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 clients. 

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, International Director at Fjällräven Outside. Amanda oversees a development 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 working into: as natural discovery shifts to AI, your product knowledge turns into the only greatest lever you truly management.

Listed below are the takeaways.

Product knowledge is your beacon

Amanda’s framing caught with the room: product knowledge is the root of every part, and in an agentic world, it is your beacon — the factor you’ll be able to truly steer.

Her reasoning was pragmatic. Natural visitors is declining, and paid cannot (and should not) backfill that hole endlessly. 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 you must begin with.”

— Amanda Carrew, International Director at Fjällräven Outside

If natural is down and paid is not a sustainable patch, the info turns into the sign you put money into to take again a measure of management, as a result of that is what brokers learn after they determine whether or not to advocate your model.

The silos are displaying and unified groups win

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

Fjällräven is ready up in a manner that helps right here, with an attention-grabbing twist. At many firms, 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’ll be able to transfer on AI discoverability.

Consistency and model management go hand in hand

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

For a model that guards its voice fastidiously, enrichment raises an apparent concern: will this transformation 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 among my copywriters mentioned, ‘We do not use the phrase cozy.’ And I mentioned, ‘Okay, properly 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 technique to clear up the historic knowledge so model tonality is lastly constant throughout the entire catalog. AI does the heavy lifting; people hold the guardrails.

Each channel speaks a distinct language

A single feed has to serve many locations, and each wants one thing completely different. Google Service provider Heart traditionally needed structured attributes. AI discovery is a distinct recreation completely; 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 knowledge, and why the info 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 arrange for a world with not simply consumers and retailers, however shopper brokers and service provider brokers, every needing knowledge in new methods.

The aim 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 strategy is a mannequin for the way to “eat the elephant.” Relatively than attempting to eat it suddenly, the group took a bite-sized check: 100 dad or mum SKUs, within the US, on Google feeds solely. No PDP adjustments and no marketplaces, simply sufficient of a pattern to see whether or not enrichment moved the needle.

The arduous 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 strategy combining:

They set shared baselines with the Feedonomics group primarily based on what’s being seen throughout the market, then gave the check 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 complete catalog.

Discovery first, checkout later

Agentic checkout will get the headlines, however Amanda was clear about the place the true 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 need somebody typing, ‘I need a pair of climbing pants that can take me to Patagonia in the course of summer season’ — and we are the first end result.”

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

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 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 check earlier than tackling inner change administration. Present affect the place it hits the highest line.

3. Take one small step. Perceive your present visibility, choose a bite-size check, show it out, and broaden to the complete catalog, then to different manufacturers and markets.

As Amanda put it: your model is on the market for customers 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 all over the place AI is wanting?

Information enrichment is the way you present up — persistently and in context — throughout each search engine, market, and AI software your clients use. See how Feedonomics may also help you get there and discover AI knowledge enrichment.