API performance issues can silently erode user experience, strain resources, and ultimately impact your bottom line. I've grappled with these challenges firsthand. Here are the critical pain points I've encountered, and the solutions that turned things around: ð¦ð¹ðð´ð´ð¶ððµ ð¥ð²ðð½ð¼ð»ðð² ð§ð¶ðºð²ð ðð¿ð¶ðð¶ð»ð´ ð¨ðð²ð¿ð ððð®ð ð£ð¿ð¼ð¯ð¹ð²ðº: Users abandoning applications due to frustratingly slow API responses. ð¦ð¼ð¹ððð¶ð¼ð»: Implementing a robust caching strategy. Redis for server-side caching and proper use of HTTP caching headers dramatically reduced response times. ðð®ðð®ð¯ð®ðð² ð¤ðð²ð¿ð¶ð²ð ðð¿ð¶ð»ð´ð¶ð»ð´ ð¦ð²ð¿ðð²ð¿ð ðð¼ ð§ðµð²ð¶ð¿ ðð»ð²ð²ð ð£ð¿ð¼ð¯ð¹ð²ðº: Complex queries causing significant lag and occasionally crashing our servers during peak loads. ð¦ð¼ð¹ððð¶ð¼ð»ð: Strategic indexing on frequently queried columns Rigorous query optimization using EXPLAIN Tackling the notorious N+1 query problem, especially in ORM usage ðð®ð»ð±ðð¶ð±ððµ ð¢ðð²ð¿ð¹ð¼ð®ð± ð³ð¿ð¼ðº ðð¹ð¼ð®ðð²ð± ð£ð®ðð¹ð¼ð®ð±ð ð£ð¿ð¼ð¯ð¹ð²ðº: Large data transfers eating up bandwidth and slowing down mobile users. ð¦ð¼ð¹ððð¶ð¼ð»: Adopting more efficient serialization methods. While JSON is the go-to, MessagePack significantly reduced payload sizes without sacrificing usability. ðð£ð ðð»ð±ð½ð¼ð¶ð»ðð ððð°ð¸ð¹ð¶ð»ð´ ð¨ð»ð±ð²ð¿ ðð²ð®ðð ðð¼ð®ð±ð ð£ð¿ð¼ð¯ð¹ð²ðº: Critical endpoints becoming unresponsive during traffic spikes. ð¦ð¼ð¹ððð¶ð¼ð»ð: Implementing asynchronous processing for resource-intensive tasks Designing a more thoughtful pagination and filtering system to manage large datasets efficiently ð£ð²ð¿ð³ð¼ð¿ðºð®ð»ð°ð² ðð¼ððð¹ð²ð»ð²ð°ð¸ð ðð¹ðð¶ð»ð´ ð¨ð»ð±ð²ð¿ ððµð² ð¥ð®ð±ð®ð¿ ð£ð¿ð¼ð¯ð¹ð²ðº: Struggling to identify and address performance issues before they impact users. ð¦ð¼ð¹ððð¶ð¼ð»: Establishing a comprehensive monitoring and profiling system to catch and diagnose issues early. ð¦ð°ð®ð¹ð®ð¯ð¶ð¹ð¶ðð ððµð®ð¹ð¹ð²ð»ð´ð²ð ð®ð ð¨ðð²ð¿ ðð®ðð² ðð¿ð¼ðð ð£ð¿ð¼ð¯ð¹ð²ðº: What worked for thousands of users started to crumble with millions. ð¦ð¼ð¹ððð¶ð¼ð»ð: Implementing effective load balancing Optimizing network performance with techniques like content compression Upgrading to HTTP/2 for improved multiplexing and reduced latency By addressing these pain points head-on, we can significantly improve user satisfaction and reduce operational costs. What challenges have you faced with API performance? How did you overcome them? Gif Credit - Nelson Djalo
User Experience for SaaS Products
Explore top LinkedIn content from expert professionals.
-
-
We spoke to 80+ SaaS leaders about onboarding, and hereâs the brutal truth we found: 49% of users donât quit at signup. They quit at the first key feature. People want to use your product. They leave when you ask them to use it. Because the âaha!â moment is buried behind setup, integrations, or data imports. Here are 3 quick fixes that actually work ð  1. A simple setup email with step-by-step visuals  2. Pre-built templates/workflows (at Mailmodo, we hand users a library so they can quickstart instantly)  3. Social proof: show how completing the step drives impact (âTeams who connect their CRM save 5+ hours weeklyâ) And this is just the surface. Our State of Onboarding 2025 report took 5 weeks of work, 80+ respondents, and the expertise of leaders like Aiza Coronado, Ramli John, and Jon Farah. We even built it as a customizable dashboard so you can filter benchmarks by your GTM model and product type, and see exactly where your onboarding stands. ð If you havenât checked it out yet, nowâs the time.
-
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. Iâm dropping the first 3 templates below, & Iâd 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 youâd like the blank versions).
-
When I was head of growth, our team reached 40% activation rates, and onboarded hundreds of thousands of new users. Without knowing it, we discovered a framework. Here are the 6 steps we followed. 1. Define value: Successful onboarding is typically judged by new user activation rates. But what is activation? The moment users receive value. Reaching it should lead to higher retention & conversion to paid plans. First define it. Then get new users there. 2. Deliver value, quickly Revisit your flow and make sure it gets users to the activation moment fast. Remove unnecessary steps, complexity, and distractions along the way. Not sure how to start? Try reducing time (or steps) to activate by 50%. 3. Motivate users to action: Don't settle for simple. Look for sticking points in the user experience you can solve with microcopy, empty states, tours, email flows, etc. Then remind users what to do next with on-demand checklists, progress bars, & milestone celebrations. 4. Customize the experience: Ditch the one-size fits all approach. Learn about your different use cases. Then, create different product "recipes" to help users achieve their specific goals. 5. Start in the middle: Solve for the biggest user pain points stopping users from starting. Lean on customizable templates and pre-made playbooks to help people go 0-1 faster. 6. Build momentum pre-signup: Create ways for website visitors to start interacting with the product - and building momentum, before they fill out any forms. This means that you'll deliver value sooner, and to more people. Keep it simple. Learn what's valuable to users. Then deliver value on their terms.
-
Imagine this: youâre filling out a survey and come across a question instructing you to answer 1 for Yes and 0 for No. As if that wasn't bad enough, the instructions are at the top of the page, and when you scroll to answer some of the questions, youâve lost sight of what 1 and 0 means. Why is this an accessibility fail? Memory Burden: Not everyone can remember instructions after scrolling, especially those with cognitive disabilities or short-term memory challenges. Screen Readers: For people using assistive technologies, the separation between the instructions and the input field creates confusion. By the time they navigate to the input, the context might be lost. Universal Design: Itâs frustrating and time-consuming to repeatedly scroll up and down to confirm what the numbers mean. You can improve this type of survey by: 1. Placing clear labels next to each input (e.g., "1 = Yes, 0 = No"). 2. Better yet, use intuitive design and replace numbers with a combo box or radio buttons labeled "Yes" and "No." 3. Group the questions by topic. 4. Use headers and field groups to break them up for screen reader users. 5. Only display five or six at a time so people don't get overwhelmed and bail out. 6. Ensure instructions remain visible or are repeated near the question for easy reference. Accessibility isnât just a "nice to have." Itâs critical to ensure everyone can participate. Donât let bad design create barriers and invalidate your survey results. Alt: A screen shot of a survey containing numerous questions with an instructing you to answer 1 for Yes and 0 for No. The instruction is written at the top and it gets lost when you scroll down to answer other questions. #AccessibilityFailFriday #AccessibilityMatters #InclusiveDesign #UXBestPractices #DigitalAccessibility
-
This is a visual representation of why your team hates Salesforce ð¡... Throughout my Salesforce journey, I've seen it all (Insert "Emotional Damage" meme ð« ). One common issue I see often are Flows that "work," but that are not optimized for scale or user experience. They cause ugly error messages, delays on future iteration, & inaccurate data that plague users on a daily basis. Check out the Flow examples below: Version 1 works. It's simple, has only 2 elements, so what's the big deal? To find out, let's look at the #'d boxes in Version 2: 1ï¸â£ Element Descriptions: Please...for the love of Benioff... document the "Why." Each element allows you to write a description, which explains what it's doing technically and why it's important to the process you're building. This context is essential for future changes and for those that come after you. If another admin can't read your descriptions and understand what it's doing, you haven't documented enough! 2ï¸â£ Decision Elements after Get Records Elements: In Version 1, the "Get Account Id" element finds a related Account record associated with the triggering Opportunity. What happens if the criteria for the search doesn't find a record? â Flow Error â. By checking to see if the Get Records element finds what it's looking for, you can prevent a poor user experience and ensure other automation runs on schedule. 3ï¸â£ Fault Paths & Error Handling: A fault path is an error handling path that triggers when the element wasn't able to process a change (Update, Create, Delete) in the database. By default, users are presented with red text and a cryptic message without enough readable context to troubleshoot themselves. In Version 2, we've add a fault path for every Create Records element to notify the Salesforce team of new errors. No one likes it when automation fails, but it's a magical experience to reach out to a user and let them know you're already working on it! ðªð©ð 4ï¸â£ Tracking Performance/Usability: This one is a game changer... What good is an active Flow if you can't measure its performance or usability? Create a custom object called "Automation Saved Time." Any time you add to a Flow, estimate the amount of time the automation saves and add it to a variable. At the end of the Flow, create a new Automation Saved Time record adding the aggregated time for all elements. It'll help answer some amazing questions: a) How much time has your Flow saved users? b) How often has Flow is been run? c) Is this Flow useful? All questions you can only assume the answers to without this data! Build a dashboard and show it to internal stakeholders, so they understand the value you're adding. 5ï¸â£ Reuse & Recycle: Rather than building a new Flow element each time you need it, connect to an existing element. In this example, we are connecting both fault paths to the same email alert. "In a world full of Version 1s, be a Version 2 ðªð»" #salesforce #salesforceflow #automation #bestpractices #benioff
-
I spent 5 years scaling Superhuman's white glove, concierge onboarding. â¦and another 2 years rebuilding it in product. My biggest lessons on effective product onboarding: It must be *opinionated*, *interruptive*, and *interactive*. â¢â¢â¢ ð§ Opinionated There's a million ways to use Superhuman, but only one correct way. We had unopinionated steps in the onboarding, like teaching "j" and "k" to navigate. But what really matters is Inbox Zero. Marking Done. Our most extreme form is Get Me To Zero â a pop-up that practically coerces you to Mark Done *everything*. This experience gets an astonishing 60% new user opt-in. New users want to experience something different; they want to learn. We pruned away the bland, and left behind pure, unfiltered opinion. Exactly what made our concierge onboarding effective. ð¥ Interruptive We've all seen them before: checklists, tooltips, nudges. Inoffensive growth clutter that piles up in the corners of your app. We shipped all this and more. But it had precisely zero impact. Our most impactful changes were interruptive: on-rails demos, full-screen takeovers, product overlays. Arresting user attention is critical: if an experience is tucked away in the corner, it will be ignored. If it's ignored, it may as well not exist. ð¹ï¸ Interactive You can't be Opinionated and Interruptive without being Interactive. It's a crime to force users to engage with non-actionable information. Instead, provide functionality: an action to take, setting to toggle, CTA to click. It's more fun AND users build muscle memory. There is something to do in every step of our onboarding. Perhaps that's how we get away with an onboarding nearly 50 screens long ð¤ â¢â¢â¢ Final thought: if you're struggling with this flow, simply watch new users. Note all the places you want to jump in â there's your onboarding ð s/o to the very thoughtful Superhumans building this: Ben â¨Kalyn Lilliana Kevin Peik Erin Gaurav ð #plg #onboarding #activation
-
New: When folks are most likely to convert from trial-to-paid. Itâs even earlier than I thought ð And itâs one of the best pieces of evidence I've seen in support of reverse trials. For B2B SaaS companies, about 5% of new trialists convert from free-to-paid within 6 months. (In B2C, it's closer to 20%.) Of these conversions: - HALF (!) happen within the first 7 days - 70% happen within the first 14 days - 90% happen within the first month - But only about 3% happen in months 3-6 The data comes from my friends at ChartMogul who looked at the GTM and conversion data of 2,500+ SaaS companies. Look, the immediate takeaway of this data is that you have a very narrow window to impress trial users. Usually within the first week -- if not sooner. My two cents about what to do with this data: re-engage signups post-trial! 1. Consider a 'reverse trial' -- letting folks downgrade to a freemium plan if they don't convert within the trial period. Why? This gives users more time to see value. It then creates additional conversion opportunities over time: users hit a CTA in the product, they run into a feature gate, they trip a usage limit. And they can become product-qualified leads for the sales team. In my recent State of B2B Monetization survey, still only 4% of software companies have a reverse trial. That's about one-tenth the rate of free trial/freemium. 2. Auto-extend free trials for accounts who reached an 'aha moment' but didn't buy. Why? In my experience, about 25-40% of new signups reach an 'aha moment' within their first week. But this data shows only about 5% of folks are buying. That leaves 20-35% of signups reaching and 'aha moment' and NOT buying! Depending on user signals, you could take a 'smart trial' approach to either (a) enforcing the trial time-limit, (b) extending the trial period and/or (c) collecting data on why they've decided not to buy. 3. Periodically re-open trial windows, especially around major milestones like new product releases or events. Why? Your product is constantly improving. Make sure lapsed trial users see that and have a chance to experience it firsthand! It's a cool report, I'll drop the link in the comments. #saas #ai #plg
-
I got to see behind the curtain of 100s of SaaS companies. hereâs what the best ones had going for them: 1ï¸â£ Customer Success truly is Product's responsibility first. After analyzing hundreds of retention patterns, the truth became clear: companies that treated CS as a product function (not just a post-sale service) consistently outperformed their peers. The most successful products had customer success principles built into their DNA, not bolted on afterward. 2ï¸â£ Build advocacy triggers into the product journey. The SaaS companies with the highest NRR didn't just deliver value - they made it unmistakably obvious when that value was delivered. Every "aha moment" was clearly marked, celebrated, and designed to be shareable. Products that made users look good in front of their teams created natural advocates. 3ï¸â£ Onboarding friction predicts churn with shocking accuracy. We could predict retention rates just by analyzing the first 30 days of engagement. Every additional step, confusion point, or moment of hesitation in early adoption correlated directly with eventual churn. The most successful products weren't necessarily the most feature-rich - they were the ones that delivered value with the least resistance. 4ï¸â£ Cross-team expansion happens through champions, not features. After analyzing hundreds of expansion patterns, we found that additional seats and modules rarely sold through traditional sales motions. Instead, they spread through internal champions who became unofficial product evangelists. The best products deliberately created and empowered these champions with resources, recognition, and tools to drive internal adoption. 5ï¸â£ Transparency builds confidence that drives renewals. The SaaS companies with the highest renewal rates weren't hiding behind quarterly business reviews and sanitized metrics. They provided radical transparency into product usage, upcoming roadmap, and even their own internal metrics. This transparency built trust that became the foundation for long-term retention. Working with hundreds of SaaS businesses showed me that successful products aren't just built differently - they're operated differently. Product decisions cascade into customer success outcomes, creating either virtuous or vicious cycles that ultimately determine company trajectory. What patterns have you noticed that separate successful SaaS products from the rest?
-
How do you figure out what truly matters to users when youâve got a long list of features, benefits, or design options - but only a limited sample size and even less time? A lot of UX researchers use Best-Worst Scaling (or MaxDiff) to tackle this. Itâs a great method: simple for participants, easy to analyze, and far better than traditional rating scales. But when the research question goes beyond basic prioritization - like understanding user segments, handling optional features, factoring in pricing, or capturing uncertainty - MaxDiff starts to show its limits. Thatâs when more advanced methods come in, and theyâre often more accessible than people think. For example, Anchored MaxDiff adds a must-have vs. nice-to-have dimension that turns relative rankings into more actionable insights. Adaptive Choice-Based Conjoint goes further by learning what matters most to each respondent and adapting the questions accordingly - ideal when you're juggling 10+ attributes. Menu-Based Conjoint works especially well for products with flexible options or bundles, like SaaS platforms or modular hardware, helping you see what users are likely to select together. If you suspect different mental models among your users, Latent Class Models can uncover hidden segments by clustering users based on their underlying choice patterns. TURF analysis is a lifesaver when you need to pick a few features that will have the widest reach across your audience, often used in roadmap planning. And if you're trying to account for how confident or honest people are in their responses, Bayesian Truth Serum adds a layer of statistical correction that can help de-bias sensitive data. Want to tie preferences to price? Gabor-Granger techniques and price-anchored conjoint models give you insight into willingness-to-pay without running a full pricing study. These methods all work well with small-to-medium sample sizes, especially when paired with Hierarchical Bayes or latent class estimation, making them a perfect fit for fast-paced UX environments where stakes are high and clarity matters.