Back in 2024 I wrote a post that leaned hard on one stat from HockeyStack: a B2B SaaS company needed 894 impressions to earn a single website visit, 71 touchpoints to generate one MQL, and 99 touchpoints to close a deal.

I still catch myself citing those numbers in decks and calls. They’re good numbers, from a real study. They’re also two years old now, in a market where LinkedIn’s auction, Google’s results page, and buyer behavior have all shifted.

So I went looking for whoever re-ran HockeyStack’s study, or the closest thing to it, to see if the number went up and by how much. Nobody re-ran it exactly. But enough dated, credible research exists now to answer the real question underneath it: is it harder to close a B2B deal today than it was in 2024? Yes. of course. Case closed.

The thesis hasn’t changed. If anything it’s stronger: paid ads are a touchpoint. Not a strategy.

The short version

HockeyStack’s original numbers came from 150 B2B SaaS companies, January to August 2024. Nobody, including HockeyStack, has published a 2025 or 2026 refresh of that specific study. The closest real successor is Dreamdata’s 2026 LinkedIn Ads Benchmarks Report: buyers now touch 88 touchpoints across 4 channels with 10 stakeholders involved, up from 76, 3.7, and 6.8 just a year earlier, and 81% of the journey now happens entirely outside the sales pipeline, up from 70%. Forrester’s 2026 buying survey puts the average buying group at 22 people. Gartner has 67% of B2B buyers now saying they’d prefer a rep-free purchase, up from 61% eighteen months ago. 6sense’s 2025 data shows the research-to-seller split moving from 70/30 to 60/40. Google zero-click search sessions went from 60.45% to 68.01% between 2024 and 2026. None of this is a clean update to 894/71/99. All of it points the same direction: it got worse.

What HockeyStack’s study actually found In 2024

HockeyStack’s numbers come from HockeyStack Labs, authored by their CRO Emir Atli, drawn from 150 B2B SaaS companies with at least $15K a month in ad spend, measured January through August 2024:

• 894 impressions to earn a first website visit, up 23% from 723 in 2023

• 71 touchpoints to generate one MQL, up 31% from 54

• 99 touchpoints for the SQL-to-Closed-Won stretch, up 22% from 81

Two corrections to how I originally framed this. First, 894 is specifically LinkedIn company-level ad impressions, not a blended number across your whole paid media mix. If you read it as impressions across Google, Meta, and LinkedIn combined, that’s not what HockeyStack measured (I hadn’t clocked this either until I went back and checked their methodology). Second, “99 touchpoints for a deal” is really just the last leg, SQL to Closed Won, not the whole journey. First touch to closed deal was 266 touchpoints and 2,879 impressions in 2024, up 19.8% and 9.5% over 2023. Growth wasn’t even across stages, either: impressions dropped 5% in the MQL-to-SQL stage while touchpoints rose in every stage.

Deal size moves these averages more than anything else does. Sub-$10K ACV deals needed 40% fewer impressions and 31% fewer touchpoints than average just to hit MQL. Deals over $100K ACV needed roughly 5,500 impressions and 417 touchpoints to close, with the SQL-to-Closed-Won stretch alone eating 2,081 impressions. If you sell enterprise, every average in this post is underselling your real funnel math.

There’s no clean 2026 update

HockeyStack did publish a separate, current 2025 LinkedIn Ads Benchmark Report (70+ companies, $28M in ad spend analyzed), which found aggregate LinkedIn CTR improving over the course of the year, from about 0.82% in Q1 to 0.96 to 1.05% by Q3. That’s a real, current data point. It’s just a different metric than impressions per company, and it doesn’t update 894.

So instead of inventing a 2026 number to make a cleaner headline, here’s the closest real evidence I found.

The closest real evidence

The best successor stat comes from Dreamdata’s 2026 LinkedIn Ads Benchmarks Report (Anna Thorsen, published March 10, 2026), built on 66 million-plus sessions and 3.5 million-plus customer journeys:

What it shares with HockeyStack’s data is the direction: buying in 2026 takes more touches, across more channels, with more people involved, than it did a year earlier. A 16% jump in touchpoints and a 47% jump in stakeholders in a single year isn’t noise.

The buying group got bigger, full stop

This part is the most solidly documented, because three research groups landed on the same conclusion using three different methods.

Forrester’s “State of Business Buying, 2026” (published January 21, 2026) puts the typical buying decision at 13 internal stakeholders and 9 external influencers, 22 people touching a single purchase, with 73% of purchases involving three or more departments. That’s well past the “6 to 10 people” figure Gartner made famous for most of the last decade. Gartner’s own newer research backs the shift: a May 2025 survey found buying groups now ranging from 5 to 16 people across up to 4 functions, and that 74% of those groups show “unhealthy conflict” during the decision process. More people in the room, more people disagreeing in the room. Selling to a committee that argues with itself internally is exactly as fun as it sounds.

That lines up with Dreamdata’s jump from 6.8 to 10 stakeholders above. Three studies, three data sources, one direction: the buying committee isn’t shrinking.

Buyers are moving faster without you

6sense’s 2025 B2B Buyer Experience Report (4,000+ buyers across North America, EMEA, and APAC) found the split between independent research and seller engagement moved from 70/30 to 60/40 in a single year. Buyers are spending relatively more time with reps, not less, which cuts against the popular “dark funnel” story in one specific way. But the same report found 94% of buying groups had already ranked a preferred vendor before first contact with sales, and bought that vendor 77% of the time. The buying cycle also compressed, from about 11 months in 2024 to 10 in 2025.

Put those together: buyers loop sales in a bit earlier than before, but only after they’ve already picked a favorite. Gartner is tracking the same shift in stated preference: 61% of buyers said they’d prefer a rep-free purchase in mid-2025, and that was already 67% by March 2026, a 6-point jump in under a year on a preference that was already a majority.

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AI adds a verification step. It doesn’t remove one

The obvious question in 2026 is whether AI is shortening this process. From what’s published so far: not really. It’s changing what buyers do with the extra time, not cutting the steps.

TrustRadius’s 2026 B2B Buying Disconnect Report (1,862 buyers, 444 vendors, published July 2026) found 63% of buyers now use AI somewhere in the purchase process, and 54% say it made research easier, up from 40% a year earlier. But 94% fact-check whatever the AI told them, and the average shortlist a buyer considers is just 2.7 products, tighter, not looser, even as the buying committee grows. Gartner’s May 2026 survey found the same pattern from the other side: 45% of buyers used GenAI during a recent purchase, mostly to research vendors, buyers consult an average of 7 information sources, and 69% say they’d still rather validate AI-generated insights with a sales rep than trust the AI alone. Which, if you’ve ever watched someone paste a vendor comparison into ChatGPT and then call the rep to double-check it anyway, tracks.

If you’re optimizing for “get on the shortlist,” 2.7 products is the single most useful number in this piece. You’re not competing against ten vendors for attention. You’re competing to be one of three names a buyer already trusts before they start looking seriously, which pushes the whole game earlier, into that 81%-outside-the-pipeline territory Dreamdata is measuring.

Why impressions probably cost more now

The underlying math has gotten worse, mostly upstream of any single ad platform.

SparkToro’s analysis of Similarweb clickstream data (Rand Fishkin, published June 2026) found 68.01% of US Google searches in the first four months of 2026 ended without a click anywhere, up from 60.45% in 2024, by their count the fastest acceleration in zero-click search in the past decade. Ahrefs analyzed 300,000 keywords via Search Console and found position-1 organic CTR on keywords that trigger an AI Overview fell from 7.3% to 2.6% between March 2024 and March 2025, a 64% relative drop. Ahrefs attributes roughly 34.5% of that decline specifically to the AI Overview’s presence, separate from the general downward trend that hit even non-AI-Overview keywords over the same period (5.6% down to 3.1%).

WordStream’s 2026 benchmarks put programmatic display CTR at 0.12 to 0.40% depending on category, meaning 250 to 800-plus impressions per click is the baseline cost of that channel before any of this year’s headwinds. Display was always the volume play. It’s a bigger one now.

What this changes about your budget

The closest adjacent methodology’s most recent jump, Dreamdata, 2025 to 2026, is 16% on touchpoints and 47% on stakeholders. Different studies measuring different things, about a year apart. Line them up and the pattern holds: whoever measures this next will very likely report a bigger number than 2024’s, probably somewhere in the 15 to 30% range both studies found independently.

If you run paid media for a B2B SaaS company, three things here should change what you do this quarter.

With 81% of the journey now happening outside any pipeline you can track, and the average buyer touching four-plus channels before converting, a media plan concentrated in one platform structurally can’t cover the journey. It never really did. The gap is just 11 points wider than it was a year ago.

The shortlist number matters more than the funnel number. A 2.7-product average shortlist means the job isn’t filling a funnel with strangers, it’s being one of three names already in a buyer’s head before they open a tab. That’s a brand problem as much as a demand-gen one, and it won’t show up in a last-touch attribution report.

Budget for more impressions per outcome, not fewer. Bigger buying groups, more channels, lower organic CTR: all of it points toward funnel math getting harder. If your 2026 plan assumes the same impressions-to-pipeline ratio you used in 2024, it’s probably underfunded.

Frequently asked questions

Is AI shrinking the B2B buyer journey? Not based on anything published so far. It’s changing what buyers do with their research time, not cutting steps. 63% now use AI somewhere in the process, but 94% fact-check it and 69% would still rather validate with a sales rep.

Did the buying group get bigger, or is that just one report? Three independent sources agree. Forrester puts it at 22 people, Gartner’s 2025 survey found groups of 5 to 16 across up to 4 functions, and Dreamdata’s data shows stakeholders per deal rising from 6.8 to 10 in a year.

How many touchpoints does it actually take to close a B2B deal in 2026?
There's no single verified number, that's the whole point of this piece. The closest real data point is Dreamdata's: 88 touchpoints per buyer on average, up from 76 a year earlier. Apply the 15 to 30% growth pattern found across every 2025/26 study to HockeyStack's original 2024 numbers and you land somewhere around 82 to 92 touchpoints for an MQL and 114 to 129 for a closed deal. Plan around a range, not a single figure.

I did in six hours what our marketing agency needed two months for. So we fired them.

Not out of spite. The math made the call for me.

Three months on retainer. $42,000 for a two-month campaign. Eight rounds of feedback and twelve meetings just to get to launch day. Then the campaign went live, broke in seven different ways, and cost us close to $100,000 before anyone could ship a fix.

Plenty of agencies are good, and later on I’ll get into exactly when hiring one still beats building in-house. What follows is the real numbers from one specific relationship, what it took to unwind it, and a framework for making this call before it costs you six figures instead of six hours.

The short version

A company I worked with paid an agency $42,000 over two months to launch a multichannel campaign: ads, landing pages, copy, visuals. Planning took 12 meetings and 8 rounds of feedback. After launch, a run of preventable mistakes cost roughly $100,000 in lost monthly revenue. I rebuilt the broken pieces myself in six hours. Moving the work in-house afterward saved $4,250 a month, cut planning-to-launch time in half, and lifted campaign ROI by around 20%.

The setup: three months, $42,000, and a rocky start

The agency had already been on retainer for three months when this campaign started. The brief wasn’t complicated: a multichannel push covering ads, landing pages, copy, and visuals, budgeted at $42,000 for a two-month sprint from planning to launch.

Planning was rocky from the first call. We kept having to re-explain who our product was for, well into month three of paying them. An agency still asking “wait, who’s this for again?” in its fourth month isn’t in an onboarding phase anymore. It never built a real model of the business in the first place.

It took 8 rounds of feedback to get the materials shippable and 12 meetings to reach go-live. Turns out that’s not just us. Setup®, which has tracked client-agency breakups for eight years, found that in 2025 “the agency didn’t understand our business” was the fourth most common reason clients left, cited by 44% of them, right behind dissatisfaction with delivery and value, both at 61% (Setup®). We were living that stat and didn’t have a name for it yet.

What went wrong after go-live

Within days, the cracks weren’t cosmetic:

• An ad went out with grammar errors. The CEO caught it and forwarded a screenshot.

• The URL on our most important landing page pointed to the wrong place.

• Some ads got served to existing clients instead of the new-prospect audience they were built for.

• Landing pages carried images that had never gone through approval.

• The A/B tests, the entire point of a controlled launch, weren’t set up correctly. No clean read on what was working.

• An influencer video posted early, out of sync with the rest of the launch.

• Our client success manager was out of office through all of it and hadn’t told us.

Three days to fix everything. By the time it was patched, we were staring at a $100,000 hit to monthly revenue, more than double what the whole two-month campaign had cost to make.

None of these were hard problems. They were carelessness. Nobody on the other side was treating our launch like it was theirs.

The real number isn’t the $4,250 a month

This story leads with the monthly savings: $4,250 once we moved in-house, about 20% more ROI, planning-to-launch time cut in half. Real numbers, and a clean headline.

But they weren’t what mattered most in the decision. The retainer savings were a rounding error next to the $100K we lost from three days of a broken campaign. Most agency-vs-in-house comparisons price the relationship by the invoice. The real cost shows up later, when something breaks and nobody senior is paying close enough attention to catch it before it ships.

That tracks with a wider pattern. A 2026 analysis of the CMO-agency relationship found dissatisfaction with delivery is the #1 reason clients leave (48%), while agencies rank it 7th on their own list of concerns. Six positions apart, same relationship. Agencies keep bracing for the budget conversation. Clients are walking over a broken URL nobody checked (Everything-PR).

The same research puts retainer churn at 18% a year, project-based churn at 42%, and the highest risk of all in the first 90 days. We were three months in. Right in that window, not past it.

Why this happens more than founders realize

I started my career at an agency. The irony isn’t lost on me: I’ve now fired more agencies than I’ve hired.

But this wasn’t one bad vendor. It’s structural. Full-service retainers typically run $3,500 to $10,000 a month, with most agencies still enforcing minimums around $1,000 to $1,500 even for smaller scopes (HawkSEM). At that price, a small account rarely gets a senior team’s full attention. It gets whoever has bandwidth that sprint. Agencies also routinely spend $400,000 or more just pitching for new business (Everything-PR), a cost structure built around landing new logos, not protecting the ones already signed.

Agencies aren’t inherently bad. The incentives on a mid-size retainer just don’t reward the kind of obsessive, would-I-ship-this-on-my-own-product attention a founder assumes they’re paying for.

Small business marketing agency vs. in-house: when the agency wins

To be fair to the other side: outsourcing execution isn’t automatically a mistake, and the data doesn’t back a blanket “always build in-house” rule either.

Companies with serious in-house marketing muscle still keep agencies around. Among ANA member companies running both, in-house handles 61% of the work on average, but 92% still use outside agencies for the rest (Marketing Charts), usually for specialized skills that don’t justify a full-time hire, or overflow during a launch push. The shift toward in-house is real (82% of ANA members have an in-house agency now, up from 58% a decade ago). It’s rebalancing who does the work more than eliminating agencies from the picture.

A 200-person company running four campaigns a quarter across a dozen channels can get outproduced fast trying to do it all internally. What happened to us was a specific, common failure: a small account, understaffed on their side, running something complex enough to need senior attention it never got.

What changed when I brought it in-house

The six-hour rebuild wasn’t heroic. Most of it was AI doing in minutes what used to sit in a design or dev queue for days: rewriting the broken ad copy, regenerating the landing page assets, fixing the tracking and A/B test setup, checking every link and image against our approved brand set.

That’s not unique to us. Traditional production of one marketing visual used to take several hours. AI-assisted teams now do it in under 30 minutes. Full campaign launch windows have gone from a typical 2–3 weeks down to under 2 days (Luma), with teams reporting roughly 10x more creative output without adding headcount. Meanwhile, an estimated 52% of content made through traditional agency pipelines never even reaches the market.

Six hours stops looking like an outlier once you put those numbers next to each other. It’s what happens when the gap between an outsourced production queue and one person with AI-native workflows finally flips.

A simple framework for the decision

Three questions do most of the work if you’re making this call yourself.

How many people do you have? Under roughly 20, an agency retainer usually buys less senior attention than you’re picturing, at a price that could fund an in-house hire plus a solid AI toolkit with room to spare.

How complex is the launch? A single-channel, evergreen campaign is a reasonable thing to hand off. A multichannel launch with tracking, segmentation, and a hard go-live date needs someone who treats a wrong URL like their own revenue, because it is.

Can you build or check the work yourself? This is the newest variable, and most agency-vs-in-house comparisons haven’t caught up to it. If a non-technical founder can use AI tools to produce and QA campaign assets directly, the old argument for outsourcing execution gets a lot weaker. What’s left to pay for is judgment and strategy, a smaller, more defensible thing to hand to someone else.

Overpromising and underdelivering is common in this business. It’s just rarely said out loud. If a vendor keeps needing you to re-explain your own company three months in, take that as the whole story, told early.

Frequently asked questions

Is a small business marketing agency worth it, or should you build in-house? Depends on scope, but the comparison usually isn’t close once you factor in failure costs. A full-service retainer runs $3,500–$10,000 a month. One in-house marketer with modern AI tools can often match or beat agency output on execution-heavy work for a fraction of that, without the risk of a launch-day mistake going unnoticed until it’s expensive.

What’s the biggest reason companies fire their marketing agency? Delivery, not price. Multi-year survey data has dissatisfaction with delivery and dissatisfaction with value tied as the top reasons clients left in 2025, both cited by 61% of them, followed by “the agency didn’t understand our business” at 44%. Agencies tend to overestimate how much budget cuts drive departures and underestimate how much delivery does.

When does it make sense to keep working with a marketing agency? When the work needs specialized skills you don’t have and won’t need often enough to justify a hire, or when you’re running enough campaigns at once that even a strong internal team needs overflow help. Even companies with mature in-house teams keep agencies for exactly this. The goal is options: an agency for specialized or overflow work, nothing mission-critical resting on just one vendor.

How long should you give an agency before deciding it’s not working? Watch the first 90 days closely, that’s statistically when agency-client relationships are most likely to fail. If you’re still re-explaining fundamentals about your own company three months in, that’s a signal worth acting on, not waiting out.

Can AI tools really replace agency execution work for a non-technical founder? For production and QA-heavy work, increasingly yes. AI-assisted workflows have cut typical campaign production windows from 2–3 weeks to under 2 days industry-wide. What AI doesn’t replace is strategic judgment, so the more defensible use of an agency going forward is strategy and specialized expertise, not execution you can verify and rebuild yourself.

Last updated: August 2026

80% of startup founders don’t understand marketing. And it’s costing them real money.

Not because founders are bad at their jobs. Most are excellent at the thing that got the company here: product, sales, fundraising, sheer will. Marketing is just a different discipline, with its own timelines and its own definition of “working,” and most founders never had to learn it before they were suddenly in charge of it.

That gap shows up as one constant, low-grade struggle inside almost every startup marketing team: executing the founder’s sporadic ideas versus doing the work the right way. Here are five places I see it happen, over and over, across companies that otherwise look nothing alike.

The short version

Founders default to instinct, because instinct is what built the company so far. Marketing runs on planning, data, and effort that compounds over months. Put those two operating modes in the same room without translation and you get chaotic priorities, a pipeline full of bad leads, and a marketing hire burning out trying to fix five problems at once. It’s fixable. It just takes a founder willing to name the gap first.

1. Focus

Founders switch priorities chaotically. A competitor’s campaign, a board member’s offhand comment, something that performed well on a founder’s personal LinkedIn post, and the whole team’s roadmap shifts for the week. Each pivot feels small in the moment. Stacked up over a quarter, it makes real planning impossible and puts constant pressure on marketing to show results before anything has had time to work.

2. The basics

Newly hired marketers routinely spend close to half their time just explaining fundamentals to the founders who hired them: why marketing needs a monthly, quarterly, and yearly plan, not a weekly one; why a campaign needs weeks to show a signal, not days. Skip that groundwork and the mismatch shows up downstream, filling the pipeline with “marketing-qualified” leads that were never really qualified. CB Insights lists poor marketing among the top reasons startups fail outright. Not a footnote.

3. Resources

Organic virality is rare, and most paid channels have gotten more expensive, not less. Building a real pipeline takes more than one marketer and a small ad budget, no matter how tempting it is to copy a competitor running a $10M budget and a global team. A good small team can be highly productive. It still can’t out-produce a company with twenty times its resources by sheer effort. The gap isn’t talent. It’s math. Content Marketing Institute puts it starkly: roughly 80% of startups with no dedicated marketing budget fail.

4. Data

Gut calls are fast. Data-driven marketing isn’t, and that’s the trade nobody wants to make out loud. Constant focus-shifting (see #1) makes it nearly impossible to measure anything cleanly, and long B2B sales cycles make the problem worse: the median B2B deal now takes roughly four to five months to close, up 20 to 30% since 2021, so cause and effect on any given campaign can be months apart. For scale, it takes an average of 894 LinkedIn impressions to generate a single website visit from an ideal customer profile match (HockeyStack). None of that shows up in a weekly check-in. It shows up in a quarter.

5. Results

Poor results are rarely one thing. Shifting focus, internal disagreement, thin resources, no benchmark data to argue from, all compounding at once, while marketers are expected to fix every piece of it simultaneously. When results don’t move fast enough, the instinct is to bring in outside help as a patch. To the team already in the room, that usually reads as a vote of no confidence. It tends to do more damage to trust than the original problem ever did.

The truth is

When founders actually understand marketing, the difference is immediate. Ideas flow in both directions instead of one. Communication stops requiring translation. Marketers take real ownership instead of just executing requests, because they’re finally being asked into the strategy instead of handed a list after it’s already been decided.

Founders, how do you make sure you’re actually on the same page as your marketing team, and not just assuming you are?

Frequently asked questions

Why don’t most startup founders understand marketing? Founders are usually strongest in the disciplines that got the company off the ground: product, sales, or fundraising. Marketing runs on a different clock, with results that compound over months instead of days, and most founders never had a reason to learn that rhythm before they were suddenly responsible for it.

What’s the actual fix for founder-marketer misalignment? Shared planning cadence, not more meetings. A founder who reviews marketing on a monthly and quarterly view, alongside the team, closes most of the gap described above. The chaos comes from founders parachuting in with weekly ideas outside that cadence, not from disagreement itself.

How long should a founder give a new marketing hire before judging results? Long enough to match the sales cycle, not the hiring itch. With median B2B deals now taking four to five months to close, a fair read on a marketing hire’s impact usually needs at least one full quarter, and closer to two for anything with a longer enterprise cycle.

Is bringing in outside marketing help always a bad sign? Not inherently, but timing and framing matter. Brought in as a genuine skills gap, external help works. Brought in as a panic response to slow results, it usually reads to the existing team as distrust, and the morale cost tends to outweigh whatever the outside help fixes.

Last updated: August 2026

Here’s a question I keep coming back to: if you haven’t canceled a $3,000 order over the phone from a housewife who, after finishing a bottle of wine, ordered a bunch of stuff at 2AM, have you really worked in e-commerce?

I open with that because it’s basically the whole point of this piece. Everyone wants to talk about AI “transforming” e-commerce. Not many of the people talking have sat inside the operation long enough to know which parts of that transformation are real, and which parts are a chatbot demo that falls apart the second a real customer shows up drunk with a credit card. I have. Ads, socials, landing pages, product catalogs, loyalty programs, and yes, the 2AM phone calls. So here’s my attempt to lay it out, function by function: where AI is doing real work right now, and where it’s still theater.

The short version

E-commerce operations break down into roughly fifteen client-facing functions, and every one of them boils down to three repeatable buckets: content creation, deployment, and analysis. AI is already very good at two of those three, for most functions. It’s still weak at the third, whenever the job requires real judgment. And it’s basically useless for anything analog: print media, BTL activations, giant SMYK-format banners. Those were never computational problems to begin with, so there was never anything for AI to solve there.

That’s the framework this whole piece runs on. For every function below, I’m scoring it against those three buckets. Can AI create the thing? Can AI deploy the thing? Can AI actually tell you what happened and what to do next?

Why this is worth writing down

Nobody plans to do just one of these things. In a real campaign, a real quarter, you’re running paid ads, refreshing the loyalty program, chasing UGC, and rewriting a dozen landing pages at the same time, in three languages, for two personas. That’s what makes e-commerce ops different from most marketing. It’s not one hard problem. It’s fifteen medium problems happening in parallel, at a volume that punishes anything that doesn’t scale.

That’s also exactly the kind of problem AI is built for: repeatable structure, high volume, low novelty per unit. Which is exactly why so much of the AI hype aimed at e-commerce misses the mark. Vendors sell you the demo (one perfect product description, one perfect ad), not the actual job (ten thousand SKUs, six markets, a Tuesday).

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Marketing channels

1. Ads & paid media

Every ad platform- Meta, Google, TikTok- is constantly tweaking its own workflow to make e-commerce advertisers dependent on staying inside the walled garden. Connect your Shopify store, and they’ll dynamically serve you an endless product carousel back at yourself. It doesn’t even work that well, technically: how many times have you been retargeted for a product you bought two days ago? The platforms’ own “AI,” the copy generation, the image enhancement, the image-to-video, is better than the ChatGPT-3.5-with-no-context era, but it’s still working off just your website. Feed it real campaign history and brand context instead, and the output stops reading like slop.

Where AI helps: campaign planning drafts, audience research synthesis, first-pass ad copy and creative variants across formats, A/B test structuring, performance summaries, budget reallocation recommendations.

Where it doesn’t: the actual creative judgment call on what’s worth testing, catching a platform-reported ROAS number that’s lying to you, and the account-level trust that no AI tool is going to build for you with a Google or Meta rep.

2. Social media marketing

Of every channel on this list, social is the hardest to defend on paper. You’re managing DMs, reacting to trends and algorithm shifts in real time, explaining to a stakeholder why the TikTok with 500,000 views produced zero purchases, and doing all of it while staying close enough to your audience that they don’t clock you as disconnected. A lot of the job is manual by nature. So the honest measure of whether AI is helping here isn’t whether it wrote the caption; it’s whether it gave the social team back enough time to actually be creative instead of just executing. Where it earns that: content calendars, platform-specific copy variants, hashtag generation, sentiment monitoring, first-pass trend spotting. Where it can’t help at all: the fast, funny, opportunistic post that nobody could have planned a week out, and the read on whether a trend actually fits your brand voice or is a trap.

3. Influencer marketing & UGC

Even small e-commerce brands need this. The pain isn’t finding influencers; it’s everything downstream: negotiating, deciding whether the economics work, sending hundreds of dollars of product for free, hoping they post on time, hoping you break even given how fast the trend cycle moves. There are now platforms generating decent AI “UGC reviews” for paid ads. Decent, not yet at the level of an experienced creator with real audience recognition and the extra organic content that comes attached to an actual relationship.

AI’s lane: influencer shortlisting and vetting at scale, outreach drafting, contract term summarization, performance tracking, repurposing UGC across formats.

Not AI’s lane: the relationship itself. AI UGC is a decent substitute for ad creative. It is not a substitute for a creator’s own audience.

4. Mobile app marketing

We don’t think about apps and app stores much, but most big e-commerce businesses run one, and running one well is no small task (ask Nike about its app-first push). ASO for the App Store and Play Store works nothing like web SEO. The analytics are a different animal from web analytics, and tying it all back together- from install to activation to retention- is real work. This is one of the more underrated places for AI to add value, because most of it is research, copy drafting, and analytics interpretation, and less pure judgment: ASO keyword research, store listing copy, push notification copy and timing tests, analytics synthesis across a fragmented toolset. What it can’t do is decide what actually belongs on the product roadmap. That’s still a product call.

5. Email marketing

This is one of the main channels for talking to people who already know you, and the volume compounds fast. Two to three emails a day is normal during a campaign, for one audience. Multiply that by multiple personas, languages, and regions, and you have a real production problem. This is where AI’s leverage is the most obvious and the least controversial: it can draft the whole sequence for you to review, edit, and schedule.

What speeds up: first-draft copy across segments and languages, subject line variants, automation logic, deliverability pattern analysis.

What still needs a human pass: the finishing touch. AI email at scale, without a human final read, reads like AI email at scale.

Website content & SEO

6. Landing pages

Landing pages resemble almost any other conversion asset, with one difference that punishes you harder than anywhere else: skip building mobile-first, and you’re setting money on fire. Plenty of companies still spend more time and budget optimizing an app than the mobile page driving most of their traffic. The lever AI pulls here is speed: drafting pages, copy, and A/B tests faster, especially for seasonal content, especially once you’ve fed it real project and brand context instead of asking it cold. What it doesn’t touch is the UX and conversion architecture itself, CTA placement, form friction, trust signal placement. That’s design judgment, not a copy problem.

7. Blog content & SEO

The defining feature of e-commerce SEO, compared to SEO for basically anyone else, is that you’re optimizing product pages in bulk, per market, and every market is its own animal. Bulgarian search behavior has nothing to do with Turkish search behavior. I once spent weeks researching and optimizing an enormous number of pages for a Bulgarian retail site, working out of one giant spreadsheet while an agency handled the ingestion. That’s the job at scale: not writing one great blog post, but keeping quality from collapsing across ten thousand product pages in six languages.

AI helps with: keyword research and clustering, content briefs, first-draft product and category copy at scale, content-gap analysis against competitors.

Still you: editorial judgment on what’s actually worth writing, and catching the multi-market nuance that an AI trained mostly on English content will miss.

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Product management

8. Product uploads & management

This is the unglamorous backbone every other function depends on, even though most marketers barely think about it. Get the data wrong here- inconsistent titles, thin descriptions, broken attributes- and it breaks dynamic ads, breaks cross-channel sync, breaks every downstream integration you were counting on. The scale problem is brutal: thousands of SKUs, a unique description expected for each, consistency required across every channel you sell on. AI is a real time-saver on the drafting side of this: SEO-friendly titles and descriptions at scale, attribute standardization, cross-selling relationship mapping. It’s no help at all on the taxonomy decisions or the quality control. Garbage attribute structure in, garbage dynamic ads out, no matter how good the copy is.

Customer experience

9. Customer support

Back to the 2AM order. Phone support is the closest thing on this list to fully solved by AI already; plenty of large companies have already done it. Everything downstream of “customer has a question with a known answer” is automatable now. Advertisers currently pay up to $124 a click on “ai customer support” as a keyword, the highest CPC in the entire research set behind this piece. Nobody bids that high on a term nobody’s actually buying against.

Where AI’s already there: first-response drafting, categorization and routing, knowledge base generation and maintenance, sentiment-based prioritization.

Where it still needs you: everything upstream of the known-answer question. The judgment call on whether to actually cancel that order, the de-escalation, the read on whether someone’s just frustrated or about to leave a one-star review that tanks your rating. That still needs a person, or at minimum a very well-trained escalation path.

10. Loyalty & rewards programs

It’s genuinely hard to be original here. Loyalty programs have existed since the 1960s, and “generate me 10 loyalty program ideas for my online shop” gets you the same generic tiers-and-points structure everyone else has. There’s real data science underneath the good ones. Ask LIDL, which reportedly burned north of $600 million on a failed SAP integration trying to get this right at scale. Most brands aren’t LIDL-scale, though, and for them AI is actually useful for sharper program ideas, once it has real context on your customers rather than a cold prompt. What it can’t do is the economics: point-to-redemption ratios and program liability are finance decisions wearing a marketing costume.

11. Reviews management

Everybody needs reviews. Nobody wants to do the actual work of collecting, moderating, and responding to them. This one’s close to a free win with the right setup: pipe reviews automatically into a shared drive, feed that into your AI tool’s project knowledge, and most of the analysis and response drafting gets handled without you touching the raw feed by hand.

AI helps with: sentiment analysis at scale, response drafting for common scenarios, theme identification for product feedback.

Still you: the negative review that needs real service recovery, and deciding what a recurring complaint theme actually means for the product.

Business optimization

12. Analytics & insights

This is the company-level version of the same problem every campaign has: turning a pile of data and reports into plain language with a next step attached. Multi-touch attribution across channels is still a genuine mess, industry-wide, and that’s not really an AI-solvable problem so much as a data-infrastructure one. AI can translate a dashboard into a plain-language summary, flag anomalies, and give stakeholders a first-pass read without making them open the report. It cannot decide which insight is actually worth acting on, and it cannot build you an attribution model that isn’t garbage. That part’s still yours.

13. Operational optimization

This bucket- cart abandonment, checkout friction, inventory forecasting- is where a marketer’s job is mostly technical A/B testing, and by now you can feel that push-to-sell motion has meaningfully improved. A general-purpose AI tool isn’t especially useful here beyond the brainstorming-and-copy layer; this is closer to an engineering and data problem than a content one. AI can help identify friction points from existing data and draft test hypotheses. It cannot do the implementation, and it definitely cannot fix the cross-department coordination that’s usually the real bottleneck. That’s a people problem, not a content one.

14. Community building

Community doesn’t have a unique workflow of its own; it’s the thread that ties affiliates, coupons, and campaigns together. What makes a community worth joining is exclusivity, and exclusivity mostly comes down to content: tailored content, member recognition, a reason to feel like an insider. That’s exactly where AI earns its keep, once it actually knows your community’s context instead of generating generic engagement bait. What it can’t do is the belonging itself. No AI tool has ever made a community feel exclusive on its own.

15. Affiliate program management

I won’t pretend this one’s solved. Creating media kits and communication guidelines at scale for a large, demanding affiliate base is still a lot of work. Worth a passing jab at the Honey scandal here too, still a relevant cautionary tale about what happens when an affiliate layer optimizes for itself instead of the customer.

AI helps with: onboarding material drafting, performance dashboard summaries, fraud pattern flagging.

Still you: the actual relationship management with your top affiliates, and the trust layer that keeps a program honest.

The pattern, if you zoom out

Look back across all fifteen, and a pattern shows up fast. AI is excellent at content creation almost everywhere: drafts, variants, first passes, at volume. It’s genuinely useful at deployment in the more structured, repeatable channels: email, ASO, product data. It’s still weak at analysis the moment a decision requires real judgment, financial risk, or reading a human being correctly. The functions where AI is closest to “basically solved,” phone support triage, email drafting, review response, are the ones that were always closer to pattern-matching than judgment. The functions still mostly resisting it, community, affiliate relationships, the loyalty program’s actual economics, were never really content problems to begin with.

That’s the test I’d apply before buying anything sold to you as “AI-powered e-commerce operations.” Ask which bucket it’s actually solving for, and whether that bucket was ever your bottleneck in the first place. Buy the wrong one, and you’ve automated a problem you never had, while the actual bottleneck sits exactly where it was.

If you only pull one lever this week, start with product data. Every other function on this list depends on it being right; it’s mechanical enough that AI can do most of the drafting today, and you’ll see whether it actually worked within a day, not a quarter.

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Frequently asked questions

Can AI actually replace an e-commerce operations team in 2026? No, and nothing here suggests that. It replaces or massively speeds up the content-creation layer across almost every function, and increasingly the deployment layer in structured channels like email and app store listings. The analysis and judgment layer, what to do with the data, how to handle an angry customer, whether a partner relationship is worth keeping, still needs a person.

Where’s the single highest-leverage place to start automating with AI? Email marketing and product data management are the two most mechanical, most repetitive, and least judgment-dependent functions on this list, which makes them the easiest wins. Phone and chat support triage is close behind, since large companies have mostly already solved it.

Is AI-generated UGC as good as a real influencer’s content yet? Not for building actual audience trust. It’s a reasonable substitute for paid ad creative, where you need volume and iteration speed. It’s not a substitute for an experienced creator’s own audience, which is the actual thing you’re paying influencer rates for.

Does AI actually reduce cart abandonment? Indirectly. AI can help identify friction points and draft test hypotheses faster, but the actual fix (payment options, shipping transparency, checkout speed) is an implementation and cross-department coordination problem, not a content one.

What about the non-digital stuff, print, BTL, partnerships? Left out of this piece on purpose. A 600cm x 200cm printed banner at production spec still needs a human and Adobe, and B2B partnerships are a relationship business by definition. Neither is a scale-and-repetition problem the way the fifteen functions above are, so AI’s leverage there is much lower.

[Last updated: August 2026]

Somebody on your team is about to forward you a PostHog email, a LinkedIn post from a competitor’s ghostwritten CEO account, or a screen recording that did 40,000 views on some marketer’s TikTok. Before you write it off as “not our thing,” it’s worth working through why brand awareness matters for a B2B SaaS company that would rather just optimize for buy-now conversions, what has to be true about your team before you start, and what to actually post once you do.

This is that framework, stripped down to what matters.

The short version

Short-form video marketing for B2B SaaS means posting frequent, unpolished, founder- or team-led vertical video (TikTok, Reels, YouTube Shorts, native LinkedIn video) to build category awareness with the buyers who aren’t ready to purchase yet. It’s not a sales channel. It’s a mindshare channel, and it only works if you commit to it for months, not weeks.

Why brand awareness?
TLDR: touchpoints.

Okay, for real: Can’t we just optimize for fast buy-buy-sell-sell as a B2B SaaS?

Patrick James Cumming laid this out well on LinkedIn: the biggest problem with marketing funnels is that they give B2B marketers the illusion of control, as if they’re the ones driving a buyer’s journey toward a perfect bottom-of-funnel piece that finally makes someone buy. It eases the anxiety, and it makes a tidy narrative for the C-suite. It’s just not how buyers actually behave.

Roughly 95% of your category’s buyers are out-of-market at any given moment. Only about 5% are in-market. And of that 5%, the majority (Wynter’s research puts it above 80%) choose a brand they were already aware of before they started shopping around. There’s a finite number of in-market buyers at any given time, and most of them default to whichever brand they already like.

So the job isn’t to convert people who aren’t buying yet. I’d say more or less build mindshare around the moments when your category comes up in someone’s head, long before they open a tab to compare vendors. Pour everything into short-term sales pushes and skip the long-term content, and you get exactly what you’d predict: a dry top of funnel, because you starved it on purpose (I got PTSD writing this, argued it at least 4-5 companies I have worked at).

To sum up: improve long-term brand recognition instead of chasing short-term marketing metrics that don’t convert anyway. That’s what sales is for. Marketing’s job is making sure people recognize you when sales calls.

Before you commit: the pros and cons matrix

This is the part most gurus skip, and it’s the part that actually decides whether this works for you - sorry, this is not for everyone.

Most “how to do short-form video” advice assumes you already have the talent, the risk appetite, and the market instincts to pull it off. Most B2B SaaS teams don’t, not on day one. Pretending otherwise is how teams post for three weeks, get discouraged by low view counts, and quietly quit.

Before you greenlight this, sit your team down and fill out a version of this matrix honestly. In short:

Signals you’ll struggle:

• You’re building content for a market you don’t live inside: a different country, a different day-to-day culture, different humor

• Limited internal capacity or on-camera talent for video creation

• Your team defaults to one “safe” platform (usually LinkedIn) over native short-form platforms, so there’s a real learning curve

• Leadership prefers a low, selective social media presence

• Your brand identity and positioning are still forming

• You’re building the entire content engine from zero: no workflow, no editor, no habits yet

Signals you’re set up to win:

• Genuine willingness to build in public and be transparent about the process

• Freedom to speak authentically, without heavy legal or corporate review slowing every post

• Real domain expertise you can draw from without extra research

• A specific regional, cultural, or niche angle nobody else in your category is using

• Strong thought leadership already inside the team, even if it’s unpolished on camera

• A blank slate, which means nothing to unlearn or walk back

• Willingness to experiment and change direction fast

Be real with yourself. If the “struggle” column runs longer, that doesn’t mean don’t do it. It means budget more time before you see anything, and say that out loud to your team before you start, not three months in when someone’s frustrated.

Here’s the part that isn’t negotiable either way: to get a fair read on whether this works, you need a daily upload for 3 to 6 months, minimum. Half-assing is pointless. Three weeks of halfway effort doesn’t produce a result you can learn from — it produces content nobody watched and a story about how “we tried video and it didn’t work.”

Why the commitment has to be real

There’s a HUGE gap for authentic, human connection online and in content right now, even on the AI topic. People get fascinated by the tech first, then forget about it a week later, because they forget to ask: what is the goal of this, does it deliver results?

The reflux of AI slop has been happening for over a year now, and you need to grasp it - one iPhone, one mic, and being yourself. No cutting corners to get an AI avatar to do it for you - think about what the goal actually is.

Also, time-capping creativity is toxic. You decide to spend 15 minutes and get an okay result, but what if you spent 20 and got the best video possible, with results that were totally worth it? Next time you share a tl;dv video, remember: they spend ~6 hours from idea to post on each one. Quality > Quantity.

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The content pillars: what to actually post

You don’t need a content calendar planned a month out. Forcing a rigid sprint doesn’t produce better videos; in fact, it does the opposite. If an idea hasn’t worked three times, planning a fourth attempt for next Tuesday won’t fix it. Your product changes, how you talk about it shifts, and a chunk of what performs is reactive to trends you can’t plan for in advance.

What you can do is define a small set of content types and rotate through them.

1. Reshares and amplification. When someone on your team posts something that lands, reshare it. Unglamorous, but it compounds: it shows up to your followers again and adds engagement to a post that already proved itself.

2. Webinar clips cut into reels. You already ran the webinar. Cutting the best 45 seconds into a vertical clip is close to free content.

3. Playbooks, use cases, and workflows in 60 seconds. Green screen, split screen, or a plain screen recording with narration. Test all three before picking one; what works depends more on your product than any general rule.

4. Opportunistic, unplanned videos. The funny ones, the reactive ones. You can’t schedule these. Leave room for them.

5. Daily reaction videos. Take the AI news of the day and react to it through your team’s specific lens, not a generic hot take anyone could post.

6. Educational breakdowns. Take one lesson or workflow and break it down properly, the way you’d explain it to a smart friend who’s never touched your product.

7. Resource or course promotion. If you have a course, template, or paid resource, this is where it earns its slot.

8. “This tool is genuinely useful” videos. Recommend something real, not something you’re paid to mention. Viewers can tell the difference immediately.

9. A running tip series. One AI tip a day, one workflow shortcut a week, whatever fits your category. The format itself becomes a reason to follow.

One piece of math worth knowing: one text post becomes maybe two pieces of content across platforms. One piece of vertical video becomes five or more (TikTok, Reels, Shorts, a native LinkedIn upload, and a clip inside a newsletter or blog post). Film once, distribute five times.

Apply a thought-leader lens to anything meant to be taken seriously. It’s the difference between “here’s a tip” and “here’s a tip, from someone who clearly does this for a living.” Also known as SME: subject matter expert.

Worth studying

A short list of accounts and clips that show these pillars in practice, across categories. Some are marketing-specific, some are just doing short-form well and the mechanics translate directly to B2B.

Daily AI news and reaction format: @justinfineberg · @willfrancis24 · @aininja.pro · @samogbornn · @build_in_public · @samdespo

Educational breakdowns and workflow teardowns: @justinfineberg · @build_in_public · @thewizardmarketing · @samogbornn · @femkeplant.ai · @officeoptout

Course or resource promotion: @jinedalessandra · @sabrina_ramonov · @juliegrumman

“This tool is genuinely useful” format: @nathanhodgson_ · @stuffaboutadvertising

Running tip series: @samdespo · @digitalsamaritan · @trystring

Playbooks and workflows in 60 seconds, worth watching to compare formats: @robtheaiguy · @brandnat · @eugbrandstrat · @samogbornn · @emilidesigns · @officeoptout

Opportunistic and funny, the kind you can only recognize, not plan: @operexx · @qautoqatar

Full accounts worth following for pattern recognition over time: @clickup (most of the people on camera here are well into their careers, not interns, so “we’re not video people” doesn’t hold up as an excuse), @semrush (worth studying for how a “boring” SaaS category makes video work), @brandino.wang, @lukethorburg, @femkeplant.ai, @ohneis652, @aininja.pro, @samogbornn, @thewizardmarketing, @samdespo, @justinfineberg, @stuffaboutadvertising, @askcatgpt

Two more worth tracking down as full references rather than single clips: a breakdown of what makes MrBeast’s production process work, and “The Long and the Short (Form) of It.” Both go deeper into why certain formats hold attention than any single tip below can.

If you want the fuller picture before you film anything: get someone on the team to run a personal TikTok account for 30 days first, consuming mostly video content in your category before producing any. You’ll absorb the pacing and format instincts faster than any list of tips can teach you.

Filming fundamentals

A few mechanics that matter more than production value.

1. Do a visual hook in the first second: appear from the edge of frame, turn around in your chair, turn on a light, sit down, talk while walking, take a drink of water, use a hand gesture with a real facial expression. The scroll decision happens before anyone processes a word you say.

2. Put your call to action somewhere in the middle of the video, not at the end. A CTA at the end signals to the viewer that the video is over and it’s safe to scroll, which lowers rewatch value and lowers the chance the video sticks. Contextual, mid-video CTAs reportedly outperform end-of-video CTAs by around 20%.

3. Length matters less than people think. What matters is whether you deliver on the hook’s promise early and stay interesting. People will watch a long video to the end if it’s worth their time, and abandon a short one that wastes it.

4. Add on-screen stickers: “save this for later,” “share with your team,” a direct CTA. Point at them physically in the video.

5. Point at things in general, regardless of format. It’s a small, almost silly move that measurably increases how long people stay engaged.

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Warming up a new account

TikTok’s algorithm treats a brand-new account like spam until it’s convinced you’re a real human, not a bot farm. Skipping the warm-up is why so many company accounts post ten videos into total silence. Superscale has a detailed breakdown of the process; the short version:

Days 1 to 3, ghost mode. Leave the profile blank, no bio, no photo. Spend 20 to 30 minutes a day scrolling content in your niche, liking, commenting, and following accounts in your space. Don’t post anything yet.

Days 4 to 7, slow introduction. Add a basic bio and profile photo. Keep the daily engagement going, 15 to 20 minutes. Start posting once every two days, and keep scrolling after each post instead of closing the app.

How you’ll know it’s working: your For You page starts showing content from your actual niche, your posts pick up initial views even if they’re small, and the comments and likes come from real accounts instead of nothing at all.

After the warm-up: move gradually to one post a day, keep the daily engagement habit alive between posts, and if you’re still getting no views after ten days of consistent posting, start the account over rather than fighting an algorithm that’s already decided you’re noise.

Frequently asked questions

Does short-form video actually work for B2B SaaS, or is that a consumer-brand thing? It works as a brand-awareness channel, not a direct-response one. Around 95% of your category’s buyers are out-of-market at any given time, and most of the in-market minority choose a brand they already recognized. Short-form video builds that recognition; it isn’t built to produce same-day signups.

How long before we see results? Commit to daily uploads for 3 to 6 months before judging the channel. Shorter windows don’t produce a result you can actually learn from, in either direction.

What if nobody on the team is comfortable on camera? That’s the “struggle” column of the matrix, not a dealbreaker. Start with formats that don’t require a face on screen: screen recordings with narration, webinar clips, split-screen workflow walkthroughs, while whoever’s most willing gets comfortable in front of the lens.

Do we need real production equipment? One phone and one microphone (I used the DJI mic mini) are enough to start. Production value matters far less than authenticity and consistency for this specific goal.

If you’re trying this out for your own company right now, I’d genuinely like to know where you land. Drop a comment!