User journey research: uncovering the what and why

Behind every smooth digital experience is solid user research. Without it, teams risk relying on assumptions that mislead more than they guide. 

User journey research grounds your understanding in reality, uncovering both the quantitative what and the qualitative why behind user behavior.

Done well, research helps you:

• Avoid wasting resources fixing the wrong problems

• Reveal hidden friction points where users rage click, abandon, or loop back

• Surface motivations and emotions that drive decisions

• Feed continuous improvement as your product and audience evolve

In this chapter, we explore the main research methods that help you build journeys based on facts, not guesswork.

Key insights:

• Combine qualitative and quantitative research to reveal both why and what: numbers alone can tell you what happened, but they rarely explain why. For example, analytics might show a 60% drop-off on a signup page—but only interviews or surveys reveal that users found the form too long or confusing. Using both methods ensures you’re solving the right problems with the right context.

• Match methods to your goals—depth vs. scale: to understand motivations or emotions, use qualitative methods like interviews or session replays. To confirm how widespread an issue is, turn to quantitative methods like analytics or large-scale surveys. Knowing when to zoom in for depth and when to zoom out for scale helps you focus effort where it matters most.

• Blend tools and methods for a complete picture of the user experience: pairing Contentsquare Heatmaps with Surveys feedback, for example, shows not just where users clicked but also how they felt about the process. By combining data sources, you get a richer, more accurate view of the user journey than you would from siloed tools.

• Treat research as a continuous cycle, not a one-off project: user behavior evolves as products change, competitors shift, or new expectations emerge. A journey study done once is quickly outdated. Instead, build research into your team’s regular rhythm—weekly analytics checks, monthly surveys, quarterly interviews—so your insights stay fresh and your optimizations stay relevant.

Going deep: qualitative user journey research methods

Qualitative research digs into why people behave the way they do on your website or in your product. It surfaces motivations, frustrations, and emotional drivers that raw numbers can’t capture.

For example, analytics might show a high exit rate on your pricing page—interviews or open-ended surveys reveal why people exit: maybe users felt overwhelmed by jargon or couldn’t easily compare plans.

Here are some common methods of qualitative user journey research:

User interviews

User and customer interviews let you capture goals, barriers, and stories in users’ own words. These conversations reveal intent and context that numbers alone can miss.

They’re especially useful early in a project, when you need to uncover assumptions and get a direct sense of user motivations. Interviews also work well alongside analytics, helping explain why certain behaviors show up in the data.

For example, if analytics show that many users abandon checkout after adding items to their cart, interviews can uncover that they hesitated because of hidden shipping costs or uncertainty about return policies. This kind of insight helps teams prioritize fixes that truly address user concerns.

Schedule and analyze customer interviews in Contentsquare Voice of Customer to bring real user voices directly into your journey research

Remote observation

User testing platforms and tools—like Contentsquare Session Replay—let you watch natural user behavior in real time, free from survey bias.

Remote observation is helpful when you want to see how people actually navigate your site or product in their own environment. It’s especially valuable for spotting moments of friction that users might not articulate in an interview, such as hesitation, repeated clicks, or sudden drop-offs.

For example, during a product launch, a SaaS company might notice a large number of users stalling on the signup page. Watching session recordings could reveal that visitors repeatedly click a non-clickable icon, assuming it’s a “Next” button. Without observing this in action, the team might misattribute the drop-off to a lack of interest rather than a simple UX misunderstanding.

Contentsquare Session Replay highlights rage clicks and API errors on a checkout page, revealing hidden friction that analytics alone can’t explain

Open-ended surveys

Open-ended user surveys are easy to scale and a great way to surface surprising patterns or sentiments without boxing users into predefined choices.

They’re especially useful when you want to capture fresh insights directly from users at scale. Because participants answer in their own words, you often uncover friction points or emotional drivers that structured surveys miss.

For example, a financial services site might notice high drop-off during an application process. An open-ended survey reveals a common theme: users don’t understand the jargon in one of the required fields. This insight, which analytics alone couldn’t have surfaced, can lead to clearer language and a smoother experience.

Keep in mind that to get meaningful results from open-ended surveys, it’s critical to phrase questions carefully. Leading or assumptive wording can skew responses. For instance:

Ask: “What nearly stopped you from completing this step?”

Don’t ask: “How much did you enjoy this page?” (leading, assumes positive intent)

Open-ended surveys in Contentsquare Voice of Customer highlight recurring themes and sentiment, turning unstructured text feedback into actionable insights

Lab observation

Lab observation—a type of user journey or usability test done in a group format—offers a structured environment for deep UX testing, accessibility validation, and observing non-verbal cues.

This method is most helpful when you need controlled conditions to focus on specific aspects of the user journey, like form completion, navigation flow, or accessibility barriers. Because participants are in the same environment, researchers can track body language, tone of voice, and real-time reactions that might be missed in remote studies.

For example, an ecommerce brand testing a redesigned checkout flow might invite users into a lab or on-site office to complete a purchase on different devices. While analytics show higher abandonment on mobile, lab observation reveals the real cause: users are visibly frustrated by the tiny tap targets and the confusing order summary. These non-verbal cues can give the design team evidence to prioritize mobile accessibility fixes.

💡 Pro tip: pair Contentsquare Journeys with Session Replay for deeper context. Journeys show where users drop off, and replays reveal what actually happened on the page. Layer in Voice of Customer feedback—like on-page surveys or follow-up interviews—and you’ll understand not just where the friction occurred, but why users behave how they do. This combination gives you a 360° view of user behavior and sentiment in a single workflow.

Contentsquare Data Connect lets you bring user behavior insights into your data warehouse, so you can pair journey analytics with other business metrics for deeper context

Measuring scale: quantitative research methods

Quantitative research explains what is happening across the broader user base. It gives the numbers that reveal user behavior patterns at scale.

For example, if analytics show that 40% of users abandon checkout after selecting shipping, you know there’s a significant blocker affecting conversions. Quantitative data helps you spot patterns and size their impact; qualitative insights help you explain and solve them.

Here are some common methods of quantitative user journey research:

Website and product analytics

Analyzing website and product analytics lets you track traffic flows, funnel progression, and drop-off points.

These metrics are especially valuable for spotting where in the journey users run into trouble. Analytics provide the scale you need to quantify issues—whether it’s a sudden spike in bounce rate, a drop-off in a multi-step form, or low engagement with a key feature.

For example, an online retailer might use funnel analytics and discover that a large share of shoppers drop off at the shipping step. The data alone highlights the problem’s size and location: nearly 30% of carts are abandoned there. That finding prompts further qualitative research, which reveals the cause: unexpected shipping fees are displayed too late in the process.

Closed-ended surveys

Closed-ended user surveys allow you to gather user behavior insights in multiple-choice or yes/no formats to validate hypotheses quickly.

These are most useful when you already suspect a friction point and need to confirm how widespread it is. Because responses are structured, you can quantify patterns at scale and compare results over time.

For example, a SaaS company might notice a high number of support tickets for account setup. To validate whether onboarding is the main pain point, they launch a short in-app survey asking, “Did you complete setup without issues? Yes/No.” A 62% “No” response confirms the problem at scale, which helps the team prioritize improvements and measure progress after making changes.

Closed-ended exit surveys capture quick, structured feedback on why users leave—making it easier to validate friction points at scale

Task-level CSAT or CES

Customer satisfaction (CSAT) and customer effort score (CES) surveys ask focused questions like “Was this page helpful?” instead of relying only on broad Net Promoter Score® (NPS) surveys that don’t explain journey-level issues.

NPS is best used post-purchase, when you’re measuring overall brand loyalty. But during the user journey, task-level surveys are far more actionable. They pinpoint satisfaction or frustration at a specific moment, giving you insights into problems you can fix right away.

For example, a bank might introduce a new online loan calculator, and want to test if customers find it useful. By adding a one-question CSAT survey at the end of the tool—“Did this calculator give you the information you needed?”—they learn that many users still have unanswered questions about repayment schedules. This task-level insight helps the team adjust the interface and improve clarity before rollout.

Task-level CSAT surveys capture satisfaction at a specific moment in the journey, highlighting friction points you can act on immediately

A/B and multivariate testing

A/B testing—and multivariate testing—show how design or content changes influence real user behavior and outcomes.

These methods are especially helpful when you want to move beyond identifying problems to validating potential solutions. Rather than guessing which change will have the biggest impact, you can experiment with multiple versions of a page, flow, or element and measure the actual user response at scale.

For example, an ecommerce brand could suspect that unclear CTA wording is hurting conversions. They run an A/B test to compare the original “Continue” button with a new “Go to Checkout” label. The revised copy increases conversions by 14%. Later, a multivariate test on button color and placement confirms that the combination of a bolder color and clearer wording performs best. Together, these tests gave the team confidence to roll out the changes site-wide.

💡 Pro tip: pair and analyze survey data with replay clips to go beyond surface metrics. For example, if analytics show a high exit rate on a checkout page, a survey might reveal “I couldn’t find the shipping cost.” The replay then shows exactly where users hesitated or abandoned. Together, the what and the why point to a clear fix.

Session replays with error tracking connect user frustration to specific technical issues, showing exactly where and why drop-offs occur

Blending approaches: tools that bridge the gap

The most powerful research comes from combining qualitative and quantitative methods to give you a more complete picture of user behavior.

For example, quantitative analytics might reveal a 50% drop-off between the product page and checkout. On its own, that data shows the scale of the issue, but not the cause. Pairing it with open-ended surveys and session replays uncovers the why—maybe users were confused by shipping details and clicked around trying to find answers. Together, both methods show the size of the problem and the reason behind it, giving the team clear direction for improving the user journey.

Here are some tools that help bridge the gap between quantitative and qualitative user journey research:

Contentsquare experience intelligence

Contentsquare combines quantitative data with qualitative evidence to connect user struggles directly to business impact. Instead of working in silos—looking at analytics in one platform, surveys in another, and replays somewhere else—Contentsquare lets teams merge these views into a single, cohesive picture. That matters because understanding what happened without knowing why it happened often leads to guesswork. With both sides of the story, teams can move from assumption to action with confidence.

For example, imagine you notice a 35% drop-off at the payment stage of your checkout funnel. With Contentsquare Funnel Analysis, you can pinpoint exactly where the drop-off occurs. From there, Session Replay shows you that users repeatedly try to apply a promo code that isn’t functioning. Heatmaps confirm that most clicks are clustered around the inactive field, and on-page VoC feedback surveys reveal frustration in the users’ own words: “My discount code didn’t work, so I gave up.” By linking these behaviors directly to conversion loss, Contentsquare quantifies the business impact and highlights a clear priority for the product and UX teams.

This blending of analytics, replays, heatmaps, and feedback makes it easier to identify problems and justify investments to fix them—because you can connect user journey friction directly to revenue.

Contentsquare Data Connect integrates with other analytics platforms to bring user behavior data together for a more complete picture of user journeys

Data visualization tools 

Tools like Miro and UXPressia help teams cluster insights, map findings, and collaborate on next steps. Instead of leaving research scattered across spreadsheets, replay clips, and survey exports, visualization platforms let you see everything in one shared space.

Clustering insights often looks like grouping sticky notes (digital or physical) around common themes—for example, “navigation issues,” “checkout friction,” or “mobile-specific blockers.” This makes it easier to see patterns across qualitative and quantitative data that might otherwise stay buried. Once organized, teams can prioritize which issues to tackle first and align on the next round of improvements.

For example, a global travel brand might run interviews, surveys, and analytics around its booking flow. Using Miro, the team can cluster insights into categories: “confusing search filters,” “pricing transparency,” and “support contact points.” Visualizing the data side by side may reveal that “pricing transparency” isn’t just mentioned in surveys but also correlates with a 22% drop-off in analytics and negative sentiment in chat logs. By making the insight visible to all stakeholders in one place, the team can rally around a clear priority for fixing the booking journey.

Visualization tools don’t replace research—they make it easier to interpret, share, and act on it. They keep user journey research from staying locked in the researcher’s head and turn it into a collaborative asset for product, design, and marketing teams.

💡 Pro tip: use Contentsquare together with a visualization tool like Miro to turn findings into shared insights. For example, you might use Contentsquare to spot a drop-off in your checkout funnel, then pull related session replays, heatmaps, and survey responses into Miro. Clustering those insights visually—“technical errors,” “form confusion,” “promo code issues”—makes it easier for cross-functional teams to see the full story and agree on priorities. This workflow bridges data and collaboration: CSQ uncovers the friction, while Miro helps align everyone on how to fix it.

Keeping research alive: a continuous mindset

User journeys are never static. New features, changing expectations, and external events all shift user behavior over time. Treating research as a one-off project risks leaving your team with outdated insights that no longer match reality.

Continual research matters because:

• User expectations evolve quickly: a checkout flow that felt smooth last year may now feel clunky compared to new competitors or industry standards

• Products change: every new feature, content update, or pricing model introduces fresh opportunities for friction

• External factors reshape behavior: economic shifts, new devices, or even seasonality can alter how people navigate your journeys

The most effective digital teams treat research as a cycle rather than a box to tick. Instead of asking, “Did we do research?” they ask, “When are we doing research next?”

Recommendations for building a continuous research cycle:

• Weekly/monthly cadence: refresh analytics dashboards, run on-page surveys, and monitor frustration signals like rage clicks

• Quarterly cadence: conduct deeper interviews, structured observation sessions, and broader surveys to catch evolving needs

• Annual cadence: step back for a holistic review of end-to-end journeys, benchmarking against competitors and industry shifts

By building rhythm into your research, you create a heartbeat for your digital experience strategy—one that keeps insights fresh, teams aligned, and optimizations tied to what users need today.

From data to decisions: the power of balanced user journey research

Strong user journey research balances empathy, evidence, and impact. By combining the story (qualitative insights) with the stats (quantitative data), teams gain a complete picture of what users do, and a deeper understanding of why it matters for their experience.

It all starts (but never ends) here:

• Use qualitative methods to capture motivations, emotions, and context

• Use quantitative methods to measure patterns and size issues at scale

• Blend both approaches—and the tools that support them—for a complete picture of the journey

• Keep research continuous so your insights evolve with your users

When you combine stories and stats, you unlock human-centered and business-relevant insights. That balance ensures you’re not just mapping the user journey—you’re improving it in ways that drive real impact.

FAQs about user journey research