User Path Analysis

A user journey is the sequence of steps a user takes within your product. Imagine a photo editing app where users explore the “Image Upscaler” before the “Shape Cropper,” leading to a 20% increase in conversion. The trick is identifying that particular user journey out of all the many permutations a user could follow in using your product. It’s hard to go over all of them, measuring the impact of each. Causal analysis is key to understanding what drives the KPI change and what to do next. Even though you might have identified some impactful user journeys, many companies struggle to translate these journeys into real actions. Let’s take a look at a few examples of what you can do next, drawn from a sample photo editing app: 1️⃣ The “Journey Reduce-Noise-Filter” → “Background Eraser” could increase Conversion by 20%. ✅ Amplify the impact of the journey: >> Highlight Reduce Noise Factor in your UI and marketing. >> Use in-app nudges to encourage and Background Eraser exploration. >>Incorporate this flow into a product Walkthrough, educational video or your onboarding process. 2️⃣ Users that complete “Clean Object” after “Cartoon Effect” are 22% more likely to convert if they complete “Clean Object” after “Glitch Video Effect.” ✅ When to promote a feature: >> Surface Glitch Video Effect earlier and provide guidance. >> Showcase success stories reinforcing this journey. 3️⃣ The Journey “Magic Eraser” followed by “Search“ increases Churn Within 2 Weeks by 15%. ✅ Reduce user churn following a journey: >> Is there a bug in the product or a gap in user expectations >> Was there something they searched for and could not find? 4️⃣ The Journey “Use Template” → “Cartoon” → “Glitch Video Effect” → “Clean Object” increases 30-Day Retention by 38%. ✅ Build winning Activation journeys: >> Guide users gradually through a user journey over the first 7 or 30 days. >> Sequentially promote these features in your onboarding process, in-app prompts, timed marketing campaigns etc. 5️⃣ The journey “Campaign= Fast Track” → “Viewed landing page = /FastTrack-US” increases conversion by 23%. ✅ Leverage the right combination of marketing campaigns and landing pages to maximize KPIs: >> Understand and promote the touchpoints that work >> Direct users through the journey with targeted campaigning, incentives, interactive guidance, and contextual nudges. 👉 Key Takeaway User journeys are gold mines of action-ready insights. 🥇 The real power lies in turning them into strategies and actions that optimize the user experience and drive growth. If you’re using Loops, you have likely uncovered high-impact sequences, both positive and negative, along with hidden user segments. I’d love to hear your story. What’s the most actionable insight you’ve gained through a user journey? 🚀 #CausalML #userjourney #productanalytics

Great journey maps start from the intersection of user touchpoints. A customer journey map shows a customer's experiences with your organization, from when they identify a need to whether that need is met. Journey maps are often shown as straight lines with touchpoints explaining a user's challenges. start •—------------>• finish At the heart of this approach is the user, assuming that your product or service is the one they choose to use in their journey. While journey maps help explain the conceptual journey, they often give the wrong impression of how users are trying to solve their problems. In reality, users start from different places, have unique ways of understanding their problems, and often have expectations that your service can't fully meet. Our testing and user research over the years has shown how varied these problem-solving approaches can be. Building a great journey map involves identifying a constellation of touchpoints rather than a single, linear path. Users start from different points and follow various paths, making their journeys complex and varied. These paths intersect to form signals, indicating valuable touchpoints. Users interact with your product or service in many different ways. User journeys are not straightforward and involve multiple touchpoints and interactions…many of which have nothing to do with your company. Here’s how you can create valuable journeys: → Using open-ended questions and a product like Helio, identify key touchpoints, pain points, and decision-making moments within each journey. → Determine the most valuable touchpoints based on the intersection frequency and user feedback. → Create structured lists with closed answer sets and retest with multiple-choice questions to get stronger signals. → Represent these intersections as key touchpoints that indicate where users commonly interact with your product or service. → Focus on these touchpoints for further testing and optimization. Generalizing the linear flow can be practical once you have gone through this process. It helps tell the story of where users need the most support or attention, making it a helpful tool for stakeholders. Using these techniques, we’ve seen engagement nearly double on websites we support. #productdesign #productdiscovery #userresearch #uxresearch

Product Analyst Guide: User Flow Analysis As a product analyst, I have to find out user drop offs in key flows. Identifying these drop-off points helps me to make specific changes that can boost engagement and conversion rates. Here's my step-by-step method to find and solve issues in user flows: 𝟭. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗞𝗲𝘆 𝗨𝘀𝗲𝗿 𝗙𝗹𝗼𝘄𝘀 ⤷ Pinpoint the main paths users follow, like checkout or registration. ⤷ Focus on flows that are critical to your objectives. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: For an e-commerce site, tracking the checkout process is essential. >> Solving Drop-Off: ⤷ Use heatmaps to see where users click most and least. ⤷ Track the average time spent on each page to spot potential issues. 𝟮. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗗𝗿𝗼𝗽-𝗢𝗳𝗳 𝗣𝗼𝗶𝗻𝘁𝘀 ⤷ Identify steps with high drop-off rates. ⤷ Compare drop-off rates at different stages to find problem areas. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Many users abandon their carts on the payment page. >> Solving Drop-Off: ⤷ Check if there are usability issues on the payment page. ⤷ Compare abandonment rates before and after recent changes. 𝟯. 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗲 𝗖𝗮𝘂𝘀𝗲𝘀 ⤷ Examine potential issues such as confusing forms/slow load times. ⤷ Gather user feedback to understand their frustrations. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Users find the payment page too complex and confusing. >> Solving Drop-Off: Conduct user interviews or surveys to pinpoint specific problems. Test different versions of the payment page to find the most effective design. 𝟰. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 ⤷ Make targeted improvements based on your findings. ⤷ Simplify processes, enhance form usability and improve page load times. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Revise the payment page to be more user-friendly and offer more payment options. >> Solving Drop-Off: ⤷ Streamline the payment form and reduce the number of required fields. ⤷ Add progress indicators and clarify error messages. 𝟱. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 ⤷ Continue monitoring and refining based on new data. ⤷ Address any new drop-off points that arise and keep enhancing the user experience. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: After initial improvements, additional optimizations may be necessary. >> Solving Drop-Off: ⤷ Regularly review user feedback and behavior to spot emerging issues. ⤷ Make iterative changes and measure their impact on user flow. Read the document below for end-to-end process.. ------------------------------------------------------------- 👉 Free Data Analyst Template (https://lnkd.in/gxrngzVg) ♻️ Found this post useful? Repost it! #product #productanalyst

My favorite tactic (or mindset) for identifying leading indicators to create more helpful Product OKRs: Looking for patterns along a user's conversion path. Here's how it works. Leading indicators are meant to predict the change of larger-scale, slower-moving metrics (lagging indicators). They help teams to map their day-to-day actions to metrics that respond more directly than the lagging indicators relying on the activities of multiple teams and longer time horizons. 𝗔 𝗵𝗲𝗹𝗽𝗳𝘂𝗹 𝘄𝗮𝘆 𝘁𝗼 𝗿𝗲𝗳𝗿𝗮𝗺𝗲 𝗳𝗶𝗻𝗱𝗶𝗻𝗴 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗶𝘀 𝘁𝗼 𝘁𝗿𝗲𝗮𝘁 𝘁𝗵𝗲𝘀𝗲 𝗮𝘀 𝘁𝗵𝗲 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗻𝗴 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝘂𝘀𝗲𝗿𝘀. Success is, of course, relative. But most of the time, it's defined by the "ultimate conversion" happening. This could include: • A completed purchase (eCommerce) • A submitted/average CES survey (universally applicable) • A generated and downloaded report (b2b SaaS) In general, reverse-engineering the (potential) path of a user from presents you with a range of leading drivers. Like: Started product searches 𝘭𝘦𝘢𝘥 𝘵𝘰 𝘮𝘰𝘳𝘦 Search result list views 𝘭𝘦𝘢𝘥 𝘵𝘰 𝘮𝘰𝘳𝘦 Article detail views 𝘭𝘦𝘢𝘥 𝘵𝘰 𝘮𝘰𝘳𝘦 "Save for later "bookmarks 𝘭𝘦𝘢𝘥 𝘵𝘰 𝘮𝘰𝘳𝘦 Add-to-cart actions 𝘭𝘦𝘢𝘥 𝘵𝘰 𝘮𝘰𝘳𝘦 Checkout started actions 𝘭𝘦𝘢𝘥 𝘵𝘰 𝘮𝘰𝘳𝘦 Completed purchases. While these are (probably) technically "correct" leading indicators, they might not all be equally effective. 𝗧𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿 𝘄𝗶𝘁𝗵 𝗮 𝗵𝗶𝗴𝗵 𝗰𝗵𝗮𝗻𝗰𝗲 𝗼𝗳 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿, 𝘄𝗲 𝗵𝗮𝘃𝗲 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗮𝗹𝗼𝗻𝗴 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗽𝗮𝘁𝗵: What are the common denominators among those users who were successful? In this case, those who completed the purchase at what time of their interaction with the product. Thomas Christensen calls this taking a "Conversion Snapshot" to spot the patterns. Conversion Snapshots primarily require quantitative insights into the actions taken along a user's journey. Still, they can be complemented by qualitative insights into why each step was taken or how it was taken. Leading indicators are also not binary: What might be a fast-moving predictor for one team and their industry can come with a lag for another. Of course, leading indicators move you further from the "final" conversion, but that's the point: You're trading certainty for responsiveness–That's why they're called indicators and not proof. 𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗵𝗼𝘄 𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗼𝗳 𝗢𝗞𝗥𝘀 𝘂𝘀𝗶𝗻𝗴 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗮𝗿𝘁𝗶𝗰𝗹𝗲: https://lnkd.in/e7FE9UAS #prodmgmt #productmanagement #okrs #outcomes #leadingindicators

🌀 User journey maps often capture “perfect” journeys users never take. We need to stop designing paths, and start designing loops, especially in AI products ↓ We use journey maps to capture, understand and refine user's experience. However, these maps are merely an idealistic view of what users SHOULD be doing, rather than what they actually ARE doing. Linear paths don't consider detours, circling back and forth, abandonments and returns and shortcuts. In fact, our interactions with reality rarely follow a well-defined, structured script; they’re a series of adjustments and feedback loops — depending on environment, disturbances, decision-making and actions. Workflows shouldn’t be perceived as a rigid cage, but as an orchestrated loop. Matt Fick and Max Peterschmidt suggest to rethink the idea of designing paths and design loops instead, especially with AI products in play. We start with a goal, make decisions, sense what’s going on, study environment, take action and then keep checking again, and again, and again. It follows a simple structure: 🎯 1. Setting a goal First, we establish a goal: what is the user trying to achieve? Desired outcome is the foundation on which the product will ground all its actions and adjustments. We must help people articulate their goal — with slow prompting and better calibration (knobs, pre-prompts, buttons, sliders). 🌡️ 2. Studying the current state (Sensors, Environment) To improve something, we must understand its current state. We find the right sources and collect the right inputs to get a snapshot of the current state. Often there are many meaningful inputs, and often they are very difficult to predict ahead of time. 🧠 3. Making decisions (Controller) Next, we evaluate the data and compare it against the goal. We come up with meaningful actions and get recommendations, grounded in trusted sources. Mapping the reasons for recommendations is critical for building trust and confidence — with AI, but not necessarily with LLMs. 🚀 4. Taking actions (Actuator) Once we decide that an adjustment is necessary, we take an action, or we ask agents to take an action — directly manipulating the environment closer to the desired outcome. The actions are typically initiated or approved by humans, and that’s what we mean with “human in the loop”. 🧲 5. Studying and refining the new state We gather data about the changed environment, and then use these inputs to suggest the next batch of changes as output. With nested loops, when many people or AI agents are involved, output in one loop becomes an input in another and informs next decisions and actions there. An interesting and realistic model in AI world, matching the complexities of the real world better than journey maps often do. Indeed, workflows aren’t rigid cages — they are non-linear, cyclic and must be highly adaptive to be meaningful. They must sense, respond and learn — and loops do just that.