User Engagement Strategies

Are you still polling your backend every few seconds just to get updates? That’s not just inefficient — it’s a terrible user experience. In my latest video, I walk you through how to implement real-time communication in .NET using SignalR. We’ll build an order tracking system that sends live updates from the backend to the frontend. ✅ Configure SignalR in .NET ✅ Build and secure your Hub ✅ Target specific users using User() ✅ Secure it with JWT ✅ Update the UI instantly via WebSocket Oh, and I’ll also show you how to make your SignalR client methods strongly typed — cleaner code, fewer bugs. Watch the full tutorial here: https://lnkd.in/e6hH5gak If you’re building modern web apps with ASP .NET Core - this is a must.

10 reflections on retention from a decade of building eCommerce & SaaS businesses: ~~ 1. Most brands focus on acquisition. The best brands focus on retention. The difference? Profitability. 2. A second-time buyer is 5x more valuable than a new customer. Yet most brands don’t have a strategy to get that second purchase. 3. The fastest way to increase LTV? Make the next purchase a no-brainer. Default-on behaviors always win - “subscribe and save”. 4. Discounts kill retention. Cashback, memberships, and loyalty perks work better. The goal isn’t to win once—it’s to win forever. 5. The best retention strategies create habits—Prime, Starbucks Rewards, Apple’s ecosystem. If you have to remind customers you exist, you’ve already lost. 6. Retention starts before the first purchase. Customers who engage with content, quizzes, and community are 2-3x more likely to buy again. 7. A VIP customer doesn’t spend 10% more—they spend 10x more. Exclusive access, priority perks, and surprise gifts turn buyers into evangelists. 8. Community is the best retention strategy no one talks about. Private groups, live Q&As, and direct brand access keep customers engaged. 9. People leave when they feel unappreciated. A simple “thank you” email, handwritten note, or surprise upgrade goes further than any discount. 10. Retention isn’t about gimmicks. It’s about delivering real, consistent value that makes repeat purchases the obvious choice. Retention is the single most important metric you’re not paying enough attention to. Follow Josh Payne for more lessons on growth, retention, and scaling profitably.

Personalisation is no longer optional for publishers; it’s the key to staying visible in Google top stories and building long-term audience loyalty. Google’s top stories carousel shapes how readers access breaking and trending news. But as algorithms shift, it’s not just about speed or keyword targeting anymore. The deciding factor is relevance, how well your content aligns with a reader’s preferences. When readers consistently engage with stories from certain publishers, those signals increase the likelihood of that publisher’s content reappearing in their personalised feeds. Publishers that drive subscriptions and repeat engagement gain more stable visibility. Encouraging audiences to subscribe via newsletters, browser alerts, or site accounts goes beyond retention. It signals to Google that your content is a preferred source. These micro-commitments contribute to authority in personalised ranking. Many publishers focus personalisation only on the homepage or recommendations widget. However, sitewide personalisation reinforces the habit. Examples include: ↳ Content tags and filters that let readers prioritise topics, authors, or regions. ↳ Dynamic internal linking based on past engagement. ↳ Contextual modules recommending similar stories or explainers. This creates a cycle where readers shape their experience, stay longer on site, and provide feedback signals that search engines can measure. Editorial and SEO teams should treat personalisation as an extension of optimisation. Beyond metadata and structured data, building systems that enable readers to express their preferences strengthens both discoverability and loyalty. Personalisation future-proofs against traffic volatility by creating recurring engagement pathways. Here are key takeaways for publishers ✅ Personalisation drives visibility – Google’s Top Stories isn’t just about speed; it’s about relevance and aligning with reader preferences. ✅ Engagement builds authority – Subscriptions, repeat visits, and micro-commitments (like saves or follows) strengthen ranking signals. ✅ Go beyond homepage widgets – Sitewide personalisation (tags, filters, dynamic links, contextual modules) builds deeper reader habits. ✅ SEO + personalisation = future-proofing – Treat personalisation as part of optimisation to reduce reliance on one-off viral hits. ✅ Stable traffic > short spikes – Creating pathways for recurring engagement ensures stronger, long-term visibility in top stories. The path to sustainable visibility in Google top stories lies in connecting two priorities: optimising for personalisation signals in search while creating pathways for readers to subscribe and shape their on-site experience. Publishers who combine both will see recurring traffic and less reliance on one-off virality. What personalisation tactics have you tested that boosted engagement or visibility? Share your experience, I’d love to compare notes. #SEO #DigitalPublishing #GoogleTopStories #Personalisation

𝗨𝘀𝗲𝗿 𝗮𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲𝗿𝘀: 𝗔 𝘀𝗵𝗼𝗿𝘁-𝗹𝗶𝘃𝗲𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻. Look, I’ve done 100s of influencer activations over the years, with the Fortune 10 down to small bootstrapped SaaS startups… 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝗴𝗿𝗼𝘄 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲𝗿 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴, 𝗸𝗻𝗼𝘄 𝘁𝗵𝗶𝘀: Influencers boost visibility and bring a surge of new users, yes - BUT, is this growth sustainable? In my experience working with #B2B #TechStartups, I've seen the initial excitement fade quickly. Just like with ads-driven methods, without having community-focused retention methods in place, users that are derived from influence campaigns often lack loyalty and engagement, which of course leads to high churn and low marketing ROI. While influencer marketing is enticing, be certain to build a strategy that fosters long-term user retention and genuine community engagement. Relying solely on influencers for long-term user acquisition is a recipe for disaster. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱: • 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝗻𝘁: First and foremost, consistently deliver value to your users. Know what “value” looks like to these people and then produce content that directly helps them achieve their goals. Keep purely promotional content to an absolute minimum.    • 𝗖𝘂𝗹𝘁𝗶𝘃𝗮𝘁𝗲 𝗮 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: Influencer promos drive awareness and leads. Once you’ve got these new people in your ecosystem, have a system in place to immediately start building relationships, both with these people and between them.    • 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝘂𝘀𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: This sounds obvious, but before launching anything, make sure your product meets the needs and expectations of the ideal customer for whom it was built. Streamline user experience to decrease time-to -value and increase engagement. Long-term growth requires a lot more than just a quick boost from influencers. It also requires a foundational strategy that prioritizes user retention and satisfaction. 𝗣.𝗦. Curious to learn more about sustainable growth strategies? Let's connect in DMs and discuss how to drive consistent, reliable revenue growth for your startup. 𝗣.𝗦.𝗦. Follow me Lillian Pierson, P.E. for more insights on AI and SaaS growth strategies. 🚀

If you think regular customer updates aren't crucial, think again. Timely communication is the backbone of customer retention. Using automation tools like HubSpot, we ensure every customer feels valued and informed. Here’s what makes it truly effective: 1/ Segment your audience: We use data to segment our customers based on behaviors, preferences, and past interactions, allowing for more targeted communications that truly resonate. 2/ Automated triggers: Our system creates triggers based on customer actions—or inactions. For instance, if a customer hasn't interacted with our emails for a while, we initiate a re-engagement campaign automatically. 3/ Drip campaigns: We've set up drip email campaigns that send messages at just the right times or in response to specific user actions, keeping our communication flow consistent and reducing team workload. 4/ Regular monitoring: Automation isn't a set-it-and-forget-it tool. We continuously monitor and optimize our automated campaigns to improve engagement and conversion rates based on performance analytics. 5/ Feedback mechanisms: We automatically send surveys and feedback forms at different stages of the customer journey, making our customers feel heard and helping us quickly identify and act on areas for improvement. This strategy not only saves time but also enhances the overall customer experience, leading to higher satisfaction and loyalty. #hubspot #customers #engagement #automation

Everyone’s talking about AI. But nobody is talking about the most important question: Can the AI find you? A recent Bain & Company study found that 80% of users rely on AI for at least 40% of their searches. That number is only going up. Traditional SEO can’t keep up. To stay visible, organizations need to shift their strategy from SEO to GEO: generative engine optimization. Here’s what that actually means: 1. Be useful to the model LLMs don’t visit your website. They rely on what they’ve been trained on. If you’re not in those sources, you’re not in the answers. What to do: • Create structured, clear content • Get cited in sources like government sites, media and journals • Avoid jargon, write plainly (Chatty hates fluff) 2. Answer real questions People don’t talk to ChatGPT like they talk to Google. Instead of a search for: “EU policy experts” It’s something much more specific like: “Who has contributed to white papers on EU data governance and AI regulation?” What to do: • Use real questions in titles, content and headlines • Map what your audience actually asks • Focus on curiosity, not keywords 3. Be the source others cite LLMs value what others link to. Be the kind of content that gets cited. What to do: • Publish reports, explainers, benchmarks • Build thought leadership that earns trust, use it for #LinkedIn, YouTube and your website. • Partner with high-authority platforms in your space 4. Train your team to prompt and be promptable It’s not just how LLMs find you. It’s how you use them to express your ideas. What to do: • Build a prompt library that matches your voice and values • Teach your team to use LLMs in a way that matches your vision • Make your tone and clarity consistent, even when the words aren’t yours SEO was about smart headlines, your own content. GEO is about the answers. Sound like a lot of work? AI can help with a lot of it. But you'll first need to define exactly what the right questions are, and what the right strategy is. In The Think Room we help you with this important shift, from strategy to actual content. Strategy first, content second. We’ll make sure ChatGPT knows your name. 📩 DM me or book a session above to meet in The Think Room Follow Liora Kern for practical insights on #branding #AI #communication #GEO #contentstrategy, tag me in relevant stories Sean Hayes BABEDA Thomas Van Oekelen Anthony Iudicello Vyella Kaptein Coleen Hartig Vikas Sharma

I recently spent 3 weeks trying to build a voice AI assistant for a client project. The result? A robotic experience with 2-3 second delays that made users want to hang up immediately. Then I discovered Agora's Conversational AI Engine, and everything changed. Here's what blew my mind: → 650ms Response Time: That's faster than most humans respond in conversation. No more awkward pauses that kill user engagement. → Real Interruption Handling: Users can actually interrupt the AI mid-sentence—just like talking to a real person. Revolutionary for natural conversation flow. → Complete Control: Bring your own LLM (OpenAI, Claude, Gemini, custom), your own TTS (Microsoft, ElevenLabs), your own everything. Zero vendor lock-in. → Built for Scale: Running on Agora's SD-RTN that handles 6+ billion voice minutes monthly. From prototype to production without breaking a sweat. The game-changer? Three lines of code. That's literally all it takes to add voice AI to your app. Built on the open-source TEN framework, they've abstracted away months of development complexity. Real-world impact I'm seeing: • Healthcare AI companions providing 24/7 emotional support • Retail assistants that actually understand complex product questions   • Gaming NPCs with dynamic personalities that remember your history • Enterprise tools that scale without losing the human touch If you're building anything that needs voice interaction, skip the months of R&D headaches. Your users will thank you for conversations that feel genuinely human. Your DevOps team will thank you for infrastructure that just works. Ready to experience the difference? → https://lnkd.in/dinYCzYA #VoiceAI #ConversationalAI #DeveloperTools #RealTimeAI #Agora #AIEngineering #TechInnovation

Introducing Compression Memory Training (CMT): Efficient Continual Learning in AI for LLM ... Large Language Models (LLMs) have become foundational in natural language processing tasks. However, their ability to adapt to rapidly changing information has posed significant challenges. A new research paper titled "CMT: A Memory Compression Method for Continual Learning" by Dongfang Li and colleagues from Harbin Institute of Technology (Shenzhen) presents an innovative solution that could redefine how we handle continuous updates in LLMs. 👉 The Challenge of Knowledge Adaptation Traditional LLM training methods are not suited for frequent updates due to their sheer size and the associated computational costs. As user needs and data streams evolve, the risk of "catastrophic forgetting"—where models lose previously learned knowledge when exposed to new data—becomes a pressing concern. This paper addresses these critical challenges by proposing a method that allows LLMs to integrate new knowledge efficiently without extensive retraining. 👉 What is Compression Memory Training (CMT)? CMT introduces a "dynamic memory bank" within the model's latent space, paralleling human memory functions. Here are its core components: - "Dynamic Memory Bank": CMT stores compressed information from new documents, allowing LLMs to retain and recall knowledge fluidly. - "Robust Knowledge Retention": By freezing the model's parameters during training, CMT minimizes the risk of forgetting previously acquired information. - "Efficient Online Adaptation": The method supports real-time learning by adapting to incoming data streams while maintaining computational efficiency. 👉 Real-World Applications and Impact CMT holds practical implications across multiple industries. Here’s how it can benefit various sectors: - "Customer Service": Businesses can provide up-to-date responses based on changing customer queries and preferences. - "Healthcare": Models can integrate new research findings or patient data swiftly, improving care and decision-making. - "Finance": Keeping financial models aligned with the latest market trends can enhance predictive accuracy and decision support. 👉 Conclusion and Future Directions The paper's findings suggest that CMT is not only efficient but also versatile across multiple datasets, showcasing improvements in performance metrics. The integration of such memory mechanisms into LLMs may pave the way for a future where AI systems can learn continuously, adapting to change without losing their foundational knowledge. I invite my connections to explore this fascinating work further. 

It's not enough to make great products; you also need a top-notch ecommerce marketing strategy to: → build brand awareness, → turn site visitors into loyal customers, → and keep them coming back. But how do you choose the right strategies? Here are 11 ecommerce marketing tactics with real-life examples to try: 1. Add User-Generated Videos to Product Pages 👉🏻 Use video content to showcase products and educate customers. ↳ Example: goPure's shoppable videos led to a 13% conversion rate. 2. Optimize Webpages with SEO 👉🏻 Focus on keyword research and site architecture to rank higher in search results. ↳ Example: Munchkin improved site navigation, reducing bounce rates. 3. Post Relevant, Informative Content on Your Blog 👉🏻 Create content that attracts readers and incorporates SEO strategies. ↳ Example: A blog post on cleaning coffee machines can link to your products. 4. Develop a Social Media Marketing Plan 👉🏻 Combine paid and organic strategies to build a loyal community. ↳ Example: Solo Stove's Instagram giveaways enhance brand awareness. 5. Set Up Google Shopping Ads 👉🏻 Place products at the top of search results with detailed ads. ↳ Use tools like Semrush’s PLA Research to refine your ad strategy. 6. Collect Email Addresses Through Innovative Pop-Ups 👉🏻 Engage visitors with unique pop-ups to build strong customer relationships. ↳ Example: Asphalte generated 4,000 leads per month with pop-ups. 7. Personalize Online Shopping Experiences 👉🏻 Tailor interactions based on previous purchases and browsing behavior. ↳ Example: Amazon's personalized product suggestions based on browsing history. 8. Create a (Branded) Loyalty Program 👉🏻 Develop a loyalty program to incentivize repeat purchases. ↳ Example: Blume's "Blumetopia" loyalty program with branded rewards. 9. Partner with Micro-Influencers 👉🏻 Collaborate with influencers who have a dedicated following. ↳ Example: LOOKFANTASTIC's micro-influencer ads resulted in higher engagement. 10. Retarget Potential Customers Across Different Platforms 👉🏻 Use ads, emails, and SMS to reconnect with potential customers. ↳ Example: Beardbrand's retargeting approach with cart recovery emails and Facebook ads. 11. Practice Ecommerce Conversion Rate Optimization (CRO) 👉🏻 Optimize your website for better conversions with clear product descriptions and streamlined checkout processes. ↳ Example: Helm Boots’ simplified checkout process enhances user experience and drives sales. Most importantly, a great ecommerce marketing strategy should reflect and connect with how people ACTUALLY shop online. What would you add?

Investment is moving from pre-training to post-training. Early LLM budgets focused on training ever-larger base models. Post-deployment, models were mostly fixed... improvements came via scaling next-gen models. Now labs split resources between pre-training and RL fine-tuning with careful inference optimization. In post-training, models learn reasoning, follow prefs and refine outputs for specific tasks. Models update more frequently, weekly or monthly vs yearly. Each iteration collects new data, extends RL training and adjusts rewards to improve reasoning. New economic models charge for updated reasoning engines vs static models. Serving costs and latency are key so providers favor efficient pro models with most accuracy at lower compute. 3 more takeaways from ICML: 1️⃣ RL is only beginning to cross into “unverifiable” domains. Traditional RL used tasks with clear auto checks (like code compilers or math calcs) for rewards. Domains like Math Olympiad or legal arguments have complex solutions that can't be auto-verified. These need more complex reward models scoring reasoning steps, argument clarity or persuasiveness vs just correctness. With solid reward models, RL could teach coherent proofs, experiment design or legal strategies. This is still early work - current systems often break tasks into verifiable parts or use human prefs, but broader paths are emerging. 2️⃣ Personalization is a safer near-term goal than true continual learning. Interest in adapting models to users is growing with 2 key approaches. Personalization tailors outputs by learning user-specific rewards from few feedbacks or adding curiosity rewards that prompt questions about user tastes. These adjust behavior without changing model weights, improving perceived helpfulness and empathy. Continual learning updates parameters in real time from all user input. But continual learning poses safety and privacy risks like overfitting, bias amp & data leakage. Personalization and context windows (remembering interactions without weight changes) are more practical and responsible. 3️⃣ RL scaling will follow multiple paths. → One path applies current RL methods (PPO, guided reward models, diffusion reg) to more domains. This incremental path needs no new algos, just better rewards, bigger varied datasets & optimization tweaks. → The second tackles sparse-reward probs with long feedback delays. Success depends on credit assignment in long seqs and off-policy learning using varied experiences. Off-policy RL supports multi-datacenter training with clusters handling acting, collection and learning. → The third involves continual learning with frequent updates using user feedback and new data. Each has trade-offs: incremental and safe, risky but domain-expanding, adaptive but complex w/ safety issues. What'd I miss?