Understanding User Experience

Brains aren’t calculators (they really aren’t). People compare, not score, so why do we keep asking for numbers when their minds work in stories and snapshots? I used to rely heavily on rating questions in UX studies. You’ve probably used them too. Rate the ease of a task from 1 to 7 or indicate satisfaction on a scale from 1 to 10. These questions feel measurable and look neat in reports, but after running enough sessions, I started noticing a pattern. A participant would finish a task and pause when asked for a score. They’d hesitate, look unsure, and eventually say something like, “Maybe a six?” followed by, “I’m not really sure what that means.” That hesitation is not about the experience itself. It’s about the format of the question. Most people do not evaluate their experiences using numbers. They judge by comparing, whether against other apps, past expectations, or familiar interactions. When I started asking questions like “How did that compare to what you’re used to?” or “Was that easier or harder than expected?” the responses became clearer and more useful. Participants shared what stood out, what surprised them, and what felt better or worse. Their answers were grounded in real impressions, not guesses. This shift from rating questions to comparison questions changed how I run research. Rating scales flatten experiences into abstract numbers. Comparison questions surface preference, context, and emotion. They help users express themselves in the way they naturally reflect on experiences. And they help researchers hear the parts of the experience that actually drive behavior. There is strong support for this in cognitive science. Tversky’s Elimination by Aspects model shows that people decide by gradually filtering out options that lack something important. Prototype theory explains that we judge how well something matches our internal image of what “good” looks like. Both models show that people think in relative terms, not fixed scores. Even heuristic evaluation in usability relies on comparing designs to expected norms and mental shortcuts, not isolated measurement. These models all point to the same idea. People understand and evaluate experiences through contrast. Asking them to rate something on a scale often hides what they really feel. Asking them to compare helps them express it. I still use quantitative data when needed. It helps with tracking and reporting. But when I want to understand why something works or fails, I ask comparison questions. Because users don’t think in scores. They think in reference points, in expectations, and in choices. That is what we should be listening to.

While traditional metrics like CSAT and NPS have long been staples in measuring customer experiences, we’re in the post-survey era and companies need to adapt. Here’s what is changing: In the next 2-3 years, we’re set to witness surveys transform into more conversational interfaces. Imagine feedback mechanisms that feel like chatting with a knowledgeable friend, rather than ticking boxes on a form. This approach not only enhances engagement but also captures the depth of customer or employee sentiment more effectively. The future is also about contextual feedback - gathering insights at multiple touchpoints throughout the journey. Moving away from transactional surveys to a journey-based approach allows for a more nuanced understanding of the experiences, identifying specific moments that matter and that have the biggest impact on your business. Moreover, the focus is increasingly on outcome-driven data. Business leaders are seeking actionable insights, not just numbers and graphs. Generative AI specifically will play a pivotal role in transforming surveys. By delivering deeper, more accurate insights and automating the process of turning data into actionable strategies, and at scale, businesses will take a broader perspective on experience-led data. As a result, survey data becomes less relevant. As you embrace these changes - or, perhaps, this new reality - one thing remains clear: The future of surveys is about making every customer feel heard and understood, turning feedback into meaningful conversations that drive business growth. #customerexperience #employeeexperience #artificialintelligence #surveys #csat #nps

Are you generating enough value for users net of the value to your company? Business value can only be created when you create so much value for users, that you can “tax” that value and take some for yourself as a business. If you don’t create any value for your users, then you can’t create value for your business. Ed Biden explains how to solve this in this week's guest post: Whilst there are many ways to understand what your users will value, two techniques in particular are incredibly valuable, especially if you’re working on a tight timeframe: 1. Jobs To Be Done 2. Customer Journey Mapping 𝟭. 𝗝𝗼𝗯𝘀 𝗧𝗼 𝗕𝗲 𝗗𝗼𝗻𝗲 (𝗝𝗧𝗕𝗗) “People don’t simply buy products or services, they ‘hire’ them to make progress in specific circumstances.”  – Clayton Christensen The core JTBD concept is that rather than buying a product for its features, customers “hire” a product to get a job done for them … and will ”fire” it for a better solution just as quickly. In practice, JTBD provides a series of lenses for understanding what your customers want, what progress looks like, and what they’ll pay for. This is a powerful way of understanding your users, because their needs are stable and it forces you to think from a user-centric point of view. This allows you to think about more radical solutions, and really focus on where you’re creating value. To use Jobs To Be Done to understand your customers, think through five key steps: 1. Use case – what is the outcome that people want? 2. Alternatives – what solutions are people using now? 3. Progress – where are people blocked? What does a better solution look like? 4. Value Proposition – why would they use your product over the alternatives? 5. Price – what would a customer pay for progress against this problem? 𝟮. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 Customer journey mapping is an effective way to visualize your customer’s experience as they try to reach one of their goals. In basic terms, a customer journey map breaks the user journey down into steps, and then for each step describes what touchpoints the customer has with your product, and how this makes them feel. The touch points are any interaction that the customer has with your company as they go through this flow: • Website and app screens • Notifications and emails • Customer service calls • Account management / sales touch points • Physically interacting with goods (e.g. Amazon), services (e.g. Airbnb) or hardware (e.g. Lime) Users’ feelings can be visualized by noting down: • What they like or feel good about at this step • What they dislike, find frustrating or confusing at this step • How they feel overall By mapping the customer’s subjective experience to the nuts and bolts of what’s going on, and then laying this out in a visual way, you can easily see where you can have the most impact, and align stakeholders on the critical problems to solve.

A hairdresser and a marketer came into the bar. Hold on… Haircuts and marketing? 🤔 Here's the reality: Consumers are more aware than ever of how their data is used. User privacy is no longer a checkbox – It is a trust-building cornerstone for any online business. 88% of consumers say they won’t share personal information unless they trust a brand. Think about it: Every time a user visits your website, they’re making an active choice to trust you or not. They want to feel heard and respected. If you're not prioritizing their privacy preferences, you're risking their data AND loyalty. We’ve all been there – Asked for a quick trim and got VERY short hair instead. Using consumers’ data without consent is just like cutting the hair you shouldn’t cut. That horrible bad haircut ruined our mood for weeks. And a poor data privacy experience can drive customers straight to your competitors, leaving your shopping carts empty. How do you avoid this pitfall? - Listen to your users. Use consent and preference management tools such as Usercentrics to allow customers full control of their data. - Be transparent. Clearly communicate how you use their information and respect their choices. - Build trust: When users feel secure about their data, they’re more likely to engage with your brand. Make sure your website isn’t alienating users with poor data practices. Start by evaluating your current approach to data privacy by scanning your website for trackers. Remember, respecting consumer choices isn’t just an ethical practice. It’s essential for long-term success in e-commerce. Focus on creating a digital environment where consumers feel valued and secure. Trust me, it will pay off! 💰

Your research findings are useless if they don't drive decisions. After watching countless brilliant insights disappear into the void, I developed 5 practical templates I use to transform research into action: 1. Decision-Driven Journey Map Standard journey maps look nice but often collect dust. My Decision-Driven Journey Map directly connects user pain points to specific product decisions with clear ownership. Key components: - User journey stages with actions - Pain points with severity ratings (1-5) - Required product decisions for each pain - Decision owner assignment - Implementation timeline This structure creates immediate accountability and turns abstract user problems into concrete action items. 2. Stakeholder Belief Audit Workshop Many product decisions happen based on untested assumptions. This workshop template helps you document and systematically test stakeholder beliefs about users. The four-step process: - Document stakeholder beliefs + confidence level - Prioritize which beliefs to test (impact vs. confidence) - Select appropriate testing methods - Create an action plan with owners and timelines When stakeholders participate in this process, they're far more likely to act on the results. 3. Insight-Action Workshop Guide Research without decisions is just expensive trivia. This workshop template provides a structured 90-minute framework to turn insights into product decisions. Workshop flow: - Research recap (15min) - Insight mapping (15min) - Decision matrix (15min) - Action planning (30min) - Wrap-up and commitments (15min) The decision matrix helps prioritize actions based on user value and implementation effort, ensuring resources are allocated effectively. 4. Five-Minute Video Insights Stakeholders rarely read full research reports. These bite-sized video templates drive decisions better than documents by making insights impossible to ignore. Video structure: - 30 sec: Key finding - 3 min: Supporting user clips - 1 min: Implications - 30 sec: Recommended next steps Pro tip: Create a library of these videos organized by product area for easy reference during planning sessions. 5. Progressive Disclosure Testing Protocol Standard usability testing tries to cover too much. This protocol focuses on how users process information over time to reveal deeper UX issues. Testing phases: - First 5-second impression - Initial scanning behavior - First meaningful action - Information discovery pattern - Task completion approach This approach reveals how users actually build mental models of your product, leading to more impactful interface decisions. Stop letting your hard-earned research insights collect dust. I’m dropping the first 3 templates below, & I’d love to hear which decision-making hurdle is currently blocking your research from making an impact! (The data in the templates is just an example, let me know in the comments or message me if you’d like the blank versions).

I recently had a chance to clock in for a 5-hour shift making coffee at one of Deputy's customers. Those hours provided a treasure trove of valuable information: which part of our product is most beloved by workers and why, what we could do better to enable worker productivity, and a wish list for new features that will help workers and managers be more connected and in sync. I also learned about how the baristas foster teamwork, what body movements and order of operations they do to maximize the amount of customers they can serve, and how they build real connection and loyalty with their buyers. It was one of the most memorable days of last year! When it comes to developing game changing innovation, "listening" to customers is no longer enough - leaders must go deeper! They actually have to step into their customers' shoes. I shared my thoughts with Forbes on the topic of human-centered design. My take? It’s about deep market research that comes with spending time with your users and building empathy through true understanding of their pain points. Translating data is something I’m passionate about, but understanding the HUMAN challenges behind the numbers is where the magic happens. Check out the 20 strategies shared by inspiring leaders, and tell me, what would you add to the list? Link here: https://lnkd.in/gH73y-nR #ForbesExpertPanel #TechnologyForGood #EmpathyInDesign #CustomerFeedback

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

Employees often miss what #CX is about, so I have an ice-breaker activity I've used at the beginning of #CustomerExperience workshops. Now, I offer this idea to you: At first, this will seem obvious and perhaps unhelpful, but stick with me, please. The activity is to have small groups spend 10 minutes discussing what drove their satisfaction and dissatisfaction with recent air travel. No, the outcomes will not be surprising—but that hides a really important point that will shake up participants' expectations and attitudes. Of course, everyone says the same things in this exercise. "I was satisfied because we arrived on time." "The snacks were better than expected." "The seats were surprisingly comfortable." "The flight attendants were attentive and pleasant." And, on the other side, "I was dissatisfied by delays." "Communications about flight changes were poor." "The seat was cramped and awkward." "The staff was grumpy and indifferent." I'll spend a few minutes collecting the drivers of satisfaction and dissatisfaction. Everyone will nod in agreement. And then comes the point of this exercise: Absolutely no one will say that a driver of satisfaction was that the airline flew them six miles in the air and delivered them to their destination safely. In other words, the CORE experience--and the most important priority of any airline--drives virtually nothing in terms of customer relationships. Getting there safely is expected, not a driver of satisfaction, loyalty, and advocacy. That's the "aha." Whether you're talking to a group of healthcare workers who think their only essential function is reducing mortality and morbidity or a room of telecom execs who feel everything hinges only on uptime, the message is that it's not what we do but how we do it that drives differentiation, satisfaction, and loyalty. We all can become so focused on the delivery of our primary product or service--or achieving the chief KPIs--that we can neglect to understand the experience from the customer's perspective. Forcing people to consider their own experiences and perceptions as customers helps them to perceive that air travelers landing safely (or patients having successful surgeries, or your phone service working) isn't what drives differentiated CX and outstanding loyalty. Don't get me wrong—you can't miss the table stakes. An airline isn't forgiven for lax safety because it has fresh nuts, nor is a telecom company pardoned for unreliable service thanks to rapid call answer times. But delivering table stakes is not what drives the kind of rabid loyalty, sales, and margin enjoyed by brands with differentiated CX. Ensuring people realize this before introducing them to customer-centric concepts and practices opens their minds to new possibilities within their existing job roles.

Ever heard of the 'peak-end rule'? This psychological principle often goes unnoticed, yet it plays a crucial role in product design. Especially within SaaS platforms. The peak-end rule suggests that our memory of past experiences is shaped not by the entire experience, but primarily by its peaks (both positive and negative) and how it ended. In other words, users will judge an entire experience based on its most intense points and its conclusion. This has profound implications in product design. It guides how we craft user journeys and interactions. Every touchpoint, feature, and interaction forms part of a user's experience in SaaS platforms. By focusing on creating positive peak moments and satisfying conclusions, we can shape a user’s overall perception of the software. Even if every single moment isn’t perfect. For SaaS products, where first impressions and overall user satisfaction are key, applying the peak-end rule can significantly enhance the 'time to value'. By designing peak moments that delight users and ensuring their journey ends on a high note, we can create a more memorable and positive experience. This encourages quicker adoption and deeper engagement. As product teams, we must recognize the power of the peak-end rule in shaping user perceptions. By intentionally designing these peak moments and satisfying endings, we can craft experiences that meet and exceed user expectations. This fosters loyalty and long-term engagement. Have you noticed the impact of peak moments and endings in your experience with SaaS products? #UXDesign #SaaS #UserExperience #DigitalStrategy #UX

While it can be easily believed that customers are the ultimate experts about their own needs, there are ways to gain insights and knowledge that customers may not be aware of or able to articulate directly. While customers are the ultimate source of truth about their needs, product managers can complement this knowledge by employing a combination of research, data analysis, and empathetic understanding to gain a more comprehensive understanding of customer needs and expectations. The goal is not to know more than customers but to use various tools and methods to gain insights that can lead to building better products and delivering exceptional user experiences. ➡️ User Research: Conducting thorough user research, such as interviews, surveys, and observational studies, can reveal underlying needs and pain points that customers may not have fully recognized or articulated. By learning from many users, we gain holistic insights and deeper insights into their motivations and behaviors. ➡️ Data Analysis: Analyzing user data, including behavioral data and usage patterns, can provide valuable insights into customer preferences and pain points. By identifying trends and patterns in the data, product managers can make informed decisions about what features or improvements are most likely to address customer needs effectively. ➡️ Contextual Inquiry: Observing customers in their real-life environment while using the product can uncover valuable insights into their needs and challenges. Contextual inquiry helps product managers understand the context in which customers use the product and how it fits into their daily lives. ➡️ Competitor Analysis: By studying competitors and their products, product managers can identify gaps in the market and potential unmet needs that customers may not even be aware of. Understanding what competitors offer can inspire product improvements and innovation. ➡️ Surfacing Implicit Needs: Sometimes, customers may not be able to express their needs explicitly, but through careful analysis and empathetic understanding, product managers can infer these implicit needs. This requires the ability to interpret feedback, observe behaviors, and understand the context in which customers use the product. ➡️ Iterative Prototyping and Testing: Continuously iterating and testing product prototypes with users allows product managers to gather feedback and refine the product based on real-world usage. Through this iterative process, product managers can uncover deeper customer needs and iteratively improve the product to meet those needs effectively. ➡️ Expertise in the Domain: Product managers, industry thought leaders, academic researchers, and others with deep domain knowledge and expertise can anticipate customer needs based on industry trends, best practices, and a comprehensive understanding of the market. #productinnovation #discovery #productmanagement #productleadership