How AI Influences Shopping Behavior

It was the best of search, it was the worst of search. It was the age of instant answers, it was the age of disappearing links. It was the epoch of personalization, it was the epoch of lost discovery. It was the season of AI-driven clarity, it was the season of algorithmic opacity. It was the spring of conversational commerce, it was the winter of ten blue links. According to Adobe Analytics, U.S. retail websites saw a 1,200% increase in traffic from generative AI sources between July 2024 and February 2025. During the 2024 holiday season alone, this figure jumped 1,300% year-over-year, with Cyber Monday traffic spiking 1,950% compared to 2023. Consumer adoption is driving the shift. A survey of 5,000 U.S. shoppers found that 39% have used generative AI for online shopping, with 53% planning to do so this year. Users rely on AI for product research (55%), recommendations (47%), deal-hunting (43%), gift ideas (35%), product discovery (35%), and shopping list creation (33%). AI-generated traffic isn’t just growing—it’s more engaged than traditional sources. Visitors spend 8% more time on-site, view 12% more pages per visit, and have a 23% lower bounce rate than those from search or social media. Conversational AI interfaces are improving consumer confidence and making online shopping more intuitive. That said, conversion rates for AI-driven traffic still lag behind traditional sources by 9%, but the gap is closing. In July 2024, the difference was 43%, signaling growing consumer trust in AI-assisted purchases. Another key insight: AI-assisted shopping is happening on desktops, not mobile. Between November 2024 and February 2025, 86% of AI-driven traffic came from desktop users—suggesting that consumers prefer larger screens for complex, AI-guided shopping experiences. While the numbers are compelling, they only hint at what’s coming. AI-driven agents won’t just assist shoppers—they’ll shop for them. The way consumers find, evaluate, and purchase products is shifting fast, and this data is just beginning to tell the story. -s

AI impact on shopping is going to be much bigger than ecommerce, because ecommerce is only a small part of retail that starts online. So many buying decisions are driven by interactions online that separating out ecommerce as an isolated channel sales channel is obsolete. To illustrate, Profitero data shows that 64% of retail sales are either ecommerce or digitally-influenced offline retail. This essentially means that even though we still rarely shop on TikTok or YouTube, we buy things because we saw them there. AI tools are now replacing Googling. Our decisions are increasingly digitally influenced by ChatGPT and others. That affects shopping too. And just like the impact of social platforms, AI tools are both direct checkout channels (many have added buy buttons to their responses) and indirect recommendation engines that inform decisions elsewhere. Think someone standing among the endless isles at Walmart chatting with ChatGPT for shopping ideas. AI is disruptive to retail. Ecommerce penetration has been growing slowly in most mature markets and this could re-accelerate it because of better personalization, discovery, and curation. Thus, one way to look at it is what percentage of ecommerce is AI (be it agentic shopping, embeded checkout, etc). Another is what percentage of all retail is influenced by AI. The first is a challenge for Amazon. The second is a challenge for retail.

New Andreessen Horowitz thesis - AI x Online Shopping! Shopping used to be a hunt. With AI, it’s ‘God Mode’. AI is transforming online shopping into something intelligent, predictive, and visually intuitive. Instead of searching for products, the right picks come to you — curated, customized, and ready to buy. Here’s how Bryan Kim and I see it playing out 👇 1/ No more “will this look good on me?”: AI try-ons let you see fit, drape, and style on your own digital twin — making shopping visual and data-driven. 2/ From “nothing to wear” to AI-curated style: AI stylists recommend outfits based on your closet, calendar, weather, and taste. 3/ From imagination to inventory: You can now design and refine custom products in real time — AI makes personalization scalable. 4/ AI finds the best deals: Smarter search surfaces affordable alternatives and secondhand picks — matching your style and budget. 5/ Brands connect at scale: LLMs run support, from refunds to shipping — with higher satisfaction and zero wait time. This is just the beginning. What’s next is predictive, personalized shopping powered by fully integrated AI assistants. Outfits are becoming first-class primitives — dynamically styled from what you already own or imagined entirely by AI. For more of our thoughts, check out the full blog post below - and we'd love to hear from you if you're building something here! 👋 https://lnkd.in/gFWB3K9b https://lnkd.in/g99-Caz7

How AI Chatbots Are Transforming Holiday Shopping Holiday shopping can be stressful, but AI chatbots are stepping in to simplify decision-making and enhance customer experiences. These virtual assistants leverage generative AI to provide tailored recommendations, answer product questions, and even suggest gift ideas using natural language inputs. Here’s a look at how they make holiday shopping easier, along with some of their limitations. How AI Chatbots Make Shopping Easier 1. Personalized Recommendations AI chatbots like Amazon's Rufus and Walmart's tools analyze user queries to suggest products that align with preferences. For instance, Rufus can recommend toys, electronics, or holiday deals based on your specific needs. 2. Ease of Comparison Tools like Perplexity AI make comparing products straightforward by pulling search results that highlight key features and prices across multiple retailers. 3. Convenience Chatbots provide immediate responses, saving you from browsing through countless pages. Whether it’s finding a gift or locating the best deal, they streamline the process. 4. Follow-Up Assistance Advanced bots don’t just stop at the first recommendation, they ask clarifying questions to refine suggestions, creating a more interactive and tailored shopping experience. 5. 24/7 Availability No need to wait for business hours, AI shopping assistants are available anytime, making them perfect for last-minute shoppers or those in different time zones. The Limitations of AI Chatbots 1. Accuracy Issues AI chatbots can occasionally provide incorrect or irrelevant suggestions, a phenomenon known as "hallucination." 2. Lack of Context Some assistants struggle to understand nuanced preferences, which may result in generic or suboptimal recommendations. 3. Limited Price Comparison While helpful, tools like Rufus aren’t always equipped to provide real-time price updates or identify the most affordable options. 4. Dependence on Training Data The quality of recommendations depends on the chatbot’s training data, which can vary in depth and accuracy. 5. Trust and Verification Consumers may remain skeptical, especially when shopping for high-value or complex items, and often double-check the bot’s advice. Future of AI Shopping Assistants To overcome their current limitations, AI chatbots need to: Integrate deeper personalization by remembering purchase history and preferences. Improve accuracy in price and product matching. Increase transparency to explain how recommendations are derived. AI chatbots are revolutionizing holiday shopping with convenience, personalization, and round-the-clock assistance. While challenges like accuracy and context remain, continuous advancements will make these tools even more reliable and user-friendly. This holiday season, they might just save you time and stress.

The retail customer experience has changed forever thanks to AI. But it's really different today vs. 5 years ago. Let me explain: Five Years Ago... “Best-seller” recommendations. AI-driven tools mostly sorted shoppers into broad categories, leading to cookie-cutter suggestions. Chatbots were scripted and inflexible, often unable to handle real-time changes in inventory or complex customer questions. Predictive analytics were nascent. Forecasting focused on historical sales data rather than real-time signals. Customer Journeys lacked synchronized data. Shoppers experienced one offer in-store and a disconnected experience online. Supply Chain insights largely relied on static spreadsheets, causing delays in restocking and missed sales opportunities. December 31, 2024... Advanced AI uses purchase history, browsing behavior, and contextual cues to shape customized product offerings. Conversational AI handles returns, provides order updates, and even suggests complementary products. Natural language processing ensures smoother, more organic interactions. Predictive intelligence uses real-time data from multiple sources such as social trends and even weather patterns to anticipate shifts in demand, optimizing inventory distribution. Omnichannel integration means buying, returns, and post-sales service function seamlessly across physical stores, mobile apps, and e-commerce sites. AI is the backbone role in harmonizing data. Smart supply chain systems adjust restocks automatically and reroute shipping for efficiency. Fewer out-of-stock items, better stock rotation, and tighter coordination between vendors and retailers. Finally, and here's the wow factor, high-fidelity image recognition, computer vision, and generative AI let consumers visualize products on themselves or in their environments with far greater realism and accuracy. Link to Kolor-Virtual-Try-On in the comments. Just amazing. #ai #retail #customerexperience #machinelearning #digitaltransformation

Will AI Agents Be The End to Retail Media? With Amazon's recent announcement of its “Nova Act” AI agent (designed to browse and shop on your behalf), the idea of AI doing the shopping for us feels imminent. After reading Kiri Masters article about this launch (link in comments), I started wondering… If AI agents are about to become the new shoppers, what happens to retail media? So I ran a few experiments. I used ChatGPT’s AI agent, “Operator,” and asked it to buy me some toilet paper. Once from Walmart and once from Target. I gave it a simple prompt: find me a great quality toilet paper at a good value. Here’s what happened: At both retailers, the agent surfaced the top four search results – At Walmart, 3 of the 4 were sponsored – At Target, 2 of the 4 were sponsored The only difference: the Target agent filtered for products with 4+ star reviews, because I mentioned quality. I asked the agent whether the sponsored ads influenced its recommendation. In both cases, the answer was “no” but that they chose items that met my criteria and were “the first ones [they] saw.” In their own words: 🛒 Walmart: “I focused on the initial visible options that fit the criteria.” 🛍️ Target: “Sponsored products appeared at the top of search results, which is why they were initially presented.”   At first, I was surprised. I didn’t expect the agent to favor ads, but I did expect it to work a little harder - maybe scan a few more rows, compare value, or check price per sheet. Instead, it took the path of least resistance. Sound familiar? It’s eerily human and very consistent with what we know from shopper psychology: most shoppers choose the path of least resistance. Based on this experiment, I think there are a few implications for how we think about retail media moving forward as AI shopping agents become more common. 1.     Winning Top of Search Placement Will Become Absolutely Critical: If agents default to what’s first and meets the basic criteria, getting those critical spots will become even more competitive, and expensive! 2.     Branded Search Terms Will Become Less Relevant: Today brands buy their branded terms to “protect the shelf”. However, if you tell an agent “buy me Charmin,” it’s likely to go straight there. No need to “protect the brand” the same way. 3.     Continue to Keep Shopper Psychology at the Center of Decisions, Even for Agents: Will online retailers be able to dramatically simplify their assortment? Fewer choices can reduce friction and increase confidence. Will all websites default to only 4 start and above products shown? The agent will not want to make a poor recommendation for you. So, will AI agents replace human shoppers overnight? Probably not. But retailers designing retail media programs and brands participating in them, should begin to consider this now. How do you think retail media will change as AI shopping agents become more mainstream? #AIinECOMM #AITrainer #AISpeaker #AIConsultant

AI has completely changed how I make purchasing decisions. I've used generative AI in both my work and life for over 3 years, and it has become second nature to use in almost anything I'm doing, including buying things! Most people haven't reached this stage of AI adoption, but marketers should pay attention to how early adopters like me use AI to buy. We will give you a window into what's coming for everybody. (It also may give you ideas for your own shopping!) Here are just a few ways AI has influenced my own recent purchase decisions for a trip to Europe I'm taking in a few weeks: 1️⃣. Booking Hotels👉 AI helped identify which neighborhoods to look at staying in for every stop. It took into account our interests, how much time we had in each location, personal sightseeing priorities, what is most important to us, etc.. Then it told me the areas to consider hotels in. In a couple of cities, AI even suggested the specific hotel we ended up booking! 2️⃣. New Luggage 👉I've never used hard-side luggage and didn't know where to start. So, I had a conversation with ChatGPT to understand what to look for. Then I gave multiple AI tools my specific requirements (like what size and features were most important to me) and got brand and model recommendations. Once I'd narrowed it down to two brands I couldn't decide between, I did full pro and con comparisons between the brands, and ChatGPT ultimately helped sway my final decision to buy an Away suitcase. 3️⃣. Shoe shopping 👉I've worn ASICS for years, but have had so many customer service issues with the company that I was ready to kick them to the curb rather than buy a new pair for my trip. So, I chatted with AI to find the best alternative shoes for all the walking we'll do. Four tools gave me the same specific alternative recommendations that I'd probably love, but ChatGPT actually convinced me to stick with my usual shoe model (but buy from REI, rather than direct from Asics). The AI just gave a very strong argument about the risks of changing shoes right before a trip with lots of walking, and convinced me it is not the time to try something new for the first time. I also used the AI to help one of my travel companions find shoes for her unique feet (she has a very hard time finding comfortable shoes). I gave 4 AIs all her criteria, and they each suggested specific shoes to try. I identified the common recommendations, and that gave her a starting point of 3 specific brands and models to actually go try on. 4️⃣. Compression Socks 👉 I knew I needed to buy them for the long flight, but had no idea what to look for. AI laid out what's most important for a flight, and gave me specific brands to look at with the pros and cons of each one. I ended up buying Bombas socks, which AI recommended as a solid option. I love their socks normally, but would never have thought to look at them for compression socks to fly! Has AI changed how you make your buying decisions yet, too?

We’re seeing AI companions start to take over as a primary interface for online shopping. So what happens to brands when there’s no results page? No ads to promote? A huge part of our work at NielsenIQ is helping brands win in search — optimizing keywords, content, and placement across platforms like Amazon and Walmart. As shopping behavior shifts toward AI agents like Amazon’s Rufus or ChatGPT plug-ins, the whole playbook changes. There's no list to rank on. No ad carousel to buy into. Just one response, powered by a model that may or may not include your brand. That’s a major challenge for anyone in eRetail or retail media, and — as with all things AI — it’s coming fast. Tools like AdFury.ai are already experimenting with how to inject brands into these new conversations. And while it’s still early, one thing is clear: brands and retailers won’t sit back and accept zero influence on the consumer journey. Discovery is shifting. If AI becomes the gatekeeper to online shopping, RMNs, agencies, and brands need to start thinking now about what comes next.

Anthropic and Perplexity just released two new features that will fundamentally change shopping. The technology (as you'll see in the below demo) is still very early, but it's here and will rapidly improve. Basically - AI can now browse, configure, add to cart, and buy on your behalf. You describe your ask ("Build me and buy a gaming PC") and it will browse the internet, find the most appropriate website to fulfill your request, incrementally build the PC using the parameters you've given it (performance specs, favorite brands, cost, etc), and then buy it for you. All without human interaction. I know that other AI vendors are all working on similar functionality. It won't be long before you can say "Hey Siri, buy me a sweater" and it'll do the shopping for you with full knowledge of what you've bought in the past, your size preference, brands you like, and how much you like to spend. This has huge implications for vendors. Rather than optimizing for humans, we'll have to also optimize for AI agents (as you can see in the demo). We could soon see vendors offering up AI-only shopping via APIs. AI thrives on well structured APIs, so why not just offer up APIs for the AI to interact with directly? We at commercetools are ready for that world and are working on some skunkworks projects to enable this. I see commerce platforms releasing "Shopping Co-pilots," which offer non-personalized assistance on a specific website. But this is a misguided strategy. Anthropic, OpenAI, Perplexity, etc spend billions on innovation and are rapidly putting their respective products in front of consumers. Apple Intelligence (backed by OpenAI) is going live to billions of iPhones next week. That's where AI innovation should happen, and we as vendors should be optimizing our products for the AI agents that consumers use - not trying to compete with them. Early demo: https://lnkd.in/gvZTZ85K