User Experience for SaaS Products

🧑🏽 Designing Better Personalization UX. With guidelines on how to better tailor content and features to user’s needs and interests. ✅ Customization allows users to choose exactly what they want. ✅ Personalization anticipates what they want behind the scenes. ✅ We personalize to match specific needs without user’s effort. ✅ We allow users to customize preferences, filters, layout, data. 🤔 But often only very few people customize their experience. 🚫 Past behavior doesn’t always predict future actions. 🤔 Users often have different needs at different times. ✅ Design a wide range of presets, templates and defaults. ✅ Track frequent actions and errors, and suggest shortcuts. ✅ Always add content, or reshuffle it, rather than removing it. ✅ Expose users to non-matching topics to avoid filter bubbles. 🤔 Often users don’t know what they need, or what they’d like. ✅ Good personalization is deeply embedded in a user journey. ✅ Search for moments when you want to win user’s attention. ✅ Ask users explicitly about their intent to learn their context. ✅ Let users override personalization if it goes against their needs. ✅ When journey breaks, don’t stitch it, but tie a beautiful bow. We can’t personalize without research. Collect reliable data about users first. Then segment users into groups with shared needs. Decide what messages you have for each group. And define a user model, content model and metadata that go along with it. Then decide on individual or role-based personalization. Choose touchpoints where personalized UX will be served. Apply the logic across your channels, but give users full control of their data. In that process, define how the team will test and measure the impact of personalization over time. Such a project might often feel like a huge leap of faith without immediate benefits. But if done well, it can increase customer lifetime value significantly — but you will need short-term victories to get a long-term commitment. So start slowly. Run experiments. Personalize where you can make the highest impact. More often than not, the outcome will be worth the effort — even although most users will never even notice it, they might stay for many years to come. ✤ Useful resources Personalization: A Practical UX Guide, by Taras Bakusevych https://lnkd.in/e8v6WF9U Five Levels Of Recommendations, by Guillaume Galante https://lnkd.in/eKqsZtJ5 Definitive Guide To Personalization (free eBook, PDF) https://lnkd.in/eCA_a5Xh Personalization Pyramid, by Colin A. Eagan M.S., Jeffrey MacIntyre https://lnkd.in/eaztWU8e ✤ Books – The Person in Personalisation, by David Mannheim – Hello {first name}, by Rasmus Houlind 🎀 – The Personalization Paradox, by Val Swisher, Regina Lynn Preciado – Personalization Mechanics, by John Berndt #ux #design

Getting the right feedback will transform your job as a PM. More scalability, better user engagement, and growth. But most PMs don’t know how to do it right. Here’s the Feedback Engine I’ve used to ship highly engaging products at unicorns & large organizations: — Right feedback can literally transform your product and company. At Apollo, we launched a contact enrichment feature. Feedback showed users loved its accuracy, but... They needed bulk processing. We shipped it and had a 40% increase in user engagement. Here’s how to get it right: — 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Most PMs get this wrong. They collect feedback randomly with no system or strategy. But remember: your output is only as good as your input. And if your input is messy, it will only lead you astray. Here’s how to collect feedback strategically: → Diversify your sources: customer interviews, support tickets, sales calls, social media & community forums, etc. → Be systematic: track feedback across channels consistently. → Close the loop: confirm your understanding with users to avoid misinterpretation. — 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Analyzing feedback is like building the foundation of a skyscraper. If it’s shaky, your decisions will crumble. So don’t rush through it. Dive deep to identify patterns that will guide your actions in the right direction. Here’s how: Aggregate feedback → pull data from all sources into one place. Spot themes → look for recurring pain points, feature requests, or frustrations. Quantify impact → how often does an issue occur? Map risks → classify issues by severity and potential business impact. — 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗰𝘁 𝗼𝗻 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Now comes the exciting part: turning insights into action. Execution here can make or break everything. Do it right, and you’ll ship features users love. Mess it up, and you’ll waste time, effort, and resources. Here’s how to execute effectively: Prioritize ruthlessly → focus on high-impact, low-effort changes first. Assign ownership → make sure every action has a responsible owner. Set validation loops → build mechanisms to test and validate changes. Stay agile → be ready to pivot if feedback reveals new priorities. — 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 What can’t be measured, can’t be improved. If your metrics don’t move, something went wrong. Either the feedback was flawed, or your solution didn’t land. Here’s how to measure: → Set KPIs for success, like user engagement, adoption rates, or risk reduction. → Track metrics post-launch to catch issues early. → Iterate quickly and keep on improving on feedback. — In a nutshell... It creates a cycle that drives growth and reduces risk: → Collect feedback strategically. → Analyze it deeply for actionable insights. → Act on it with precision. → Measure its impact and iterate. — P.S. How do you collect and implement feedback?

In customer experience (CX), the closed-loop feedback (CLF) model has been a cornerstone for over two decades, originally designed to ensure responsiveness and adaptation. It's time for a change. With the advent of artificial intelligence, it's clear that merely adapting this model isn't enough. It's old tapes. It needs to evolve. Here's what's next: Real-time Interaction Management: Traditional CLF reacts to feedback after the fact. And, traditionally, closing the "inner loop" requires a human to follow up. AI turns this on its head. Imagine a system that adjusts the customer journey in real-time based on predictive analytics, reducing friction points before they affect the customer experience. Large Action Models: We all know that AI can dive deep into data lakes to instantly identify patterns and root causes of customer dissatisfaction. This rapid analysis allows companies to not only close the feedback loop faster, but also implement more effective solutions. This will come in the evolution of Large Language Models, or LLMs, to LAMs, or Large Action Models. Continuous Learning Systems: AI transforms CLF from a loop that ends into continuous cycle of improvement. These systems learn from each interaction, constantly updating and refining strategies to enhance the customer experience. This means that the feedback loop is ever-evolving, driven by AI's ability to adapt to new information and complex variables, seamlessly. CX leaders have to embrace AI's potential to redefine our foundational practices. It's time to innovate beyond the traditional CLF and leverage AI to deliver personalized experiences, and at scale. How are you thinking about adaptive, predictive, and personalized CX strategies? Your answer can't be to hire more people to close more loops. #customerexperience #ai #journeymanagement #survey #CLF

Every company says they listen to customers. But most just hear them. There's a difference. After spending years building feedback loops, here's what I've learned: Feedback isn't about collecting data. It's about creating change. Most companies fail at feedback because: - They send random surveys - They collect scattered feedback - They store insights in silos - They never close the loop The result? Frustrated customers. Missed opportunities. Lost revenue. Here's how to build real feedback loops: 1. Gather feedback intelligently - NPS isn't enough - CSAT tells half the story - One channel never works Instead: - Run targeted post-interaction surveys - Conduct deep-dive customer interviews - Analyze product usage patterns - Monitor support conversations - Build customer advisory boards - Track social mentions 2. Create a single source of truth - Consolidate feedback from everywhere - Tag and categorize insights - Track trends over time - Make it accessible to everyone 3. Turn feedback into action - Prioritize based on impact - Align with business goals - Create clear ownership - Set implementation timelines But here's the most important part: Close the loop. When customers give feedback: - Acknowledge it immediately - Update them on progress - Show them implemented changes - Demonstrate their impact The biggest mistakes I see: Feedback Overload: - Collecting too much data - No clear action plan - Analysis paralysis Biased Collection: - Listening to the loudest voices - Ignoring silent majority - Over-indexing on complaints Slow Response: - Taking months to act - No progress updates - Lost customer trust Remember: Good feedback loops aren't about tools. They're about trust. Every piece of feedback is a customer saying: "I care enough to help you improve." Don't waste that trust. The best companies don't just collect feedback. They turn it into visible change. They show customers their voice matters. They build trust through action. Start small: 1. Pick one feedback channel 2. Create a clear process 3. Act quickly on insights 4. Show results 5. Scale what works Your customers are talking. Are you really listening? More importantly, are you acting? What's your approach to customer feedback? How do you close the loop? ------------------ ▶️ Want to see more content like this and also connect with other CS & SaaS enthusiasts? You should join Tidbits. We do short round-ups a few times a week to help you learn what it takes to be a top-notch customer success professional. Join 1999+ community members! 💥 [link in the comments section]

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

Customer needs are evolving and they need more personal attention. How to scale personal attention? AI is the solution for brands to start offering personalization at scale. AI solutions are transforming how businesses engage with customers, making experiences more tailored, efficient, and impactful. Here’s a quick AI Personalization Checklist to ensure your strategy hits the mark: ↳ Understand Your Audience: • Analyze customer data for preferences, behavior, and trends. • Identify key segments and personas to tailor experiences effectively. ↳Use Data Wisely: • Leverage historical and real-time data for dynamic personalization. • Ensure data privacy and compliance with regulations like GDPR/CCPA. ↳Deliver Relevant Recommendations: • Implement AI to suggest products, services, or content based on individual needs. • Personalize marketing campaigns with customer-specific offers and messaging. ↳Optimize User Journeys: • Use AI to predict and address pain points in the customer journey. • Provide seamless multi-channel experiences across web, app, and support. ↳Continuously Adapt: • Monitor AI model performance and retrain as customer behaviors evolve. • Incorporate customer feedback for improved personalization. ↳Delight with Details: • Offer subtle yet impactful touches like dynamic visuals (e.g., Netflix thumbnails). • Ensure AI-driven solutions feel intuitive and human-centric. ↳Measure Success: • Track KPIs like engagement rates, conversion rates, and customer retention. • Use insights to refine and iterate your personalization strategies. Bonus: Check the popular use cases from the 8 product leaders in the deck below. 👇 Follow Lokesh Gupta and ProductHood School for more such resources. PS - Want to become better at building products. Join our membership.

The biggest mistake I made as a 10-months old SaaS founder (so far) 👇 → Not trying to spend more time with users who churned from Scalelist and understand in details why they churned. It sounds stupidly obvious, But this is something I didn’t do well. I spent the last 4 days in the USA with Youssef and 50 other B2B SaaS founders. 3 key elements that constantly came back from the discussions we had there were: 1- If you feel stuck, talk to your users, 2- If you need answers, talk to your users, 3- If you want to satisfy your clients, talk to your users and those who churned, Long story short, No matter what, communicate with your current and previous users. Get information from them will help you avoid taking stupid decisions based on gut feelings. Yesterday, Youssef and I met with Dominic, a former customer. Here’s what I discovered during our conversation: - He canceled Scalelist because of Sales Navigator’s limitations - He decided to stop any form of automation for a while - His outreach process was painfully time-consuming - Automation often lacks the personal touch he values and solves him a lot of time - Switching between prospects is exhausting. But most importantly: We weren’t solving his real problems. Dominic told us he sometimes spent 6 hours reaching out to 200 prospects. He dreams of doing that process by just talking to a software or his phone and ideally in a few minutes each day. Talking to Dominic gave me clarity. It’s not just about building features. It’s about understanding and potentially solving the frustrations of real people. - - Lesson learned: Churn isn’t failure. It’s feedback in disguise. Would love to hear your thoughts—what’s your biggest SaaS lesson (Comment below) 👇 ?

That’s the thing about feedback—you can’t just ask for it once and call it a day. I learned this the hard way. Early on, I’d send out surveys after product launches, thinking I was doing enough. But here’s what happened: responses trickled in, and the insights felt either outdated or too general by the time we acted on them. It hit me: feedback isn’t a one-time event—it’s an ongoing process, and that’s where feedback loops come into play. A feedback loop is a system where you consistently collect, analyze, and act on customer insights. It’s not just about gathering input but creating an ongoing dialogue that shapes your product, service, or messaging architecture in real-time. When done right, feedback loops build emotional resonance with your audience. They show customers you’re not just listening—you’re evolving based on what they need. How can you build effective feedback loops? → Embed feedback opportunities into the customer journey: Don’t wait until the end of a cycle to ask for input. Include feedback points within key moments—like after onboarding, post-purchase, or following customer support interactions. These micro-moments keep the loop alive and relevant. → Leverage multiple channels for input: People share feedback differently. Use a mix of surveys, live chat, community polls, and social media listening to capture diverse perspectives. This enriches your feedback loop with varied insights. → Automate small, actionable nudges: Implement automated follow-ups asking users to rate their experience or suggest improvements. This not only gathers real-time data but also fosters a culture of continuous improvement. But here’s the challenge—feedback loops can easily become overwhelming. When you’re swimming in data, it’s tough to decide what to act on, and there’s always the risk of analysis paralysis. Here’s how you manage it: → Define the building blocks of useful feedback: Prioritize feedback that aligns with your brand’s goals or messaging architecture. Not every suggestion needs action—focus on trends that impact customer experience or growth. → Close the loop publicly: When customers see their input being acted upon, they feel heard. Announce product improvements or service changes driven by customer feedback. It builds trust and strengthens emotional resonance. → Involve your team in the loop: Feedback isn’t just for customer support or marketing—it’s a company-wide asset. Use feedback loops to align cross-functional teams, ensuring insights flow seamlessly between product, marketing, and operations. When feedback becomes a living system, it shifts from being a reactive task to a proactive strategy. It’s not just about gathering opinions—it’s about creating a continuous conversation that shapes your brand in real-time. And as we’ve learned, that’s where real value lies—building something dynamic, adaptive, and truly connected to your audience. #storytelling #marketing #customermarketing

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

I'm going to say it: SaaS products need to stop over-relying on onboarding tours. Every user doesn't have the patience to watch the fancy onboarding video and 10-slide carousels. They've got places to be. According to Chameleon, the average completion rate for product tours is 61%. In a recruitment tech product I worked in, I found that 1 out of 4 users would happily skip onboarding prompts to get straight into action. With the rise of prompting, that trend is only going to rise. Users won't want to "learn the product" to get stuff done. They want the product to "learn what they want" and take orders. Over the years, exposure to a high volume of B2B SaaS products has made users more confident in riding in without training wheels. This is especially seen in category-aware and solution-aware users. Even more so if they are migrating from a competing product. So, yes, there is a place to have an "onboarding" flow. I'm not asking to drop that. But you need a Plan B for users who bail out from that path. You need to support "Noboarding". A few things Product Managers, Designers & Marketers need to get right: 1) Navigation → use jargon-free labels. → too many items in settings? Offer search. → sequence items based on usage frequency. → interview users to identify familiar nomenclature. 2) Product microcopy → clear titles and descriptions for each screen. → proofread success, error, and transactional emails. → write copy with HEAL in mind (helpful, empathetic, actionable, linear) 3) Help users visualize the end → use familiar web constructs. → offer templates where possible. → show sample data on blank states (with an easy way to delete it). Above all, have internal and external users test your "no-boarding" flow and critically assess how it holds up. What are some products that do this well? Canva: users zipline to the editor and figure it out with drag-n-drop. Lovable: simple chat interface with a preview pane. Loom: single prominent CTA ("Record a video"). -- What do you do for users who choose to "no-board"?