What Fjällräven Realized Constructing for AI Discovery

Within the age of AI brokers, your product knowledge is a lot greater than a back-office asset. It is a direct line to your prospects.

Gaps in that knowledge can quietly block your merchandise from surfacing throughout marketplaces, engines like google, and the rising wave of agentic and answer-engine surfaces. Miss the context an AI wants, and also you miss the sale.

That was the premise of our CommerceNext session within the Omnichannel Transformation Observe, the place Feedonomics’ Sharon Gee sat down with Amanda Carrew, World Director at Fjällräven Outside. Amanda oversees a progress org spanning paid media, e-mail, ecommerce, and customer support — throughout Fjällräven and 4 sister manufacturers.

That uncommon, unified vantage level gave her a front-row seat to an issue increasingly more manufacturers are operating into: as natural discovery shifts to AI, your product knowledge turns into the one largest lever you really management.

Listed here are the takeaways.

Product knowledge is your beacon

Amanda’s framing caught with the room: product knowledge is the root of every partand in an agentic world, it is your beacon — the factor you may really steer.

Her reasoning was pragmatic. Natural site visitors is declining, and paid cannot (and should not) backfill that hole eternally. So the place does a model regain leverage? Within the knowledge that now feeds the LLMs and brokers answering buyer questions.

“Our product knowledge is like your beacon. What it’s important to begin with.”

— Amanda Carew, World Director at Fjällräven Outside

If natural is down and paid is not a sustainable patch, the information turns into the sign you spend money on to take again a measure of management, as a result of that is what brokers learn once they resolve whether or not to suggest your model.

The silos are exhibiting and unified groups win

One of many clearest patterns Feedonomics sees throughout prospects: the groups that personal efficiency knowledge and the groups that personal the PIM traditionally have not needed to speak a lot. In an AI-driven world, that is a legal responsibility. The info feeding third-party channels and the information feeding your product catalog now should be constant and far richer in context.

Fjällräven is ready up in a means that helps right here, with an attention-grabbing twist. At many corporations, ecommerce owns the PIM. At Fjällräven, advertising and marketing owns it. Amanda’s background makes that work: a grasp’s in knowledge science paired with a advertising and marketing profession provides her the flexibility to learn the tea leaves within the knowledge and translate them right into a income thesis her management can get behind.

The lesson for everybody else: the nearer your advertising and marketing and ecommerce knowledge capabilities sit, the quicker you may transfer on AI discoverability.

Consistency and model management go hand in hand

With 1000’s of SKUs, Fjällräven got here to the desk with what Sharon referred to as a “grade-A feed.” However even robust knowledge carries years of drift, which incorporates inconsistent model tonality and accuracy accrued throughout a catalog constructed over greater than a decade.

For a model that guards its voice rigorously, enrichment raises an apparent concern: will this alteration how folks speak about us?

The reply was to maintain a human firmly within the loop. To do that, Fjällräven fed its model tips into the enrichment course of, iterated with its personal copy and model groups, and used a scoring system to approve outputs, with checks and balances at each step.

Amanda’s favourite instance says all of it:

“One in all my copywriters mentioned, ‘We do not use the phrase cozy.’ And I mentioned, ‘Okay, effectively then change it.”

With enrichment guidelines in place, “do not use cozy” turns into a ruled instruction the system applies at scale — and, simply as importantly, a method to clear up the historic knowledge so model tonality is lastly constant throughout the entire catalog. AI does the heavy lifting; people preserve the guardrails.

Each channel speaks a distinct language

A single feed has to serve many locations, and each wants one thing totally different. Google Service provider Heart traditionally needed structured attributes. AI discovery is a distinct recreation solely; it is about giving an AI the context to reply a natural-language, conversational question.

That is why enrichment has to deal with each structured and unstructured knowledgeand why the information pipeline has to dynamically form itself to every vacation spot’s schema whereas staying constant beneath. On the Feedonomics aspect, this meant actual engineering funding,  together with working with the Google group on Common Commerce Protocol, to organize for a world with not simply customers and retailers, however shopper brokers and service provider brokers, every needing knowledge in new methods.

The objective is not to optimize one channel. It is to construct a knowledge basis that may enrich knowledge for any channel — your PDPs, Google, marketplaces, and agentic surfaces — from one ruled supply of fact.

Begin small, then measure what issues

Fjällräven’s method is a mannequin for the right way to “eat the elephant.” Moderately than attempting to eat it , the group took a bite-sized take a look at: 100 guardian SKUs, within the US, on Google feeds solely. No PDP modifications and no marketplaces, simply sufficient of a pattern to see whether or not enrichment moved the needle.

The exhausting half wasn’t the enrichment, however measurement. As Amanda put it, there is not any established benchmark and no playbook for monitoring AI visibility but. So the groups constructed one collectively — what she referred to as “the stool,” a multi-legged measurement method combining:

They set shared baselines with the Feedonomics group primarily based on what’s being seen throughout the market, then gave the take a look at three months to show out. On simply 100 SKUs, the natural elevate was marginal — precisely as anticipated at that scale — and sufficient of a sign to justify increasing throughout the total catalog.

Discovery first, checkout later

Agentic checkout will get the headlines, however Amanda was clear about the place the actual alternative is proper now: discoverability. Customers aren’t but handing brokers their bank cards en masse, however they are asking AI conversational questions and trusting the solutions.

Her north star:

“I would like somebody typing, ‘I desire a pair of mountain climbing pants that may take me to Patagonia in the midst of summer season’ — and we are the first end result.”

That is the section most manufacturers ought to give attention to: getting the information proper so that you present up, with the fitting info, when a buyer describes their journey or their path in their very own phrases. The acquisition rails will mature, however the manufacturers that win once they do would be the ones already discoverable right now.

Your Commerce subsequent steps

Amanda closed with sensible recommendation for anybody beginning their LLM visibility journey:

1. Audit your catalog. Have a look at what knowledge goes in and the way you are talking to every channel. Keep in mind, it is now not simply structured knowledge. It is unstructured, intent-rich, conversational content material too.

2. Construct the enterprise case, consumer-first. When management asks, “What are we doing with AI?”, lead with a consumer-facing, revenue-driving take a look at earlier than tackling inner change administration. Present impression the place it hits the highest line.

3. Take one small step. Perceive your present visibility, decide a bite-size take a look at, show it out, and develop to the total catalog, then to different manufacturers and markets.

As Amanda put it: your model is on the market for shoppers to find and purchase. The query is whether or not your knowledge is able to meet them the place they’re now.

Able to make your catalog discoverable in every single place AI is trying?

Knowledge enrichment is the way you present up — constantly and in context — throughout each search engine, market, and AI software your prospects use. See how Feedonomics might help you get there and discover AI knowledge enrichment.