LinkedIn's B2B Institute argued on October 30, 2026 that companies selling to other businesses have been underusing one of their most persuasive assets - their own customers - and that the shift of vendor research into AI assistants makes the oversight more expensive.

The argument appeared in a post on the LinkedIn Marketing Blog titled "Your Customers Are Your Most Undervalued Media Channel," written by Mimi Turner, Global Head of Thought Leadership Strategy at the institute. It is opinion and thought-leadership content from a company that sells the advertising it recommends, and the page closes with an invitation to "Reach the right audience with LinkedIn ads." Its proposal is simple. Customer testimonials, long parked in sales decks and late-stage persuasion, should move into brand campaigns, social content, PR and events, and should be carried by always-on paid media. To support it, the post leans on four pieces of evidence: a 5.8x return LinkedIn attributes to a Rippling campaign, a survey of 750 senior B2B buyers run with Bain & Company, an analysis of 700 award-entered B2B campaigns produced with WARC and LIONS Advisory, and a Sage brand campaign from 2023.

In Short

LinkedIn published a blog post saying that companies selling to other businesses should show off their happy customers much earlier and much more often, including in paid ads. LinkedIn says this matters because buyers now describe their exact needs to AI assistants, and stories from similar customers help a vendor look like the right fit. Most of the evidence comes from LinkedIn and its research partners, and LinkedIn sells the ads it is recommending, so the numbers come with a commercial interest attached.

What the post argues

The post's summary sets out the thesis in one sentence. "AI is shifting B2B discovery from brand claims to buyer-defined needs, which means customer evidence now has a bigger job to do than closing late-stage deals," according to LinkedIn.

Turner starts from the familiar place of the customer testimonial in business marketing. "For as long as B2B marketers have been in business, customer testimonials have been the workhorse of the marketing department," she writes. Their use runs from late-stage customer conversations through events to sales enablement, and the combination of "a professional face, success quote and performance statistic" provides, in her words, "a mirror for prospects to see themselves in."

Yet that material is treated as second-tier. According to the post, customer-centred materials "rarely get always-on paid media support or entered for awards. And they do not enjoy the lavish budget of high profile brand campaigns." The conclusion is blunt: "It is time for marketers to rethink this low-key approach."

The reason offered is AI, which the post says is rewiring how B2B brands are discovered, how they establish credibility and how they win over the internal buying group. In that environment, "the humble customer success story turns out to be able to do all three jobs," according to LinkedIn. The stated goal goes beyond conversion: "increasing the likelihood of being bought at all."

Turner is not new to this line of argument. In June, PPC Land covered her June 19 post arguing that AI had scrambled the B2B buying formula, when she was described as Head of Marketplace Innovation at LinkedIn. The October 30 byline carries the new title and places her inside the B2B Institute.

Rippling's 5.8x, and what it leaves out

The opening proof point is a single advertiser. "When San Francisco-based software development firm Rippling used customer stories to power the launch of their AI-first platform, it saw a 5.8X return on its total campaign investment," according to the post, which links to a Rippling customer story on LinkedIn's business site. "This result is not an outlier," it adds.

The figure arrives without a definition. The post does not say whether the return is measured in revenue, pipeline or attributed conversions, over what period, against what spend, or under which attribution model. Nor does it give a launch date for the AI-first platform. The same gap appeared in another LinkedIn customer story this autumn: PPC Land's analysis of LinkedIn's claim that Zembl won 82% more ABM budget found no baseline, time period, deal count, spend figure or attribution model in that post either.

Rippling's head of customer marketing, Emmy Quinn, is quoted in a graphic within the post. "Every company is making AI claims right now. The way to cut through isn't a louder claim, it's hundreds of real customers sharing how their work has actually changed. LinkedIn let us champion those stories at scale," Quinn said.

The post illustrates the campaign with an April 9 video post from Bonnie Dilber, a recruiting leader at Zapier, which opens: "I don't think HR professionals are afraid of AI, we just want AI that does more than write emails or summarize documents." It shows 120 reactions, 20 comments and 5 reposts. The post does not say what relationship Dilber or Zapier has with Rippling, or whether her post was promoted with paid media. Rippling has a wider commercial relationship with LinkedIn's creator products: it was named among the early customers when LinkedIn launched Creator Marketplace and BrandWorks in June.

The buyer survey behind "Buyability"

The second strand is buyer research. "Research carried out by LinkedIn and Bain & Company shows that among 750 senior B2B buyers, 'being heavily recommended by customers and colleagues' was more than three times as influential in moving a vendor from the shortlist to being bought as 'a lower price' or 'a better product,'" according to the post.

The same research, the post says, found that customer recommendations act as "career-defence collateral" after the purchase. "'Companies like us recommended them' was the most defensible reason buyers felt they would present to defend their original decision, in the event that the decision went wrong," according to LinkedIn. A chart in the post gives the shares: 58% chose "Companies like us recommended them," 39% "They were the market leader," 12% "We took a risk on ambitious innovation" and 6% "They were not well known but a lot cheaper."

That chart carries the logos of LinkedIn, the ANA, the IAA and NewtonX, not Bain's, even though the text attributes the finding to "this same research" with Bain. PPC Land's June coverage traced a 750-person NewtonX buyer survey to a June 18, 2025 LinkedIn publication by Turner and Jann Martin Schwarz. The October post gives no fieldwork dates, markets, industries or company sizes for the 750 buyers.

From this work LinkedIn draws three "Buyability" signals that, it says, move a brand "from shortlist to being chosen":

  • Recommendations: "when a customer, peer or expert vouches for the brand"
  • Relationships: "when familiarity, knowledge or previous relationship exists between buying organization and brand"
  • Relatability: "when the buyer can see themselves in the proof the brand offers"

A graphic headed "Buyers are looking for reputational and relational signals" lists the questions behind them: "Do companies like us recommend them?", "How specialized are they in situations like ours?", "Have we worked with them before?", "Is their working style like ours?" and "What does my network think?"

The framework has been built up over the year. When LinkedIn and Bain published "The Principles of Buyability" on June 11, the research said 40% of deals stall because the buying group cannot agree, and that peer-recommended vendors were chosen three times more often. A week later, Turner's June 19 post said video from a similar customer was more than four times as influential as messaging from a market leader.

700 campaigns, 64% against 16%

The newest numbers in the post come from "How To Build More Buyable Brands," a report by WARC and LIONS Advisory produced with LinkedIn. According to the post, the analysis covered "700 B2B award-entry campaigns that were scored across seven observable signals of Recommendations, Relationships, and Relatability."

The headline finding concerns testimonials. "Customer testimonials, use cases and recommendations drove the single largest divide, appearing in 64% of the High Buyability campaigns versus just 16% of Low Buyability campaigns," according to LinkedIn. "This 48 percentage point gap was the biggest divide between High and Low Buyability campaigns across any signal."

The commercial claims follow. According to the post, High Buyability campaigns were 1.91x more likely to report improvement in mid-funnel brand metrics such as consideration, preference and purchase intent, and 2.1x more likely to report incremental revenue growth than Low Buyability campaigns.

The report itself, whose cover is dated September 2026, adds detail the blog omits. According to WARC and LIONS Advisory, the 700 were B2B effectiveness case studies from the WARC database - campaigns that had been entered for, or had won, awards - classified as High or Low depending on how many signals were present. The average campaign carried just 1.6 of the seven. The report puts the revenue ratio at 2.09, which the blog rounds to 2.1. It also says High Buyability campaigns were 1.62 times more likely to report uplifts in ROI, a figure the post does not use, and it notes that some of its findings rest on relatively small bases.

Three features of the method shape what the numbers can show. First, the groups are defined by how many signals a campaign contains, and testimonials are one of the seven signals; a campaign with more signals is, by construction, more likely to contain any one of them. The 48-point gap shows which signal separates the groups most, not that testimonials cause better results - a reading that is PPC Land's own, not the report's. Second, the outcome measure is what a campaign entry chose to report. "More likely to report incremental revenue growth" measures how often entrants claimed a result, which is not the same as how often revenue grew. Third, award entries are a selected population: campaigns whose makers believed they had worked well enough to submit.

Sage's 2023 campaign

The final example is Sage, the UK software company. According to the post, Sage put "customer voices at the heart of its brand campaign targeting small business owners." The creative shown features Toks Aruoture of The Baby Cot Shop in Chelsea on a large roadside digital screen under the line "The Baby Cot Shop is bossing Chelsea."

"Spontaneous awareness grew by 12 percentage points and annual recurring revenue grew by 7.2% in 2023 as a result of the campaign, the company said," according to the post. The link on the campaign points to the WARC report rather than to Sage, and the report places the revenue figure in Sage's 2023 financial year. The causal claim is the company's. Neither the post nor the passage it cites explains how the campaign's contribution was separated from pricing, product changes, sales activity or other factors that also move recurring revenue.

The AI discovery argument

The core of the post is a claim about how B2B buying has changed. A LinkedIn graphic contrasts an "Old World" sequence - "Who is well known and thought of?", "What exactly can they do?", "Could our unique needs be met by them?" - with a "New AI World" one: "Here are specific details of our unique needs", "Which vendors can do this exact thing best?", "How do I defend / validate this choice in my org?"

"For the first time, buyers can front-load AI assistants with their own use case, business context and success criteria at the outset of their journey," according to the post. That, it says, "makes it easier to filter vendors against specific user requirements because the cost of extra effort has been flattened." A pull quote states the consequence: "being relevant earlier in the buying journey matters much more."

The distinction the post draws is between two kinds of proof. "Late-stage recommendation works because the buyer already knows the vendor - and is looking for permission to choose," Turner writes. "Early stage relatability works differently. It provides evidence of relevance that can travel directly into AI-mediated discovery."

How that evidence travels is not spelled out. The post offers no figure on how many buyers use AI assistants and no data showing that customer stories are retrieved or cited by those assistants more often than other material. One mechanism is implied by its first recommendation, which wants customer voices freed from "gated white papers": content behind a lead form is not available to systems that assemble answers from the open web, a concern at the centre of GEO.

Other data PPC Land has covered gives the claim some context. LinkedIn's own three-phase launch framework in May cited a 6sense finding that 94% of buying groups consult large language models before talking to a salesperson, and a Bain figure that 86% of B2B buyers start with a day-one vendor list, with 81% buying from it. Demandbase reported in August that ChatGPT referrals to B2B sites grew 303% in a year, from about 645,000 monthly visits in June 2025 to 2.6 million in June 2026. And a study by the UK agency Recrawled found that ChatGPT, Gemini and Google never named 9 of 40 UK software firms in shortlists, with the share of firms named dropping from 63% on a UK-specific question to 0% when the question compared a leader with its nearest rival.

LinkedIn is also a beneficiary of AI citation. Semrush's March study found LinkedIn ranked second among domains cited in AI answers, appearing in about 11% of responses across ChatGPT Search, Google AI Mode and Perplexity. Customer stories published on LinkedIn would therefore sit on a domain those systems already cite heavily.

Three changes LinkedIn wants

The post closes with three recommendations, which it frames as the way B2B marketers can "capitalize on the opportunity created by these new shifts."

The first is to "move customer voices upstream so buyers encounter them earlier in the journey." Instead of being "trapped in gated white papers, sales decks and conversion content," customer voices "should strategically appear in brand campaigns, social content, PR, events and other public channels," according to LinkedIn.

The second is to "organize customer stories around situations that buyers recognise." Stories that explain "the customer's situation, the obstacles they faced and how they overcame them" enhance relevance, the post says, "right down to the sector, scale, pressures, ambitions and vulnerabilities."

The third is the one with a direct line to LinkedIn's revenue: "Put always-on paid activity behind customer voices to engage prospects even when they are not in-market." Here the headline claim is restated: "Customer voices are one of B2B's most undervalued media channels. Paying to make those voices visible to the right audience builds familiarity and relevance long before the process begins." In practice this describes paid amplification of customer content - buying distribution for material that already exists.

The post ends on a positioning statement rather than a number. "If customer voices help drive discovery, they deserve a place closer to the heart of brand strategy than the sales enablement role they have performed for many years," Turner writes. "Customer advocacy has spent decades behind-the-scenes. It's time for B2B's unsung hero to become a headline act."

Why it matters for marketers

The post is less a study than a budget argument, and its target is a line item that rarely gets media money. B2B advertisers already route a growing share of paid spend through LinkedIn: Dreamdata's March benchmarks put LinkedIn at 41% of B2B paid budgets, up from 39%, with a 121% ROAS and buyer journeys of 272 days. A recommendation to keep customer stories running on always-on paid media, published by the platform that would carry much of that spend, asks marketers to add a new standing budget line, and LinkedIn would be among those collecting it.

There are costs that the post does not discuss. Lunio's June report found LinkedIn delivered the most bot clicks per ad dollar among the platforms it studied, with an average invalid traffic rate of 15.34% across IT and security accounts between the third quarter of 2025 and the first quarter of 2026. Measurement is another gap. LinkedIn's own measurement guide in June cited research showing that 64% of B2B leaders do not trust their own data, and an always-on campaign aimed at buyers who are "not in-market" is among the hardest kinds to attribute.

The emphasis also differs from earlier B2B Institute work. In December 2025, its research argued that building owned prominence beats rented ads in B2B marketing. The October post does not contradict that - customer stories are owned assets, and the paid spend buys their distribution - but the emphasis has shifted towards paying to put them in front of buyers.

Is the underlying idea new? Not really. Peer proof has been a staple of B2B selling for decades, and PPC Land's coverage of the LinkedIn and Ipsos "Trust is the New KPI" study in 2025 recorded that 39% of marketers seeking to demonstrate real-world value used customer success story videos. What is new is the claim that AI assistants turn customer evidence from a closing tool into a discovery asset. The claim is consistent with the AI shortlist data third parties have published this year, but the post does not test it. Its evidence shows that buyers say they value recommendations and that award-entered campaigns with more trust signals reported results more often; it does not show that customer stories get B2B brands named more often by AI assistants, or that always-on paid support for them pays back.

The most concrete question the post raises is where customer evidence is published, since material behind forms or inside sales decks is hard for buyers to find early and out of reach of systems that compile AI answers from the open web. Whether paying LinkedIn to distribute it is the best answer is a separate matter, and one on which the seller of the media is not a neutral party.

Timeline

Summary

Who: LinkedIn's B2B Institute, through a blog post by Mimi Turner, Global Head of Thought Leadership Strategy, drawing on research with Bain & Company, WARC and LIONS Advisory, and on campaign examples from Rippling and Sage.

What: An opinion post arguing that customer testimonials are B2B marketing's most undervalued media channel and should be moved earlier in the buying journey and carried by always-on paid media. It cites a 5.8x return for a Rippling campaign (undefined), a 750-buyer survey, testimonials in 64% of High Buyability award entries against 16% of Low ones, and Sage's 12-point awareness and 7.2% recurring revenue gains.

When: The post was published on October 30, 2026. The WARC and LIONS Advisory report it draws on is dated September 2026, and the Sage results refer to 2023.

Where: The LinkedIn Marketing Blog, in its Research and insights section. The campaign examples cover a US software company and a UK one.

Why: LinkedIn argues that AI assistants let buyers start vendor research from their own needs, so evidence that a vendor has solved the same problem for similar customers matters earlier than before. The recommendation to fund always-on paid activity behind that evidence also points spend towards the platform publishing the advice, while the evidence offered does not show that customer stories improve how often AI assistants name a brand.