Complete Market Research Guide for Shopping Apps

A shopping app carries a strange burden. When a customer opens it, the app is only the window. Behind it sit a catalog, a warehouse, a delivery partner, a returns desk and a customer service team. When something goes wrong anywhere in that chain, the review lands on the app's page, and the star rating drops for the app.

That is why reviews of shopping apps tell two stories at once. One is about the app itself: how fast it loads, whether search finds the right product, whether checkout crashes at the payment step. The other is about the business behind it: late deliveries, refunds that take weeks, a support chat that never answers. Product and marketing teams that read both stories as one often fix the wrong thing, rebuilding a screen when the real problem was a courier, or blaming operations for a bug in the cart.

This guide shows how the team responsible for a retailer's or e-commerce brand's mobile app can turn that feedback into real market research about its own app, its competitors and the market it serves, and how to keep the two stories apart.

Key Research Questions

Research starts with the questions it has to answer. For a shopping app, they fall into three groups: questions about your own app and its customers, questions about the apps and stores your customers compare you with, and questions about how shopping habits in your category are changing.

Your App

The first questions follow the customer through a purchase. How do they find products, and how often does search fail them? Where do they hesitate: on the product page, in the cart, at the payment step or when they see the delivery fee? What happens after the order is placed, and how does that experience change the way they describe the app? Which moments do customers mention when they explain a five-star review, and which when they explain a one-star review?

The second set separates the app from the business. When a customer is unhappy, is the cause something the app team can change, such as a confusing filter or a failed payment, or something that sits elsewhere, such as stock levels or a delivery partner? Both matter, but they go to different teams, and the research is only useful if it sends each finding to the right one.

Your Competitors

Your competitors include other shopping apps in your category, large marketplaces and the mobile websites of other stores. Customers also compare your app with your own website and your physical stores. What do customers praise in competing apps that yours never mention? What do they complain about that you already do well? Reviews of competing apps are as public as yours, and customers often name the alternative they switched from or switched to.

The Shopping App Market

The third group is about demand. Which features are customers starting to expect, and which ones do they ignore? How do they feel about notifications, loyalty points and app-only offers? When do they prefer the app, and when do they go back to the website? These questions are hard to answer from downloads and conversion rates alone, because those numbers show what customers did, while their reasons sit in what they write.

Sources of Evidence

Evidence about a shopping app comes from several places, and none of them has to come first. You can analyze reviews without running a survey, and you can study competitors before you have interviewed a single customer. The strongest research combines what your own customers tell you, what customers of every app in your category write in public, and what you learn by asking.

Your Own Customer Feedback

Support tickets, chat transcripts, answers to post-purchase surveys and return reasons are the most direct feedback a shopping app has. Return reasons are especially useful, because they are written at the moment a purchase failed. Collected in one spreadsheet with columns for the date, the channel, the platform, the product category and the order status, they become the start of a customer feedback analysis practice.

App and Store Reviews

App Store and Google Play reviews are the main public source for any shopping app, and they cover your app and every competitor in the same detail. Reviews often name the device, the version and the step where something went wrong, which makes them useful for product decisions. Trustpilot adds reviews of the company behind the app, which usually focus on delivery, returns, refunds and customer service. Reading both together is the basis of e-commerce feedback analysis: the app stores show how the app works, and Trustpilot shows how the business behind it is experienced. Reddit adds longer discussions, where customers compare stores and explain why they stopped using one.

Surveys and Interviews

Traditional methods still have a place. A short survey after a first order can ask what the customer expected and whether the app delivered it. A conversation with a frequent customer, or with someone who deleted the app, shows what made the difference. Store staff and customer service agents are another source, because they hear the same complaints every day. Notes from these conversations are qualitative research, and they need the same structured analysis as reviews to become more than a set of impressions.

Researching Shopping Apps with Kimola

Kimola is an AI platform that turns customer feedback into enterprise-grade market research. For a shopping app, that means taking reviews of your app and of the apps you compete with, together with Trustpilot reviews of the businesses behind them and your own support tickets and return reasons, and turning them into a structured study of how customers shop, where purchases fail and which part of the business each problem belongs to. The steps below follow the order a researcher would work in, and you can start with a free account, without a credit card.

Building the Dataset

After signing up, go to the Create your report area on the Home screen and paste the link to your app's page on the App Store or Google Play, or search for the app by name. With the Add Multiple option you can paste several links, one per line, and Kimola collects the reviews from all of them into a single dataset. A practical first report includes your app on both stores and three or four apps your customers compare you with. The same works for Trustpilot pages, so the company reviews can sit in the same dataset. The support article on creating a report by adding links walks through each screen.

For Reddit threads, Kimola's browser extension, Airset Generator, collects the discussion from a page and sends it to your account as an Airset, which you can turn into a report. Your own feedback can be uploaded as an .xls, .xlsx, .csv or .tsv file. If the file has columns such as platform, channel, product category, order status or date, Kimola reads them, and you can filter and compare the results by those values.

By default, Kimola finds the themes in the data itself. If you prefer a classifier built for this kind of feedback, the Advanced option lets you choose one of Kimola's pretrained models, such as the Mobile App Feedback Classifier for app reviews or the E-Commerce Feedback Classifier for feedback about orders, delivery and returns.

Choosing Dimensions

Dimensions are the research questions Kimola asks of every piece of feedback. You choose them when you create the report or add them later, and the guide to Dimensions explains each one. For a shopping app, five are especially useful, and two of them do most of the work of separating the app from the business.

Experience Stages places each comment at a moment in the purchase: discovery, the product page, the cart, checkout, delivery and returns. Because customers usually say where things went wrong, this Dimension turns a pile of reviews into a map of the journey, and the first half of that map belongs mostly to the app team while the second half belongs mostly to operations. Underlying Causes then asks why. A complaint about a missing order may come from a delivery partner, from a stock error or from an order confirmation the app never showed. Together, these two Dimensions show where a problem happens and who can fix it.

Pain Points asks what goes wrong, which in this category often covers slow loading, search results that miss the product, payment failures, unexpected fees and slow refunds. Unmet Needs shows what customers ask for that no app in your report offers yet, such as better size guidance or a clearer view of where an order is. Co-Occurrences shows which topics appear together. If complaints about late deliveries often appear with complaints about support, the real problem may be that customers cannot find out where their order is, which is something the app can address.

Reading the Report

The Overview shows the main themes in the feedback and how sentiment is distributed. Kimola finds those themes in the data itself instead of sorting comments into a fixed list of categories, so a review can be tagged with something as specific as discount codes that fail at checkout or filters that reset after every scroll.

Each review can mention several themes, and each theme gets its own sentiment. A customer who loves the product range but is furious about a refund contributes a positive mention of the catalog and a negative mention of returns. A single star rating would hide that the app and the business behind it are being judged separately.

The Performance screen ranks themes with a Satisfaction Score and a Dissatisfaction Score. These scores account for how many reviews mention a theme as well as the percentage, so a theme backed by five reviews does not outrank one backed by fifty. This protects against the most common mistake in small-scale research, which is changing the product because of a handful of strong opinions.

Signals

Signals are findings that have passed a statistical test. Kimola crosses every theme and Dimension with the columns in your dataset, such as app, store, star rating or date, and checks whether a topic is discussed more often or more negatively in one group than in the others (Differences) or more often recently than before (Trends). Only significant results become Signals, and they are ranked so the most important ones come first. The explanation of how Signals are tested covers the method in more detail.

For a shopping app, the platform column is especially revealing. A Difference might show that payment complaints are concentrated on Android, or that search complaints appear mostly on iOS, which points to a platform-specific problem that an average rating would hide. In a dataset with dates, a Trends Signal can show that complaints about delivery grew after a change of partner, or that checkout complaints rose after a release. Signals need enough feedback and at least some selected Dimensions. When the data does not contain a pattern strong enough to report, the report shows none, which is an honest result.

Compare

Compare puts segments of a report side by side: different apps, different stores, or one-star and five-star reviews. Up to five can be compared at once, without using extra queries. In a report built with Add Multiple, the most valuable use is placing your app next to the apps your customers compare you with and seeing the volume of feedback, the sentiment, the themes you share and the themes that appear for only one app. Comparing your app store reviews with your Trustpilot reviews is just as useful, because it shows which complaints belong to the app and which belong to the business.

Action Plans

Action Plans turn the findings into steps. You start by choosing or creating a Profile that describes your app, what it sells and who it serves, then select a perspective: Our own offering, A rival or Our market. These match the three groups of questions at the beginning of this guide. A sensible first plan uses Our own offering with the Product Development goal, which focuses on the app's features and flows. Once your report includes competing apps, a plan with the Competitive Strategy goal focuses on how to position your app against them, and a plan with the Customer Experience goal focuses on delivery, returns and support.

Kimola builds hypotheses from the report, tests each one against the feedback and drops the actions the evidence does not support. Each remaining action comes with a confidence level and explains why it matters, who it is for, when and where to act, the risk involved and how to see that it worked, along with the evidence behind it. A plan contains up to 12 actions. Plans work best when the report already has Signals, as the support article on how actions are built explains.

From Findings to Decisions

Research is only useful if it changes a decision. For a shopping app the decisions are concrete, and each one connects to a specific part of the report.

Search and Discovery

A customer who cannot find a product does not buy it, and often does not say why. Pain Points and the discovery stage in Experience Stages show how customers describe search: results that ignore the words they typed, filters that are missing or reset, categories that do not match how they think. Unmet Needs shows what would help them decide, such as better photos, size guidance or reviews from other buyers.

Checkout

Checkout is where the app team has the most direct control. When complaints concentrate on this stage, Underlying Causes helps separate technical failures, such as crashes or rejected cards, from design problems, such as forced account creation or fees that appear only at the end. A Difference between platforms often points to a bug, and a pattern across both platforms often points to a design choice.

Delivery and Returns

Delivery and returns usually sit outside the app team's control, but the app still shapes how they are experienced. When Co-Occurrences shows that delivery complaints appear together with complaints about not knowing where an order is, better tracking in the app may reduce frustration even if the courier stays the same. When returns are the problem, Trustpilot reviews often explain where the process breaks, and those findings go to operations with the evidence attached.

Notifications

Notifications can bring customers back, and they can also be the reason customers delete the app. Pain Points shows when customers describe them as useful, such as order updates and price drops, and when they describe them as noise. Experience Stages shows whether the complaints come from new customers or from long-term ones, which changes what the fix should be.

Loyalty Programs

Loyalty programs succeed when customers understand them and feel the reward is worth the effort. Pain Points shows where points expire unexpectedly or rules feel unclear, and Unmet Needs shows what customers would value instead. Compare shows how customers describe the programs of competing apps, which is often a useful benchmark.

App Versus Web

Every shopping app has to justify itself against the store's own website. When customers explain why they use the app, they describe what it offers that the website does not, such as faster reordering, saved sizes or app-only offers. When they explain why they went back to the website, they describe what the app lacks. Both groups of comments tell you what the app is for, which shapes the roadmap more than any single feature request.

Ongoing Research

A single report gives you a snapshot, and a shopping app changes with every release, every season and every change in the business behind it. Repeating the research after major releases, a redesign of checkout or a new delivery partner shows whether the change moved the themes it was meant to move. For retailers with strong seasons, repeating it after peak periods shows which problems appear only under pressure.

On paid plans, Feeds keep the research running continuously. A Feed collects public mentions from social media, news sites, blogs, forums, YouTube and review sites, based on the keywords and links you define. Alerts email your team when something important happens, and Mailings send a regular summary. Over time, Trends Signals show which topics are rising and which are fading.

Retailers and E-Commerce Brands

Larger retailers and e-commerce brands face the same questions across several apps, markets, store formats and service channels. On the Enterprise plan, each source of feedback, such as an app, a market, a support channel or a post-purchase survey, can become a Space, a continuously growing source of feedback classified with the same Funnel, which is a shared set of custom models and Dimensions. Because every source is analyzed the same way, results stay comparable over time and between channels. Spaces also calculate an NPS from what customers write, on a scale from -100 to 100. It is derived from the positive and negative share of written feedback, so it is not the same as a Net Promoter Score from a 0 to 10 survey, and NPS targets can be set for each area.

When a finding needs action, Tasks assign it to the right team, whether that is the app team, logistics or customer service, with the underlying feedback attached as evidence, and Tasks can be sent to Jira. After a Task is closed, Kimola measures feedback on the same topic again two and four weeks later, so a change can be checked against what customers say afterwards. Executive summary screens give leadership a single view of what customers are saying across the organization. Enterprise accounts are set up together with a dedicated Account Manager, and you can request a demo to discuss your setup.

Frequently Asked Questions

A few practical questions come up when a shopping app team starts researching with customer feedback for the first time.

Should app reviews and Trustpilot reviews be analyzed together?

Usually yes, as long as the source is kept as a column. One report then shows the whole experience, and Compare shows which complaints belong to the app and which belong to the business behind it.

What if most complaints are about delivery?

That is common in this category, and it is still useful for the app team. Underlying Causes and Co-Occurrences show whether part of the frustration comes from missing information in the app, such as tracking or delivery estimates, which the app team can improve.

How many reviews do I need before the findings mean anything?

There is no fixed threshold, and the ranking on the Performance screen is designed to protect you from small samples by weighing how many reviews support each theme. Adding a few competing apps to the same report is the easiest way to reach a dataset large enough for Signals.

Getting Started

Your customers, and the customers of every shopping app in your category, have already written down much of what you need to know about where purchases fail and what brings people back. Create a free Kimola account, add your app and a few competitors to a single report, and see at which stage of the purchase customers talk about giving up. The market research guides for fashion stores and Amazon sellers apply the same approach to neighboring fields, and the market research guide for small businesses covers the method for any industry.