Customer-Focused Innovation

Your Retail Strategy Was Built for Customers Who No Longer Exist. Today's customers grew up with smartphones in their hands. And they're breaking every rule you built your retail strategy around. They research online.  Buy in store.  Return via app.  Complaining on social media. And just when you figure them out they change again. Most retailers are still doing it the old way. Loyalty programmes they ignore. Emails they never open.  In-store experiences built for their parents. No wonder fewer people are coming in and fewer are buying online. These aren't difficult customers.  They're just different. One day they want AI to find the best deal instantly.  Next they want a real person to help them decide.  They'll buy from a social media video at midnight and return it in store the next morning and expect both to feel effortless. The numbers are clear: → 𝟒𝟑% of Gen Z search on TikTok not Google or Amazon  → $𝟏𝟎𝟎 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 in US social commerce sales projected in 2026 → Global social commerce heading to $𝟖.𝟓 𝐭𝐫𝐢𝐥𝐥𝐢𝐨𝐧 𝐛𝐲 𝟐𝟎𝟑𝟎 The right approach is simple: 1. One view of every customer across every channel  2. Personalisation that adapts right product, right person, right time  3. Seamless returns and support wherever they shop  4. Trends spotted in real time not too late  5. Inventory connected directly to where Gen Z shops Build one experience and hope customers adjust or build a system that adjusts to customers. What part of your customer experience still feels like 2015? Drop it below. #RetailTransformation #CustomerExperience #Omnichannel #GenZ #SocialCommerce #DigitalRetail

📈 New Adobe data just dropped: generative AI traffic to retail sites surged 4,700% year-over-year in July 2025. But it's not the growth, while impressive, that has me thinking...it's what happens next. Here's what caught my attention and what I think marketing leaders need to pay attention to: AI-referred shoppers spend 32% more time on sites, view 10% more pages, and bounce 27% less. These aren't casual browsers. They're research-driven consumers who arrive knowing exactly what they're looking for. Three things brands need to start thinking about, because the shift in consumer behavior in the Agentic Web is happening fast: - We're optimizing for the wrong thing. 73% of AI users cite LLMs as their primary research source. Keywords won't cut it anymore. Brands need to be the authoritative source that AI systems reference when customers ask questions. - The attribution models are broken. AI traffic converts 23% less but generates 84% more revenue per visit than six months ago. These customers research through AI, then convert elsewhere. How are we tracking that journey? - The infrastructure shift is real. Consumer Electronics and Tech lead in AI visit share because complex purchases benefit most from AI research. But every category will follow. The question isn't if—it's when. For brands who have built great visibility in the current digital economy and are wondering what is happening to their metrics, It feels like the early days of digital all over again, equal parts terrifying and exhilarating. We're not just adding another channel. We're witnessing the emergence of the Agentic Era where AI agents become the new front door to discovery. The brands that recognize this shift and adapt their content, measurement, and customer journey strategies now will own the next decade. Read the full insights from our team at Adobe Digital Insights: https://lnkd.in/g2mGVGud What are you seeing in your data? How are you preparing for this shift? #MarketingStrategy #GenerativeAI #CustomerJourney #DigitalTransformation #AEO #GEO #AISearch #AdobeLLMOptimizer

Back from Hong Kong conducting 𝐀𝐈 𝐰𝐨𝐫𝐤𝐬𝐡𝐨𝐩𝐬 𝐚𝐧𝐝 𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐞 𝐍𝐕𝐈𝐃𝐈𝐀 𝐇𝐏𝐄 𝐀𝐈 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 and the most revealing conversations weren't about 𝐆𝐏𝐔 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐨𝐫 𝐦𝐨𝐝𝐞𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞. They were about a fundamental shift in customer expectations. Gen Z arrives at every interaction already armed with AI-generated research, comparisons, and questions. They've done their homework before the conversation even starts. Here's what my GenAI workshop with customers uncovered: Every enterprise is looking at "How do we serve customers who are already using GenAI?" They want to redefine their customer journey using GenAI. What I observed across industries: • Customers who use ChatGPT daily expect every business interaction to be equally intelligent. • They arrive with pre-researched questions that expose knowledge gaps in traditional customer service. One insurance executive shared: "Our customers now ask about policy exclusions we never thought they'd discover. They're coming prepared with AI-generated scenarios we've never considered." The companies adapting fastest aren't just implementing AI. They're redesigning entire customer journeys for AI-informed users. What we discussed around GPU infrastructure: The conversation wasn't just 𝐍𝐕𝐈𝐃𝐈𝐀 versus alternatives like 𝐌𝐄𝐓𝐀𝐗 or 𝐇𝐮𝐚𝐰𝐞𝐢. It was about building AI systems that can handle complex, context-rich interactions without burning through compute budgets. These aren't simple chatbots. They're reasoning engines and AI Agents that need to understand financial regulations, customer history, and emotional context simultaneously. The enterprises getting this right aren't buying more powerful chips. They're designing smarter architectures. The broader pattern emerging: The competitive advantage isn't having better AI than your customers. It's having AI that can engage with customers who already use AI. This requires a complete rethink of interaction design. When your customer can generate a detailed comparison of your product versus competitors in 30 seconds, your value proposition better be bulletproof. The enterprises getting this right are building systems that assume customer intelligence, not customer ignorance. 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐬𝐞𝐞𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐬𝐡𝐢𝐟𝐭 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲?   

Are we finally entering the Golden Age of Consumer Insights? I think we are. But not because we’ve figured everything out—because we’re finally building tools that let us explore what we don’t know yet. In the last few weeks, I’ve been testing a behavioral simulator built on real stigma dynamics, powered by synthetic personas. The response? Passionate, skeptical, curious—and incredibly thoughtful. We’re not talking about replacing real data. We’re talking about extending it. Not projecting from weak samples, but simulating directional shifts when traditional research can’t keep up. This post is about: 1. The difference between inference and simulation 2. Where synthetic data fits—and where it doesn’t 3. The real feedback we got from peers (and what we learned) 4. Why this isn’t about dashboards—it’s about adaptability If you're navigating uncertainty, volatility, or cultural complexity—this might spark something for you. Full post below—and I’d love to hear your take. What role should simulation play in the next era of insights? #ConsumerInsights #SyntheticData #AI #Simulation #BehavioralScience #MarketingLeadership

Consumer Insights is no longer about reporting what happened.  It’s about predicting what to do next. For years, Insights teams were measured by the quality of dashboards, reports, and presentations. Today, AI is changing the game. In leading organizations, Consumer Insights is moving from: Reporting → Predictive decisioning Data analysis → Real-time recommendations Research cycles → Continuous intelligence Here’s what AI is enabling: 1. From hindsight to foresight AI models now predict demand, churn, price sensitivity, and customer behaviour - before it impacts revenue. 2. Real-time decision support Business teams don’t want reports. They want answers like: Which segment to target? What price to optimize? Which channel to invest in? 3. Insights embedded into operations From dynamic pricing to personalization and media optimization - Insights is becoming a core growth engine, not a support function. In the fast-moving retail and digital ecosystem, the question is no longer: “Do we have data?” The real question is: “Are our insights driving decisions at speed?” The future of Consumer Insights leaders will be defined by one capability: Turning intelligence into revenue impact. How is AI changing the role of Consumer Insights in your organization - reporting, predicting, or directly driving decisions? Would love to hear your perspective. #ConsumerInsights #AIinBusiness #CustomerIntelligence #DataDrivenDecisionMaking #AnalyticsLeadership #CX #RetailUAE #EcommerceUAE #UAEMarket #DigitalTransformation #GrowthStrategy #MarketingAnalytics

What happens when you use AI to reframe your business entirely around your customer? For a well-known enterprise in the UAE, I used AI to ingest every customer touchpoint... every webpage, document, action, phone call, email, and more. Each was reframed, reimagined, and mapped to a core “Customer Need” in 3D space. With “Customer Need” as the common denominator, we can map the customer at any given time in the 3D space. It could be a single instant or a customer pattern forming over days or weeks. And this is where it gets powerful... proximity reveals opportunity. Once we know the customer’s location (image 2/3), we can INSTANTLY see the needs sitting closest to them in 3D space. These nearby needs highlight the most relevant data, content, actions, and context to deliver a superior experience. This is the foundation for the next generation of digital products. A hyper-personalized web experience. A chat experience more powerful and meaningful than traditional RAG. An app feature that appears only when relevant. Contexual campaigns and emails triggered at the perfect moment. A CRM enriched with deeper, more meaningful insights. Or even.. the input and context for a fully custom AI agent built for that specific customer, in that specific moment. From reacting to anticipating. From guessing to knowing. And from serving customers to serving them exactly when it matters most. This is the future. A transition from customer experience (Cx) to relationship experience (Rx).

In today’s hyperconnected world, understanding your customers no longer means tracking clicks or counting conversions - it means decoding the full narrative of how people move, decide, and connect across every channel. Customer Journey Analytics turns fragmented data into a unified, behavioral map that reveals the true flow of experience behind every purchase, sign-up, or interaction. Journey analytics follows behavior as it unfolds - how someone discovers a brand on social media, compares options on mobile, signs up through an email, and completes a purchase in-store. Each of these steps reflects both data and intention, and when linked together, they reveal the underlying logic of decision-making. This clarity allows organizations to see where attention drifts, where delight occurs, and where friction stops momentum. At the heart of the practice is journey mapping - the process of visualizing the full customer lifecycle from awareness to advocacy. By combining behavioral data with emotional and contextual signals, teams can understand what customers feel at each stage and design experiences that match those expectations. Touchpoint analysis adds another layer of insight by evaluating which interactions truly drive engagement and which need rethinking. The modern customer journey is fluid. People start on one device, switch to another, and complete their actions elsewhere. Cross-channel optimization connects those pathways, merging data from social, web, mobile, and physical environments. Machine learning models can then detect patterns and predict what happens next, empowering teams to act at the right moment with precision and empathy. Path and attribution analysis refine this even further. Rather than crediting the last click, advanced models assign value across every contributing touchpoint - ads, emails, search, and referral traffic- clarifying which combinations of actions actually lead to conversion or retention. But data alone isn’t enough. The most effective journey analytics strategies blend quantitative patterns with qualitative understanding - surveys, interviews, and sentiment analysis that explain the emotional “why” behind behavioral “what.” A drop-off on a checkout page might be clear in the numbers, but only customer feedback reveals whether it’s caused by confusion, lack of trust, or poor usability. Leading organizations already use journey analytics to bridge this gap between insight and action. Retailers link online behavior to in-store experiences, streaming services personalize recommendations in real time, and airlines trace the entire travel journey to enhance loyalty. Each case demonstrates how connecting data and human understanding reshapes the way companies anticipate needs, reduce friction, and build stronger relationships.

For years, "customer insights" has mostly meant a few standard metrics — NPS, CSAT, sentiment, and categories. Those helped us measure satisfaction. But they've never really helped us understand our customers. Today, with advances in AI and large language models, that limitation is disappearing. We can now extract entirely new dimensions of intelligence from language itself — insights that were invisible before. We can detect reasons behind churn, drivers of loyalty, product confusion patterns, sales opportunities hidden in support logs, and emerging customer behaviors — all from the raw conversations companies already have every day. This isn't just more analytics. It's a new layer of understanding. At Dimension Labs, we're building the technology that makes this possible — transforming unstructured data into structured intelligence every team can act on. 👇 I wrote about this shift in my latest piece, "Going Beyond Sentiment and Categories." If your company still measures customer understanding through NPS and sentiment alone, this will change how you think about insights entirely.

𝗔𝗜 𝘅 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 A decade ago, social listening primarily involved analyzing data from platforms such as Twitter, Facebook, and Instagram. Over time, these platforms evolved, and API restrictions tightened due to privacy guidelines. To adapt, industry leaders began incorporating additional data sources to enhance insights. A key strategy was mapping the broader customer journey: 𝙄𝙣𝙩𝙚𝙣𝙩/𝘼𝙬𝙖𝙧𝙚𝙣𝙚𝙨𝙨 - Leveraging search data to understand consumer intent. 𝙋𝙪𝙧𝙘𝙝𝙖𝙨𝙚/𝙊𝙬𝙣𝙚𝙧𝙨𝙝𝙞𝙥 - Using reviews data to track purchase and ownership experiences. However, with the rise of Gen AI, my personal experience has seen a significant shift. After using mobile apps like Perplexity and ChatGPT, I've noticed that 𝙢𝙮 𝙧𝙚𝙘𝙚𝙣𝙩 𝙥𝙪𝙧𝙘𝙝𝙖𝙨𝙚 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣𝙨 𝙣𝙤 𝙡𝙤𝙣𝙜𝙚𝙧 𝙨𝙩𝙖𝙧𝙩 𝙬𝙞𝙩𝙝 𝙂𝙤𝙤𝙜𝙡𝙚 𝙨𝙚𝙖𝙧𝙘𝙝, 𝙖𝙣𝙙 𝙩𝙝𝙪𝙨 𝙡𝙚𝙖𝙫𝙚 𝙣𝙤 𝙨𝙚𝙖𝙧𝙘𝙝 𝙝𝙞𝙨𝙩𝙤𝙧𝙮 𝙗𝙚𝙝𝙞𝙣𝙙. Instead, they begin with AI-driven recommendations that directly link me to relevant products. Moreover, we've all heard about brands manipulating reviews across platforms. With AI, there's potential for automation in generating reviews, especially at scale, which I believe some brands must have already started using. In essence, with 𝗔𝗜 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗻𝗴 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆𝘀, it will have a direct impact on how marketers map the consumer insights. It will be interesting to see how, if and when the Gen AI platforms such as ChatGPT will open up their databases for marketers to tap into the relevant consumer patterns. 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸? #ConsumerInsights #GenAI #SocialListening #SocialIntelligence #ConsumerTrends #ConsumerResearch

💰 $140 Billion. That’s how much companies spend each year trying to understand their customers, according to Andreessen Horowitz. But here’s the problem: Most of that money goes into outdated methods such as static surveys, lagging panels, and quarterly reports that are obsolete before they’re read. That world is collapsing. 🚀 AI is not just enhancing market research . it’s reinventing it. We’re now seeing the rise of synthetic customers such as generative agents that simulate human behavior at scale. These AI-driven digital consumers evolve, react to marketing stimuli, browse virtual stores, and offer continuous, real-time feedback. Think: Instead of asking a thousand people a few questions… You simulate 100,000 dynamic agents who behave like real consumers and test everything on them before touching the market. The implications are staggering: 🔹 Faster insights: Real-time dashboards and instant data processing cut weeks down to minutes. 🔹 Smarter strategies: Predictive models and NLP uncover trends and sentiments before humans even spot them. 🔹 Scalable research: AI doesn’t just make research cheaper but it makes it limitless in scope and speed. 🔹 New data types: Digital twins and synthetic data are enabling experiments that were previously impossible. 🧠 Platforms like Quantilope, CrawlQ, and AI-native co-pilots are automating every stage from survey generation to data reporting to strategic recommendations. 📊 Harvard Business Review calls this “a new insight infrastructure.” Andreessen Horowitz says it’s “the end of lagging research.” Let’s be clear: this is not the future, it’s already happening. The companies adopting AI-driven research workflows aren’t just saving time but they’re changing the game: • Predicting customer needs before they arise • Tailoring experiences at the micro-segment level • Making faster, bolder, data-driven bets The rest? Still waiting on the next quarterly report. — 💬 Are you still relying on old playbooks? Or are you building insight engines that run in real time?