Data-Driven UX Decisions for SaaS

Data doesn’t have to define your design process. But failing to use it is a big mistake. In our process, we use data from the beginning to draw inspiration, then use data to guide our prototyping decisions, and eventually make more data-driven choices. The process is more flexible than people often think. The goal isn’t to use data–it’s to make more informed decisions that ultimately improve user and business outcomes. Here’s how: → Data-Inspired Design (Frame the Challenge) We use data to inspire and shape our understanding of the design problem. The aim is to find insights that lead to creative solutions while considering what users need, how they behave, and why they act in specific ways. We find up to 100 opportunities to create lift in a design initiative. Helio UX metrics help us gather early user feedback or signals, highlighting where users struggle or where new opportunities lie. We can set a clear direction for the design process by using these early insights and proxy metrics. We also do interviews. Our team focuses on collecting these early signals to understand the reasons behind user actions. → Data-Informed Design (Assess the Potential) We weigh the benefits and risks of different ideas. Data helps guide the design process, but intuition and insights are just as important as measurable factors. In more significant engagements, we collect answers from up to 30,000 participants in this phase. Helio is handy here, as it allows teams to test early prototypes on a large scale, gathering UX metrics crucial for evaluating design choices. Data storytelling and analyzing user research turn insights into practical feedback. Collaboration across teams also ensures that the design meets user and business needs. We gather feedback through usability tests and measure task completion rates, helping link early design ideas to clear success criteria. → Data-Driven Design (Finalize the Choices) Data helps us make decisions that align with business and user goals. The focus is refining the design using feedback and data to make it as effective as possible. Once the design is live, we connect early metrics with analytics. Helio helps us collect data, such as success rates, user satisfaction, and task completion. These figures provide the confidence needed to finalize design decisions. We align UX metrics with business goals, focusing on clear outcomes like improved usability, higher feature adoption, or revenue growth. Design KPIs and early signals play a role, guiding us in making final decisions based on how well the product performs against these success metrics. —–––––– Data can be applied differently throughout the design process—from an initial source of inspiration to a guiding force in assessing potential and ultimately as the driver of final decisions. We use data differently in each design phase, balancing creativity and analysis. Interested? DM me. #productdesign #productdiscovery #userresearch #uxresearch

Conjoint analysis has long been a powerhouse for understanding how users make trade-offs - but the field has evolved far beyond the traditional models most UX teams still use. Classic methods like Full-Profile, Adaptive, and Choice-Based Conjoint taught us how to quantify preference and predict demand. They remain powerful for testing early concepts, subscription options, or pricing tiers. Yet, as digital products became more complex - configurable SaaS dashboards, adaptive apps, or multimodal experiences - these methods began to show their limits. Modern conjoint frameworks now bridge behavioral realism with computational sophistication. Hierarchical Bayesian (HB) models reduce survey fatigue by inferring individual preferences from minimal data - perfect for agile UX cycles where speed matters. Hybrid conjoint designs (like HIT-CBC) combine ratings and choices to capture nuanced trade-offs without overwhelming respondents. Menu-Based Conjoint (MBC) takes things further by mirroring the way users actually interact with digital products: they build their own bundle. This method captures real configuration behavior seen in subscriptions, feature toggles, and personalization flows - while managing cognitive load through progressive disclosure. Dynamic Choice Modeling introduces a time dimension. It tracks how preferences evolve with feedback or experience, making it ideal for studying onboarding journeys or adaptive recommendations where past actions influence the next choice. Virtual and Immersive Conjoint (VR/AR) place participants in simulated environments - digital storefronts, 3D layouts, or interface prototypes - to measure how spatial design and aesthetics shape real decisions.

For the past 8 years as a PM/Engineer/Analyst, I've always been working on data products or utilizing them in one way or another. The importance of data can never be emphasized enough. Let me share my insights from a long journey so far! I kickstarted my journey in two very early-stage startups; I was the first data person in one company, and the first team member/engineer/analyst/PM in another. There was barely any work done with regards to data. The advantages were the freedom to mold and shape from scratch, crafting solutions tailored to the unique needs of the business. However, this freedom came with challenges – the absence of existing frameworks meant there was a need to build everything from the ground up; so much to do, but barely any available resources or capacity time-wise. Following this, I transitioned to two later-stage startups, where data products had already taken shape. Here, the advantages were evident – a foundation was laid, and there was a wealth of historical data to tap into. Yet, challenges emerged in the form of information overload, and how to improve the quality of some data points with known data issues. -- Useful data is not just about numbers; it's about ensuring they are: 🚀 Actionable: Transforming data into strategic decisions. You can think of possible actions based on every data point. 💡 Easily Understandable: Anyone with the right context should easily understand this data - there should be very little to no confusion. 🤔 Reasoned: Every data point has a purpose, a reason why it was tracked, shared or visualized. -- How are data products essential? 🛠️ Improving UX Optimizing the user experience was a key function I worked on in one of the companies I joined. For example, we optimized the UX of the onboarding process in for a client-facing system. reducing the steps from ~8 to ~4. The result? A doubling of the conversion rate, showcasing the tangible impact of data on user-centric enhancements. 📝 Improving Content/Copy Crafting compelling messages is an art. Adjusting the header of a step in the onboarding process, guided by data insights, resulted in a 10% reduction in dropoff. This underlines how data-driven decisions can significantly impact user engagement. ➕ Building New Features Innovation is fueled by understanding user needs. Think about Spotify's Year in Review – a data product that not only provides insights but also adds a viral touch. A strong understanding of user needs can lead to the creation of features that truly have a positive business impact. 🔍 Allowing for A/B Tests Once you have a live product in place, it’s likely you’ll need some optimizations, and one way to do that is through A/B tests. It's made better when rooted in a deep understanding of user challenges. By knowing how users interact with a system, A/B tests can be tailored to address specific pain points, ensuring more meaningful and impactful results. -- How have you utilized data in your product activities?

A product only scales when its strategy is tied directly to business goals. Otherwise, features become noise, and teams burn months on “nice to have” work that doesn’t move revenue, retention, or efficiency. Business alignment means: ✓ Every feature connects to metrics that matter ✓ Every design decision supports growth or cost optimization ✓ The roadmap speaks the same language as the leadership team. ⸻ Example: Healthcare Case I worked with a medical SaaS platform that had a backlog of 120+ features. Developers pushed new releases every two weeks, but churn was growing and revenue wasn’t scaling. I ran a UX–Business audit: — Mapped every feature to a business KPI — Cut 40% of backlog items that had zero business impact. — Rebuilt the roadmap so that every quarter focused on one clear business lever . Result after 3 months: ✓ Customer support tickets dropped by 22% ✓ Retention improved by 15% because patients were guided better through their journey. ✓ Leadership got visibility: for the first time, the roadmap was linked directly to revenue forecasts. ⸻ Example: Fintech Case In a fintech startup, leadership struggled to raise the next round because their pitch deck showed features, not impact. I restructured the product narrative: — Aligned UX flows with financial metrics: fewer failed transactions, faster onboarding, higher account activation. — Designed a demo around money saved and money earned, not UI screenshots. — Synced the product roadmap with the CFO’s model, so investors could see cause–effect clearly. The outcome: They closed a $7M round. Investors saw a product tied to growth levers, not just design polish. ⸻ My takeaway Business alignment is not paperwork. It’s the discipline of turning UX work into financial outcomes. When I step in, I translate design into numbers the boardroom understands — retention, efficiency, growth. That’s how design stops being a cost center and becomes a driver of business decisions. ⸻ I’ve spent over 8 years in UX and 7 years in branding, marketing, and PR. What I do is not just design — I architect clarity between product and business goals. That’s why my work stabilizes teams, speeds up decision-making, and helps products grow in markets under pressure. 

69% conversion increase. 32% boost in average order size. 27% more membership sign-ups. These are actual results our clients have actually achieved through data-driven design. Design is subjective, but it should also be objective. When clients come to Metajive, they rarely view design as purely aesthetic — the "make it pretty" phase. We have smart clients who knows pretty doesn't pay bills. Results do! So we approach design differently. We start with KPIs and business goals, then work backward: 1. What specific metrics need to improve? 2. Who's the actual buyer? (For premium golf simulators, it's 40+ high-income men, not 20-year-old athletes) 3. What visual language creates both familiarity AND differentiation in market? This matters because when we designed for School of Rock, every visual choice supported their primary KPI: lead conversion. The clean layout, strategic form placement, and intuitive hierarchy weren't just aesthetic — they were conversion machines. For our golf simulator client, instead of just studying other golf companies, we analyzed Nike, Under Armour, and Tag Huer. "If we want to feel more tech-focused, how do we drive that tech look? If we want to feel more sports-oriented, what visual elements are common across sports brands but absent in golf?" The science behind our approach: A. Market Analysis: Study both direct competitors AND adjacent spaces that appeal to the same demographic B. Psychology Mapping: Match visual elements to buyer psychology (bright colors create emotion, black backgrounds can make products premium) C. Strategic UX: If there are 50 tech features of a printer we align those to benefits and communicate clearly. A website isn't art — it's your hardest-working salesperson. It should be measured and optimized like one. Design subjectively. Validate objectively. Your business deserves both.

UX design without data is like driving blindfolded. But at the same time, data alone won't tell you the whole story. Here’s how we balance both for stellar results at UN/COMMON: ↓ 1️⃣ Start with well-tested strategies After building hundreds of eCommerce funnels, we’ve seen certain UX approaches consistently perform well. We focus on designs that: -> Keep users moving down the funnel -> Guide them smoothly from home page to checkout …this sets the foundation. 2️⃣ Dig into the numbers Leveraging data platforms like Triple Whale and GA4 allow us to understand consumer behavior in a funnel at a micro level. They let us analyze every step of the user journey. We use them to: -> Find winning patterns -> Spot conversion roadblocks -> Make data-backed UX decisions From home page to the “thank you” page, we leave no stone unturned. 3️⃣ Get inside customers’ heads Numbers tell a story… …but they don’t tell the *whole* story. So, we put ourselves in the shopper’s shoes and ask: -> How does this design make them feel? -> What motivates them to keep clicking? -> Where might they get stuck or confused? To make conversions, we don’t only analyze behavior— We decode the human behind every click. Because at the end of the day, we’re all consumers— We shop. We browse. We buy. …and the best UX taps into that shared experience. 4️⃣ Balance quant and qual Magic happens when we combine hard data with human insight. This dual approach helps us: -> Validate our hunches with numbers -> Explain our numbers with real user feedback The result? ↳ UX that’s both data-driven *and* user-centric 5️⃣ Keep learning and applying Every project and partnership is a chance to get better— We take lessons from each client and apply them to the next. This constant evolution means: -> Our designs keep improving -> Our strategies stay current -> Our results get stronger At UN/COMMON, we’re never satisfied with “good enough.” The bottom line? Great UX is where quantitative analysis intersects with human psychology. It's not just about data or design. It's about decoding human behavior at scale— That's how we create experiences that convert.

Does your SaaS product check all the boxes on paper and yet struggles to attract and retain customers? You're not alone. Many B2B SaaS companies invest heavily in building a great product but overlook the importance of customer experience. This is a huge missed opportunity. 📉 For B2B SaaS companies, customer data can provide unique insights to improve customer satisfaction and retention. Delivering an exceptional customer experience is arguably more important for SaaS companies than traditional product-focused businesses. Why? Because SaaS is an ongoing service, not a one-time transaction. Customers must repeatedly interact with your platform and support team. Any friction during onboarding, usage, or support is amplified. But how can you identify and fix customer experience issues? The key is utilizing data-driven insights. Modern SaaS platforms generate a goldmine of behavioral data. Analyzing this can uncover exactly where customers struggle or churn. 📌 For example, you may find: 1. Excessive in-app help requests around a certain feature, indicating confusion 2. Spikes in churn after a specific onboarding step, suggesting it's too complex 3. Low adoption of high-value features needed for renewal With these insights, you can take targeted actions to smooth out the customer journey, such as optimizing your onboarding flow, improving self-service help content, or proactively guiding customers to get value from overlooked features. The result? Happier, more engaged customers that fully leverage your product and are more likely to renew. Plus, reducing churn saves you money on customer acquisition. Focusing on the customer experience pays dividends across the board. Don't let your data just sit there. Put it into action 👇: ✔ Audit your current CX ✔ Analyze customer data ✔ Implement data-driven CX improvements. You can see the impact immediately when you turn mere data into delighted customers and revenue growth. #customerexperience #saasgrowth #saasleadgeneration #b2bsaas #saasmarketing #data

𝐒𝐚𝐚𝐒 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 𝐓𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭𝐬 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 A recent Forbes Technology Council Expert Panel explored strategies for SaaS companies to leverage data analytics in refining offerings, improving customer retention, and driving profitability. As one of the contributors, I shared insights on utilizing digital twins to predict user responses and optimize product features before deployment. Key takeaways include: 📊 Segment Customers By Needs: Use data analytics to understand and address unique customer priorities 💡 Monitor Usage To Increase Adoption: Analyze feature adoption patterns to focus on impactful areas 🚀 Optimize High-Value Features: Prioritize development of features that drive engagement and satisfaction 🔍 Spot Early Signs Of Churn: Employ predictive models to identify and mitigate churn risks proactively 🔄 Predict Responses With Digital Twins: Simulate user interactions to test and optimize features in controlled environments 👥 Contributors: Raghu Ram Bongula, Virgil Bretz, Laurent Barcelo, Devin Redmond, Evan Schwartz, Josh Dunham, Song Bac Toh, Aditya Ganjam, Paul Kovalenko, Oleg Lola, Balaji Dhamodharan, Savitri Sagar, Adrian Stelmach, Jagadish Gokavarapu, Anoop Gupta, Sebastiaan Debrouwere, Nihinlola Adeyemi #ForbesTechnologyCouncil #SaaS #DataAnalytics #CustomerRetention #Profitability #DigitalTwins #FeatureOptimization

Data is a superpower in product design. Without data, we open ourselves up to: - Biases - Opinions - Confusion - Misalignment When we are data-informed and that data is accurate, we can truly make educated product decisions. I like to think of data in two layers: 1. What’s happening 2. Why it’s happening Let’s break it down. 1. What’s happening: ↳ Business data tells us how the business is doing ↳ Marketing/sales data tells us where our customers come from ↳ Retention data tells us when and why customers are leaving us ↳ Engagement data tells us how customers are using our product 2. Why it’s happening: ↳ User research gives us rich insight into why something is happening ↳ Voice of the customer data shows us how customers talk about our product ↳ Usability scores show us how people perceive our experience in a measurable way ↳ Product market fit & satisfaction scores give us a simple and actionable metric to track and improve over time In terms of accessing that data, methodologies vary, but generally speaking, I always advise the following: 1. Get access to growth and retention data through business dashboards. 2. Get access to product data through your product analytics tool. 3. Set up a cadence to gather customer reviews & comments. 4. Set up a cadence to speak to your users continuously to answer the why. 5. Set up a recurring survey to track satisfaction and usability. PS: The list of metrics is indicative: Actual metrics will differ significantly from one company to another and largely depend on the industry, niche, and data setup. — If you found this useful, consider reposting ♻️ #productdesign #uiux #uxdesign