Why 2027 is the year of brand - and how to get ahead
Alicia Skubick, Chief Customer Officer, Trustpilot
The brand-performance pendulum
Performance marketing did its job: it delivered defensible ROI and safe board reporting when volatile markets demanded certainty. But efficiency has its limits. Relying on short-term conversion alone has created diminishing returns and a costly dependency on paid acquisition. It’s a reality I hear firsthand as Trustpilot’s Chief Customer Officer in my conversations with marketing leaders.
The pendulum is swinging back, and industry data has caught up to what we’ve been feeling. This year’s McKinsey’s State of Marketing Report marked a sea change: brand building was ranked as the number one priority for marketing leaders; even outranking gen AI.
Rising paid acquisition costs have turned brand equity and reputation into the next decade’s commercial necessities.
The true force behind this shift, though, is AI search.
The new search narrative
We've all seen AI dominate the stage at events over the last few years, but much of the conversation stays at the 30,000 foot view. Marketers are ready to move from speculation to execution - we want real-world examples, hard data, and playbooks to implement.
Meanwhile, consumer search habits have fundamentally changed, and we’re under increasing pressure to do something about it. Audiences are typing complex questions into AI tools, and our teams are having to quickly work out how to land our businesses in the answer.
I read an interesting piece in Fast Company that captures this transition well. It argues that the marketing funnel has been replaced by a flywheel:
Priming (for humans): Building cultural relevance, emotional resonance, and memory structures before a consumer enters the market.
Proving (for machines and humans): Surfacing the objective, verified data that both consumers and AI models require to validate brand claims.
This is where the old brand playbook breaks. You used to be able to buy brand scale; deploy enough broadcast, OOH, and paid display, and you controlled the narrative. But that doesn’t work when AI tools crawl multiple sources to check your marketing claims against customer experience. WARC’s recent study on LLM visibility found that 63% of a brand's presence in AI search is driven by long-term brand equity. Put simply: shorter-term tactics influence the margins, but true AI visibility is won through trust.
It’s against this landscape that brand building is returning, as an actionable and measurable growth engine.
Brand equity in 2027 is the measured gap between what you promise, and what your customers report. If you can’t provide AI engines with authentic, independent validation, your business simply will not be recommended.
Brand equity, reintroduced
Lagging customer surveys struggle to keep up with today’s brand equity, which forms infrastructure that dictates whether AI engines recommend or omit your business.
Without a footprint, there are two risks:
Low visibility (AI engines omit the business from answers)
Unaddressed negative sentiment (AI surfaces complaints)
To quantify how AI engines evaluate brands, we recently commissioned a global research study with Seer Interactive on AI search. It analyzed 804,491 generative AI responses across ChatGPT, Gemini, Perplexity, and Google AI Overviews.
Key findings include:
Trust and review sites are the #2 most-cited source category overall, second only to the general open web.
Trustpilot is the #1 most-cited review platform across every major AI engine tested.
AI relies more heavily on reviews the closer a consumer gets to a transaction. Citations of review platforms climb 12x from the Awareness stage (2%) to the Intent stage (24%).
The study also showed the measurable penalty that comes with an unmanaged footprint. When ChatGPT investigated a brand and found no review profile, it explicitly cited the lack of independent customer feedback as a trust risk in 34.4% of responses.
So, neutrality does not exist. The absence of evidence is actually treated as evidence of risk.
Three pillars for building new brand equity
Leading through this shift requires structural changes. Here are three places to start:
1. Close the gap between marketing and customer teams
Make time to tackle this silo. Customer operations are your future marketing, because answer engines scan reviews to judge your business. How you approach this will depend on your organization. At Trustpilot, my own role expanded from Chief Marketing Officer to Chief Customer Officer years ago, combining marketing, brand, and CX under a single mandate. That’s just one way to solve the problem.
Different paths to explore:
Create a shared executive mandate: Unify your CMO and Head of CX under shared targets, tying a portion of incentives directly to customer satisfaction and review health.
Embed CX data analysis into marketing: Keep the departments independent, but embed customer operations data directly into your growth and performance teams. Give ad buyers a direct line to product return data and support backlogs so they can pause or adjust campaigns in times of customer friction.
Fix operational gaps before AI finds them: Whichever structure you choose, stop treating customer feedback as a passive metric for support teams. Treat recurring customer complaints as an early warning system to fix bottlenecks before AI models use them to steer buyers away.
2. Invest in trust as a board-level metric
If 63% of your visibility in AI search is driven by brand equity, your budget allocation needs to change. You can’t spend your way out of low visibility by buying more ad space. Instead, you need to treat independent customer feedback as core data that informs these new search tools.
In their report, Budget Planning Guide 2027: B2C Marketers, Get Ready For New Ways Of Working, Forrester advises B2C marketers to prioritize brand, reputation, and community, because review and trust platforms often outrank a brand’s own paid and owned media in AI search.
This is where investing in an open platform like Trustpilot moves from spending budget on a traditional reputation tool, to funding critical infrastructure. AI engines behave exactly like cautious consumers: they look for fresh proof, evaluate rating trends, and flag empty spaces.
3. Capture competitor market share through AI traffic
Bidding on competitor keywords can be an expensive, low-margin cash burn. AI search provides an organic alternative via the "co-mention effect."
The Seer Interactive study revealed that AI engines routinely recommend alternatives when users research a specific competitor. Maintaining an active, verified Trustpilot profile with recent reviews makes your brand 9.5x more likely to win this competitor traffic.
This completely changes the economics of growing your market share, with two new advantages:
Intercept buyers at the point of decision, whilst they’re evaluating your competitors.
Grow your organic share of voice without paying inflated ad prices.
Depending on your organization's size, you can lean into a specific competitive advantage:
For challenger brands: This shift drastically evens the playing field, because AI engines value unedited human proof over glossy ads. So if you’re a challenger, you have the chance to outrank market leaders by building an active, highly verified public record.
For enterprise brands: Your advantage is sheer transaction volume. You can generate customer proof at a scale that challengers simply can’t match. But this only works if you actively collect data. Enterprise leaders need to automate feedback on every touchpoint to turn daily interactions into the continuous, fresh data stream that AI models want.
Winning in 2027
AI search has collapsed the divide between brand and performance. Because answer engines use verified customer feedback to drive recommendations, authentic brand equity is essential.
The marketing leaders who win in 2027 will be the ones who understand that brand is back, in a new form. And they have a plan to act on it.
You can find the full Seer Interactive analysis below.
Author
Alicia Skubick
Chief Customer Officer