AEO as a separate service later.

    Do AI Overviews appear on B2B searches?

    Yes, and more often on the informational, research-style queries at the top of the B2B funnel. They appear far less on transactional and brand queries. The exact trigger rate for your category shifts over time, so check a sample of your own target keywords rather than trusting a blanket figure. [DATA — VERIFY: current B2B trigger rate.]

    How do I track traffic lost to AI Overviews?

    Google doesn’t label AI Overview impressions separately in Search Console yet, so it’s imperfect. Watch for pages holding their ranking position while click-through rate drops. That gap is the usual signature of an Overview appearing above you. Segment by query type to see whether informational pages fall faster than commercial ones.

    Where to start

    Pick your 10 highest-traffic informational pages this week. For each, rewrite the first 100 words so it answers the target query directly, in a statement a model could quote, then check whether the keyword still returns a clickable results page. That single pass tells you which pages to defend, rebuild, or retire.

    If you want the wider system this fits into, read our guide to building a full-funnel B2B strategy. And if you’d rather we ran the audit with you, book a growth audit.

  • Top Digital Marketing Trends Every B2B Company Should Know in 2026

    Top Digital Marketing Trends Every B2B Company Should Know in 2026

    B2B marketing in 2026 doesn’t look like it did even two years ago. The buyer changed, the channels changed, and the tactics that used to carry your growth quietly stopped working. None of what follows is a forecast. It’s measurable, it’s happening now, and the only real question is whether your marketing has caught up.

    The buyer now sells to themselves

    The single most important number in B2B is this: Gartner finds buyers spend only about 17% of the entire purchase journey meeting with any potential supplier, and when they’re weighing several vendors, a single sales rep may get as little as 5% of their time. Put differently, roughly 80% of the decision now happens before you’re ever in the room.

    This isn’t a blip. Gartner’s 2026 sales research found 67% of B2B buyers now prefer a rep-free buying experience, up from 61% a year earlier. Your website, your content, and your reviews are doing the selling whether you’ve resourced them to or not.

    AI moved from novelty to infrastructure

    The experimental phase is over. McKinsey’s State of AI reports that roughly two-thirds of organisations now use generative AI regularly, yet only about a third have scaled it across the business. That gap, between teams who fold AI into how the work gets done and those still bolting tools onto broken processes, is where the advantage now lives. And it’s on both sides of the table: Gartner found 45% of B2B buyers used AI during a recent purchase.

    For a founder, the lesson isn’t to buy more AI. It’s to ask how it changes the economics of the work: how much faster you can test, how many more variations you can run, how much sooner a useful signal appears.

    “Search” stopped meaning Google

    Buyers increasingly get answers synthesised by AI assistants instead of clicking a list of blue links, and Gartner has warned that traditional search volume will fall sharply as AI search and chatbots absorb discovery. Classic SEO still matters, but it now shares the stage with answer-engine optimization (being cited inside AI-generated answers) and discovery on the platforms and communities where your buyers actually spend their attention.

    Fig · SEO, AEO, and AI answer engines

    First-party data is the only data that’s safe

    Third-party cookies, tightening privacy rules, and platform lockdowns have made borrowed audiences unreliable. The durable asset is data you own outright: email lists, communities, logged-in users, real conversations. It’s slower to build and far harder for a single platform change to take away.

    Trust became a measurable growth lever

    Buyers are quietly ruthless about credibility. Gartner reports that 73% of B2B buyers actively avoid suppliers who send irrelevant outreach, and 69% notice inconsistencies between what a company’s website says and what its salespeople say. Sloppy, contradictory, spammy marketing doesn’t just underperform, it disqualifies you before a conversation ever starts.

    The buying committee got bigger, and quieter

    The average B2B purchase now involves a buying group of six to ten people (Gartner), and much of their deliberation happens in private channels you can’t see or track. Marketing’s real job is to arm an internal champion you’ll never meet with material that travels, one-pagers, comparisons, proof, so the case for you gets made when you’re not in the room.

    None of these trends is exotic. They’re the new baseline. The advantage in 2026 doesn’t go to whoever spots them first, everyone has. It goes to whoever adjusts fastest.

  • Building a Full-Funnel Digital Marketing Strategy for B2B Growth

    Building a Full-Funnel Digital Marketing Strategy for B2B Growth

    Ask most B2B teams to describe their strategy and you’ll get a list of tactics: some paid search, a bit of LinkedIn, a newsletter, an SEO project. Tactics aren’t a strategy. A full-funnel strategy is the decision about how those pieces hand off to each other, and how you split the budget between the buyers ready now and the far larger group who will be ready later. Get that split wrong and growth stalls no matter how good the individual tactics are.

    The 95-5 rule changes where the money goes

    The most important research in modern B2B marketing is deceptively simple. Professor John Dawes of the Ehrenberg-Bass Institute, working with the LinkedIn B2B Institute, showed that at any given moment only about 5% of potential buyers are in-market, actively looking to buy. The other 95% are out-of-market, and won’t buy for months or years. Because most B2B contracts run for years, a company might be genuinely in-market for only a few weeks out of every sixty.

    The implication is uncomfortable: a funnel built entirely to capture that 5% ignores 95% of your future customers and competes for the same scarce demand as everyone else, which drives up costs. A full-funnel strategy funds both the harvest and the planting.

    Top of funnel: build memory, not just leads

    The job at the top is to be remembered by the 95% before they need you, because the brand a buyer already recognises is the one that makes the shortlist. Judge this stage by whether the right audience is growing and by branded search, not by last-click conversions, and don’t cut it because it doesn’t convert on the spot. Les Binet and Peter Field’s long-running effectiveness research points to roughly a 60/40 split between long-term brand building and short-term activation for sustainable growth. Most B2B teams are inverted, spending almost everything on activation.

    Middle of funnel: turn interest into intent

    This is where most strategies are thinnest. Someone knows you exist and has a problem, now what? Nurture, proof, and education live here: case studies, comparison content, email sequences, webinars. With buyers spending roughly 80% of the journey self-educating (Gartner), the middle of the funnel is now doing work your sales team used to do in meetings. Resource it like it matters, because it does.

    Fig · Nurture and proof: the middle funnel

    Bottom of funnel: make the decision easy

    At the bottom the buyer is close, so the work is removing risk and friction: clear pricing, honest comparisons against alternatives, strong proof, and a low-commitment first step. High-intent search and review sites like G2 and Capterra live here. It’s the cheapest place to win because demand already exists, which is exactly why it’s so tempting to over-invest here and starve the stages that create demand in the first place.

    The stages everyone forgets: the committee, and after the sale

    Two things quietly decide B2B economics. First, the buying committee: Gartner puts the average buying group at six to ten people, and Forrester’s State of Business Buying found that 86% of B2B purchases stall at some point. Content that helps your champion build internal consensus, ROI models, one-pagers, risk summaries, is often what unsticks a deal. Second, retention and expansion: a funnel that ends at the sale treats every month as a fresh acquisition problem, when your existing base is the cheapest growth you have.

    The point is the handoffs

    A full-funnel strategy isn’t doing everything. It’s being honest about where your funnel leaks, funding demand creation and demand capture in deliberate proportion, and refusing to judge every activity by the same short-term metric. Strategy is deciding what connects to what, and then holding the line long enough for it to compound.

  • How AI Is Transforming Digital Marketing Campaigns

    How AI Is Transforming Digital Marketing Campaigns

    AI has been sold to marketers as both a threat and a miracle. The data tells a more useful, more boring story: it changes the economics of the work. Tasks that were once too slow or expensive to do well, deep research, dozens of creative variants, always-on analysis, are now cheap enough to do routinely. That shift is real and measurable. What it doesn’t change is who’s accountable for the decisions.

    Adoption is near-universal. Results are not.

    Roughly 91% of marketers now say they use AI in their work (Jasper’s 2026 State of Marketing AI), and McKinsey finds about two-thirds of organisations use generative AI regularly. But the same research exposes the gap: only around a third of companies have scaled AI across the business, and the share of marketers who can actually prove ROI from it has fallen year over year, not risen. Adoption went up; accountability went down.

    The prize is real: McKinsey estimates generative AI could unlock $0.8 to $1.2 trillion in annual value in marketing and sales alone. Capturing any of it depends less on which tool you buy and more on whether you redesign the workflow around it.

    What actually improves: the speed of learning

    The biggest change isn’t quality, it’s cycle time. Research that took days takes hours; ten creative variations that were once a luxury become the default. Because marketing is fundamentally a learning system, the faster you put a real idea in front of a real audience, the faster you learn what works. Semrush reports that 68% of businesses have seen higher content marketing ROI from AI-enhanced workflows, and the operative word is enhanced, not automated.

    Personalisation finally becomes practical

    Tailoring messages to segments was always a sound strategy; almost no one had the capacity to actually produce and manage that many variations. AI removes the production bottleneck, letting you adapt messaging by industry, role, and buying stage without a proportional increase in headcount. This is where much of that marketing-and-sales value pool McKinsey describes actually sits.

    The quality trap

    Left unsupervised, AI drifts toward the average, the safe, the generic. Analyses of organic performance consistently find that human-led content still outperforms purely AI-generated content by a wide margin on traffic and engagement. The teams winning with AI treat it as AI-enhanced (human strategy and judgement, AI execution and scale) rather than AI-generated. The difference shows up in the results.

    Where the human stays in charge

    AI produces options, drafts, and signals; it does not carry accountability. The decision to move budget, the read on whether a bold creative will land or backfire, the judgement of brand fit, the client call when something breaks, these stay with people. Gartner predicts that by 2028 a majority of brands will use agentic AI in customer interactions, which makes human oversight more important, not less: agentic AI without strategic direction is just faster chaos.

    The practical stance

    Treat AI as leverage on your team’s judgement, not a replacement for it. Let it take the hours, the research, the drafts, the variants, the first-pass analysis, and keep the decisions with people who can explain and defend them. The campaigns that win with AI aren’t the most automated. They’re the ones where good judgement now gets applied far more often, because everything leading up to the decision got faster and cheaper.

  • How to Build a Predictable B2B Lead Generation Engine Using Digital Marketing

    How to Build a Predictable B2B Lead Generation Engine Using Digital Marketing

    Most SaaS and IT companies don’t have a lead generation problem, they have a predictability problem. Leads arrive in bursts: a strong month after a big push, then a drought. That pattern is what happens when you run campaigns instead of building a system. An engine has a known input, a known output, and a rate you can forecast. Getting there is less about clever tactics and more about connecting the parts so they compound.

    Why lead flow is unpredictable in the first place

    Two structural facts explain most of the volatility. First, at any given moment only about 5% of your market is in-market to buy (Ehrenberg-Bass and the LinkedIn B2B Institute), so a program aimed only at ready-now buyers is fishing in a tiny, contested pond, and results swing with every competitor’s budget. Second, buyers now spend around 80% of the journey researching on their own (Gartner), so much of what determines your pipeline happens where you can’t see it.

    Predictability doesn’t come from spending more into that small pond. It comes from building a system that also creates future demand, and from measuring each stage so you can see exactly where the flow breaks.

    Separate demand capture from demand creation

    These are two different jobs, and confusing them is where budgets get wasted. Demand capture reaches the ~5% already looking: high-intent search, comparison content, review sites, retargeting. It’s cheap and converts fast, but it’s capped by existing demand. Demand creation reaches the 95% who have the problem but aren’t searching yet: content, social, thought leadership. It’s what raises the ceiling. A predictable engine funds both on purpose, and judges each by the right metric: pipeline for capture, reach and branded search for creation.

    Define the buyer before you spend

    Unpredictable flow usually traces back to a fuzzy definition of who you’re for. Loose targeting produces wild swings, some months you accidentally reach the right accounts, some months you don’t. Precision comes first: the specific accounts and roles worth reaching, and the exact problem they’re trying to solve. It also spares you the 73% of buyers who, Gartner found, actively avoid vendors that send irrelevant outreach.

    Build for the committee, not a single lead

    The MQL-chasing model assumes one champion moves neatly down a funnel. Reality: Gartner puts the average buying group at six to ten people, and Forrester found that 86% of B2B purchases stall. A durable engine produces assets that help a committee reach consensus, ROI models, one-pagers, security and risk summaries, because a stalled deal is a lead you already paid for that never converts.

    Instrument everything, then fix the weakest stage

    You can’t forecast what you can’t see. Tracking set up properly from first touch to closed deal is what turns lead gen from a feeling into a number. Once the system is instrumented, growth becomes mostly a matter of finding the weakest stage, improving it, and moving to the next. It’s unglamorous, and that’s exactly why it works.

    Give it time to compound

    Engines are rare because they don’t pay off in week one. Demand creation, organic visibility, and nurtured relationships build slowly and then accelerate. Teams that abandon the system after a quiet month never reach the point where it compounds. The ones that hold the line get to a place where leads arrive at a rate they can actually plan around, which was the entire point.