The Rise of Research Driven Shopping explores why buyers now research everything, and what that means for retailers navigating AI.
Key Takeaways
- Online research now precedes 61% of in store shopping trips, up sharply from just a few years ago
- AI tools are reshaping discovery, but only 14% of shoppers trust AI recommendations outright
- Retailers should prioritize clean product data, since ChatGPT sources most listings from Google Shopping
- Traditional market research still matters, catching gaps that AI-driven visibility strategies alone can’t find
You check your phone before you’ve even picked up the item. A friend recommends a blender, you say “sounds great,” then you open five tabs to compare it against three other blenders anyway. That’s not indecision. That’s just how buying things works now.
Why Shoppers Research Everything Now
Something shifted in how people approach even ordinary purchases. Research used to be reserved for big ticket items, a car, a mattress, maybe a laptop. Now it happens before a $30 pair of headphones gets added to a cart. According to 1WorldSync’s 2023 Consumer Product Content Benchmark, 61% of in store shopping trips now begin online first, a 25% jump from 2022. That’s not a marginal shift. That’s a new default.
Capital One Shopping’s 2026 data backs this up from a different angle: 57% of consumers say they research their intended purchases extensively before buying. And the depth of that research keeps growing. A joint study from Impact.com and eMarketer found that shoppers typically check a product three times before committing to it. For 60.2% of households earning over $250,000 a year, that climbs past five separate research sessions. The more money on the table, the more the shopping journey stretches out.
The Modern Shopping Journey: From First Search to Final Click
The path from “I need this” to “I bought this” isn’t a straight line anymore. It bends through several distinct stages, and most shoppers move through all of them without thinking twice.
- Discovery: a need surfaces, often triggered by a search, an ad, or a friend’s mention
- Comparison: multiple products get lined up side by side, usually across different sites
- Validation: reviews get read, sometimes obsessively, before trust is granted
- Purchase: the actual transaction, which can happen online or in a physical store
- Post purchase research: confirming the choice was right, checking return policies, reading more reviews after the fact
Back in 2018, Bazaarvoice’s ROBO study (Research Online, Buy Offline) found that 82% of smartphone users consulted their phones before an in store purchase, and 45% read reviews first. That’s worth treating as a historical marker rather than current behavior, a snapshot from a much earlier phase of this shift. Set next to Capital One’s 2026 figure of 57% doing extensive research, the trend line is obvious: what looked like an emerging habit in 2018 has become standard practice now.
In Store Research Hasn’t Disappeared It’s Changed Shape
Walking into a store doesn’t mean the research stops. It just moves into your pocket. SellersCommerce’s 2026 Ecommerce Statistics report, citing Klarna Insights, found that 83% of shoppers research online before ever stepping foot in a physical location. The habit doesn’t end at the door.
What that research looks like has evolved too. Shoppers aren’t just glancing at their phones anymore, they’re actively cross referencing what’s in front of them.
- Price checking a product against three other retailers while standing in the aisle
- Pulling up reviews for the exact item on the shelf before deciding
- Comparing the in store price to what the same product costs online, sometimes from the same retailer
Most of that homework still happens before the trip, though. The same SellersCommerce report, drawing on data from Invoca and HelpLama, found that 66% of shoppers do their research on a PC or laptop at home, compared to just 15% on mobile. The store visit is often the last step, not the first.
How AI Is Reshaping Where Shoppers Go to Research
A new layer has entered the research process, and it’s growing fast. Salsify’s Q4 2025 Ecommerce Pulse Report found that 64% of consumers now use AI tools to discover or research products. That’s a substantial share of shoppers routing part of their decision making through a chatbot instead of a search bar.
Trust hasn’t caught up to adoption, though. Only 14% of shoppers say they trust AI recommendations outright. People are using these tools, but they’re not handing over full confidence just yet. Adoption also skews younger. A June 2025 survey from Future Commerce and Commerce, fielded by Centiment with a sample of 1,000 respondents, found that 33% of Gen Z and 26% of Millennials now turn to AI platforms for product research. It’s real, but it’s not universal, and it’s not blind trust either.
AI Shopping Visibility: Best Practices for Retailers
If shoppers are researching through AI tools, retailers need a plan for showing up inside those results. This isn’t a future consideration. It’s already determining which products get surfaced and which don’t.
The mechanics behind this matter more than most retailers realize. A study by Tom Wells, a GEO researcher at Peec AI, analyzed over 43,000 ChatGPT shopping carousel products across ten industry verticals and found that 83% of them closely matched Google Shopping listings. In other words, the foundation you’ve already built for traditional search is quietly doing double duty.
That means AI shopping visibility best practices for retailers largely come down to getting the fundamentals right, not chasing some separate AI specific playbook.
- Keep product data feeds clean and fully structured, no missing fields or inconsistent formatting
- Maintain accurate, real time pricing and inventory across every channel
- Mark up customer reviews so AI systems can cite them with confidence
- Answer comparison style questions directly in product content (“best for,” “vs.,” “alternative to”)
- Treat your Google Shopping feed as core infrastructure, not an afterthought
Retailers Using AI for Styling and Personalization What It Looks Like in Practice
Fashion retailers have moved past basic recommendation engines and into something closer to a digital stylist. Wardrobe analysis tools now study what a customer already owns and suggest pieces that actually complete an outfit, rather than just pushing whatever’s trending.
Virtual try on has followed a similar trajectory, letting shoppers see how something fits before committing to a purchase. Then there are AI outfitting engines, which generate complete, shoppable looks at scale instead of relying on a merchandising team to hand curate every combination.
The results retailers are reporting back this up. Stylitics, whose platform powers many of these outfitting tools, reports that Rhone saw a 39% increase in average order value from curated outfit recommendations, along with a 10x return on investment within the first 100 days. JD Sports reports that its “Shop the Model” feature drives 50% of the total revenue generated through the Stylitics widget. Worth noting: both figures come from Stylitics’ own case studies, so they’re vendor reported results rather than independently audited numbers. Still, retailers using AI for styling aren’t just experimenting anymore. They’re building it into core merchandising strategy.
The Risks of Leaning on AI for Retail Planning and Discovery
None of this comes free. As more of the shopping journey gets routed through AI platforms, retailers are trading away things they used to control outright.
Etsy, Target, and Walmart all deepened their partnerships with Gemini and Copilot for agentic commerce in January 2026, according to Retail Dive, even knowing the tradeoffs involved. That’s a signal worth paying attention to: major retailers are making this bet with eyes open, not by accident.
The risks stack up quickly once you look past the visibility gains.
- Data ownership erodes when the shopping journey starts and ends inside an external AI platform, cutting retailers off from the browsing and search behavior they used to capture directly
- Disintermediation grows more likely, since an AI agent sitting between the shopper and the retailer weakens the direct customer relationship
- Over reliance on AI forecasts without a human checking the output invites errors that compound before anyone notices
- Brand loyalty takes a hit as AI agents compress discovery, comparison, and checkout into a single interaction where the retailer’s name barely registers
These are exactly the kind of ai retail planning risks that don’t show up in a quarterly report until the damage is already done.
Why Ecommerce Businesses Still Need Traditional Market Research
AI visibility tactics solve one problem. They don’t solve all of them. There’s a whole category of insight that only comes from asking real people real questions, and no amount of feed optimization replaces that.
Ecommerce market research still means sitting down with competitor pricing data, running actual customer interviews, and surveying people about needs your product line hasn’t addressed yet. It means testing messaging on a small audience before scaling ad spend behind it. This kind of market research for ecommerce isn’t glamorous, but it catches problems AI visibility work simply isn’t built to catch.
The stakes for getting this right keep rising. Research and Markets values the AI in retail market at $15.4 billion in 2025, projecting it to reach $20 billion in 2026, a growth rate of roughly 30% a year. That kind of spending pace makes disciplined, human led research on ecommerce more valuable, not less. When budgets move that fast, someone still needs to be checking whether the strategy is actually working.
What This Means for Retailers Going Forward
None of this points to AI replacing how people research before they buy. It points to AI becoming one more channel layered on top of habits that were already well established. The 57% of shoppers doing extensive research, the 61% starting in store trips online, the 64% now touching AI tools somewhere in the process. These numbers describe the same underlying behavior, just expressed through more surfaces than before.
For retailers, the response isn’t complicated, even if the execution takes real work.
- Audit product data quality first, since that’s the foundation everything else in AI visibility depends on
- Don’t abandon human led research methods, they still catch what AI tools miss
- Treat AI visibility as one channel among several, not a full replacement for the strategy that got you here
Shopping research keeps expanding into new territory. The retailers who stay ahead of it will be the ones who show up wherever that research happens next, without betting the whole business on any single platform doing it for them.
Sources
- 1WorldSync 2023 Consumer Product Content Benchmark, via PR Newswire
- Capital One Shopping, Consumer Behavior Statistics 2026
- Impact.com joint study with eMarketer, 2024
- Forbes (John Ellett), on the Bazaarvoice ROBO study, 2018
- SellersCommerce, Ecommerce Statistics 2026 (citing Klarna Insights and Invoca/HelpLama)
- Salsify Ecommerce Pulse Report, Q4 2025
- Future Commerce + Commerce / Centiment survey, June 2025, via Chain Store Age
- Peec AI study by Tom Wells, via Search Engine Land
- Stylitics — Rhone case study
- Stylitics — JD Sports case study
- Research and Markets — AI in Retail Market Report
