Fix product problems with user research in 4 steps 🔍 ✅

Who designed this?

Have you ever opened an app, tried to complete a simple task, and thought, "This tool is completely useless...who designed this?"

When a product feels confusing or fails to solve your problem, it usually means the team skipped user research. As a product manager (PM), your job isn't just coming up with ideas — it's understanding user problems so your team builds the right solutions.

User research is a product management pillar that makes a product both usable (easy to navigate) and useful (solves a real problem).

Research isn't a one-time task. It's an ongoing cycle that changes based on where your feature is in its life cycle stage:

• Discovery: Keeps you from wasting time and money on features nobody wants.

• Build: Catches confusing designs before developers spend time writing code.

• Post-launch: Fixes hidden reasons why live users drop off or leave your app.

While research and testing take time upfront, it saves massive amounts of time and money in the long run by ensuring every hour your developers spend writing code actually pays off.

Before interviewing users, define who experiences the problem and where competitor apps fail to provide a solution.

Target Audience

Marketing and product teams work together to define the user vs. buyer.

• Marketing teams build buyer personas. Focus on age, income, ad channels, and who pays for the app.

• PMs build user personas. Focus on daily user habits, app problems, sizing needs, and why shoppers hesitate at checkout.

• When to align? Meet with marketing before discovery to get market trends, during discovery to share direct user quotes, and before launch to align on product positioning.

Market Gap vs. User Research vs. SWOT Analysis

• Market gap analysis: Auditing competitor features and drop-off points tells you where competitors fail.

• User research methods: Interviews and observation tell you why users struggle and how to solve it.

• SWOT analysis: Evaluate internal business strategy, not specific user friction.

Market Gap

Imagine you work for a clothing company called FitSelect. Here's how you can find your market gap:

• Check 3 direct competitors: Look at the exact screens where your users drop off (e.g., between product pages and checkout).

• Compare features: List what competitors offer vs. where user anxiety stays high.

  • Product photos: Competitors offer clear, high-quality photos, which FitSelect already has.

  • Sizing info: Competitors only offer basic, static size charts.

• Find the underserved need: Notice where competitors give simple information (like basic size charts) that leaves users unsure. Replace it with helpful tools that give users the confidence to buy.

  • Example: Offer interactive "True-to-Size" bars based on real customer feedback.

Takeaway: Market gap analysis shows you where competitors fall short. Next, use conduct research in Step 2 to discover why users hesitate and how to design the solution.

Imagine you are a product manager for an online clothing app. Your data shows that 75% of shoppers put fitted jeans in their cart, but leave without buying them.

Your team's first reaction might be: "Let's give them a discount code pop-up!"

Stop!

Before your team designs screens or writes code, you must run Discovery research to answer one key question: "What exact problem are our shoppers facing?"

User Research Method: Exploratory Research

Find out what's happening by checking competitor apps, reading online reviews, and talking 1-on-1 with real shoppers.

How to validate the problem (using the FitSelect example from above):

• Try buying something on 3 competing apps. Look at what they offer vs. what they miss (for example, if everyone uses text-only size charts, shoppers everywhere are left guessing).

• Read 1-star App Store reviews or search Reddit and social media apps for real complaints like "sizing is way off" or "returns take forever."

• Interview 5 people who recently bought clothes online. Ask open-ended questions about past shopping struggles to see whether size uncertainty is the real reason people leave.

Don't ask:

• "Would you use a discount pop-up if we made one?"

• "Do you like our checkout page?"

Ask:

• "Tell me about the last time you ordered clothes online that did not fit."

• "How did you choose your size the last time you bought jeans online?"

How to test the solution:

• A problem is proven real when at least 4 out of 5 interviewed shoppers complain about the same issue (like jean size uncertainty) without you bringing it up first.

• Turn the proven problem into a clear project goal: "If we help shoppers find their exact jean size, more people will complete checkout."

Quiz: Social Media Feature

Scenario: You are a product manager for a social media app. Analytics show that while 80% of active users open the new "Group Stories" tab, less than 5% ever post a story. Before your team designs new features, you need to run Discovery research to find out why posting activity is so low.

Which action best helps you discover the root user problem?

A. Launch a forced pop-up notification asking every user to post a story today for a chance to win a free digital badge.

B. Interview 5 active creators who checked the tab but never posted, asking them open-ended questions about what held them back.

C. Ask your dev team to immediately add 10 new face filters to make posting feel more fun.

D. Email a 15-question survey asking users: "Would you post more if we let you pin stories to your profile?"

Through discovery, you learned that shoppers abandon their carts because they aren't sure which jean size will fit.

To solve this, your FitTest team designs a low-fidelity interactive "True-to-Size" slider. Before your engineers write a single line of real code, you must test this prototype during the build phase.

User Research Method: Unmoderated or Moderated Usability Testing

Conduct usability testing with an interactive, low-fidelity clickable prototype (e.g., built in Figma).

How to validate the problem:

• Give 5 target shoppers a specific prompt: "Find a pair of fitted jeans and complete checkout using the new sizing tool."

• Watch where users hesitate, miss buttons, or get confused. Do not help them or explain how the slider works.

How to test the solution:

• Track task completion rates. The solution is validated when users can complete checkout smoothly without hitting design roadblocks.

Quiz: Secretary AI Bot

Scenario: You're building an AI secretary bot designed to help busy students auto-schedule study sessions and meetings. Your team created a low-fidelity clickable Figma prototype of the calendar-sync setup flow. Before writing code, you need to test if the setup flow makes sense to real users.

Which testing approach gives you the most reliable behavioral data?

A. Walk 5 students step-by-step through the setup flow, explaining how the AI permissions work so they don't get stuck.

B. Send an email poll asking 100 students: "Would you pay $3/month for an AI bot that auto-books your calendar?"

C. Give 5 target students the goal: "Set up your first automated study reminder." Observe where they hesitate without giving hints.

D. Ask 5 friends in an interview: "Do you think existing calendar apps are annoying to set up manually?"

Your team builds the feature and rolls it out to live app traffic. Now you use post-launch research to track performance and fine-tune the experience.

User Research Method: Mixed-Methods Research

Combine quantitative funnel analytics (e.g., checkout drop-off rates) with qualitative feedback (e.g., exit micro-surveys or 1-on-1 interviews using the 5 Whys framework).

How to validate the problem:

• Funnel analytics show where drop-offs happen.

  Example: Cart abandonment drops from 75% to 25% after adding the slider!

• Exit surveys or short interviews explain why the remaining 25% still leave.

  Example: "I love the slider, but I'm afraid to order because I don't know if returns are free."

How to test the solution:

• A/B testing: Test small live updates based on feedback — such as adding a "Free 30-Day Returns" badge next to the slider — and measure the impact on checkout conversions.

Quiz: Gaming Gear Site

Scenario: On your gaming gear e-commerce site, live analytics show that 60% of shoppers add custom mechanical keyboards to their cart, but drop off at the final payment screen. Exit feedback reveals buyers aren't sure if the key switches are compatible with their existing setup.

Applying post-launch best practices, what should you do next?

A. Rebuild the entire checkout page and payment gateway from scratch.

B. Run a live A/B test adding a direct "Switch Compatibility Guide" link right above the checkout button, measuring purchase conversion impact.

C. Send a follow-up survey asking users to rate the visual color design of your checkout page from 1 to 5.

D. Put a site-wide 20% discount banner on the homepage to boost total sales volume.

Photo by Jakub Żerdzicki on Unsplash

Put these user research techniques into practice with this step-by-step action plan: