Digital Innovation Tools

Introducing the web's first market map of the Product Analytics Market: I was floored when I couldn't find one of these online. Surely, Gartner or CBInsights or A16Z would have created one? It turns out not. So I spent the past 3 months: • Talking with 25 buyers • Researching the space myself • Interviewing 5 product leaders at key players This is what I learned about the most significant players in each space: (that PMs and product people need to know) 1. Core Product Analytics Platforms     The foundational tools for tracking user behavior and product performance Amplitude : The leader, an all-in-one platform for PMs to master their data Mixpanel : The leader in easy UX and pioneer in event-based analytics Heap | by Contentsquare: The automatic event tracking and real-time insights leader 2. A/B Testing & Experimentation     Platforms for analysis Optimizely : The premier tool for sophisticated A/B and multivariate testing VWO : The best for combining A/B testing with heatmaps and session recordings AB Tasty: The all-in-one solution for testing, personalization, and AI-driven insights 3. Feedback & Session Recording     Capture qualitative insights and visualize user interactions Medallia: The top choice for comprehensive experience management Hotjar | by Contentsquare: The go-to for visual feedback and user behavior insights Fullstory: The best for detailed session replay and user interaction analysis 4. Open-Source Solutions     Customizable, free analytics platforms for data sovereignty Matomo: The robust, privacy-focused open-source analytics platform Plausible Analytics: The lightweight, privacy-first analytics solution PostHog: The versatile, open source product analytics tool 5. Mobile & App Analytics     Specialized tools for mobile and app performance analysis UXCam: The best for in-depth mobile user interaction insights Localytics: The leader in user engagement and lifecycle management Flurry Analytics: The comprehensive, free mobile analytics platform 6. Data Collection & Integration     Gather and unify data across platforms Segment: The top choice for effortless customer data unification Informatica: The enterprise-grade solution for data integration and governance Talend: The flexible, open-source data integration tool 7. General BI & Data Viz     Non-product specific tools for data analysis and visualization Tableau: The leader in interactive, rich data visualization Power BI: The best for deep integration with Microsoft tools Looker: The modern BI tool for customizable, real-time insights 8. Decision Automation & AI     Systems for automated insights and decisions Databricks: The unified platform for data and AI collaboration DataRobot: The leader in automated machine learning and AI Alteryx: The comprehensive solution for analytics automation Check out the full infographic to see where your favorite tools fit and discover new platforms to enhance your product analytics stack.

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Blockchain in business proves effective when it is used to solve real problems, guided by the strength of widely adopted networks, leveraging public chains, enabling smart contracts for value creation, and fostering collaboration across firms. When I look at how blockchain is being adopted, I see that success depends less on the technology itself and more on the way it is applied. The companies that benefit most are those that focus on solving clear challenges rather than migrating existing processes that already work. Data shows that public blockchains create a more open playing field, where participation is encouraged and value is generated through shared trust. Smart contracts and tokenization are not abstract concepts but mechanisms that simplify complex operations and ensure consistency across transactions. Their integration marks a real shift in how business logic can be automated and made reliable. Equally important is the capacity to connect multiple external parties through a common infrastructure, as value grows when collaboration extends beyond the borders of a single organization. Reflecting on these dynamics, the question is how leaders will balance innovation with practicality, ensuring that blockchain is adopted with clarity of purpose rather than as a mere trend. #Blockchain #BusinessTransformation #SmartContracts

Your users leave a trail of behavioral breadcrumbs with every transaction, and your recommendation engine might be stepping right over them. A new study by Upwork analyzed 9M marketplace users across 62M interactions and found that combining text-based profile analysis with behavioral data improved matching accuracy by 8-12% compared to text-only approaches. The system learns simultaneously from what users write about themselves and how they actually behave on the platform. Who they hire, what they buy, which connections succeed. This architecture works anywhere you're connecting two sides of a market. - Airbnb matching guests to hosts. - Amazon connecting buyers to sellers. - Uber pairing riders with drivers. - Dating apps. - B2B sales platforms. The pattern is the same. You have profiles (text people write about themselves), and you have behavior (the trail of interactions in your database). Most recommendation systems use one or the other. Combining both produces substantially better matches. If you run a two-sided marketplace, your transaction and interaction logs are an underutilized asset. The patterns of who your users connect with contain a real signal about who you should connect them with next.

Blockchain is changing industries in ways we didn’t expect. We often hear about blockchain when it comes to finance, but its impact goes much further. Here's a look at how it's shaking things up in different areas: Supply Chain Management: Blockchain is improving transparency and tracking. Take IBM's Food Trust network, for example—it lets consumers see where their food comes from, all the way from the farm to their plate. Healthcare: Blockchain helps keep patient records safe and makes it easy for healthcare providers to share info. MedRec Technologies, for instance, uses it to manage electronic medical records, ensuring privacy and accuracy. Voting Systems: With blockchain, we can build voting systems that are harder to tamper with. Voatz, for example, tested blockchain-based voting in U.S. elections, letting military members vote securely via their phones. Music Industry: Artists can keep control and get paid fairly. Platforms like Ujo Music let musicians publish and sell their music directly, without middlemen. Real Estate: Blockchain is making property transactions smoother and more transparent. Propy Inc., for example, helps with international real estate deals, simplifying buying and selling. Gaming: Players can truly own their in-game items. Decentraland, a virtual world, lets users buy and sell virtual property through blockchain. Intellectual Property: Blockchain securely records patents, trademarks, and copyrights, making it harder for anyone to steal them. Insurance: Blockchain is speeding up claims and policy management. Etherisc is working on decentralized insurance systems that help with quicker payouts. Education: Blockchain makes it easier to verify diplomas and certifications, cutting down on fraud. MIT Media Lab is looking at how blockchain could verify academic credentials. Charity and Philanthropy: Blockchain brings transparency to donations. The BitGive® (acquired by Heifer) Foundation, for instance, shows exactly how donations are used. Blockchain isn't just changing finance—it’s transforming industries, making systems more efficient, transparent, and trustworthy across the board.

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 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐢𝐬 𝐥𝐲𝐢𝐧𝐠 𝐭𝐨 𝐲𝐨𝐮. Here’s why 👇 You launch a Meta ad. Retarget on Google. Layer in TikTok for reach. Close the deal via email. And every platform says it got the sale. 💰 The result? ❌ Double-counted conversions ❌ Misallocated budgets ❌ Terrible ROI Here’s the hard truth: Each tool only sees a sliver. Your data? It’s a war zone of conflicting claims. So I wrote a guide that breaks it all down: ✅ What attribution models actually mean ✅ Which platforms are the most (and least) reliable ✅ Why 3rd-party tools like Wicked Reports, Hyros & Northbeam give you the full picture ✅ Real user journeys + visual breakdowns ✅ Idiot-proof tables for quick comparison If you’re a marketing leader who wants results—not vanity metrics— this one’s for you. 📖 Read the full guide below. 👇 ✉️ Subscribe to my newsletter 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 for more deep dives like this #MarketingStrategy #DigitalAttribution #MarketingAnalytics #MarketingLeadership #ScalingSuccess #MarketingROI #DataDrivenMarketing #MarketingTips #GrowthMarketing

Talking with 40+ Marketing Leaders in the last 90-day...here's what most seem to be thinking/planning for 2026 👇 1. Marketing Efficiency - Yes yes yes..the old "do more with less" seems to still be here and many marketing budgets are either holding steady and needing to improve efficiency...or maybe getting some cuts and looking to squeeze more out of each channel/play. -My advice here? Look for channel combos that build off each other vs thinking about net new channels. A. Convert more google traffic by using LinkedIn retargeting ads to qualify and convert that traffic (only channel that can do this natively). B. Leverage LinkedIn Organic + LinkedIn thought leadership ads for a very efficient middle of funnel play. This outperforms most other MOFU types. C. Use Sales Navigator to Monitor ICP fit profile viewers and set up a basic connect + message flow D. Set up simple message flow to ICP fit website visitors. E. Use LinkedIn ads company hub data to spot the accounts most exposed and engaged with LinkedIn ads to create a heat map for sales team. 2. Marketing Attribution - I believe because there is additional pressure on marketing to do more with less...attribution has become more important than ever for marketers to "justify" spend and channels. No surprise but LinkedIn is one of the hardest channels to show attribution for which is bad because it's one of the best B2B channels for Demand Gen style motions and ABM targeting/activating. -My advice for attribution? A. Set up ruthless channel attribution for each channel - key page views, lead fills, booked calls, newsletter subscribers etc. B. Implement self-attribution - ask each call booker how they found you **required free form field C. Consult software attribution- something like Dreamdata can be great but there are other lightweight options. D. Show account penetration + movement - One thing I've realized is the for B2B...we realize not all accounts are in market and yet we mostly only report on metrics for those in market. But here's what you could report on: -Accounts that moved from unaware to aware -Accounts that moved from aware to engaged -Number of accounts showing engagement -Ranking of accounts showing highest engagement/intent This can be equally valuable data to both prove effectiveness of the motion and show the leading signs we are moving in the right direction. 3. AI Overload/FOMO/Nightmares - AI is here and you need to "AI everything" or your boss will think you're not keeping up...I get it...the pressure is real here. My Advice - You're not as far behind as you think but you should prioritize time to dabble, test, measure, and start understanding how it actually will/won't impact you. For marketers I see a few high priority AI intitiatives you should tackle in 2026 But i'm out of room.... So you tell me : ) Website LinkedIn Ads Agency: https://lnkd.in/guEafPKk B2B Strategies and Guides: https://lnkd.in/gB-WQ82f

Nestlé and Digiyatra. What could they have in common? Well, they both used Blockchain to establish trust. A couple of days back, I spoke about how failed pilots shouldn’t stop innovation with example of VeChain and Wallmart China. However, there is another reason why Enterprise Blockchain suddenly feels like one of the biggest trends for Web3 2026. Its because enterprise blockchains succeed quietly, without hype. Because not every win looks like a headline. The example: Global food traceability built on Hyperledger Fabric, used by Nestlé through IBM Food Trust. For global food brands, the challenge isn’t tracking data. They already have data. Lots of it. The real challenge is this: How do you create shared trust across farmers, processors, logistics providers, retailers, and regulators, with a key feature - without exposing sensitive commercial information? That’s where this worked. Why Hyperledger Fabric fit the problem: Permissioned architecture → enterprises control who sees what Private channels → competitors collaborate without data leakage Modular design → integrates cleanly with legacy systems Governance-first approach → aligns with regulatory realities This wasn’t about decentralization for ideology’s sake. It was about coordination at scale. And again, this wasn’t a lab experiment. It ran in production, across global supply chains. What it enabled: Proven origin and traceability of food products Faster response to contamination and recalls Higher trust with regulators and consumers Operational transparency without compromising privacy My takeaway: Different problems demand different chains. And that’s a sign of maturity, not fragmentation. We have spent one whole hype cycle on interoperability solutions when they were not even that relevant. Interoperability is a natural evolution waiting for its turn to happen more appropriately. If you remember an enterprise problem that “blockchain couldn’t solve back then,” share it here. Chances are, the solution already exists today. #Blockchain #EnterpriseBlockchain #Web3 #SupplyChain

🔹 Day 21 – Product Manager Interview Prep Series 🔹 🎯 RCA-Based Question: “Your team just launched a new onboarding flow. Instead of increasing activation, it's led to a spike in churn. How would you analyze and resolve this issue?” 📌 Step-by-Step Breakdown – Root Cause Analysis (RCA) As a PM, your goal is to understand user behavior, pinpoint the friction, and fix the flow without compromising long-term retention. 1️⃣ Clarify the Problem 🔍 Define “churn”: Is it users dropping mid-onboarding? Or completing onboarding but not returning? Ask: -What’s the exact drop-off point in the new flow? -Is the churn immediate (same day) or delayed (after 1–2 days)? -What does churn look like compared to the previous flow? 2️⃣ Quantify & Segment the Impact 📊 Dive deep into analytics: 📈 Timeframe: When was the new flow launched? Sudden spike or gradual rise in churn? 👥 User Segments: Are new users from a particular platform (iOS/Android/Web) churning more? 🌐 Geo/Cohort Analysis: Are certain regions, age groups, or acquisition channels seeing higher churn? 🧪 AB Testing: Compare churn between users on old vs. new flows (if test is live). 3️⃣ Identify Potential Root Causes 🧠 UX/UI Issues: -Too many steps or confusing layout? -New permission asks too early (e.g., location, notifications)? -Value not shown quickly enough? 🔧 Technical Issues: -App crashes, lags, or slow load times? -Broken API, failed calls, or validation errors? 🧭 Psychological Friction: Users feeling overwhelmed or not understanding the benefits? High cognitive load in first interaction? 4️⃣ Talk to Stakeholders & Users 👂 User Feedback: - Session recordings (Hotjar/FullStory) - User interviews or feedback surveys - App store reviews post-launch 🤝 Internal Teams: - Engineering: Check for bugs, crashes, error logs. - Design: Walk through usability testing insights. - Data Science: Get funnel drop-off visualization. 5️⃣ Suggest Short-Term & Long-Term Improvements 🛠 Short-Term Fixes: - Roll back the most friction-heavy step. - Add in-line help or tooltips at high drop-off points. - Highlight core product value earlier. 🚀 Long-Term Initiatives: - Redesign onboarding based on user mental models. - Introduce progressive disclosure – don’t show everything at once. - Run usability tests before full rollout. 6️⃣ Measure Success Track: ✅ Increase in activation rate 📉 Drop in onboarding churn 🧠 User comprehension (measured via surveys or task success rate) 🎯 Retention metrics over Day 1, Day 7, Day 30 🔁 PM Mindset Tip: Onboarding is your first impression. Make it intuitive, not intimidating. Test thoroughly, talk to real users, and iterate until value is delivered with clarity and ease. 💬 How would YOU debug a broken onboarding flow? Let’s brainstorm in the comments 👇 #ProductManagement #PMInterview #RootCauseAnalysis #Onboarding #UserChurn #UserExperience #LinkedInDaily #ActivationStrategy #ProductDesign #LinkedInNewsIndia