Online Shopping Behavior Trends: What Changed
- Brand trust is now a stated purchase criterion: 88 percent call it important or critical, level with quality (89 percent) and value (88 percent), per the 2026 Edelman Trust Barometer Special Report.
- Social platforms are the top product discovery channel for Gen Z, Millennials and Gen X (HubSpot 2025 Consumer Trends Report), while word of mouth remains the most trusted channel at 88 percent (Nielsen, 2021).
- AI-referred traffic to US retail sites grew 393 percent year over year in Q1 2026 according to Adobe Analytics, and those visitors read more pages and stay longer than average.
- Baymard Institute puts average cart abandonment at 70.22 percent across 50 studies, with unexpected extra costs the leading stated reason at 48 percent.
- Most retail sales still finish offline but are decided online, so an online session that ends without an order is not automatically a lost session.
Most trend articles age badly because they describe a moment rather than a behavior. The safest way to read online shopping behavior is to separate the two: what shoppers want has barely moved in a decade, while the places they go to satisfy it change every couple of years.
This article covers seven behaviors that are visible in current, named research, what each one means for a brand selling online, and how to check whether it is true of your customers specifically. Where a claim from an earlier version of this article no longer holds, it has been removed rather than restated.
What changed in online shopping behavior since 2023
It is worth being explicit about what has been dropped from this article, because a stale trend is more expensive than no trend at all.
- The "everything moves online permanently" framing is gone. Online sales kept growing, but as a steady share gain rather than the step change of the early 2020s. Physical retail did not collapse. Planning for a one-way shift produced a lot of over-built digital and under-served stores.
- "Social media is for awareness, not sales" is no longer accurate. In-app checkout on social platforms is now a real revenue line rather than an experiment.
- "Search means a search engine" is no longer safe. A meaningful share of product research now happens inside an AI assistant that summarizes rather than lists.
What survived is more useful than what changed. Value, trust, convenience and recognition were the drivers in 2023 and they are the drivers now. The rest of this article takes each in turn.
Shoppers compare value, not just price
Value-seeking is easy to misread as price-seeking. The two behave very differently. A price-seeking shopper leaves for a cheaper competitor immediately. A value-seeking shopper is doing arithmetic that includes delivery cost, the chance of having to return the item, how long the product will last and how much hassle is involved if something goes wrong.
The 2026 Edelman Trust Barometer Special Report on brands put value at 88 percent as an important or critical purchase criterion, effectively tied with quality at 89 percent. The two are read together, not traded against each other.
What to do about it. Make the full cost visible early, including shipping and any fees, so the comparison a shopper is already running does not break down at checkout. Put specifications, materials, sizing and warranty terms where they can be read without scrolling for them. Say what the product is not good for. Categories with heavy comparison shopping benefit more from an honest limitation than from another superlative.
Trust is now an explicit purchase criterion
The 2026 Edelman Trust Barometer Special Report is useful precisely because it puts trust on the same scale as quality and price rather than treating it as a separate emotional layer. A shopper who does not trust a store will not buy from it at any price.
For an eCommerce brand, trust is mostly operational rather than philosophical. It is the delivery date being right, the return being painless, the reviews being real, the price at checkout matching the price on the product page and the support reply arriving.
What to do about it. Publish your returns policy where the buying decision is made rather than only in the footer. Show real reviews, including the mediocre ones, because a page of perfect scores reads as filtered. State delivery windows you can actually meet. When something goes wrong at scale, say so before customers have to ask. If sustainability or sourcing claims are part of your positioning, make them specific and verifiable, since vague claims now cost more credibility than they earn.
Ask your customers what stopped them from buying, then test the fix rather than guessing at it.
See how Omniconvert Explore handles surveys and A/B testing →Discovery moved to social feeds and creators
Two findings sit behind this. HubSpot's 2025 Consumer Trends Report found social media is the top product discovery channel across three generations, not just the youngest one, and that roughly one in four social media users had bought something directly through a social app in the previous three months, rising to 43 percent of Gen Z. Nielsen's long-running Trust in Advertising work puts recommendations from people you know at the top of the trust ranking, well above any paid channel.
Put together, they describe a single behavior rather than two. Shoppers find products where other people are talking about them, and they believe a person more than they believe a brand. Creator content works because it sits between the two: a person, with an audience, talking about a product.
The platform detail matters less than the pattern, and platform detail is exactly what dates fastest. TikTok Shop is a substantial commerce channel in 2026, in-app checkout is live across the major platforms, and none of that guarantees the same ranking in two years. Build for the behavior, stay portable across the platforms.
What to do about it. Make your products easy for other people to talk about: clear names, shareable images, a claim that survives being repeated by someone who does not work for you. Work with creators whose audience matches your buyer rather than the largest audience you can afford, and give them the product rather than the script. Ask for reviews systematically after delivery, since organic word of mouth is the channel you can influence most cheaply.
Research increasingly starts inside an AI assistant
This is the genuinely new behavior on this list, and the one absent from the 2023 version of this article. Instead of scanning a page of links, a shopper asks a question and receives a summary. The brand either appears in that summary or it does not.
The traffic that results is small relative to search but is growing quickly and arrives further along in the decision, which is why Adobe's data shows longer sessions and deeper browsing rather than the shallow visits you would expect from a new referral source.
What to do about it. Write product and category pages that answer questions directly, in plain sentences, near the top. Keep specifications in structured markup rather than only inside images. Make prices, availability and shipping terms machine-readable. Publish comparison and buying-guide content that states conclusions explicitly instead of implying them, because an assistant can only summarize what is actually written down. Then track AI referrers separately in analytics so you can see the channel rather than guess at it.
Online research still ends in an offline purchase
This behavior has been stable for over a decade and it is still the most commonly mismeasured one. If a shopper reads three of your product pages, checks stock at a nearby store and then buys there, your on-site conversion rate records a failure. Optimizing purely against that number pushes you to remove exactly the content that did the work.
It also runs the other way. Shoppers browse in a store and buy online later, which is why in-store price and availability information needs to match what is published online. Inconsistency between the two is one of the fastest ways to lose a sale you had already won. This is a familiar problem in customer journey optimization: the journey crosses channels even when your reporting does not.
What to do about it. Keep stock levels, prices and store hours accurate and easy to find. Make product listings searchable, including on the marketplaces your buyers use. If you have stores, offer collection and returns in store and measure them as digital outcomes. Where you cannot connect the two directly, use assisted metrics or a simple post-purchase question rather than pretending the offline half does not exist.
Tolerance for friction has collapsed
Baymard's cart abandonment figure is an aggregate rather than a target, and a lot of the abandonment it counts is ordinary browsing behavior. The valuable part is the reason list, because the top reasons are almost entirely under the retailer's control.
The pattern holds across categories. Shoppers do not leave because the product was wrong. They leave because a cost appeared that they had not been told about, or because completing the order required more effort than it was worth at that moment.
What to do about it. Show shipping cost, tax and fees before the cart, not after. Offer guest checkout. Cut checkout fields to the minimum that fulfillment actually needs. Give a delivery date rather than a delivery range. Support the payment methods your market prefers, including the wallets that remove typing entirely. Then test each change rather than shipping all of them at once, so you know which one moved the number.
Service is expected at any hour
The questions people ask before buying are mostly the same twenty questions: will it fit, when will it arrive, can I return it, does it work with the thing I already own, is this the right size for me. Most of them can be answered on the page, which is cheaper and faster than answering them in a conversation.
What has changed since this article was first written is the quality bar for automation. An automated assistant that answers well is now expected. One that loops through a menu and cannot escalate reads as an obstacle deliberately placed between the customer and a person. If you are weighing AI in customer support, the handover path matters more than the answer quality.
What to do about it. Answer the top twenty pre-purchase questions on the product and policy pages first. Then add automated support for what is left, with an obvious route to a human and honest response times when no human is available. Log what people ask, because the questions are a free list of everything your pages fail to explain.
What shoppers do after the first order
A trend report will tell you where shoppers are. It will not tell you which of your own customers are worth keeping. That answer sits in data you already have.
Recency, frequency and monetary value are enough to separate customers who are about to stop buying from customers who never really started. The signals below are the ones worth watching, and none of them require new tracking.
| Behavioral signal | What it tells you | What to do about it |
|---|---|---|
| Recency is slipping for previously frequent buyers | Churn is starting, while the customer is still reachable | Win-back contact before the relationship goes cold, not after |
| Large first-time cohort, low second-order rate | Acquisition is working and onboarding is not | Fix the post-purchase experience before buying more traffic |
| Repeat rate varies sharply by acquisition channel | Some channels bring customers who never return | Re-weight spend on lifetime value, not cost per order |
| Repeat rate varies sharply by first product bought | Certain entry products attract one-time buyers | Change what you promote to new visitors |
| A small segment produces most of the revenue | Averages are hiding where the value actually is | Serve that segment deliberately instead of treating everyone alike |
Nexus by Omniconvert automates this segmentation, cohort analysis and customer lifetime value tracking, and pushes the resulting segments into the channels where you would act on them. It is built on 13 years of data across 7,000+ websites and 15+ industries, with 248+ audit criteria behind the analysis. For the underlying theory, our guide to consumer behavior patterns, types and segmentation covers how these groupings are formed.
How to act on these trends without chasing them
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Find your own number for each trendShare of new customers from social and AI referrers, checkout abandonment by step, second-order rate, proportion of sessions ending in a store visit. Seven trends, seven numbers, one afternoon.
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Rank by gap, not by noveltyThe trend that is most talked about is rarely the one where you are furthest behind. Work on the two or three with the largest distance between your number and a reasonable target.
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Ask customers why, before deciding what to changeAn on-site survey at the point of hesitation, or a short post-purchase question, usually explains a drop-off faster than another analytics session will.
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Write the change as a hypothesisState what you will change, for whom, and which number you expect to move. A trend is a reason to form a hypothesis, not a reason to skip having one.
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Test it instead of rolling it outA/B test the change on the segment it targets. Across 70,000+ experiments run on Omniconvert Explore, the average uplift for a winning variation is 23.2 percent, and plenty of confident ideas do not win at all.
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Re-check in six monthsDiscovery channels move faster than buying behavior. Re-pull the same seven numbers twice a year and you will see channel shifts early, without needing anyone's trend list.
Frequently Asked Questions
The main online shopping behavior trends in 2026 are value comparison over impulse buying, brand trust as an explicit purchase criterion, product discovery on social platforms and through creators, research that starts inside AI assistants instead of a search box, online research that still ends in a physical store, very low tolerance for checkout friction, and an expectation of round-the-clock service. Each of these is a behavior you can measure in your own data rather than a prediction.
Three things changed. Discovery moved further away from the search box toward social feeds, creators and AI assistants. Value replaced growth-era impulse buying, so shoppers compare more and buy less often. And the pandemic-era assumption that every category would keep shifting online stopped holding, because physical stores recovered while the research that leads to them stayed digital. The underlying expectations, value, trust and convenience, did not change. The channels where they are formed did.
Yes, and trust now sits alongside quality and price as a stated buying criterion. In the 2026 Edelman Trust Barometer Special Report on brands, 88 percent of respondents said trusting the brand is an important or critical purchase criterion, essentially level with quality at 89 percent and value at 88 percent. Trust is built through transparent pricing, honest delivery promises, easy returns, visible ownership of mistakes and consistent product quality.
Social platforms are the top product discovery channel for Gen Z, Millennials and Gen X according to HubSpot's 2025 Consumer Trends Report, and a growing share of those discoveries convert inside the app rather than on the brand site. Word of mouth remains the most trusted channel of all. Nielsen's 2021 Trust in Advertising study, covering 40,000 respondents in 56 countries, found recommendations from people you know are trusted by 88 percent of consumers, ahead of every paid format.
Yes, and the volume is growing quickly. Adobe Analytics reported that AI-referred traffic to US retail sites grew 393 percent year over year in the first quarter of 2026. Visitors arriving from AI sources also behave differently once they land, spending 48 percent longer on site and viewing 13 percent more pages per visit. In practice this means product information has to be readable by a machine as well as by a person.
It does, in every category that has physical retail. Shoppers check price, availability and reviews online, then finish the purchase in a store. A large majority of retail sales are now shaped by a digital touchpoint somewhere in the journey, even when the money changes hands in a store. The practical consequence is that an online session that ends without an order is not automatically a failed session, so measuring only the on-site conversion rate understates what digital is doing.
Mostly because of things the retailer controls. Baymard Institute puts the average documented cart abandonment rate at 70.22 percent, calculated across 50 studies, and identifies unexpected extra costs such as shipping, tax and fees as the leading stated reason at 48 percent. Forced account creation, slow or complicated checkouts and unclear delivery timing follow. These are the cheapest online shopping behavior problems to fix because they need no new traffic.
Check each trend against your own data before acting on it. Look at where new customers actually arrive from, what they read before ordering, where they drop out of checkout, and whether they come back. RFM segmentation in Nexus by Omniconvert turns that history into segments you can act on, and A/B tests in Omniconvert Explore tell you whether the change you made because of a trend actually moved anything.
A trend list is only useful once it is checked against your own numbers. Take the seven behaviors on this page and, for each one, find the matching figure in your own analytics: the share of new customers arriving from social and AI sources, the checkout step where the most people leave, the proportion of orders placed by returning customers, the categories where online sessions end in a store visit. Some trends will be strongly present in your data and some will not apply to your category at all. Work on the two or three that are, test the changes rather than assuming them, and re-check in six months. That is a slower answer than a trend list usually promises, but it is the only one that survives contact with your own customers.
See which of these behaviors show up in your own data
Nexus by Omniconvert turns your order history into RFM segments, cohorts and CLV, so you can see where your customers actually come from, which ones come back and which ones quietly stop buying. Built on 13 years of data across 7,000+ websites and 15+ industries.