Analyzing User Pathway Metrics

How well does your product actually work for users? That’s not a rhetorical question, it’s a measurement challenge. No matter the interface, users interact with it to achieve something. Maybe it’s booking a flight, formatting a document, or just heating up dinner. These interactions aren’t random. They’re purposeful. And every purposeful action gives you a chance to measure how well the product supports the user’s goal. This is the heart of performance metrics in UX. Performance metrics give structure to usability research. They show what works, what doesn’t, and how painful the gaps really are. Here are five you should be using: - Task Success This one’s foundational. Can users complete their intended tasks? It sounds simple, but defining success upfront is essential. You can track it in binary form (yes or no), or include gradations like partial success or help-needed. That nuance matters when making design decisions. - Time-on-Task Time is a powerful, ratio-level metric - but only if measured and interpreted correctly. Use consistent methods (screen recording, auto-logging, etc.) and always report medians and ranges. A task that looks fast on average may hide serious usability issues if some users take much longer. - Errors Errors tell you where users stumble, misread, or misunderstand. But not all errors are equal. Classify them by type and severity. This helps identify whether they’re minor annoyances or critical failures. Be intentional about what counts as an error and how it’s tracked. - Efficiency Usability isn’t just about outcomes - it’s also about effort. Combine success with time and steps taken to calculate task efficiency. This reveals friction points that raw success metrics might miss and helps you compare across designs or user segments. - Learnability Some tasks become easier with repetition. If your product is complex or used repeatedly, measure how performance improves over time. Do users get faster, make fewer errors, or retain how to use features after a break? Learnability is often overlooked - but it’s key for onboarding and retention. The value of performance metrics is not just in the data itself, but in how it informs your decisions. These metrics help you prioritize fixes, forecast impact, and communicate usability clearly to stakeholders. But don’t stop at the numbers. Performance data tells you what happened. Pair it with observational and qualitative insights to understand why - and what to do about it. That’s how you move from assumptions to evidence. From usability intuition to usability impact. Adapted from Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics by Bill Albert and Tom Tullis (2022).

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

Typically, I see growth teams focusing on the biggest funnel drop, but this is usually not the biggest opportunity for growth, and unproductive. Let me explain by going deeper into a more holistic approach to managing growth funnels. Most of the analytics tools available today offer limited funnel metrics: funnel drops and completions. It’s therefore understandable that teams focus on the biggest drop. The truth is - most users won’t complete your funnel anyway. Your product probably wasn’t built for them, there’s no product-market fit, and changing their low intent is unlikely. Optimizing might keep them 1-2 more stages, but they’ll likely churn at the next. Move on! Your best opportunity lies with high-intent users who don’t complete the funnel. They have a good product-market fit and should complete. First identifying this group is crucial to understanding why some don’t succeed. How to identify High-Intent Users: Try changing up your analytics approach, put the dashboards, #correlation, and lengthy #abtesting aside for a minute. Here are a few ways to help you identify your high-intent users. Search for the signals of intent: Shorter time to complete steps, differences in onboarding questions and responses, permissions etc. Group users into segments, such as the marketing received, localizations, user properties, and behavioral groups. Calculate the likelihood of users in a sub-segment completing the funnel. Then, upon aggregating all the sub-segments together, you understand the quality and intent of the segment. Users with the most signals of intent are your high-intent users. Find high-intent users automatically. Consider leveraging a causal model. Loops, for example, automatically identifies high-intent users, by looking at the sub-segments and finding intent signals. It can otherwise be a very manual process when you are limited to funnel drop and completion metrics. How to Identify the Biggest Opportunities: Once you have identified your high-intent users, you need to size the opportunity before starting to form hypotheses. Opportunity size is based on the questions: Assuming this segment completed this step of the funnel, what would be the effect on the total funnel completion rate. Loops automatically presents you the biggest opportunities to improve your funnel. It calculates what would be the impact on the total funnel completion rate, if you improve a specific step of the funnel. Action the Insight: By identifying high-intent users and their pain points and motivations you can better shape the top of the funnel and increase completions. Armed with the confidence and impact insight of your biggest opportunity, you can turn your attention to the specific actions needed for funnel completion, as expected. Remember, most users will drop. Invest your time in identifying and understanding high-intent users. Causal inference models can help you find the answer, with less time, effort, and stress. #productledgrowth #causalml #growth

Leading 7 product lines as Socure, I place a significant emphasis on tracking engagement metrics. These metrics provide crucial insights into how customers are interacting with our platform and indicate their overall satisfaction. For us, it's not just about the numbers themselves but what they signify. And sometimes it takes trial and error to find the right metrics to track. For example, increasing session duration and the number of touchpoints might seem particularly important because it demonstrates deeper engagement and ongoing interaction with our services. It tells us that our customers are finding value and are actively using our platform to meet their needs. BUT… Looking at session duration in isolation could be misleading. For example, what if long sessions mean that compliance managers are having a hard time navigating the analytics features. And they are spending all this time because of product deficiencies... That’s why talking to customers daily is so important. Only through close customer collaboration did we land on one KPI that tells a complete story: FLOW It relies on engagement and time to value. 1️⃣ Engagement = Daily Active Users/Monthly Active Users. Higher Engagement improves the FLOW score. 2️⃣ Time-to-Value = total time spent in decision work streams/total number of decisions. Lower Time-to-Value (faster decisions) also improves the FLOW score. FLOW = Engagement / Time to Value FLOW is a Frictionless Loop of Work. By monitoring these KPIs closely, we can adjust our strategies to enhance user experience, optimize our offerings, and ultimately drive growth.

Your analytics show users spend 3 minutes on a page.  But they don’t tell you if those users are:   → Engaged and reading every word.   → Confused and scrolling in circles.   → Frustrated and hunting for a link that doesn’t exist.  Data without context is like a map without landmarks, it gives you direction but no understanding of the terrain.  To uncover the why, you need tools that go beyond surface-level metrics:   → Heatmaps to see where users hover, click, or ignore.   → Session recordings to watch how they navigate (or struggle).   → Scroll depth analysis to learn if they actually read your content.  Because time on page doesn’t matter if users leave without converting.   The real metric is: Intent.  - Are they finding what they came for, or are they trapped in a loop of confusion?  So, next time you see a “high engagement” page, ask:   → Is this page solving a problem or creating one?   → Are users taking the desired action, or just killing time? 

🔹 Day 17 – Product Manager Interview Prep Series 🔹 🎯 Diagnosis Question: “Friend requests are down 10%. As a PM at Facebook, what would you do?” 📌 Structured RCA Framework 🏢 Company Vision: “Connect people meaningfully.” 🎯 Product Goal: Increase user engagement via social connections. ⚙ How Feature Works: User visits profile → Clicks “Add Friend” → Request sent & logged. 👥 Problem It Solves: Enables social discovery & connection. 🧪 Check for A/B Test Impact: Was an experiment running? If yes, segment accordingly. 📉 Drop Type – Gradual or Sudden? → Gradual? Environment or user behavior shift → Sudden? Likely tech issue, feature change, or external trigger 🧭 User Journey Diagnosis: → Feed/Profile visit → Visibility of friend button → Button click responsiveness → Success/failure in request → Confirmation UX → Notification to the recipient 🔍 Potential Root Causes: - New UI hiding friend button? - App crash or server failure post-click? - Delay in confirmation feedback? - New users not finding suggestions? 📊 Is Drop Real? → Metrics calculation changed? → Tooling/data pipeline error? → One-off vs trend? 📍 Segment Data by: Platform (iOS/Android), Geo, User cohort (new vs power), Language 🗺 External vs Internal: Start internal (logs, funnels, bugs) → Then external (market event, PR issue, platform outage) 📈 Check Funnel Conversion: Profile Visits → Friend CTA Impressions → Clicks → Sent → Accepted 🧠 Hypotheses: - iOS app crash post-release - Friend button below fold post-redesign - Decrease in profile views due to algo tweak - Misfiring analytics causing under-reporting 🔁 PM Tip: Metric dips aren’t just dashboards blinking—they’re user frictions waiting to be discovered. Think systematically, validate rigorously. 💬 How would you debug this friend request dip? Let’s hear it👇 #ProductManagement #PMInterview #RootCauseAnalysis #LinkedInDaily #UserEngagement #UXDebugging #ProductThinking #LinkedInNewsIndia #PMLife

A structurally bizarre aspect of working with data is realizing that intricately connected user flows and business processes are artificially broken up during measurement only to rely on heroic data modeling efforts to piece it all back together. In daily organizational workflows, it feels like utilizing chopped up pieces of a whole where the pieces don’t fit as easily like lego blocks or as cohesively like a puzzle. This is the default pattern in organizations. Take a SaaS business for example: the user flow starts with marketing generating leads, which are then funneled into sales, then onboarding, followed by customer success, and eventually churn or renewal. All of these steps are deeply interrelated, yet the data around each of these steps is often captured via different APIs in separate systems, with differing levels of accuracy, time grains, and dimensions, making it impossible to track the full user journey without doing extensive work. To address this challenge, data teams execute valiant data modeling efforts. First, they clean up the raw facts to ensure they are accurate and consistent. Then, they recognize the key entities or dimensions and the associated attributes that give context to these measurements. Once the facts and dimensions are in place, they move on to creating meaningful metrics that represent the business’s key performance indicators (KPIs). At this stage, many organizations end up with a set of reports or dashboards powered by these data models. But even after going through these steps, we still encounter a major challenge: how do we connect these metrics and dimensions into cohesive, unified models that reflects the entire business process? The state of the art for this is additional painstaking work in spreadsheets. This is where metric trees come in as they represent the pinnacle of data modeling. They go beyond the basic elements of facts, dimensions, and metrics to model the complete flow of a business process, illustrating how different metrics interconnect and influence each other across various stages of the business cycle. For instance, in the case of a SaaS business, the metric tree would start with the highest-level output metric, such as revenue, and branch out to show how acquisition metrics (e.g., new customer leads), activation metrics (e.g., onboarding success), retention metrics (e.g., churn rates), and expansion metrics (e.g., upsell and cross-sell) all interconnect. This structure mirrors the actual dynamics of the business and reflects how changes in one area affect other areas, allowing for a more holistic view of operations. In short, metric trees represent the end state evolution of data modeling from fragmented measurements to a unified, connected view of the business.

Here’s how a customer we work withincreased ROAS 99% with a data-led approach And how you can do the same for your brand by cutting fluff & focusing on the metrics that move the needle. These are the exact 5 steps they used: ↳ Track the right metrics They used PenPath’s Purchase Intent Rate (PIR) dashboard as a guiding metric. Instead of relying solely on ROAS or CVR, they analyzed customer buying signals: - Adding to cart - Begin Checkout - Site searches - Email signups ↳ Clean up campaign data Set up clean campaign naming conventions to make data analysis easy & actionable. Specifically making things segmented by prospecting, retargeting, and by product category. ↳ Optimize by funnel stage Measured PIR by source, medium, and campaign to understand baselines for each stage of the funnel to measure interest for each traffic source and by product categories. ↳ Focus on what’s working For TOF effort with high PIR, they scaled or kept them even when ROAS was not performing and cut the rest. For BOF, they cut any campaign with low ROAS or PIR. This is an over simplification but that was the general approach. ↳ Scale high-intent audiences Lastly, they used purchase intent data to created improved retargeting audiences on Google and Meta. The Results? ✅️ ROAS skyrocketed from 1.35x to 2.69x (+99.555) in three months ✅️ Ad spend increased by 243% --- with no wasted dollars Pro Tip: Map your customer journey with intent-driven metrics. Focus on actions that align with each stage of your funnel (TOF, MOF, BOF) to uncover where customers drop off—and where to double down on winning strategies. If you’re an ecommerce decision maker, what data have you used to scale ROAS as quickly as possible? #Dataanalysis #Ecommercetips #Adspend #Ecommercesolutions

Among the UX metrics worth tracking, there’s one I watch particularly closely: Time to Action (TTA). What is TTA? It’s the time between a user’s first interaction and their first meaningful action—whether that’s signing up, completing a task, or making a purchase. While bounce rate and time on page can be useful, TTA offers something more valuable: A direct window into the user journey—and where friction lives. Why is TTA so powerful? 🧭 User Efficiency Shorter TTA often points to a more intuitive, task-focused interface. 🧠 Task Clarity When TTA is high, it can reveal that your next step isn’t as obvious as you think it is. 📈 Conversion Optimization Reducing TTA can lift completion rates—not through persuasion, but through better UX. How to use TTA in your strategy: 1️⃣ Identify key actions What are the “moments that matter”? Track how long they take. 2️⃣ Analyze flow friction Map out drop-offs, detours, and second-guessing points. 3️⃣ Iterate and test Design with momentum in mind — and validate it. 4️⃣ Pair with qualitative insights Numbers show where the problem is. Feedback tells you why. TTA reframes UX around momentum. It’s not just about getting users to the right screen — it’s about getting them to act, with confidence, and without friction. Let me know if you want to pair this with a swipeable mini-carousel or repurpose it into a weekend “micro-breakdown” series post. #uxdesign #productdesign #uxmetrics ⸻ 👋 Hi, I’m Dane—I love sharing design insights. ❤️ Found this helpful? 'Like’ it to support me. 🔄 Share to help others (& save for later). ➕ Follow me for more like this, posted daily.

Your buyers are telling you what’s wrong with your site—are you listening? Behavioral insights reveal exactly how users interact with your site, showing where they hesitate, where they drop off, and what’s stopping them from converting. Tracking these key behavioral metrics can turn traffic into revenue: 👉 Scroll Depth: If visitors aren’t reaching your key CTAs, they’re invisible. Move them higher or refine your content flow to keep engagement strong. 👉 Click-Through Rates (CTR): A low-performing CTA isn’t just bad luck—it’s a signal. A/B test different copy, colors, or placements to find what works. 👉 User Flow: A complicated conversion path kills leads. If users struggle to navigate, simplify steps, remove friction, and guide them toward action. SEO brings visitors in. Behavioral insights make sure they stay—and convert.