Retailgentic | Consumer Behavior & Retail Trend

This week is a product deep dive with a fresh funding story attached. eComID just closed Europe’s largest fashion tech seed round, and the company is building something agentic commerce needs: shopper context that belongs to the shopper and goes wherever they shop. The ID was born inside H&M, it already runs on about 70 brand sites, and it’s now headed for the US.
Highlights 
𝗧𝗵𝗲 $𝟭𝟳.𝟰𝗠 𝘀𝗲𝗲𝗱 𝗿𝗼𝘂𝗻𝗱: Europe's largest fashion tech seed round. Oscar says it's the second-biggest in the world, led by London's Systemiq Capital, and Nordic sports retailer Stadium (one of eComID's biggest customers) joined as a strategic investor. Scot rang the gong 🔔
𝗕𝗼𝗿𝗻 𝗶𝗻𝘀𝗶𝗱𝗲 𝗛&𝗠: Oscar pitched eComID internally in a Dragon's Den–style process and H&M's startup arm invested from day one.
𝗔 𝘃𝗲𝗿𝘆 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝘀𝘁𝗿𝗮𝗻𝗴𝗲𝗿: Models are very capable, catalogs are open to agents and payments are getting solved. But without shopper context, AI "still starts from zero." It doesn't know your size, your taste or what you sent back.
𝗧𝗵𝗲 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗽𝗮𝘀𝘀𝗽𝗼𝗿𝘁: One source of truth for your size, taste and preferences, tied to your email. It goes with you across eComID brands like H&M, Björn Borg, Peak Performance and Haglöfs. "We want to democratize personalization."
𝟵𝟭% 𝗯𝘂𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘀𝗶𝘇𝗲𝘀: 91% of shoppers buy different sizes from different brands. Oscar says that shows how broken sizing is.
𝗧𝗵𝗲 𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: About 70 merchants, about 20 million shoppers reached each month, and 15% of users already in the US. Shoppers who use eComID have about 30% lower return rates and about 10% higher conversion.
+𝟮𝟲% 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗽𝗲𝗿 𝘃𝗶𝘀𝗶𝘁𝗼𝗿: In a head-to-head test, eComID's AI search beat traditional on-site search by 26% on revenue per visitor.
𝗞𝗶𝗹𝗹 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗼𝗿𝗱𝗲𝗿 𝘃𝗮𝗹𝘂𝗲: "Average order value is a completely useless metric." What matters is average kept order value, because each handled return costs brands $21–46.
𝗧𝗿𝘂𝘀𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁: If an agent spends $1,000 for you, you need to know whose agent it is, what you authorized, what context it used and what happens if something goes wrong. Oscar argues Europe's GDPR-first approach suits agentic commerce.
𝗦𝗵𝗼𝘄 𝘀𝗵𝗼𝗽𝗽𝗲𝗿𝘀 𝘁𝗵𝗲𝗶𝗿 𝗿𝗲𝘁𝘂𝗿𝗻 𝗿𝗮𝘁𝗲: 92% of shoppers want to see their own return rate, and 75% say seeing it makes them want to return less. The live demo on Houdini Sportswear shows the search, sizing and nudge modules working together.
Timestamps:
00:35 Welcome to Retailgentic
01:50 Announcement: Kiri Masters launches Adgentic
05:00 Welcome Oscar Rundqvist, CEO & founder of eComID
05:25 Big news: $17.4M, Europe's largest fashion tech seed round
06:15 Oscar's background at H&M
07:35 Lessons from H&M: why personalization never worked
08:55 Pitching eComID inside H&M's startup studio
09:40 Why we need eComID: AI without context starts from zero
10:55 The shopping passport: portable shopper context
13:20 Building the consumer side through brand modules
14:45 How the ID works across brands
16:30 Shopper control and a visible shopping algorithm
18:05 Beyond sizing: capturing taste and intent
20:35 The merchant suite: search, sizing, personalization, agents
23:00 Modular by design
23:50 Traction: 70 merchants, 20M shoppers, -30% returns
24:50 Why average order value is a useless metric
25:35 Use of proceeds and coming to the US
26:30 The fundraising journey and cap table
28:50 Fashion, shoes and future categories
30:15 Why AI can crack sizing when others couldn't
32:50 Product-led growth: no marketing, no sales
33:35 Agentic commerce adoption in Europe
37:15 AI platforms vs. brand sites and product context
38:55 Live demo: eComID on Houdini Sportswear
42:30 Wrap-up and how to reach Oscar
👉 Connect with Oscar: https://www.linkedin.com/in/oscarrundqvist/
👉 Learn more about eComID: https://www.ecomid.com/
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Retailgentic listeners: this week we're sharing an episode of AdGentic, our sister show in the ReFiBuy Media Network. Retailgentic covers how AI agents are changing shopping. AdGentic covers what happens to advertising when your agent does the shopping. If you like this one, subscribe to AdGentic for more.
Personal AI agents feel magical right now, and part of the magic is what's missing: ads. Kiri Masters walks through how Google, Facebook, WhatsApp and Amazon all started clean and ended up ad-funded, why AI is following the same script, and why that might not be the worst outcome. The real question isn't whether ads come to your agent. It's how bad they'll be.
In this episode:
→ The broken no-ads promises of Google, Facebook and WhatsApp
→ Ensh*ttification, and why ads are still the least-worst way to pay for consumer tech
→ Subscriptions vs fees vs ads, and why fees quietly turn into ads
→ What Meta's Muse, OpenAI's Dots and Instinct have said about making money
→ The ad formats starting to appear in AI, and the ones agents might borrow
Chapters
00:25 Welcome to AdGentic
00:50 The magic of an ad-free agent
01:55 Every platform starts like this: Google
02:30 Facebook and the scarf
03:05 WhatsApp's "No Ads!" note
03:25 Amazon: the store became the content
04:00 Ensh*ttification
04:40 Why I'm an advertising maximalist
05:05 Three ways to pay for an app
06:20 How Muse, Dots and Instinct plan to make money
07:05 The problem with subscriptions
07:30 Why fees turn into ads
08:25 How bad will it be?
09:15 AI ad formats so far
09:55 Formats agents could borrow
10:25 Off-site: ads without ads
11:20 Kiri's wish list for Ad Santa
11:50 The dark scenario
12:30 Ads are the least worst
Subscribe to AdGentic
Substack: https://www.adgentic.ai/
LinkedIn: https://www.linkedin.com/company/adgentic-podcast
Podcast: https://adgentic.transistor.fm/
🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This episode was recorded live at the RetailClub AI Festival in Huntington Beach (Sept 22–24), where Jason Goldberg and I set up shop to record with retail and e-commerce leaders between sessions. You’ll hear a few nods to the festival in the conversation, and if there’s a little extra crunch in the audio, blame the snacks.
We start with a quick tour of every CPG brand Justin has worked for, and the very scientific method he uses to pick the next one. From there it turns into a crash course in how challenger brands actually grow: the brutal economics of the physical shelf, why big brand houses are reshuffling their portfolios, and what it really takes to get into Costco and stay there.

There’s also a snack reveal and a crunch our audio engineer will never forget. Hit play.
Highlights 
𝗧𝗵𝗲 𝘀𝗵𝗲𝗹𝗳 𝗶𝘀 𝘇𝗲𝗿𝗼-𝘀𝘂𝗺: “We haven’t added any square footage into a shelf.” When an emerging brand earns its way in from the innovation aisle, something else has to come out.
“𝗜 𝘄𝗮𝘀 𝘄𝗿𝗼𝗻𝗴”: A year ago Justin would have said TikTok Shop would never be a main part of his job. Then came “a tsunami of interest,” and today Wilde runs live selling on TikTok Shop and Whatnot.
𝗧𝗵𝗲 𝗖𝗼𝘀𝘁𝗰𝗼 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱: Landing a Costco rotation is the moment brands post the hype video. If you miss the sales threshold, you don’t get a repeat, and the regional buyers talk to each other.
𝗦𝗽𝗲𝗲𝗱 𝘀𝗲𝗹𝗹𝘀: “Conversions go up the faster you get delivery.” Justin says ship-from-store is the fastest-growing part of Walmart digital, and if your product isn’t in that store, the conversion you paid Walmart Connect for disappears.
𝗘𝘃𝗲𝗿𝘆 𝘀𝗮𝗹𝗲, 𝗲𝘃𝗲𝗿𝘆 𝗭𝗜𝗣 𝗰𝗼𝗱𝗲: With two years of Amazon sales mapped by ZIP code, Justin can see that barbecue over-indexes in the Southeast and sea salt and vinegar in the Northeast. With AI, building creative for those regions is now quick.
𝗢𝗻𝗲 𝗰𝗲𝗻𝘁𝗿𝗮𝗹 𝗻𝗲𝗿𝘃𝗼𝘂𝘀 𝘀𝘆𝘀𝘁𝗲𝗺: Instead of building agents on top of separate data silos, Wilde built a data lake. Target store data, Roundel media, Shipt, Instacart, and DoorDash data all sit in one place, so a sales lead can ask a small language model what to do next.
𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗼-𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁: When a retailer’s promotion drops a price on Instacart, one of Justin’s agents spots it through the API and flags it. He then decides whether to push ads to new-to-brand shoppers or pause them for 30 days and let the promo do the work.
𝗨𝘀𝗲 𝘁𝗵𝗲𝗺 𝗮𝗹𝗹: Justin builds mostly in Claude but stays LLM-agnostic, trying Grok, Muse, Kimi, and NVIDIA’s open models. “Don’t let your frame just get put into one.”
𝗗𝗼𝘇𝗲𝗻𝘀 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀: “I have dozens of agents that I’m working with. That is the new norm.”
I learned more about how CPG actually works in 30 minutes with Justin than I have in years of nodding along when Jason explains it. Grab a bag of something crunchy and enjoy the conversation.
Timestamps:
01:47 Disclosure: Justin is on the ReFiBuy advisory board
02:00 Every CPG brand Justin has worked at, and the spouse test
03:00 How challenger brands grow: from D2C to Walmart, Target, and Costco
03:55 Acquired or spun off: why CPG portfolios are reshuffling
04:10 Turns and the zero-sum shelf
05:10 PopCorners and the protein pivot
06:10 Social commerce is here: TikTok Shop and Whatnot live selling
06:50 Snack reveal: "I was wrong" about TikTok Shop
08:40 Plot twist: chips made of chicken breast (plus the crunch)
10:00 Making it in Costco: pallets, thresholds, and regionality
11:10 The Justin playbook: it starts with Amazon data
12:55 DoorDash comes to Costco, and why Instacart vs. DoorDash isn't zero-sum
14:00 Jason's take: another nail in the coffin for Costco?
15:05 Ship-from-store, speed, and conversion
17:15 Who gives brands better data: Amazon, Walmart, or Target?
18:15 Every sale by ZIP code: flavor geo-targeting with AI
19:15 How AI changed everything: one data lake for Target, Roundel, Shipt, and more
21:35 Walmart's 4.7-petabyte data lake and Code Puppy
23:00 The AI cheerleader and climbing the agent ladder
24:05 Instacart media and the promo-watching agent
26:00 Claude, Grok, Muse, Kimi: use them all
27:45 What's next for CPG: dozens of agents
28:45 Wrap-up

👉 Connect with Justin: https://www.linkedin.com/in/justin-bomberowitz-%F0%9F%90%94-07030414/
👉 Learn more about Wilde: https://wildebrands.com/
🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

We pick up where we left off a year ago and get into what has changed since. Ekta walks us through how e.l.f. is structuring its AI work, what kind of person they’re hiring to do it, and how a public company makes the case for investing before the ROI is fully provable. The live audience jumps in with some sharp questions along the way.
We close by looking ahead to what Ekta thinks brands need to get ready for in the next 12 months. Hit play.

Highlights: 
𝗟𝗲𝘁 𝗶𝘁 𝗿𝗶𝗽 𝗼𝗿 𝗹𝗼𝗰𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: Ekta says companies tend to pick one of two AI philosophies, giving it to everyone or locking it down. e.l.f. has tried to do both so it doesn't kill innovation.
𝗧𝗵𝗿𝗲𝗲 𝗽𝗼𝗱𝘀: e.l.f. runs three AI pods covering agentic commerce/growth, intelligent operations, and the human-agent workforce. Each pod owns data, change management, integrations, and training. The agentic growth roles are now being hired as dedicated full-time positions across e.l.f.'s brands.
𝗡𝗼 𝗼𝗻𝗲 𝗵𝗮𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗳𝗶𝗴𝘂𝗿𝗲𝗱 𝗼𝘂𝘁: "Anybody that tells you they have figured it out haven't figured it out." Ekta measures readiness instead, and focuses on the products where e.l.f. has the right to win.
𝗥𝗲𝗮𝗹 𝗽𝗿𝗼𝗺𝗽𝘁𝘀, 𝗳𝗶𝗻𝗮𝗹𝗹𝘆: Jason points out that most visibility tools rely on synthetic prompts. Google's new Agentic Insights tab in Merchant Center is the first look at what real people actually type, at least on Google surfaces.
𝗕𝗮𝗱 𝗱𝗮𝘁𝗮 = 𝗯𝗮𝗱 𝗔𝗜: Ekta's case for fixing the foundation first. If you don't solve the plumbing, you can't scale.
𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘃𝗼𝗶𝗰𝗲𝘀 𝗹𝗶𝗴𝗵𝘁 𝘁𝗵𝗲 𝗳𝗶𝗿𝗲: About 14 months ago e.l.f. brought in an outside search expert, and that put "fire in the belly." 
𝗦𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗶𝘀 𝘁𝗵𝗲 𝘀𝗹𝗲𝗲𝗽𝗲𝗿: e.l.f. has invested in Lyric for forecasting and demand planning. Its planners "are actually now engineers," and AP, AR, payroll and inventory teams are building their own AI workflows.
𝗔𝗴𝗲𝗻𝘁 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Ekta's prediction is that Meta wins the agent shopping race. She says brands need to put agent identity in place now, so they know which agent is arriving and which customer it represents. It's the next CDP/CRM-style plumbing project.
Huge thanks to Ekta for running over from the main stage, and to our live audience at RetailClub for the great questions. Enjoy the conversation.

Timestamps:
01:17 Live from Retail Club: meet Ekta Chopra & co-host Jason Goldberg
02:04 One-year reunion and a new title
02:38 e.l.f. Beauty: a house of brands, 30 straight growth quarters
03:19 Eyes, lips, face… and the Jeopardy! moment
03:43 Ekta's role as Chief Technology & AI Officer
04:02 From the main stage: let it rip or lock it down
05:04 e.l.f.'s three AI pods
06:14 Making agentic growth a full-time job
07:00 Who do you hire for agentic commerce?
08:33 Jason's "you've reached your quota" hiring hack
08:48 Selling AI investment to a public company board
10:06 Measuring readiness when no one has measurement figured out
11:31 "My board member couldn't find our product"
13:06 Synthetic prompts vs. real prompts: Google's Agentic Insights
14:00 Audience Q: build vs. buy
14:52 Where to start: external voices and fire in the belly
16:23 Foundation first: bad data equals bad AI
17:47 Back office, supply chain, and Lyric
19:52 Personal shopping assistants and agents
20:59 Serving a house of brands and working with retail partners
22:55 Audience Q: is governance keeping pace?
24:50 Next 12 months: Meta, agent identity & agent relationship management
26:20 Wrap-up 
👉 Connect with Ekta: https://www.linkedin.com/in/ektachopra/
👉 Learn more about e.l.f. Beauty: https://www.linkedin.com/company/e-l-f-beauty/
🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

This week is a research deep dive. ReFiBuy commissioned a behavioral study of how real people shop inside ChatGPT and Google's AI Mode, and Scot sat down with the researcher who ran it to walk through what was found. The full study is published today This is the conversation behind the numbers.
Highlights: 
𝟴𝟰% 𝗼𝗳 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 𝘀𝘁𝗮𝗿𝘁 𝗼𝗻 𝗮 𝗰𝗮𝗿𝗱: About 84% of product choices in the study came from a product card. That's 83.7%, and Scot rounds it up "if you allow."
𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗲𝗲 𝗯𝗲𝗮𝘁𝘀 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝘁𝘄𝗼: The first card wins by a wide margin, with roughly 43% of picks. But card three outperformed card two in both ChatGPT and AI Mode. Eric's take is that "it's not really a ranking." Scot's guess is curiosity, because card three is only partly visible in the carousel.
𝗣𝗶𝗰𝘁𝘂𝗿𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗽𝗮𝗿𝗮𝗴𝗿𝗮𝗽𝗵𝘀: About 75% of shoppers skipped the AI's written answer and went straight to the product cards. They'd come back to the text later, but only as a second step.
𝟵𝟴% 𝗼𝗳 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝗲𝗱 𝗰𝗮𝗿𝗱𝘀: Purchase-intent prompts like "I want to buy X today" brought up product cards almost every time.
𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗼𝗳𝗳𝗲𝗿 𝘄𝗶𝗻𝘀: When shoppers had to click out to a retailer, they leaned even harder toward the first offer than toward the first product card. The likeliest path is first card, first offer.
𝗔𝗱𝘀 𝘁𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗶𝗸𝗲: About 6 in 10 free-plan users saw ads or sponsored content on a task. 71% rated them at least as useful as Google search ads, and about 35% said an ad influenced what they considered buying.
𝗧𝗵𝗲 𝗿𝗲𝘃𝗶𝗲𝘄𝘀 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: Reviews were the main reason for a pick only 2% of the time. Afterward, about 75% said reviews mattered. Scot's explanation is that nobody reads them, but everyone takes the 4.8 over the 3.2.
𝗧𝗿𝘂𝘀𝘁, 𝘂𝗻𝘁𝗶𝗹 𝗶𝘁 𝗯𝗿𝗲𝗮𝗸𝘀: Eric says the biggest surprise after two years of these studies is "the extent to which people trust, even when they say they don't." A mismatched price or a wrong spec breaks that trust quickly.
𝗧𝗵𝗲 𝗸𝗲𝘆𝘄𝗼𝗿𝗱 𝗷𝗮𝗶𝗹 𝗲𝗿𝗮: Scot compares 20 years of chasing "men's running shoe" on Google with a world where shoppers let the AI choose for them.
𝟰𝟬 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗺𝗼𝗻𝘁𝗵𝘀 𝗼𝗳 𝘄𝗼𝗿𝗸: The study had 40 remote participants doing 30-minute think-aloud sessions on screen recordings. The team logged small behaviors down to seconds spent and scroll percentages.
Scot came out of the Amazon era believing the buy box was everything, and this study tells him ChatGPT has one too. Enjoy the conversation.
Timestamps:
01:57 Meet Eric Van Buskirk, Clickstream Solutions
02:15 Why ReFiBuy commissioned this study
02:48 Eric's background: SEMrush, Backlinko, Kevin Indig
04:05 Stated preference vs. observed behavior
06:05 How a behavioral study works
08:30 Why 40 people is enough
10:20 Who took part, and who got cut
11:55 The Amazon buy box and agentic commerce optimization
14:25 Finding #1: Pictures beat paragraphs
16:40 Card position: #1 wins, #3 beats #2
18:55 84% of choices come from a product card
20:35 Google SEO vs. AI: the "keyword jail" era
23:40 When do shoppers click out?
25:30 75% skip the text and go straight to the cards
27:25 Offer cards: the first offer dominates
30:20 Ads in ChatGPT: what shoppers really think
35:50 Trust and the reviews paradox
40:25 When bad data breaks trust
41:35 Where to get the study and work with Eric
44:55 Wrap-up
👉 Connect with Eric: https://www.linkedin.com/in/vanbuskirk/
👉 Learn more about Clickstream Solutions: https://clickstream.cc/
🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic