User Experience Design for Gaming

Predicting user behavior is key to delivering personalized experiences and increasing engagement. In mobile gaming, anticipating a player’s next move, like which game table they’ll choose, can meaningfully improve the user journey. In a recent tech blog, the data science team at Hike shares how transformer-based models can help forecast user actions with greater accuracy. The blog details the team's approach to modeling behavior in the Rush Gaming Universe. They use a transformer-based model to predict the sequence of tables a user is likely to play, based on factors like player skill and past game outcomes. The model relies on features such as game index, table index, and win/loss history, which are converted into dense vectors with positional encoding to capture the order and timing of events. This architecture enables the system to auto-regressively predict what users are likely to do next. To validate performance, the team ran an A/B test comparing this model with their existing statistical recommendation system. The transformer-based model led to a ~4% increase in Average Revenue Per User (ARPU), a meaningful lift in engagement. This case study showcases the growing power of transformer models in capturing sequential user behavior and offers practical lessons for teams working on personalized, data-driven experiences. #DataScience #MachineLearning #Analytics #Transformers #Personalization #AI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gJR88Rnp

Some user groups have distinct usability needs, and to design experiences that truly meet those needs, we need to identify patterns in how different users interact with a product. Clustering helps group users based on shared behaviors rather than broad assumptions, allowing UX researchers to uncover deeper insights, optimize design decisions, and improve the overall experience. One of the most common clustering methods is k-means, which groups users around central points based on similarity. It is widely used for segmenting personas and analyzing behavioral trends but requires predefining the number of clusters, which can be a limitation. Hierarchical clustering offers an alternative by building a tree-like structure that reveals relationships between different user groups. This method is particularly useful for mapping engagement levels and understanding how different users interact with an interface. Density-based clustering, such as DBSCAN, identifies areas of high user activity while automatically separating outliers. This method works well for analyzing drop-offs, onboarding friction, and engagement patterns without assuming a fixed number of clusters. Gaussian Mixture Models take a probabilistic approach, allowing users to belong to multiple clusters at once. This is particularly useful for analyzing hybrid user behaviors, such as those who switch between casual and expert usage depending on the context. Fuzzy clustering is another approach that enables users to be part of multiple groups simultaneously. This is helpful when behavior is fluid and does not fit neatly into distinct categories. It is often used in personalization systems where engagement modes shift dynamically. Constraint-based clustering applies predefined business rules to the process, making it ideal for segmenting users based on factors like subscription tiers or access levels. Grid-based clustering, including the BIRCH algorithm, is particularly useful when working with large-scale datasets. Unlike other methods, BIRCH processes large amounts of data efficiently, making it a valuable tool for analyzing heatmaps, session recordings, and high-volume engagement metrics.

How to Evaluate Game FTUE In collaboration with Keewano, we're breaking down how to evaluate your game's FTUE: 𝟭. 𝗣𝗹𝗮𝘆 𝗮𝘀 𝗮 𝗣𝗹𝗮𝘆𝗲𝗿 🎮 𝗙𝗶𝗿𝘀𝘁 𝗜𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀: • Start with a clean install and record 20-40 minutes. • Note the hook, emotions, surprise, and confusion. • Log any "I wanted to..." moments or blockers. 𝗞𝗲𝘆 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: • Do I like this moment? Why? • Is the goal crystal clear? • Do the controls feel natural and responsive? • Is failure fair and teachy, or just punishing? 𝟮. 𝗣𝗹𝗮𝘆 𝗮𝘀 𝗮 𝗚𝗮𝗺𝗲 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗿 🧠 𝗙𝘂𝗻𝗻𝗲𝗹 & 𝗣𝗮𝗰𝗶𝗻𝗴: • Map the user funnel: install → first tap → first win. • Analyze the core loop and progression. • Check the pacing of currencies, resources, and unlocks. 𝗨𝘀��𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: • Evaluate the tutorial style: hard vs soft, gating. • Check readability: UI cues, VFX/SFX, feedback clarity. • Assess UI states: empty, error, next-step guidance. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀' 𝗙𝗧𝗨𝗘 🕵️ 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: • Pick 3-5 top subgenre comparable games. • Run "player first, designer second" passes on them. • Use a standard scorecard for hook, clarity, and session length. 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: • Chart the reveal order: core loop → USP → meta → shop. • Log friction like forced taps, waits, and paywalls. • Capture first asks: rate, push, account, currency. 𝟰. 𝗖𝗼𝗺𝗽𝗶𝗹𝗲 & 𝗙𝗶𝗻𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 📊 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀: • Merge the player diary and designer notes into one doc. • Build a first-session funnel with timestamps and clicks. 𝗔𝗰𝘁𝗶𝗼𝗻 & 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻: • Draft fixes: cut steps, embed teaching, reward action. • Close the loop with usability tests and store review mining.

Can UX researchers put a p-value on qual data? In a recent study, I had two groups of players trying out two versions of a game. Crunching the quant data took no time: metrics, ratings, playtime, done. The interviews, though? Two sets of long, messy, deeply human conversations. And of course, the client wanted to know one thing: do these interviews show a real difference between the groups? Can we say feature X is really important to them or not? Here’s the uncomfortable truth: interviews don’t work that way. With 8–15 people per group, you almost never have the statistical power to detect significance and forcing p-values onto qualitative data gives you either meaningless non-significance or fragile, misleading results. So here’s what I actually did (and what I’d recommend to any UX or games researcher facing this): 1. Code the qualitative data first (I used AI for this). I systematically coded transcripts into countable categories, mentioned frustration with controls: yes/no, number of pain points, sentiment. Once qual becomes quant, comparison becomes possible. 2. Use the right tests for small samples. Fisher’s exact test for proportions, Mann-Whitney for scores. They work with small n, just know that only large differences will surface. 3. Embed numbers inside interviews. A simple 1–10 satisfaction rating or a short scale during the session gives you directly testable data without losing the conversation. 4. Let each data type do its job. My quantitative data showed that a difference existed. The interviews explained why. Mixed methods isn’t about squeezing stats out of stories. It’s about pairing evidence types so each one carries the weight it’s built for. #UXResearch #GamesUserResearch #MixedMethods #ABTesting #PlayerExperience #ResearchMethods

🚀 UX Audit Analytics: Unlocking User Behavior for Improved Experiences! 🔍 When it comes to enhancing user experience, data is a UX designer’s best friend! A UX audit powered by analytics can uncover hidden insights, helping refine your platform and address pain points that may otherwise go unnoticed. Here’s a breakdown of key analytics to consider in a UX audit: 1. 🌐 User Flow Analysis What it is: Tracks user navigation paths within your website or app. Why it matters: Reveals where users drop off, feel confused, or fail to complete key actions, providing insight on areas that need adjustment for smoother journeys. 2. 🔥 Heatmaps What it is: Visual maps of user interactions, showing clicks, scrolls, and hovers. Why it matters: Highlights the areas users find engaging and points out elements that might need more visibility. 3. 🎯 Conversion Rate What it is: Percentage of users completing a desired action, like purchases or sign-ups. Why it matters: Pinpoints stages in the journey where users may be dropping off, helping refine these touchpoints to boost conversions. 4. 🚪 Exit Pages What it is: Pages where users often leave your site. Why it matters: Identifies pages that may need improvement to prevent users from exiting prematurely. 5. ⏱ Load Time Analysis What it is: Measures page load times. Why it matters: Slow speeds frustrate users, increasing bounce rates. Faster load times enhance user satisfaction. 6. ✅ Task Success Rate What it is: Percentage of users completing tasks without errors. Why it matters: A clear indicator of usability, reflecting how easily users navigate key processes. 7. 💬 User Insights What it is: Feedback from surveys or usability tests. Why it matters: Provides qualitative insights, explaining user behavior and adding depth to other metrics. Takeaway: UX audit analytics are essential in identifying roadblocks and refining user pathways, ultimately boosting satisfaction and conversion. Happy users mean a thriving platform! 🌟 #UXAudit #UserExperience #Analytics #UserInsights #OptimizeUX

When monitoring application performance, focusing solely on server metrics like CPU, memory and disk usage only tells part of the story. These metrics are essential for infrastructure health but don’t reveal how users interact with the application. Let’s take an online gaming service as an example. Simply tracking server load won’t tell if players are dropping off mid-game, facing lag issues, or waiting too long for a match. To make the app better for users and grow the business, we may need to look at what users are actually doing inside the app. Lets understand this with some sample example metrics: 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗣𝗹𝗮𝘆𝗲𝗿𝘀 𝗱𝘂𝗿𝗶𝗻𝗴 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗶𝗺𝗲𝘀 𝗼𝗳 𝗱𝗮𝘆: • Helps scale servers dynamically to prevent overload. • Can be segmented by region, game mode, or device type. Waiting 𝗧𝗶𝗺𝗲 (𝗔𝘃𝗴. 𝗤𝘂𝗲𝘂𝗲 𝗧𝗶𝗺𝗲 𝗽𝗲𝗿 𝗚𝗮𝗺𝗲 𝗠𝗼𝗱𝗲) • Tracks how long players wait before joining a game. • Long queue times may indicate an imbalance in player availability issues. 𝗗𝗿𝗼𝗽-𝗢𝗳𝗳 𝗥𝗮𝘁𝗲 (𝗣𝗹𝗮𝘆𝗲𝗿𝘀 𝗟𝗲𝗮𝘃𝗶𝗻𝗴 𝗠𝗶𝗱-𝗚𝗮𝗺𝗲) • Tracks how many users disconnect before a match ends. • Helps detect frustrating game mechanics or connectivity problems. 𝗗𝗮𝗶𝗹𝘆/𝗪𝗲𝗲𝗸𝗹𝘆/𝗠𝗼𝗻𝘁𝗵𝗹𝘆 𝗔𝗰𝘁𝗶𝘃𝗲 𝗨𝘀𝗲𝗿𝘀: • Provides insights into player retention. • A declining user ratio could indicate engagement issues. 𝗜𝗻-𝗚𝗮𝗺𝗲 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲𝘀: • Tracks revenue-generating activities. • Helps measure the effectiveness of monetization strategies. 𝗕𝘂𝗴 & 𝗖𝗿𝗮𝘀𝗵 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 𝗽𝗲𝗿 1,000 𝗦𝗲𝘀𝘀𝗶𝗼𝗻𝘀: • Measures game stability. • A rising trend may signal a need for urgent fixes. 𝗖𝗵𝗮𝘁 & 𝗦𝗼𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗽𝗲𝗿 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 • Helps analyze engagement in multiplayer environments. • Can indicate whether social features enhance user experience. Application level metrics can go beyond infrastructure health and provide actionable insights that directly impact user experience and business success. Are you tracking any application level metric in your project, do add in the comment section. #ApplicationLevelMetric #PerformanceTesting

🎉 Epic Games keeps raising the bar for creators, and the new Island Satisfaction tab in Creator Portal is another big step forward. Creators now have access to player sentiment data collected through post-game surveys. While only 1 in 500 plays triggers a survey for now, the feedback is invaluable for tracking how players feel about your island over time. Here’s what players are being asked: 🎮 Did you have fun in the last game? 📈 On a scale of 1-10, how would you rate the last game? 📊 Was the game “Way Too Easy,” “Just Right,” or “Way Too Difficult”? This data empowers creators to fine-tune gameplay, improve experiences, and ensure their maps resonate with audiences. It’s a critical tool for achieving long-term success in Fortnite, where understanding player preferences is key to standing out. The best part? Fortnite continues to invest in tools like this to help creators build thriving, data-informed experiences. It’s not just about creating islands—it’s about building better, smarter, and more sustainable worlds. #FortniteCreative #UEFN #GamingAnalytics #CreatorTools