Data-Driven UX Design

🎡 How To Measure And Show UX Impact. With practical guidelines on how to track and articulate business impact of design work ↓ 🚫 Business rarely sees the value of UX the way designers do. ✅ To many, it shows up merely in good outcomes of A/B tests. ✅ To some, it’s reflected in satisfaction surveys (NPS, CSAT). 🤔 But most UX work goes unnoticed, and so does its impact. ✅ To change that, we can measure and report design success. ✅ Identify 10–12 representative tasks that users must do well. ✅ These tasks must reflect business priorities, get signed off. ✅ Your goal is to achieve 80%+ success rate for these tasks. ✅ Focus on task success rate and task completion times. ✅ You need before/after snapshots to explain your UX impact. ✅ Choose metrics to track impact of your UX changes. ↳ Global KPIs: success for key tasks in a customer journey. ↳ Local KPIs: success for key tasks in a single touchpoint. 🤔 Explain and report your impact with KPI trees/graphs. ✅ Show how your design KPIs reinforce business flywheels. UX work often appears to be disconnected from the heart of the business. As we tirelessly iterate on flows and features, it’s often very hard to make an argument that a design change that we've made recently had a profound impact on key business metrics. The reason for that is that, unlike other departments, we rarely have a set of widely established and regularly reported design KPIs. These KPIs are UX metrics that are tied to business metrics that they are impacting. Design KPIs: https://lnkd.in/eEihbU7S Design KPI trees: https://lnkd.in/eTB3wrs9 Design KPI Graphs, by Ryan Rumsey https://lnkd.in/e5M2G-uu Business flywheels: ↳ https://lnkd.in/eJKuYu3R, by Timothy Timur Tiryaki, PhD ↳ https://lnkd.in/epNeWFWS, by Amplitude To visualize UX impact, we often use design KPI trees or design KPI graphs (shown above). Both are different ways to visualize how design initiatives help reach business goals, and show the dependencies between them. Another way is to show UX impact within business flywheels — an artefact companies use to explain their business models. Basically they are self-reinforcing cycles of business growth, and design work typically enables these cycles to function. Study where exactly your work fits in those flywheels, and attach design KPIs to them to reinforce the value that UX is driving. Surely not all design work is impactful. It depends on the audience it addresses, and the value it delivers. But by measuring what matters, we can get a trackable record of the changes we enable over time — and once you shed light on it, it might change how your work is seen much faster than you think. #ux #design

Here's the exact framework our clients use to tie UX research directly to revenue. It's called the I.M.P.A.C.T method: 👉 I - Identify high-friction touchpoints Systematically gather data on where customers struggle most in your product journey. Focus on high-traffic areas first. 👉 M - Measure the business cost Calculate the direct cost of each friction point: - Conversion drop-offs - Support ticket volume - Churn related to specific features 👉 P - Prioritize by revenue potential Rank issues by potential revenue impact, not just severity or ease of fix. 👉 A - Act with evidence-based solutions Design solutions based on actual user behavior, not assumptions. 👉 C - Communicate in business terms Present findings as "This issue is costing us $X per month" rather than "Users are confused by this flow." 👉 T - Track improvements continuously Measure the before/after impact of changes in business terms. With this, you can move the perception of a UX team to a strategic partner When you can say "We increased conversion by 22% through research-driven changes," executives listen differently Does your team have a framework for tying research to revenue? I'd love to hear about it!

🔎 UX Metrics: How to Measure and Optimize User Experience? When we talk about UX, we know that good decisions must be data-driven. But how can we measure something as subjective as user experience? 🤔 Here are some of the key UX metrics that help turn perceptions into actionable insights: 📌 Experience Metrics: Evaluate user satisfaction and perception. Examples: ✅ NPS (Net Promoter Score) – Measures user loyalty to the brand. ✅ CSAT (Customer Satisfaction Score) – Captures user satisfaction at key moments. ✅ CES (Customer Effort Score) – Assesses the effort needed to complete an action. 📌 Behavioral Metrics: Analyze how users interact with the product. Examples: 📊 Conversion Rate – How many users complete the desired action? 📊 Drop-off Rate – At what stage do users give up? 📊 Average Task Time – How long does it take to complete an action? 📌 Adoption and Retention Metrics: Show engagement over time. Examples: 📈 Active Users – How many people use the product regularly? 📈 Churn Rate – How many users stop using the service? 📈 Cohort Retention – What percentage of users remain engaged after a certain period? UX metrics are more than just numbers – they tell the story of how users experience a product. With them, we can identify problems, test hypotheses, and create better experiences! 💡🚀 📢 What UX metrics do you use in your daily work? Let’s exchange ideas in the comments! 👇 #UX #UserExperience #UXMetrics #Design #Research #Product

If you're a UX researcher working with open-ended surveys, interviews, or usability session notes, you probably know the challenge: qualitative data is rich - but messy. Traditional coding is time-consuming, sentiment tools feel shallow, and it's easy to miss the deeper patterns hiding in user feedback. These days, we're seeing new ways to scale thematic analysis without losing nuance. These aren’t just tweaks to old methods - they offer genuinely better ways to understand what users are saying and feeling. Emotion-based sentiment analysis moves past generic “positive” or “negative” tags. It surfaces real emotional signals (like frustration, confusion, delight, or relief) that help explain user behaviors such as feature abandonment or repeated errors. Theme co-occurrence heatmaps go beyond listing top issues and show how problems cluster together, helping you trace root causes and map out entire UX pain chains. Topic modeling, especially using LDA, automatically identifies recurring themes without needing predefined categories - perfect for processing hundreds of open-ended survey responses fast. And MDS (multidimensional scaling) lets you visualize how similar or different users are in how they think or speak, making it easy to spot shared mindsets, outliers, or cohort patterns. These methods are a game-changer. They don’t replace deep research, they make it faster, clearer, and more actionable. I’ve been building these into my own workflow using R, and they’ve made a big difference in how I approach qualitative data. If you're working in UX research or service design and want to level up your analysis, these are worth trying.

💡Measuring UX using Google HEART HEART is a framework developed by Google for evaluating the user experience of a product. It provides a holistic view of the UX by considering both qualitative & quantitative metrics. HEART stands for ✅ Happiness: How satisfied users are with using your product. It can be measured through surveys and ratings (quantitative) and reviews and user interviews (qualitative). Tracking happiness is right when you analyze the general performance of your product. ✅ Engagement: How actively users are interacting with the product. This includes metrics like the number of visits, time spent on the product, frequency of interactions, and the depth of interactions (e.g., the number of features used). Analyzing engagement will help you understand how compelling & valuable the product is to users. ✅ Adoption: How effectively the product attracts new users and converts them into active users. Key metrics include user sign-ups, onboarding completion rates, and activation rates (e.g., the percentage of users who perform a key action after signing up). Understanding adoption helps identify barriers during product onboarding. ✅ Retention: How well the product retains its users over time. It focuses on reducing churn and keeping users engaged over the long term. Metrics like retention rate and cohort analysis are used to measure retention. Improving retention involves addressing pain points, providing ongoing value, and fostering a sense of loyalty among users. ✅ Task success: How effectively users can accomplish their goals or tasks using the product. This includes metrics like task completion rate, error rate, and time to complete tasks. User journey mapping, user interviews, and usability testing can help identify usability issues and optimize the user flow to enhance task success. ❗ Top 3 mistakes when using HEART 1️⃣ Placing too much emphasis on quantitative metrics at the expense of qualitative insights. While quantitative data is valuable for analysis, it's essential to complement this with qualitative data, such as user feedback and observations, to gain a deeper understanding of user behavior and preferences. 2️⃣ Ignoring the context of interaction: Failing to consider the context in which users interact with the product can lead to misleading interpretations of the data.  3️⃣ Lack of user segmentation: Not segmenting users based on relevant factors such as demographics, behavior, or usage patterns can obscure important insights and lead to generic conclusions that may not apply to all user groups. 📺 Guide to using Google HEART: https://lnkd.in/dhkwy_jN 🚨 Live session "How to measure design success" 🚨 I will run a live session on measuring design success in February. Will talk about how to choose the right metrics for your product & how to measure product's success in meeting business goals   https://lnkd.in/dgm6t_jf #UX #design #productdesign #metrics #measure

Is there room in UX research for complicated statistics like mixed-effects models, mediation analysis, factor analysis, etc? 🏁 Yes! This is often confused with the question: should I show my stakeholders all of the work that went into my complicated statistics? 🚩 No! Advanced modeling can be extremely useful in a UX researcher's toolkit to help show things like causality or grouping/clustering (just to name two examples). Stakeholders, like product managers, often want to know if X causes Y or how certain groups of features or users are similar to each other. You've probably heard that we need to ditch our academic statistics because no one cares and they're too slow. 🧐 No one cares? On one hand, this is true. Nerd out with your UXR friends about the cool model, stow it in the appendix, and then put the simple insight/recommendation out front for your stakeholders. Some of my most impactful findings have been distilled into a short sentence, but they're sitting on a deep regression analysis that led to the insight. You can run an entire regression without needing to show one chart and sometimes be more effective as a researcher. ⌛ It's too slow? It might be if you're using excel or SPSS, doing it manually each time. Using things like R or Python you can pre-code your analysis with dummy or partial data while a survey fields. Then you're ready to go the moment you have all of your data. You can even reuse that code after the fact for fast analysis in similar projects. Quantitative analysis takes time, but so does any qualitative theming (just ask any qualitative UXR). It doesn't have to take that more *more* time than other methods. Bottom line: you don't need to forget all of your statistics you learned in school, just implement in a fashion and timeline that meets the expectations of a product team.

The top challenge I hear from UX researchers who want to quantify their impact 👇 'I don't have access to business and product metrics.' This can be for a few reasons: 1. Your organisation doesn't facilitate access to product/business data. 2. It takes a long time for your work to influence those metrics. 3. You've left the organisation. Whatever the reason, we need strategies to show our impact even when we don't have access to those metrics. Here are 6 ways UX researchers can quantify impact without access to product metrics (with examples in the carousel!) 1. Quantify the number of customers reached by your work 2. Quantify the reach of your work internally 3. Quantify your research activities 4. Quantify your thought leadership 5. Quantify your training efforts 6. Quantify your process improvements How else can we quantify UXR impact without using product or business metrics?

If you’re in UX or Human Factors, you know data only makes an impact when it’s clearly communicated. Visualization helps turn raw insights from surveys, usability tests, and interviews into clear stories that guide better design. Below is a curated list of free, practical courses to help you build data visualization skills specifically for UX and HF research. R for UX/HF Data Visualization: IBM: Visualizing Data with R (edX) – Learn ggplot2, map plots with Leaflet, and R Shiny dashboards. https://lnkd.in/eudDH5nF Data Visualization (Ball State University, Coursera) – Focuses on visualization quality, color, encoding, and EDA using R and RStudio. https://lnkd.in/es-8Zc7f Psy 6135: Psychology of Data Visualization (Michael Friendly) – Covers perception, cognition, accessibility, and visual design using R, ggplot2, and Tidyverse. https://lnkd.in/eZJ2JP5i Python for UX/HF Data Visualization IBM: Data Visualization with Python (edX) – Includes Matplotlib, Seaborn, Plotly, and Folium with geospatial and interactive charts. https://lnkd.in/emwUyXqj Data Visualization (Kaggle) – A beginner course using Seaborn to build line, bar, scatter, heatmap, and distribution plots. https://lnkd.in/eBw3fZh5 Tableau for Interactive UX/HF Dashboards Share Data Through the Art of Visualization (Google, Coursera) – Teaches data storytelling, accessible visuals, and design thinking in Tableau. https://lnkd.in/eshqyqq4 Data Visualization (UMBC, USMx, edX) – Use Tableau to create interactive dashboards and compelling data narratives. https://lnkd.in/eg7hX68k Power BI for UX/HF Reporting Free Power BI Certification Course 2025 (Power BI Plus) – Hands-on dashboard building with DAX, no coding needed, certificate included. https://lnkd.in/e-Xm8rf8 Data Visualization With Power BI (Great Learning) – Intro to BI, practical dashboard creation with Power BI and DAX. https://lnkd.in/eVsMXXRc Foundational Principles & Other Essential Tools Data Analysis: Visualisations in Excel (OpenLearn) – Covers basic plots like histograms and scatter diagrams using Excel. https://lnkd.in/eAPJqGza Data Visualization and Reporting with Generative AI (Microsoft, Coursera) – Teaches AI-assisted visual design, dashboards, and accessibility. https://lnkd.in/eTe-vwGT These resources are all free, practical, and directly relevant to the kind of data we handle in UX and HF. Enjoy!

I've always championed a product-based approach to data management. A decade ago, this perspective was often dismissed, as products were merely seen as buttons on a digital interface. However, that view is shifting as AI initiatives now place data at the forefront. The cornerstone of data product design is user centricity. Today's users could be agents or humans, but the principle remains the same. Do you have clear user profiles? Are you analyzing queries in your environment like we do with clickstream data on websites? How can you improve data navigation? Is your data model intuitive? Are you providing the right aggregations for frequently used queries? Do you have a searchable catalog that helps users find data? Are your data models designed for easier visualization? There are many simple questions to consider. My first step, even as an executive, is to examine the data model and see if I can write a query on the first day. If you're not SQL-savvy, ask your analyst to show you one of theirs. If table names require a PhD to decipher (like a schema name such as ABCD12345), it's clear that the data isn't user-friendly; it’s designed as a technical output. We can all work towards making data more accessible. It just takes a bit of observation, active listening, and thoughtful analysis.

During meetings with stakeholders, we often hear about 𝒓𝒆𝒅𝒖𝒄𝒊𝒏𝒈 𝒃𝒐𝒖𝒏𝒄𝒆 𝒓𝒂𝒕𝒆𝒔, 𝒊𝒏𝒄𝒓𝒆𝒂𝒔𝒊𝒏𝒈 𝒓𝒆𝒕𝒆𝒏𝒕𝒊𝒐𝒏, 𝒂𝒏𝒅 𝒐𝒑𝒕𝒊𝒎𝒊𝒛𝒊𝒏𝒈 𝒄𝒐𝒏𝒗𝒆𝒓𝒔𝒊𝒐𝒏 𝒇𝒖𝒏𝒏𝒆𝒍𝒔. If you're feeling confused and overwhelmed about how to do all of this, you're not alone. Here's something for those new to the world of metric-driven design. Trust me, your designs can make a real difference :) 𝗙𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗶𝗿𝘀𝘁, 𝗴𝗲𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀 𝗔𝗡𝗗 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 → Talk to real users. Understand their pain points. But also, grab coffee with the marketing team. Learn what those metrics mean. You'd be surprised how often a simple chat can clarify things. 𝗠𝗮𝗽 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗹𝗼𝘄 → Sketch it out, literally. Where are users dropping off? Where are they getting stuck? This visual approach can reveal problems you might miss otherwise and which screens you need to tackle. 𝗞𝗲𝗲𝗽 𝗶𝘁 𝘀𝗶𝗺𝗽𝗹𝗲, 𝘀𝘁𝘂𝗽𝗶𝗱 (𝗞𝗜𝗦𝗦)→ We've all heard this before, but it's true. A clean, intuitive interface can work wonders for conversion rates. If a user can't figure out what to do in 5 seconds, you might need to simplify. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗲𝘀𝗶𝗴𝗻 → Trust isn't built by security badges alone. It's about creating an overall feeling of reliability. Clear communication, consistent branding, and transparency go a long way. 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 → Transform mundane tasks into engaging experiences. Progress bars, thoughtful micro-animations, or even well-placed humor can keep users moving forward instead of bouncing off. Remember, engaged users are more likely to convert and return, directly impacting your key metrics. 𝗧𝗲𝘀𝘁, 𝗹𝗲𝗮𝗿𝗻, 𝗿𝗲𝗽𝗲𝗮𝘁 → Set up usability tests to validate your design decisions. Start small - even minor changes in copy or button placement can yield significant results. The key is to keep iterating based on real data, not assumptions. This approach improves your metrics and also sharpens your design intuition over time. 𝗗𝗼𝗻'𝘁 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁 𝘁𝗵𝗲 𝘄𝗵𝗲𝗲𝗹 → While it's tempting to create something totally new, users often prefer familiar patterns. Research industry standards and find data around successful interaction models, then adapt them to address your specific challenges. This approach combines fresh ideas with proven conventions, enhancing user comfort and adoption. Metric-driven design isn't about sacrificing creativity for numbers. It's about using data to inform and elevate your design decisions. By bridging the gap between user needs and business goals.