Qualitative methods can help you gather data. But without a structure and testing system, it’s difficult to know which hypotheses hold up and which don’t. To extract real insights, you need to use rigorous approaches to testing perception, reaction, and meaning. Below, we’ve outlined four types of testing that are most commonly used in Qualitative Market Research.
What is Qualitative Market Research?
Qualitative Market Research analyzes text, speech, actions, and reactions rather than numbers. The research is based on open-ended questions, interviews, call transcripts, user session recordings, reviews, and messages. This data allows us to understand what specific words customers use, how they describe the problem, and what associations arise when interacting with a product or brand.
The goal of qualitative research is to identify real barriers, arguments, and emotions that influence choice. For example, when researching the reasons for unsubscribing, users may not mention the price directly, but describe that “the service has not become a habit,” “there was not enough benefit,” or “too many options.” Such signals are not visible in churn metrics but become obvious in the context of phrases and wording.
Unlike surveys with ready-made answer options, the value here is in free speech, intonation, and argument construction. AI helps to systematize such data, classify semantic blocks, compare emotional shades, and find phrases that are repeated among different segments.
AI market research tools in this context make it possible to process a large volume of incoming data: from 10 interviews to 10,000 reviews. They automatically group topics, highlight hidden patterns, analyze the structure of speech, and visualize the connections between signals. This is critical when launching a product, developing a new USP, adjusting a pricing model, or checking advertising messages.
Qualitative Market Research uses methods that provide access to the wording, logic of thinking, and emotional reactions of the client. Unlike questionnaires, there are no fixed answers — the research is based on observation, decoding, and in-depth analysis of open information.
Qualitative Market Research Methods
We have identified the key methods used in qualitative analysis and explained how they are enhanced by AI research tools.
1. In-depth interviews
Conducted one-on-one, online or offline. The researcher asks open-ended questions, listens to how the person describes the problem, compares options, and makes decisions.
AI helps at the decoding stage: it identifies topics, emotions, speech speed, pauses, and clarifications. Tools like Hume and Grain.ai visualize emotional peaks and allow you to find strong signals faster.
2. Observational Research
The researcher records how the user interacts with the product: what steps they take, where they stop, and how they react to the interface or message.
AI is used for video analysis, screen session processing, pattern categorization. Examples: click analysis, delays, repeated actions. It is used in UX research, especially when launching new features.
3. Analysis of user reviews and comments
Reviews in the App Store, Trustpilot, Reddit, and Twitter provide valuable signals about what the client really thinks.
AI modules process hundreds of texts: they determine key topics, and tonality, highlight emotional patterns, group phrases by meaning. This is the basis for quick segmentation and identifying growth points. At M1-Project, we use GPT models and clustering to build insight maps from open sources.
4. Focus Groups
Several people discuss a product, idea, or advertising message. The researcher observes which arguments are heard more often, what causes resistance, and how the participants influence each other.
With AI, you can automatically analyze transcripts, determine the influence of individual participants, the pace of the discussion, and repeat phrases. This reduces subjectivity in data processing and speeds up the extraction of conclusions.
5. Analysis of open forms and feedback
Questions like “What would you like to change?”, “What didn’t work?” in questionnaires often give unexpected answers. Unlike the quantitative block, they are the ones that reveal weaknesses.
AI helps to automatically group responses, highlight new topics, sort by priority based on frequency, emotional tone, and relationship with user actions.
All these methods are enhanced when AI segmentation, text processing, speech recognition, and pattern visualization are included in the study. This not only saves time, but also allows you to identify things that are not visible in manual processing.
4 Key Types of Qualitative Market Research Testing Methods
Interviews alone are not enough to conduct Qualitative Market Research. Without testing the perception of key elements — message, concept, interface, visuals — product decisions remain assumptions. We have collected four methods that are used for testing and described how they work in real teams.
1. Message Testing
You write a headline. One version says: “Automate reporting without code.” Another: “Save 10 hours a week on preparing reports.” Which one will trigger a reaction? That’s what message testing is for. It’s a study in which you check which formulations are clear, which sound convincing, and which ones cause doubt or irritation.
Product marketers, copywriters, and sometimes the brand team are involved in the process. Materials for the test: headlines, paragraphs, value prop, landing pages, email. You show them to target users (including future ones), collect feedback in an open form or through interviews, and then analyze the reactions.
2. UX behavioral testing
When a user clicks “skip” or closes the screen, it’s not an accident. It’s a signal. UX behavioral testing is a method in which you observe how the user behaves within the product: clicks, pauses, micronavigation, and return points.
This type of behavioral testing involves the product, UX design, and analytics. Developers often participate if the behavior is related to interactive elements. Materials — prototypes, real screens, onboarding scenarios. You show the interface, record actions, transcribe voice comments (if any), and collect a behavior map.
3. Concept Testing
The idea may seem obvious within the team. But when you show it to the user, reality begins. Conceptual testing is a way to show a rudimentary version of a product, feature or UX solution and record how the audience perceives it.
Product, research, PMM, and sometimes the CEO are involved — especially at the early pivot stage. Mockups, text descriptions, presentations or even just scenarios are included in the work. The user sees the “future” and comments: on what is clear, what causes mistrust, and what is missing.
4. Emotional Response Testing
You can ask “did you like it?”, or you can just see how the person reacts. Emotional testing is recording intonations, facial expressions, pauses, microtextures, and word choice. Everything that a person does not say directly, but shows through reaction.
Used in advertising campaigns, videos, redesigns, and presentations. The following are involved: brand, creative, PMM, and, increasingly, AI analytics. Tests are conducted via Zoom, interviews or even video analysis.
Examples of AI-Powered Qualitative Market Research
AI is increasingly becoming a part of deep analysis — not for the sake of hype, but for the sake of speed and accuracy. Below, we have collected cases in which large companies applied AI in qualitative research to quickly understand user behavior, adapt the product, and strengthen marketing. Each example is not just a tool, but a changed decision-making process.
When the Microsoft team decided to delve deeper into the user experience of Microsoft Teams, they encountered a scale that was impossible to process manually. Millions of comments came through feedback forms, support, forums. These words contained precise signals — but no one saw repeating patterns in the flow of text. The Applied Sciences team implemented NLP models that learned to classify phrases, group topics, and highlight recurring pain points. One of the findings was that users were annoyed by delays in opening chats with attachments. Before that, the complaints sounded too vague to be included in the product priority. After implementing the AI framework, the team reduced the time between signal collection and interface changes from months to days.
Unilever had a different task — to understand how the audience reacts to video advertising on an emotional level. Instead of classic focus groups, they connected AI analysis of facial expressions. The camera recorded the reaction to each episode of the video: where the audience smiled, where they lost attention, and where they became slightly wary. One of the creatives caused excellent engagement in the first half, but failed in the last scene. Analysis showed that the problem was in the tone of the voiceover and visual dissonance. Re-shooting the finale increased the overall brand recall by more than 20% according to testing results.
Spotify approached AI from a different angle. Their team conducted a series of user interviews to understand how Discover Weekly is perceived. Previously, analysts relied on metrics — play, skip, save. But the metric does not tell why the user returns to the playlist every week. The interview transcripts were run through an AI model that grouped phrases by meaning. This resulted in three scenarios: ritual listening on Monday mornings, searching for inspiration while working, and a spontaneous evening launch. These insights influenced not only the description of the feature, but also how Spotify designs weekly content visually and verbally.
Procter & Gamble worked with AI on a different level — analyzing open comments in dozens of product categories. Through the Qualtrics platform, they processed feedback on skin care, household chemicals, and hygiene. One of the unexpected signals: the smell of the cream became a frequent barrier that users talked about indirectly, rather than directly — through associations, metaphors, and comparisons. The AI classified topics that “bypassed” the direct question, and the team recorded a problem that had previously been ignored. After changing the scent of one of the key products, sales in the test region increased by 14%.
L'Oréal decided to test the visual perception of the new packaging in several countries at once. Instead of surveys, AI-analysis of videos was used: gaze, facial expressions, changes in reaction rhythm. As a result of the tests, it became clear that minimalist design with warm accents inspires trust in the skin care segment, while cold colors are associated with pharmaceuticals. These nuances influenced the redesign of two product lines at once and were confirmed by the subsequent increase in engagement.
Finally, HubSpot used AI to analyze user interviews when preparing to update the CRM interface. The team processed hundreds of hour-long conversations. In order not to waste weeks on manual interpretation, they used an AI model that classified key phrase patterns: irritation, hope, misunderstanding, and enthusiasm. Based on this data, they rewrote the onboarding message and updated the training sequence within the product. The result is increased retention among new users and a reduction in the time to the first activation action.
These stories show exactly how AI works in qualitative research. It does not replace thinking but accelerates the path to the point where decisions can already be made. And the faster this feedback is built, the stronger your product becomes.
Best AI Tools for Qualitative Market Research
When the goal is to deeply understand the customer, and not just count responses, it is important to choose tools that work with speech, behavior, emotions and context. Below we have collected solutions that have proven their value in real companies and cases. These are not universal “platforms for all occasions”, but point tools, each of which enhances a certain stage of Qualitative Market Research.
1. Hume AI
A platform developed by researchers in the field of affective computing. Hume analyzes intonation, pauses, speech rate, as well as phrases said with confidence or doubt. It is used in interviews, support, and customer experience research. It is especially effective where it is not the information that is important, but the state of the client.
2. Grain
A tool for automatic analysis of video interviews. Recognizes key topics, creates time codes, and highlights quotes. Helps product and UX teams quickly move from video to conclusions. Grain is already integrated into Zoom and Slack processes, making it convenient for collaboration within teams.
3. Realeyes
A platform that analyzes facial expressions, gaze, and engagement when interacting with visual content: videos, animations, and landing pages. Used in L'Oréal, Mars, Coca-Cola. Allows you to determine an emotional response in the first seconds when the user has not yet had time to formulate an opinion.
4. Qualtrics XM Discover
A solution focused on analyzing texts from feedback, chats, reviews, and social networks. Uses NLP and AI models to determine the tonality, topics, and frequency of mentions. Used in P&G, Dell, and other companies where the scale of feedback exceeds the capabilities of manual processing.
5. Recollective
A platform for conducting qualitative research: online interviews, focus groups, and diary studies. Has built-in AI that analyzes text responses, highlights topics, and helps facilitate moderators' work. Suitable for agencies and in-house teams.
Conclusion
Qualitative Market Research is a way to understand how people perceive a product, language, interface, and user experience. It is used in the early stages of launch, when changing positioning, in times of doubt, and before entering a new market. It helps identify not only problems, but also the formulations that remain in memory and the reactions that influence decisions. With the help of AI, such research becomes scalable: new insights can be obtained not from ten people, but from thousands — without losing depth.
.svg)
