Build Faster Feedback Loops Using Qualitative User Research

Nadia’s SubstackSubscribeSign inBuild Faster Feedback Loops Using Qualitative User ResearchIntroduction: Why Getting Early Feedback on Ideas or Products Is Useful Nadia EldeibApr 24, 20254ShareUser research “in the wild” can, like an actual safari, lead to surprises and learnings. If you’re an early-stage startup founder, learning velocity is critical. Being able to test and reject or double-down on hypotheses to help you establish if you’re building the right business and product for the right customer and market is essential.When you’re in the wilds of pre-product market fit and navigating the idea maze, you need ways to orient and learn whether you’re going down the right path – or about to hit a dead end.Why I’m Writing About Gathering Qualitative User FeedbackWhile The Mom Test book by Rob Fitzpatrick is an oft-referenced and useful resource, I thought I’d share a bit about how I’ve approached getting feedback on early explorations as a founder for CodeYam and over the previous decade while working at technology startups. This is a practice I’ve honed over time and I’m still constantly learning, improving, and experimenting.This journey began when I discovered the Design Sprint book and process developed by the team at GV while working at a startup called Kamcord roughly circa 2017. On and off (as needed) over the years since then, I have been using variations of that process, along with the accompanying GV Research Sprint created by Michael Margolis, to help get unstuck, speed up learnings, and test out new ideas in low-risk ways.One of the first sprint timelines at Kamcord, circa May 2017.If you worked at a larger technology company, “design sprint” often comes with a very different set of connotations; you might imagine designers blocking off a week (or more!) of time on the product and development team calendars and spending it working on ideas that, while fun or interesting, are never going to be priorities to build. This whiteboard whimsy that leads to no real results is the opposite of what I’m talking about, and using facets of the sprint process to accomplish, here.Instead, we’re trying to get real feedback from potential customers and/or users of a product (or that might be users of a potential future product that hasn’t yet been built). We are trying to get relevant feedback from a small, representative group as fast as we can to test our risks, hypotheses, assumptions, and to inform how we successfully meet our goals (or fail faster and move on with the learnings).A Note on Using AI Tools for User ResearchNew AI tools can likely be a big boost in terms of getting useful feedback faster. However, I’m still experimenting with how best to use those in this process. I will share what I’m doing today, although I anticipate this may change.That said, as an early-stage founder, it is fundamentally important to be “in the arena” and talking to the people that are, or might become, your buyers and/or users. Even if your product is meant to be used by AI agents, there’s likely a human somewhere along the way responsible for those agents and/or buying your product and deciding to deploy it. Find and talk to those people.Use AI tools to sharpen hypotheses and accelerate testing, but don’t use it to replace talking to your human customers or users.How I’ve Learned from User ResearchSome areas where I, often with a small team although sometimes solo, sought qualitative feedback and used elements of the sprint process successfully include:Testing out new product ideas (happening now)Testing out value propositions and messaging (also happening now)Testing out landing pagesLearning about a user group or marketValidating (or invalidating) that you’re actually tackling a meaningful problem / pain pointGetting early signal about willingness to try a product or serviceLearning about willingness to payFiguring out what parts of a product’s UI / UX are working or are confusing…among other use cases.One counter-intuitive insight is that user research is an excellent tool to help you realize when you’ve failed to achieve your objective. Maybe the idea you fell in love with just doesn’t do it for the group you thought would be your customers. Maybe you realize the market is too small or too hard to reach. One of the biggest values of qualitative research is being able to fail, and learn from those failures, faster.By reducing the amount of time and effort it takes to realize something doesn’t work, you’re extending your runway to experiment and iterate to get to something that is extraordinary.If you’re a venture-backed startup, you’re probably taking a big, ambitious swing (we are at CodeYam!). Being able to learn through faster feedback loops that qualitative research unlocks is immensely valuable. It helps you make progress, or pivot, faster and with greater confidence.Some of our earliest and most ambitious Sprint goals as a new founder in April 2021.Whether we’re testing an actual software product or just raw ideas through design prototypes, we’re able to get a “good enough” version of our hypothesis in front of our target audience and learn from their honest reactions.What This Series Will Be AboutThis post kicks off a new series (length TBD) that dives into a bunch of connected user research topics; from figuring out who to talk to, where to find them, how to ask the right questions, and how to gather useful qualitative feedback. I’ll be pulling in real examples from CodeYam and past research to bring this all to life.Some themes I’m thinking about covering:How to recruit the right people for user research, especially pre-productDesigning a solid research guideHow to run a research interviewDeciding what to test (and when)How we’re approaching user research at CodeYamHard-earned lessons from past research effortsSpeeding up research workflows with AITools we’re using such as FigJam, Craigslist, Superhuman, ChatGPT, Claude, etc. and how they fit into the processWhile startups' needs are never one-size-fits-all, my goal in sharing this is to help other founders, particularly those who are pre-product-market fit or conducting R&D to decide if they should pivot or double-down on a strategy or product direction. My hope is this gives other founders and their teams actionable insights and helpful tools to speed up their own feedback loops.If you’re doing user research to explore startup ideas or make product or engineering decisions and have questions or feedback, I’d be happy to chat. Reach out any time at nadia [at] codeyam.com . Thanks for reading! To follow this series and my personal writing, you can subscribe for free.SubscribeIf you’d like to follow what we’re building and exploring at CodeYam, you can also subscribe to our company’s blog. 4ShareDiscussion about this postCommentsRestacksTopLatestDiscussionsNo postsReady for more?Subscribe© 2026 Nadia Eldeib · Privacy ∙ Terms ∙ Collection notice Start your SubstackGet the appSubstack is the home for great culture

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The process of gathering qualitative user research is presented as a critical strategy for early-stage founders seeking to accelerate learning velocity and navigate the challenges of establishing product-market fit. The author emphasizes that when operating in the dynamic environment of pre-product market fit, the ability to test and adjust hypotheses is essential to determine if a venture is aligned with the right customer and market. This approach is rooted in practices honed through experience in technology startups and exposure to frameworks like the Design Sprint process.

The author refutes the conventional perception of design sprints as simply time-consuming sessions for abstract ideation, instead framing them as methods to obtain tangible feedback from potential customers or users regarding a potential product or future product. The primary goal of this research is to secure relevant input from a small, representative group rapidly to test inherent risks, assumptions, and hypotheses, enabling founders to either meet their goals or fail faster and extract necessary learnings.

While acknowledging the potential of artificial intelligence tools to expedite research, the author stresses that engaging directly with human customers or potential users remains fundamentally important. The core message is that founders must remain actively engaged in the market, talking to the people who will eventually be buyers or users, regardless of the product's underlying technology. AI tools should be used to sharpen hypotheses and accelerate testing, but they must not replace essential direct human interaction.

The practical applications of qualitative user research, as demonstrated by the author's experience, span several critical business areas. These include testing novel product ideas, evaluating value propositions and messaging, assessing landing page effectiveness, understanding specific user groups or markets, validating whether a meaningful problem or pain point is being addressed, gauging the willingness to try a product or service, understanding willingness to pay, and analyzing the efficacy of product user interface and user experience.

A counter-intuitive insight shared is that user research is highly valuable because it facilitates the recognition of failure. It allows founders to understand when an idea, despite personal enthusiasm, does not resonate with the target customer or if the market is too small or difficult to reach. By facilitating faster realization of these failures, qualitative research extends the experimental runway, allowing teams to iterate and achieve extraordinary outcomes more quickly. This capability to learn from failure rapidly is presented as immensely valuable, particularly for venture-backed startups attempting ambitious ventures.

The author plans to delve deeper into several interconnected themes in a forthcoming series, focusing on the practical execution of user research. These themes include defining how to recruit appropriate individuals for research, designing robust research guides, executing effective research interviews, determining what specific elements to test and when, detailing the approach to user research within a specific company context, absorbing hard-earned lessons from past efforts, and leveraging AI tools to streamline research workflows, such as using tools like FigJam, Craigslist, and various large language models. The overarching aim is to provide other founders with actionable insights and tools necessary to speed up their feedback loops when making decisions about strategy or product direction.