Product and User Research landscape
User research tools help product teams collect and analyze qualitative data to understand what users need. This data can be intentionally gathered through surveys and user interviews or extracted from existing feedback, such as support tickets, product reviews, or online conversations.
Qualitative data has long been the holy grail of product discovery because it reveals why customers need a product—not just how they use it. Understanding the “why” empowers product teams to develop innovative solutions to customer problems, rather than merely optimizing around existing usage patterns. This focus on qualitative insights is especially critical for startups that may not yet have product usage data to analyze.
However, collecting and analyzing qualitative data has traditionally been time-consuming. You needed to recruit participants, design surveys, and manually review large amounts of data. These limitations led teams to use qualitative research sparingly and to complement it with any available quantitative data, such as behavioral data and heatmaps.
Recent breakthroughs in large language models have disrupted user research. Teams can now instantly analyze vast amounts of user feedback and online reviews to uncover actionable product insights. AI has also introduced entirely new research methods, such as real-time interviews with millions of users, using personalized questions to uncover unique insights at scale.
These new AI-powered tools are being adopted by team members beyond traditional user researchers. Product managers, designers, and engineers bring different requirements to user research: they prioritize tools that deliver quick, actionable insights without requiring extensive manual effort or specialized research expertise.
Amid these sweeping changes, the user research tool landscape has grown both vast and dynamic with 60+ different tools. To help navigate this evolving space, I’ve mapped some of the most popular tools along two key parameters: (1) the breadth of insights they uncover (y-axis) and (2) the effort required to collect insights with each tool (x-axis).
What do I know 🙂
I’m the founder of Kraftful, the fastest-growing user research solution. Kraftful was the first user research platform to fully leverage the LLMs that power ChatGPT. In just over a year, more than 50,000 product teams have adopted Kraftful to supercharge their research and seamlessly integrate insights into the product development process.
Building Kraftful has given me the opportunity to work with thousands of product teams to deeply understand their research workflows. I’ve also drawn on my own experience leading product development and research. Before founding Kraftful, I led product teams at various tech companies, including serving as PM #2 at a fast-growing unicorn and heading product teams responsible for products used by millions of users.
My personal frustrations with product research inspired me to build Kraftful. Kraftful’s AI-generated surveys and user interviews allow teams to collect new data with just a few clicks. It also integrates with key user feedback sources to instantly analyze online reviews, support tickets, call transcripts, and Slack comments.
Legacy user research tools
To set the stage for the latest user research solutions discussed below, let’s start with a quick overview of legacy tools, which remain the default for many research teams today.
Legacy user research tools predate LLMs and rely on traditional natural language processing (NLP) techniques, such as clustering and sentiment analysis, to process text from survey responses and other unstructured data. While effective for identifying themes and basic sentiment (positive, neutral, or negative), these methods are inherently limited in their depth and contextual understanding.
Some legacy solutions have since integrated LLMs, enabling researchers to ask open-ended questions through ChatGPT-like interfaces. However, because these tools were not built with LLMs at their core, they fail to fully harness LLMs’ potential to deliver rich, contextual insights automatically—without requiring extensive manual prompting through a chat interface.
Although legacy tools don’t fit neatly into my 2x2 matrix, here’s how they might look if we added them to the map:
LLM-first analysis tools
Newer user research solutions are built specifically for leveraging LLMs to analyze qualitative data in novel ways.
1. Kraftful
Kraftful automatically collects and analyzes user feedback from support ticket platforms, CRMs, call transcripts, Slack, App Stores, Amazon, G2, Trustpilot, Reddit, and Kraftful Surveys. It extracts actionable insights, such as top feature requests and complaints, while making it easy to see how often issues arise and review mentions in context to understand user needs.
Unlike most user research tools, Kraftful empowers the entire product team to act on user insights. After surfacing key findings and sharing them in Slack, Kraftful can transform feedback into PRDs or user stories that sync directly with Jira or Linear. Teams can also build their product roadmaps in Kraftful, leveraging all the user feedback in their repository.
Kraftful is LLM-first and includes a proprietary hallucination detection mechanism to ensure insight accuracy. It can analyze feedback in hundreds of languages, translating insights into English or the team’s preferred language.
User feedback analysis in Kraftful
2. Notably AI
Notably is purpose-built for user researchers, not product managers, designers, or engineers. It uses AI to streamline the analysis of unstructured data, such as interview transcripts or focus group notes.
The platform identifies themes, patterns, and key insights, enabling researchers to quickly grasp takeaways without manually sifting through large volumes of information. It also assists in coding data by automatically tagging relevant themes or topics. Researchers can refine these AI-generated tags or create custom ones to better align with their specific needs.
Notably offers various visualization options to reveal connections and patterns in the data. Once analysis is complete, it helps generate reports summarizing insights, making it easier to share findings with stakeholders or use them to inform decisions and future research.
User feedback analysis in Notably AI
3. User feedback analysis within non-research tools
Several tools originally designed for other purposes, such as Figma, Productboard, Pendo, Miro, and Notion, now include AI features for feedback summarization. However, these features provide only high-level summaries and lack integrations with user feedback sources.
User feedback summarization in Figma
Survey tools
Survey tools enable product teams to collect fresh data, complementing insights already available from sources like support tickets, online reviews, and customer calls.
Survey tools come in many forms:
Kraftful: AI-generated surveys and user interviews with instant analysis. Kraftful AI creates surveys in minutes, leveraging past user insights or a specific topic of interest—like having an experienced user researcher on demand. It can also generate personalized, real-time questions based on users’ previous responses, enabling interviews of thousands or even millions of users in minutes.
Delighted: Focuses on quick, single-question surveys to capture customer feedback, often used for NPS. Recently added LLM analysis of the responses.
Sprig: Gathers real-time product feedback through in-product surveys during user interactions. Recently added LLM analysis of the responses.
Canny: A survey formatted as a feedback board where users can post their ideas or issues and other users can vote on the submitted feedback to give a sense of the highest demand. Canny has started adding integrations and AI summarization to expand past their original feedback board product.
Typeform: Creates visually appealing surveys that show one question at a time.
Kraftful survey generation
Other research tools
Beyond qualitative insight tools, there’s research tools that focus on understanding how users interact with products by analyzing their behavior and validating design concepts. They help teams uncover friction in the user journey, optimize user experiences, and validate decisions before committing to development. By combining data-driven insights with feedback from users’ interactions, these tools can complement qualitative and survey-driven approaches.
Hotjar: A user analytics and feedback tool that helps teams understand how users interact with their website. It offers features like heatmaps, session recordings, and surveys to visualize user behavior and gather direct feedback, enabling teams to improve UX and optimize conversion rates.
FullStory: Provides digital experience analytics, focusing on session replay and detailed user journey insights. It helps teams uncover friction points, debug issues, and improve overall product experience by delivering a complete picture of user interactions across web and mobile apps.
Maze: Empowers teams to validate design concepts and product decisions. It enables remote usability testing, prototype feedback, and surveys, providing actionable insights quickly to streamline the design and development process.
Insights in a Hotjar dashboard
Picking a user research tool
Choosing the right user research tool depends on your team’s unique goals, workflows, and budget.
The best tools are those that save time, integrate seamlessly into your processes, and deliver accurate, actionable insights that drive impactful product decisions.
Whether you’re adopting LLM-first solutions like Kraftful or exploring survey tools like Typeform and Sprig, the key is to select a tool that the team will actually use to learn user needs.
With the pace of development in this space, now is the perfect time to revisit your user research stack. Modern tools not only reduce the friction of collecting and analyzing data but also provide richer, more actionable insights—ensuring your product team stays ahead in delivering what your users truly need.
