User Engagement Strategies

Stop Posting Just Room Photos on Socials! Here’s What Guests Actually Want to See. Be honest with yourself, how many times has your hotel’s socials posted a perfectly polished room photo with a caption like: "A cozy escape awaits. Book now. ✨" And how many times did that post actually drive engagement… or, better yet, a booking? The truth? Guests don’t book because of a bed and four walls. They book for the experience. Yet, so many hotels (and restaurants) flood their feeds with soulless, salesy photos that look just like every other property. If your social media feels more like an online catalog than a destination, you’re missing the point. And here’s the real game-changer: With AI improving at an insane pace, software will soon generate better images of our hotel rooms than we ever could anyway! Perfect lighting, flawless composition, AI will do it all. Just like the image in those post. So, what will actually make a difference? The stories and experiences we share. What Should You Post Instead? 📍 The Destination : Guests aren’t just staying at your hotel; they’re visiting your city. We’re focusing on highlighting local gems, hidden spots, and experiences they won’t find on TripAdvisor. 👩🍳 Behind-the-Scenes Stories : Meet the chef behind your restaurant. Show how your cocktails are crafted. Introduce the team that makes the magic happen. People connect with people, not just places. 🎥 Guest-Generated Content : A guest’s TikTok or Instagram Story will always feel more authentic than a corporate post. That’s why we’re actively encouraging and sharing real experiences from real people. 🐶 Unique Experiences – Is your hotel pet-friendly? Show a guest’s dog getting VIP treatment. Do you have a rooftop with an insane sunset view? Capture it in the moment. We’re prioritising content that makes guests feel something. 😂 Relatable Moments – The WiFi struggle at check-in. The joy of room service at midnight. The feeling of slipping into a fresh hotel robe. We’re leaning into humour, nostalgia, and moments guests actually remember. The Bottom Line? Our guests don’t just want a room. They want a story to tell and a memory to take home. That’s exactly why our hotel group has shifted the focus of our social media strategy. Less staged perfection, more real experiences with the teams on the ground diving right in to get on board! AI will generate the polished images, but it won’t replace human connection. What’s the best-performing post you’ve seen from a hotel or restaurant? Drop a link, I’d love to check it out! 👇🏼

🔕 Design Guidelines For Better Notifications UX (https://lnkd.in/ehgF7Taa), with practical techniques on how to make notifications more useful and less annoying — with snooze mode, by exploring how and when they are triggered and measuring their use (scroll down for the newsletter ↓). 🚫 High frequency of notifications is a very frequent complaint. ✅ Not all notifications are equal: some are more useful than others. ✅ Users value updates from close contacts, transactions, insights. 🤔 Users ignore automated, irrelevant, promotional notifications. ✅ Sending fewer messages can improve long-term product use. ✅ Let users choose notification modes (silent, regular, power). ✅ Suggest switching from push notification to email digests. ✅ Let users snooze, pause, mute if high volume is expected. ✅ Track how often notifications are ignored and acted upon. 🚫 Avoid disruption and notification fatigue by sending less. And most importantly: scrutinize the decision tree to find the right timing to send the right types of notifications. Experiment with wording, timing, grouping and frequency for different user segments. And when in doubt, postpone, rather than sending through. --- 👋🏼 I'm Vitaly Friedman, and you can find useful UX resources on my profile. I’m also running “Smart Interface Design Patterns” 🍣 (https://lnkd.in/d4CNaTxR) with a friendly video library and live UX training. 😊 #ux #design #notifications

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

In our trainings with schools and districts, we anchor our workshops on hands-on prompting with GenAI foundation model chatbots. At AI for Education, we believe that prompting techniques and mindsets still are important to learn and experience. We have consistently seen that when someone has effective prompting techniques they find more value from the tools and leads to more relevant outputs, saves time, and drives AI literacy. While new reasoning models require different techniques, here are four strategies for using models like ChatGPT-4o and Sonnet 3.5: • Zero-shot: Best for quick, general-purpose responses • Few-shot: Best for generating specific responses that need to confirm to an established standard • Chain of thought: Best for encouraging a more thoughtful and accurate response • Explain-then-respond: Best for ensuring that foundational understanding is accurate When developing the prompts for our Prompt Library, we focus on zero- or few-short prompts with additional strategies for delving deeper into their outcome-specific prompting. As always, this list is not exhaustive and prompting is not an exact science. Let us know if you have any other favorite techniques. Link in the comments for a PDF version you can download. #ailiteracy #genAI #AI #prompting

What’s the secret to building a thriving community? I’m still figuring it out. But after 12 months of building the Lightbringer community, which has grown to 671 members across 16 countries, I’ve started to uncover what it truly takes to bring people together around a shared vision: getting paid to tell our stories, inspiring lives through our words, and creating impact on a global scale. It hasn’t been easy, but every challenge has been a stepping stone. Through it all, I’ve uncovered powerful truths about what makes a community thrive. Some lessons were anticipated, others caught me off guard but every single one has been transformational. Let me share a few that have made the biggest difference: 1️⃣ Empower Others to Lead A community is strongest when everyone feels ownership. When members step into leadership roles, their contributions ripple out and energize the entire group. 2️⃣ Foster Genuine Connections Thriving communities aren’t built on numbers, they’re built on relationships. The deeper the connections, the more resilient and impactful the group becomes. 3️⃣ Celebrate Every Contribution Even the smallest act of generosity adds value to the whole. Recognizing and celebrating these moments fosters a culture of gratitude and inspires others to give. 4️⃣ Create a Shared Vision A community without a shared purpose is just a crowd. When members rally behind a unifying goal, their efforts amplify each other, creating something far greater than the sum of its parts. 5️⃣ Adapt and Evolve Change is inevitable, but growth is a choice. Communities that listen, adapt, and evolve stay relevant, even as the world shifts around them. 6️⃣ Lead with Service True leadership in a community isn’t about control, it’s about care. When leaders serve their members, trust flourishes, and the community thrives. In a world that can often feel divided, our community has taught me this: Alone, we flicker; together, we shine. This is just the beginning. I know there’s so much more to learn about building communities that last. If you’re a community leader or part of a thriving group, I would love to hear from you. What’s one lesson you’ve learned about growing or leading a community? #StrongerTogether #BuildingCommunity

X just open sourced their algorithm. No hand-tuned features. No manual weights. Just a Grok transformer learning from your behavior. Every time you open X, the system needs to answer: out of hundreds of millions of posts, which ones should appear in your feed? The old approach involved manually designing features and carefully tuning how much each feature mattered. But the new system uses a Grok-based transformer looks at your engagement history (what you've liked, replied to, shared) and learns what's relevant to you. The pipeline has two stages: 1️⃣ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 Narrows millions of posts down to ~1000 candidates from two sources: 𝗧𝗵𝘂𝗻𝗱𝗲𝗿: An in-memory store that does sub-millisecond lookups of recent posts from accounts you follow. 𝗣𝗵𝗼𝗲𝗻𝗶𝘅 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: This is more interesting. It uses a 𝘁𝘄𝗼-𝘁𝗼𝘄𝗲𝗿 𝗺𝗼𝗱𝗲𝗹 to find relevant posts from accounts you don't follow. - User tower: Encodes your features and engagement history into a normalized embedding - Candidate tower: Encodes all posts in the corpus into embeddings - Similarity search: Retrieves top candidates via dot product similarity 2️⃣ 𝗥𝗮𝗻𝗸𝗶𝗻𝗴 The Grok-based transformer takes your engagement history and the candidate posts, then predicts probabilities for ~15 different actions: liking, replying, reposting, clicking, but also negative actions like blocking or muting. The model outputs probabilities for all these actions, which get combined into a final score. Positive actions get positive weights and negative actions get negative weights, pushing down content you'd likely dislike. What you're seeing in your feed right now is the product of this system processing your engagement history through a transformer architecture - originally designed for language modeling, now repurposed to predict what content keeps you scrolling 🤔 GitHub repo: https://lnkd.in/d2vvF5ee

Exciting research from Snap Inc.'s engineering team! Just came across their paper on Universal User Modeling (UUM) that's revolutionizing how they handle cross-domain user representations. The team at Snap has developed a framework that learns general-purpose user representations by leveraging behaviors across multiple in-app surfaces simultaneously. Rather than building separate user models for each surface (Content, Ads, Lens, etc.) and combining them post-hoc, UUM directly captures collaborative filtering signals across domains. Their approach formulates this as a cross-domain sequential recommendation problem, processing user interaction sequences of up to 5,000 events and using sliding windows of 800-length subsequences to balance computational efficiency with capturing long-range dependencies. The architecture leverages transformer-based self-attention mechanisms to model these sequences, with a clever design that projects feature vectors from different domains into a shared latent space before applying multi-head attention layers. The results are impressive! After successful A/B testing, UUM has been deployed in production with significant gains: - 2.78% increase in Long-form Video Open Rate - 19.2% increase in Long-form Video View Time - 1.76% increase in Lens play time - 0.87% increase in Notification Open Rate They're also exploring advanced modeling techniques like domain-specific encoders and self-attention with information bottlenecks to address the challenges of imbalanced cross-domain data. This work demonstrates how sophisticated user modeling can drive substantial engagement improvements across multiple recommendation surfaces within a large-scale social platform.

Your first 90 days with a customer can make or break the entire relationship. I've seen it happen too many times: - Great sales process - Solid product demo - Strong contract value - Excited stakeholders Then onboarding happens. And everything falls apart. Why? Most companies treat onboarding like a checklist: - Setup call ✓ - Product training ✓ - Technical integration ✓ - Documentation shared ✓ But here's the truth about onboarding: It's not about your process. It's about their success. After managing hundreds of onboarding sessions, here's what I've learned: The best onboarding isn't standard. It's personalized. Think about it: - Every customer has different goals - Every team has different challenges - Every organization has different paces - Every stakeholder has different priorities Your onboarding needs to reflect this. Here's what works: 1. Start with clear expectations - Define success metrics upfront - Set realistic timelines - Map out key milestones - Align on responsibilities 2. Build a dedicated team - Assign specialists who understand their industry - Create cross-functional support - Have clear escalation paths - Enable quick problem-solving 3. Monitor health signals - Track early usage patterns - Watch engagement levels - Note stakeholder participation - Measure progress velocity 4. Automate the right things - Regular check-in reminders - Progress updates - Resource sharing - Usage alerts But here's where most companies fail: They don't plan for challenges: - Low customer engagement - Complex technical integrations - Unclear success metrics - Resource constraints - Scalability issues The solution? Build feedback loops: - Collect input at every stage - Adjust plans based on signals - Iterate on materials - Improve processes continuously Remember: Onboarding isn't about getting customers to use your product. It's about helping them achieve their goals through your product. The first 90 days set the tone for everything that follows. Make them count. What's your approach to customer onboarding? What challenges have you faced? ------------------ ▶️ 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 1993+ community members! 💥 [link in the comments section]

Mastering Real-World App Performance: Our Strategy at Space-O Technologies In the dynamic world of mobile app development, testing and monitoring app performance under real-world conditions is crucial. At Space-O Technologies, we’ve developed a robust approach that ensures our apps not only meet but exceed performance expectations. Here’s how we do it, backed by real data and results. 📊📱 1. Real-User Monitoring (RUM): Our Tactic: We use RUM to gather insights on how our apps perform in real user environments. This has led to a 30% improvement in identifying and resolving user-specific issues. Benefit: By understanding actual user interactions, we've increased user satisfaction rates by 20%. 2. Load Testing in Realistic Conditions: Strategy: We simulate various user conditions, from low network connectivity to high traffic, to ensure our apps can handle real-world stresses. This approach has reduced app downtime by 40%. Outcome: As a result, we've seen a 25% increase in user retention due to improved app reliability. 3. Beta Testing with a Diverse User Base: Method: Our beta testing involves users from various demographics and tech-savviness. This diverse feedback led to a 35% increase in the app’s usability across different user groups. Impact: Enhanced user experience has led to a 15% increase in positive app reviews and ratings. 4. Performance Analytics Tools: Application: We employ advanced analytics tools to continuously monitor app performance metrics. This has helped us in optimizing app features, resulting in a 20% increase in app speed and responsiveness. Advantage: Improved performance metrics have directly contributed to a 30% growth in active daily users. 5. AI-Powered Incident Detection: Innovation: Using AI for incident detection and prediction has been a game-changer, reducing our issue resolution time by 50%. Result: Faster issue resolution has led to a 60% reduction in user complaints related to performance. 6. Regular Updates Based on Performance Data: Practice: We roll out updates based on concrete performance data, which has led to a 40% improvement in feature adoption and efficiency. Return on Investment: This strategic update process has enhanced overall app engagement by 25%. 🔍 Ensuring Peak Performance in the Real World At Space-O Technologies, we’re committed to delivering apps that perform flawlessly in the real world. Our methods are tried and tested, ensuring that our clients’ apps thrive under any condition. If you’re striving for excellence in app performance, let’s connect and share insights! https://lnkd.in/df_Pj6Ps Jasmine Patel , Bhaval Patel, Ankit Shah , Vijayant Das, Priyanka Wadhwani , Amit Patoliya , Yuvrajsinh Vaghela , Asha Kumar - SAFe Agilist #AppPerformance #RealWorldTesting #MobileAppDevelopment #TechInnovation #mobileappdevelopment #mobileapp #mobileappdesign

You’ll spend $30 on a single Google click and then forget that buyer exists. No segmentation. No retargeting. No second touch. Meanwhile, that same prospect is still comparing vendors for the next 90 days. Google captured the intent. LinkedIn could’ve closed the deal. That’s the disconnect killing most B2B funnels right now. 1. Capture intent on Google When someone searches “cybersecurity platform for fintech” or “B2B lead gen agency”, they’re not browsing. They’re buying. That’s high-intent traffic, and it’s expensive for a reason. But if all you do is send them to a landing page and hope they convert, you’re wasting the signal you just paid for. Don’t treat Google as a lead source. Treat it as your intent signal engine. Every click is a clue about who’s in-market right now. 2. Qualify and nurture on LinkedIn Install the LinkedIn Insight Tag and start identifying the companies hitting your site. Industry. Size. Seniority. Now you can separate random visitors from ICP-fit accounts. That’s the difference between guessing and operating with data. Google finds the demand. LinkedIn confirms the fit. Retarget those high-fit accounts with: Thought Leader Ads from your CEO or SME Short-form videos that teach and earn trust Content-led nurture instead of a pushy CTA You’re not chasing a form fill. You’re building familiarity and trust with people already looking for a solution like yours. 3. Activate and close the loop Most just collect engagement data but never act on it. Top-performing B2B systems use ads plus activation loops. When a company watches your videos, clicks your ads, or visits key pages, that’s a trigger. Feed those companies into SDR sequences, retarget them on Meta, or sync them into your CRM through DemandSense to identify exact visitors. If I were starting from zero today, I'd do the following 1. Group Google keywords by intent tier. 2. Install the Insight Tag sitewide. 3. Build a “Google visitors” audience in LinkedIn. 4. Filter for ICP-fit companies. 5. Retarget them with human, value-led content for 60 days. 6. Trigger outreach once engagement peaks. 7. Track pipeline movement and account penetration, not clicks. The problem isn’t the platforms. It’s the lack of connection between them. If your Google campaigns perform but can’t scale, or your LinkedIn ads build awareness but not revenue, this is the missing piece. LinkedIn-first. Tech-enabled. Full-funnel. That’s how modern B2B growth actually works. Website LinkedIn Ads Agency: https://lnkd.in/guEafPKk B2B Strategies and Guides: https://lnkd.in/gB-WQ82f Impactable YouTube Channel: https://lnkd.in/emYVDn_T