The Challenge of Qualitative User Analysis
Why Manual Review is Crucial in Analyzing Data from Hotjar and MS Clarity
In the age of digital analytics, tools like Hotjar, Microsoft Clarity, Mixpanel, Lucky Orange, Peer for Shopify, and Shopify Analytics offer incredible insights into user behavior. These platforms provide heatmaps, session recordings, funnel tracking, and event-based analytics that help businesses understand how users interact with their websites. However, while these tools are powerful, they are not without challenges. One of the biggest gaps in automated analytics is the inability to fully contextualize user behavior, making manual review and categorization essential for effective conversion optimization.
The Challenges of Analyzing Data from Multiple Analytics Tools
I love digging into qualitative user behavioral data in tools like Hotjar or Microsoft Clarity. I am a big fan of Peek for Shopify and advocate for user behavior tracking on any website. So, you might know that something is wrong with your website, but you will never know ‘why’ before we dig into user behavior patterns. But it’s hard, digital products are complex, funnels are different depending on whether they are for B2B, e-commerce, lead generation, or a marketplace, and most of the time there is A LOT of data.
Lately, day and night, I’ve mostly been thinking about how to solve this big data analysis issue in the best way possible, to find patterns in the data.
1. Data Overload and Noise
Each analytics tool generates an overwhelming amount of data - thousands of session recordings, heatmaps covering every page, and extensive event tracking. Sifting through this data to identify meaningful insights can be daunting, especially when a significant portion consists of random, unimportant user interactions.
Solution: Implement a systematic review process to prioritize key pages, user journeys, and anomalies. Use manual categorization to filter out noise and focus on meaningful trends.
2. Lack of Qualitative Context
While heatmaps and click tracking show what users are doing, they don’t explain why. Did users rage-click because of a broken button, unclear messaging, or slow load times? Did they abandon checkout due to pricing concerns, lack of payment options, or a complicated process?
Solution: Manually reviewing session recordings and cross-referencing with customer feedback, chat logs, and support tickets can uncover underlying reasons behind user behavior.
3. Cross-Platform Discrepancies
Data often varies across platforms. Shopify Analytics may show a high cart abandonment rate, but Mixpanel might indicate users dropping off at a specific checkout step. Meanwhile, Hotjar may reveal rage clicks on an unresponsive button, and Lucky Orange could highlight hesitation near a form field.
Solution: Manually reconcile insights across platforms to form a holistic picture of user experience issues and pinpoint exact friction points.
4. Inaccurate Attribution and False Positives
Automated analytics tools sometimes misinterpret user behavior. A long session may not mean engagement—it could indicate confusion. A high click rate on a specific element might suggest interest, but it could also be a sign of user frustration.
Solution: Manually categorize sessions into relevant buckets—frustrated users, engaged users, lost users—to separate false positives from actual behavioral patterns.
5. Failure to Identify UX and Accessibility Issues
Most analytics platforms are not designed to detect usability or accessibility issues. For example, a user struggling to navigate a form with a screen reader may appear as someone simply dropping off the page.
Solution: Conduct manual audits of key workflows, cross-checking session replays with usability testing and accessibility best practices.
6. Difficulty Analyzing Qualitative Insights
Quantitative analytics tools provide numerical data but often miss the subjective insights that explain user behavior. Tools like Lucky Orange and Peek for Shopify offer qualitative insights through surveys, user recordings, and direct feedback, helping businesses understand user motivations. However, analyzing qualitative data can be difficult due to its unstructured nature and the challenge of drawing actionable patterns.
Solution: Combine qualitative insights with quantitative data by categorizing survey responses and identifying trends in user feedback. Use a mix of manual review and AI-assisted sentiment analysis to extract meaningful insights that complement traditional analytics.
7. Quantifying User Struggles and Understanding Their Impact
Identifying a user struggle is only the first step—understanding how big the issue is and why it happens is just as critical. For example, a form validation issue might cause frustration, but how many users are affected? How many rage-clicks or abandoned sessions does it generate?
Solution: Assign numerical values to struggles by tracking session counts, drop-off rates, and error occurrences. Combine this with qualitative insights from session replays and surveys to determine the root cause. This helps when prioritizing fixes based on real impact rather than assumptions.
8. Leveraging AI to Review Session Recordings and Extract Insights
With thousands of session recordings and behavioral data points, manually reviewing everything is time-consuming and inefficient. AI-powered analytics tools can help by automatically detecting patterns, summarizing common issues, and flagging anomalies in user behavior.
Solution: AI tools can analyze session recordings at scale, highlighting frequent frustration signals like rage clicks, excessive scrolling, or form abandonment. Machine learning models can categorize sessions based on behavior types, making it easier to pinpoint usability issues. Additionally, AI can generate structured hypotheses by clustering common pain points, allowing teams to prioritize optimizations based on impact and frequency.
Why Manual Review and Categorization is Essential
1. Identifying and Prioritizing Real Issues
Not all user struggles are equal. Some friction points are minor, while others severely impact conversions. Manual categorization allows teams to prioritize issues based on severity, business impact, and recurrence.
2. Uncovering Hidden Patterns
Automated reports don’t always surface subtle patterns. By manually reviewing sessions, teams can detect behavioral nuances—like users hovering over a CTA but not clicking—that might indicate hesitation due to lack of trust or missing information.
3. Validating A/B Test Hypotheses
Before running an A/B test, understanding why users behave a certain way ensures that experiments address real problems. Manual review helps validate whether a proposed solution is necessary and what variations might work best.
4. Building Stronger Stakeholder Buy-in
Data-backed insights are powerful, but stakeholders often respond better to real user stories. Showing recordings of actual users struggling with a feature can be more persuasive than just presenting numbers. Don´t miss to look for patterns as well, because a single recording is not representative of the thousands of problems your users have.
Final Thoughts
While automated analytics tools are invaluable for collecting and visualizing data, they lack the human intuition needed to interpret complex user behavior. Manual review and categorization help bridge this gap, ensuring businesses make informed, user-centric improvements. However, AI tools can act as force multipliers by automating repetitive review processes and surfacing structured insights, making it easier to act on real user struggles efficiently.
By balancing automation with hands-on analysis, product managers and UX teams can drive more meaningful optimizations that improve conversions and overall user satisfaction.
How do you incorporate AI and manual analysis into your data review process? Share your thoughts in the comments!