Retailgentic | Consumer Behavior & Retail Trend

When you think about vertical shopping agents, the AI agents built to solve shopping in one category, one name keeps coming up: Daydream. We’d wanted to get the Daydream team on Retailgentic for a while, and this week we finally did. This one is part founder story and part deep dive into what a fashion-native agent learns when shoppers stop typing keywords and start describing their lives.

Highlights: 
𝗧𝗵𝗲 $𝟱𝟬𝗠 𝗿𝗮𝗶𝘀𝗲: Daydream has raised $50 million. The Pinterest exit kept earlier investors on board and brought in new ones who had wanted into The Yes before it was acquired.
𝗢𝗻𝗹𝘆 𝟯% 𝗼𝗳 𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝘀 𝗻𝗮𝗺𝗲 𝗮 𝗯𝗿𝗮𝗻𝗱: In Lisa’s Google days, brand search drove everything. On Daydream, people search by occasion, lifestyle and body type.
𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗴𝗲𝘁 𝗱𝗲𝗲𝗽𝗲𝗿: New users start with Google-style searches like “red dress.” The more often they return, the longer and richer their queries get. One real example: “Going to a Tulum vacation for five days. Help me dress like a Jacquemus ad.”
𝗦𝘁𝘆𝗹𝗶𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗼𝗰𝗸𝗲𝘁: “You never ask your personal assistant to actually pick it out for you.” Lisa says that gap is where Daydream fits.
𝗟𝗶𝘃𝗲 𝗶𝗻 𝘁𝘄𝗼 𝗱𝗮𝘆𝘀: Daydream works from brands’ existing Google or affiliate feeds, takes its commission through affiliate networks, and gets most partners live within about two days. The catalog covers 350+ partners, 10,000 brands and 3 million products.
𝗧𝗵𝗲 𝗳𝗮𝘀𝗵𝗶𝗼𝗻 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗴𝗿𝗮𝗽𝗵: Daydream doesn’t take a feed as finished. It enriches each one using text, images and input from real stylists, so it can tell that a skirt is both A-line and pleated, and that a white dress is the wrong answer for a wedding guest.
“𝗠𝘆 𝗳𝗲𝗲𝗱 𝗶𝘀 𝗴𝗮𝗿𝗯𝗮𝗴𝗲”: It’s what Lisa hears most often from brands. Daydream’s goal is to level the field so a brand with a 25-person feed team doesn’t automatically beat a one-person Shopify shop.
𝗧𝗮𝘀𝘁𝗲 𝘃𝘀. 𝘀𝗽𝗲𝗰: Lisa splits shopping into two kinds. In spec-based buying (“the Samsung Frame TV in 65 inches, just buy me the best price”), agents will act fast. In taste-based categories like fashion and home decor, people “want agency in” the process.
Lisa has spent her whole career bringing fashion along into what’s next, and Daydream may be the clearest picture yet of what a category-native agent can do.
Timestamps:
03:55 Welcome Lisa Yamner, Co-Founder of Daydream
04:45 Lisa's background: 12 years at Google
05:35 Condé Nast, The Yes and the Pinterest acquisition
07:40 The original 2023 Daydream pitch
10:05 Raising $50M
10:45 What Daydream learned about consumer behavior
12:40 The stylist in your pocket
13:55 Getting fashion brands on board
16:25 The SoHo pop-up, brand activations and AI Decoded
19:30 How merchants get started
21:10 Powered by Daydream
21:50 Building B2B and consumer at the same time
23:00 Conversion and the "shop with AI" problem
26:00 What brands learn from natural-language search
28:50 Profiles, context and personalization
30:40 Product data gaps and the fashion knowledge graph
34:55 What's next: a proactive AI stylist
39:25 Will consumers let agents buy? Taste vs. spec
45:05 Protocols and payments
46:55 Agentic commerce in 2030
47:50 Wrap-up

👉 Connect with Lisa: https://www.linkedin.com/in/lisayamnergreen/
👉 Learn more about Daydream: https://daydream.ing/
🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
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This week is a research-heavy episode with a conversation of one of the sharpest observers of how real people search and shop now that AI sits between them and the products they want. Kevin’s user-behavior research helped inspire our own product card study at ReFiBuy, so we’ve wanted to have him on for a while.
Highlights 
𝗧𝗵𝗲 𝘀𝗵𝗼𝗿𝘁𝗹𝗶𝘀𝘁 𝗲𝗳𝗳𝗲𝗰𝘁: Over 88% of participants in Kevin’s user-behavior studies didn’t question the frame from AI. “When AI says, ‘These are the best X,’ people barely question it.”
𝗧𝗼𝗽 𝗼𝗳 𝘁𝗵𝗲 𝗹𝗶𝘀𝘁 𝘄𝗶𝗻𝘀: Almost 75% of people picked the item at the top of the shortlist. The top-heaviness of classic Google search carries straight over into AI mode.
𝗢𝗻𝗹𝘆 𝟯% 𝘃𝗲𝗿𝗶𝗳𝘆: In a ChatGPT study, only 3% of people checked the shortlist anywhere else on the open web: not publishers, not affiliates, not YouTube.
𝗧𝗿𝘂𝘀𝘁 𝗶𝘀 𝘁𝗵𝗲 𝘀𝗵𝗼𝗿𝘁𝗰𝘂𝘁: The one thing that breaks the pattern is a brand the shopper recognizes and trusts. Show up as Nike in position five and people who trust Nike will pick you over the #1 item.
𝗧𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝘁: Five user-behavior studies over roughly 24 months, with 100+ participants and 400+ tasks. Every click, hover, and scroll was recorded, and participants talked through what they were thinking.
𝟵𝟲% 𝗰𝗮𝗿𝗼𝘂𝘀𝗲𝗹𝘀: Kevin analyzed 4,000+ shopping queries on regular Google (not AI mode). Over 96% showed at least one product carousel, and when one appears, click-through on classic blue links drops by at least 50%.
𝗔𝗱𝘀 𝗶𝗻 𝗱𝗶𝘀𝗴𝘂𝗶𝘀𝗲: Google shows more ads, bigger ads, and ads that are harder to tell apart from organic results. “I’ve been in this space for over 15 years. I more and more accidentally click on ads.”
𝗤𝘂𝗲𝗿𝘆 𝗳𝗮𝗻-𝗼𝘂𝘁𝘀: AI engines run live searches on anywhere from two to hundreds of keywords before they answer. Ranking better on those fan-out queries has repeatedly increased citation rates and AI visibility.
𝗠𝗲𝗿𝗰𝗵𝗮𝗻𝘁 𝗖𝗲𝗻𝘁𝗲𝗿 𝗼𝘃𝗲𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 𝗖𝗼𝗻𝘀𝗼𝗹𝗲: Google’s Merchant Center now says more about how products show up in AI than Search Console does, which is why Kevin calls commerce the beachhead for AI optimization.
𝗧𝗵𝗲 𝗼𝘃𝗲𝗿/𝘂𝗻𝗱𝗲𝗿: For AI-assisted product research, Kevin takes the over on 10–20% of shoppers. For fully agentic checkout, he expects under 1% over the next 12 months.
Kevin is one of the best at separating what the data says from what the hype says. Subscribe to Growth Memo after you subscribe to Retailgentic, and enjoy the conversation.
Timestamps:
05:00 Welcome Kevin
05:25 From SEO to growth: Meta, Reddit, Ramp & Shopify
06:40 The growth ninja: why companies call Kevin in
07:45 Three turnaround patterns: strategy, AI erosion, broken channels
09:20 Can AEO/GEO make up for lost SEO traffic?
11:10 The 4,000-query study: 96% show product carousels
12:10 Ads are taking over Google
14:55 Is Google pushing people into AI mode?
17:00 Will AI mode become the default?
20:05 Universal cart, UCP & richer context for ads
21:05 User-behavior findings: shortlist, slot #1, trust & 3% verify
23:30 Surveys vs. observed behavior vs. panel data
26:30 How do you get slot one? AEO vs. GEO
27:55 Query fan-outs & classic SEO tactics that still work
29:35 ACP, UCP & the product feed
30:40 Merchant Center data & closed loops
32:55 "More butter out of the dish": agents sniff out false claims
34:05 Websites matter less & direct checkout is inevitable
35:10 Back-endification: buyer data, retention & direct traffic
38:15 OpenClaw, Muse & the new consumer AI agents
40:00 Agents as a threat to marketplaces: Amazon vs. Shopify
41:20 20% agentic by 2030? Kevin's over/under
43:45 Wrap-up & where to find Growth Memo

👉 Connect with Kevin: https://www.linkedin.com/in/kevinindig/
👉 Learn more about Growth Memo: https://www.growth-memo.com/
🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
📰 Subscribe to the free Substack: retailgentic.ai
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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/
🧠 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

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