Ecommerce Market Research: A 4-Stage Framework for 2026

Ecommerce market research is the process of gathering and analyzing data about customers, competitors, products, and market conditions to help guide commerce decisions.

In 2025, global retail ecommerce sales hit an estimated $6.4 trillion and are projected to approach $9 trillion by 2030. The opportunity, now, is increasingly found in niche markets: Shopify data shows that product categories outside the top 100 now account for nearly 55% of total sales.

“From trading cards to horse hay nets…no niche is too niche,” says Erin Upton, senior associate, external communications at Shopify. 

The question that remains is which signals point to a real market. Ahead, we’ll show you how to spot and act on those signals.

How often should you conduct ecommerce market research?

There are no one-size-fits-all rules for when and how often to conduct market research. The cadence depends on what you're researching; and the timing should follow the decision you’re trying to make and how quickly the market around it is changing.

For product development, for example, Circana recommends allowing up to a year to research close-in extensions or item launches, while more disruptive launches may require 12 to 24 months.

But launch research only covers one slice of the calendar. The markets are moving fast enough now that standing still between launches is a risk in itself, and the rapid rate of technological advancements is playing a significant role. Gartner predicts that by 2028, more than 80% of companies will make significant changes to their brand identity just to keep pace with AI's effect on markets.

“CMOs have an opportunity to help their organizations define what makes them distinctive, trusted, and relevant as customer expectations and competitive dynamics shift,” says Julie Reeves, VP analyst.

The rapid rate of change makes ecommerce market research a continuous process. You may not need a full study every month, but you do need to keep checking whether customer behavior, competitors, pricing, and demand are telling you something new.

Below, we’ll examine the four stages of an effective market research effort.

Stage 1: Size your market and identify trends

Learn all that you can about the size of the market you’re selling in. A large category can contain multiple, very different pockets of growth, while a small category may be expanding quickly enough to deserve a closer look. 

Shopify’s own framework for choosing products is a good starting point for trend discovery. We recommend looking at Google Trends, social media activity, competitor reviews, and third-party trend data together.

Calculate your total addressable market (TAM)

Your total addressable market (TAM) is the maximum annual revenue available if you captured 100% of the market for a product or service. This gives you the outer boundary of an opportunity before you start asking how much of it you can realistically reach.

A starting formula is:

TAM = Number of potential customers x Average annual spend per customer

You can estimate it in two ways:

• Top down: Start with published category revenue from government data, trade associations, analyst reports, or sources such as Statista, then narrow it by geography, customer type, channel, or product segment.
• Bottom up: Estimate how many customers fit your target market and multiply that number by what they could realistically spend each year. 

Say an activewear brand wanted to assess the opportunity for a larger premium athleisure push in China. Grand View Research valued China’s athleisure market at $25.3 billion in 2025 and expects it to grow 11.3% annually through 2033, with premium athleisure the fastest-growing segment.

That $25.3 billion gives us a useful top-down TAM. Next, we’d narrow it to the customers, price points, product categories, and sales channels the brand is likely to serve. That smaller figure becomes the serviceable addressable market (SAM), a much closer estimate of an achievable market share.

Use Google Trends for ecommerce market research

Google Trends can answer where demand is moving in your product category. This is a starting point for understanding what customers are searching for when researching products to sell.

There’s evidence that search behavior can reveal commercial shifts early. In new research by K. Ramesh and Gary Lind, a review of Google search patterns for nearly 200 publicly traded US retailers between 2004 and 2019 found that increases in search volume tended to precede revenue increases. Plus, investment strategies built around those search trends outperformed traditional models by 2% to 3%.

Here’s how to implement a Google Trends strategy:

• Enter a product, category, or customer problem. Start with the wording customers are likely to type into Google, then test close variants.
• Set the market you care about. Choose a country or region if you’re evaluating demand in a specific expansion market.
• Change the time range. Look at the past five years to distinguish sustained growth from seasonality, then zoom into the past 12 months for recent movement.
• Set the category and search type. Filtering by category can remove unrelated searches. You can also compare Web Search with Google Shopping when purchase interest is more useful than general curiosity.
• Compare terms and inspect related searches. Add competing products, category names, or use cases, then check Related queries and regional interest for smaller trends within the category.

For example, search interest for “pokemon cards” rose from the low 40s in August 2025 to the mid-80s a year later, with a peak of 100 along the way. 

Bear in mind that the scores show relative performance over a period of time for the specific terms you are evaluating. Google normalizes search activity for the selected place and period, then scores it from 0 to 100. A score of 100 marks peak relative interest in that view; 50 means interest was half as high relativeto that peak.

Pro tip: Check out the top 10 trending products to sell in 2026 validated by Google Trends data. 

Use Reddit and Quora for niche demand signals

Search in conjunction with Reddit and Quora can show you how customers describe what they want before that language appears in a formal research report. You can see the products people name, the features they compare, what frustrates them, and the jobs they want a product to do.

Search Google or another search engine using queries such as:

• best [product] reddit
• [product] recommendations reddit
• best [product] for [use case] reddit
• best [product] quora
• [product] for [problem] quora

Then work through the results:

• Check the timestamp. Prioritize recent threads, particularly in categories where products and preferences change quickly.
• Look at where the discussion is happening. The subreddit itself can reveal a customer segment or motivation. In the water-bottle results above, threads from r/BuyItForLife and r/Anticonsumption suggest an audience interested in durability and longevity. Also pay attention to the popularity of the subreddit.
• Record the brands people recommend. This gives you a competitor set based on products customers bring up themselves. In this search, Owala, Hydro Flask, Yeti, and Stanley all appear.
• Note why each product gets recommended. Treat those as hypotheses about purchase criteria that you can test against reviews, surveys, search data, or sales data.
• Save the customer’s wording. Repeated phrases can give you vocabulary for survey questions, FAQs, product descriptions, keyword research, and customer segmentation.
• Follow adjacent searches. Google’s People also search for (PASF) section can lead to another round of research. In the Quora search example below, it surfaces Owala, stainless steel water bottles, and other product-specific terms.

For midmarket and enterprise teams, turn this into a structured research log. Build a running list of qualifiers—"best," "vs," "worth it," "alternative to [competitor]"—and a short list of subreddits or Quora topics relevant to your category. Then set a recurring cadence to rerun the searches and log what's new.

Over time, you can compare those patterns across customer groups, products, and markets.

Compare those outside signals with what customers are doing in your own store. In Shopify, you can build custom reports or ask Sidekick to create one for you, then use customer segments to investigate patterns by location, purchase history, spend, and other available customer data.

Validate demand before building

Before committing to inventory or a new market, test the idea with the smallest credible version of it. From our research on the nonalcoholic drinks category and the Shopify businesses currently succeeding in it, we have a few tried-and-tested methods. 

Take Ritual Zero Proof. They started as a blog, The Zero Proof, documenting their founders’ alcohol-free lifestyle. As traffic grew, it gave them evidence that an audience existed, and only then did they build a Shopify store.

"Ritual was created because we love a good cocktail, with or without alcohol," says cofounder David Crooch.

Curious Elixirs tested the market through community. Their founder JW Wiseman was developing alcohol-free cocktails in 2015, before “sober curious” had become an established category. The brand launched on Kickstarter not only to raise money, but to build a community and educate potential customers from the start. That kitchen experiment eventually became an eight-figure business.

“In a young category, your community isn’t just a target market—it’s your R&D lab,” says Joy Blenman, a senior content designer at Shopify. 

Ghia, on the other hand, took a more product-led approach, testing roughly 1,000 samples with friends and chefs before settling on their formula.

“We wanted for people to not feel like they were having a lesser version of anything,” founder Melanie Masarin says. Ghia hit $2.5 million in revenue in their first year. 

Use content to test audience interest, samples to test the product itself, and a waitlist or preorder to see whether people are willing to take the next step. If you’re on Shopify, Shopify Forms can collect signups and customer information for a launch. 

Once you’re ready to test purchase intent, Shopify also supports preorders, which can help forecast demand before a product is available.

Stage 2: Research your customers

While market data can tell you how much demand exists, customer research tells you who that demand comes from, what problem people are trying to solve, and what would make them choose your product over a competitor.

The strongest data usually combines quantitative research, which shows how common a preference or behavior is, with qualitative research that explains why.

Doe Lashes founder Jason Wong used both before launching the brand. He surveyed prospective customers and collected about 130 responses on the lash brands they used, what they liked and disliked, and what they wanted changed. He also bought roughly 20 competing products and ran informal product-study groups.

“The first thing I like to do is to see if there's any history of that product, if someone has tried to create something [similar], and if so, why did they fail? Why did they not get to market? Is it because of product defects, or is it because the market wasn't ready for it?” says Jason.

Due in part to the strength of Jason’s research, Doe Lashes grew from a $500 startup investment into a multimillion-dollar business. 

Design a customer survey that produces usable data

To create an insightful survey, start with the decision. For a new product line, for example, you might need to know which alternatives customers already use, where those products fall short, and which features or benefits would justify switching. Build the survey around those decisions.

From our Guide to Customer Survey Design, a few rules are especially useful for market research:

• Survey the right customer group. Existing customers, high-intent non-buyers, first-time buyers, and loyal customers can give very different answers. Shopify customer segments can create groups based on purchase history, location, spend, and other customer data you decide who to ask.
• Mix closed and open questions. While closed questions give you comparable data, open questions capture answers you may not have anticipated. Pew Research Center found that people can answer the same underlying question quite differently depending on whether choices are supplied.
• Keep it short. SurveyMonkey found that abandonment starts rising once surveys pass seven to eight minutes, with completion rates dropping by 5% to 20%.
• Leave room for the unexpected. End with an optional open-text question. Chattermill’s survey-question library includes helpful prompts like what nearly stopped a purchase and which competitors a customer considered. Making it optional means you won’t drive off people who are in a rush, while leaving an opening for potentially valuable answers to questions you didn’t think to ask.

For surveys sent to existing customers, Shopify Forms can collect responses on your storefront, while Shopify Flow can help automate when feedback requests are sent. 

Run a focus group for product research

Focus groups are semi-structured discussions guided by a topic guide and a trained moderator, and they give you the opportunity to hear customers work through a product or problem in their own words.

Start with a specific research question, then recruit people who match the customer group you want to understand.

For Shopify businesses, your own commerce data can help with recruiting. The British jewelry brand Abbott Lyon, for example, uses Sidekick to identify high-value customers and how often they return, then has their customer service team invite those shoppers to focus groups as the company studies loyalty.

“If we didn't have Sidekick, it would take so much longer to go through all the customer orders and identify a valuable one for us,” says Isabel Howorth, trading and commercial director.

For each session:

• Recruit people who fit the market you’re researching. The Marketing Research Society (MRS) frames focus groups around understanding the attitudes, experiences, and motivations of a target audience, so participant selection should follow the customer group or buying behavior you want to study.
• Prepare a discussion guide. Use a limited set of open-ended questions, with prompts that let the moderator probe interesting responses.
• Put the product or concept in front of them. UserTesting’s guidance recommends focus groups during early product development to gather feedback on a product or concept.
• Record both agreement and friction. Pay attention to repeated needs and objections, but also to where participants disagree or change their minds after hearing someone else.

Look for other opportunities to obtain the type of data you get from a focus group. A pop-up, for example, can create a different kind of opportunity to watch customers interact with a product and for you to ask questions in the moment.

Dossier used their Manhattan pop-up this way. The brand’s founder Sergio Tache walked the two-hour line asking customers why they’d come. They answered that they wanted to try the perfumes, and that helped confirm demand for permanent retail. The pop-up also revealed stronger in-person interest in Dossier’s Originals Collection than the brand had seen online.

“We want to meet customers where they are,” says Sergio.

Stage 3: Analyze your competitors

A competitor analysis gives you a reference point for the market you’re entering. The analysis can show you which products and price points are already crowded and how other brands are competing for attention in search.

WSI recommends looking across competitor websites, search visibility, content, paid search, social activity, and conversion paths to understand how competitors attract and convert customers. For ecommerce market research, we can apply that same logic to products, reviews, pricing, positioning, and organic search.

Then, look for gaps between what customers want and what the current market offers.

Use SERP analysis for competitive intelligence

A search engine results page (SERP) shows you who owns attention for the questions customers are asking. That may include direct competitors, marketplaces, publishers, Reddit threads, YouTube videos, and brands you hadn’t originally put on your competitor list.

Start with a small, repeatable query set:

• Choose 20 to 50 searches that represent the market. Include category terms, needs, comparisons, and purchase-led searches.
• Search each term in the market you’re researching. Record the brands and domains that appear repeatedly across organic results, Shopping results, ads, People Also Ask (PAA), discussion forums, and AI Overviews.
• Look at what earns the placement. For each recurring competitor, note the product range, price point, claims, customer segment, content format, and proof used to support those claims.

The SERP now extends beyond the familiar list of blue links. Semrush analyzed more than 10 million keywords and found that AI Overviews expanded into commercial and transactional searches during 2025. The share of queries triggering them with commercial intent rose from 8.15% to 18.57%, while transactional queries rose from 1.98% to 13.94%.

McKinsey found that a brand’s own website can account for just 5% to 10% of the sources referenced by AI search, so competitive visibility increasingly depends on what the rest of the web says about your brand.

So when you analyze a SERP, note who gets mentioned, which sources Google cites, and what each brand is being associated with.

Take the Google search for “best women’s activewear.” From one results page, you can see competing brands, price points, customer segments, positioning claims, and the third-party sources shaping AI recommendations.

Rainbow Shops takes that kind of competitor monitoring a step further. The retailer, which competes with Amazon, Walmart, and Shein, “constantly benchmarks” their ecommerce experience against competitors, looking at which features they offer, which Rainbow should match, and where they can improve on them.

The company is literally analyzing a billion shopping searches each day and applying the knowledge they learn to the site.

“So let's take a little black dress, right? A common item that a woman shops for. How many different ways are there to say a little black dress? Sometimes people abbreviate it as LBD. They have lots of different names for it—more text than we could possibly have on the page,” says David Cost, VP of digital and ecommerce.

“Yet Google, because they know what somebody wants when they type in any one of these hundreds of phrases, accurately returns a product set every time. These are the things that help us win and stay ahead in the marketplace.”

Mine customer reviews for competitive gaps

A competitor’s reviews tell you where a product’s positioning meets the customer’s experience, and where it’s currently falling short.

More and more, customer reviews carry immense commercial weight. A 2026 study of more than 9,000 shoppers, published in Harvard Business Review, asked customers which associations came to mind for drugstore giant CVS and competing brands, then tracked their spending for three months. Each additional positive association correlated with an 18% increase in spending, while each negative association correlated with a 12% decline.

And in a 2026 Walr study of 2,000 US Gen Z consumers, 72% of those surveyed said customer reviews were a source they trusted when evaluating a brand.

Here’s how to start:

• Build a comparable review set. Pick three to five direct competitors, plus one lower-priced and one premium brand. For each, collect reviews for equivalent products from the same period so an older bestseller with 10 years of reviews doesn’t distort the comparison.
• Code each review the same way. Create columns for brand, product, rating, date, use case, feature mentioned, praise, complaint, competitor named, reason for switching, and exact customer phrase.
• Count repetition across products and brands. If the same request appears across several competing products, flag it for further research. Also note features customers repeatedly associate with one brand; those can reveal positioning competitors already own.
• Separate product gaps from experience gaps. A complaint about material quality points to a product opportunity; a complaint about slow support points to a service opportunity. Both are important, but they get solved differently.
• Pull the customer language into the next stage. Save phrases that recur verbatim or in close variants.

Take the Dyson Airstrait. On Amazon, the review summary groups hundreds of ratings into recurring themes such as hair straightening, drying time, heat reduction, smoothness, value for money, and shine.

Then compare the same product on Nordstrom. One of the product’s most helpful positive reviews frames the $499.99 tool against the cost of keratin treatments and praises healthier, silky hair with no frizz. A prominent critical review says the opposite—the customer’s hair remained frizzy and not straight.

At enterprise scale, this doesn’t have to mean reading thousands of reviews one by one. A 2025 study in the Journal of Retailing and Consumer Services shows how text-mining and sentiment analysis can turn unstructured review text into structured signals, alongside ratings and engagement data such as likes and replies. 

AI tools can also help teams classify large review sets by feature, complaint, sentiment, use case, or competitor mention. 

Stage 4: Use first-party store data as a continuous research engine

The research methodologies we’ve covered so far have collected data from outside of your organization, like sizing markets or watching competitors. But your own store is already collecting research every day, whether you're using it that way or not.

In Shopify’s own personalization research, 74% of retailers surveyed by IDC selected better understanding customer preferences and behaviors as a top digital commerce priority.

That’s one reason first-party data has become such a priority. According to the 2026 IAB “Outlook Study”, first-party data ecosystems are maturing as brands put more emphasis on loyalty, repeat purchase, and reaching known customers.

“AI—particularly agentic AI—is poised to enable marketers to activate those data assets more intelligently, driving personalization, loyalty, and repeat purchase at scale,” says Chris Bruderle, vice president, industry insights and content strategy at IAB.

Use onsite search data for product research

Your store’s search bar is a running record of what shoppers came looking for.

That makes onsite search data a strong source for finding products, styles, sizes, colors, and use cases customers want but may not be finding in your current assortment.

In Shopify, start with Search & Discovery analytics. Look at:

• Top search queries to see what customers ask for most
• Searches with no results to find products or terminology your catalog doesn’t currently satisfy
• Searches with no clicks to spot cases where products appear, but shoppers don’t consider the results relevant enough to open
• Search conversion to separate heavily searched terms from searches that go on to generate purchases

Then compare search behavior with sales and conversion data before making a merchandising decision. 

Take Allied Medical. The New Zealand supplier of mobility aids and rehabilitation equipment previous platform's poor search functionality meant a simple typing mistake could send a customer to an error page and end the session.

After migrating to Shopify and implementing Shopify's Search & Discovery app, their bounce rate nearly halved, dropping from 35% to 17.6%.

“We’ve moved from a platform that held us back to one that empowers us to scale, automate, and provide a better experience for our customers,” says Katie Noble, managing director.

Apparel brand Maggy London does this with Shopify Sidekick. Their ecommerce team analyzed the top terms customers searched onsite over three months, then cross-referenced them with search-driven traffic and conversion data. The comparison showed the team what customers were looking for versus what the site was presenting. 

The team takes the same approach further into buying. Maggy London uses Sidekick to pull historical performance by style, identify hero products, examine size-selling data, and recommend unit quantities against their financial plan. Those findings have informed both their seasonal buy road map and product development decisions.

"We cross-reference with other AI tools, but Sidekick has the context of our store, our products, our customers. That's what makes it different," says Sara Bako, president.

Build a recurring market research rhythm with store data

Put market research on the operating calendar. A quarterly review gives teams enough time to see changes in customer behavior without waiting for the next launch or annual planning cycle.

A starting cadence could look like this:

• Pull the same core signals each quarter. Review top onsite searches, no-result searches, product and category sales, conversion rates, returns, repeat purchases, average order value (AOV), and changes by customer segment or market.
• Compare quarter over quarter and year over year. Look for movements that persist across more than one metric.
• Turn the changes into research questions. A rise in searches with no results, for example, might prompt an assortment study.
• Choose what to test next. Feed the strongest questions into surveys, focus groups, competitor research, merchandising tests, or product experiments, then bring the results back into the following quarter’s review.

Shopify Analytics can take some of the manual monitoring out of this cycle. Insights analyzes sales, sessions, and fulfillment data every day and flags changes based on their statistical significance and business impact. You can filter reports using your own metafields and metaobjects, while Shopify Flow’s Get analytics data action can run ShopifyQL queries on a schedule and trigger an action when a condition is met.

AI-powered ecommerce market research in 2026 

AI can compress parts of market research that often take hours of manual sorting, like reading thousands of reviews or finding patterns in store data.

Bain & Company recommends starting with AI as an augmentation layer for existing research; use it to narrow options, pressure-test assumptions, and focus human research on the questions worth pursuing.

Use Shopify Sidekick to interrogate your own store data

We’ve already seen how Maggy London uses Sidekick to interrogate their own commerce data. Jaded London uses it to compare customer rates across stores, analyze city-level sales patterns, and assess potential expansion markets. 

“I literally used Sidekick the other day to analyze potential markets...The data helps us make faster, more confident decisions,” says Jamie Evans, head of ecommerce. The team has also used those geographic insights to inform pop-up locations across several markets.

Sidekick Pulse takes that a step further by proactively surfacing recommendations from store data, with up to five suggested actions at a time. Select one, and Sidekick opens a conversation and builds a to-do list. Read more about how enterprise teams are using Sidekick.

Use ChatGPT for ecommerce market research

ChatGPT can pick up where your store data, and Sidekick, stops. The two together, however, cover both halves of the research stack: Sidekick for what's happening inside your business, and ChatGPT for what's happening outside it.

ChatGPT Search retrieves current web information with citations, and ChatGPT’s advanced AI agent Deep Research investigates multi-step questions across many sources. And data analysis can work directly with uploaded spreadsheets and CSV files.

Here are four ways to use Shopify Sidekick and ChatGPT in tandem:

1. Investigate a market signal from Shopify. Sidekick might show that sales or customer density are growing in a particular city. Take that finding into ChatGPT and ask:
• “We’re seeing customer growth in Seoul. Research the South Korean premium streetwear market, including demand, competitors, pricing, and ecommerce behavior. Use recent, credible sources.”
2. Test whether a product gap exists outside your store. Say your Shopify search data shows customers repeatedly searching for a product or feature you don’t carry. Ask ChatGPT:
• “Customers are searching our store for ‘petite wide-leg work trousers.’ Who already sells them in the US, at what prices, and what do reviews say customers still want?”
3. Analyze customer language at scale. If you’ve collected reviews, survey responses, or other qualitative research into a spreadsheet, upload the file and ask:
• “Analyze these customer responses by use case, feature request, complaint, praise, and reason for switching. Show the most common themes and the source responses behind them.”
4. Ask it to challenge your research. Once several signals point in the same direction, use ChatGPT to look for reasons your interpretation could still be wrong:
• “Our Shopify data, Google Trends, and competitor reviews suggest demand for [product] is growing. What else could explain these signals, and what should we test next?”

ChatGPT, or any other large language model (LLM), can speed up the research process, but the outputs always need scrutiny. Keep these qualifications in mind:

• A citation doesn’t guarantee the claim is right.
• A pattern in a dataset doesn’t prove it applies to the whole market.
• A web search can miss paywalled, blocked, or poorly indexed sources.
• An AI-generated hypothesis still needs to be tested with customers or first-party data.