Daniel W.

It was incredible to share how we’re reimagining AI in retail at Kushals Fashion Jewellery, and how we are moving to the predictive AI phase. 3 key ideas from my talk: 1. Data becomes powerful only when it gains context. (Our AI Tagging System is turning products into stories.) 2. Prediction is probabilistic pattern-sense, refined over time. (Our Pincode OS learns local buying rhythms and maps it with AI tagging system.) 3. The future of AI is intuitive. (Machines predict; humans interpret.) Grateful to the ad:tech India, Jaswant Singh Shiney Eapen Neha Takkar and everyone who joined the conversation on building the future of predictive retail. #PredictiveAI #RetailTransformation #DigitalInnovation #KushalsFashionJewellery #iMedia2025

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Dr. Ramla Jarrar, an MMM expert, shares an important perspective every marketer using MMM should read. The customer journey is complex, and while MMM is a powerful tool for understanding how spend impacts that journey, its limitations are often overlooked and nuances brushed over once results are packaged in polished decks. The tension I often see: marketers need one strong KPI to track large marketing investments, but the complexity of measurement means no single framework provides that holy grail metric. Measurement methodologies are sophisticated and technically robust, yet each has constraints. Triangulating across multiple frameworks—MMM, incrementality testing, attribution, brand tracking—provides better insights, though interpretation remains as much art as science. This requires partnership. Marketers need to understand what each methodology can and cannot deliver. Analytics partners, whether internal teams or external consultancies, have the responsibility to explain assumptions, acknowledge limitations, and help interpret results responsibly. When both sides engage with this complexity honestly, we make better decisions. A question for marketers: where do you find the biggest gaps between marketing performance measurement and the questions you need answered? #MarketingMixModeling #MMM #MarketingAnalytics #MarketingMeasurement #MarketingROI #DataDrivenMarketing #MarketingStrategy

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The retail execs who figured out AI in 2025 got promoted. The ones who didn't are updating their resumes. I've watched this split happen in real time. Same industry, same technology available, completely different outcomes. The difference wasn't budget. It wasn't talent. It was how they approached the problem. What the promoted execs did differently They stopped chasing "AI projects" and started solving business problems that happened to use AI. They moved from chatbots to agentic commerce. Their AI doesn't just answer questions, it executes. "Plan a 3-day camping trip for a family of four and cart everything I need." Done. They got granular with inventory. Predictive models that account for a high school championship game or a localized weather front. Some cut out-of-stocks by 30%. They made checkout invisible. Computer vision that identifies items without barcode scans. Customers walk out, transaction complete. What got people fired? Automating broken processes. I've seen 40% of retail AI projects fail because someone bolted AI onto a workflow that was already a mess. Faster mess is still a mess. Generic personalization. Customers are allergic to "Hello [Name]" emails now. If your AI doesn't understand context, like why someone's style changed after a move, you're creating noise, not value. Outsourcing the customer relationship. Relying on third-party AI agents to sell your products. If the AI buys for the customer, you become a faceless commodity. The three things that determined who survived 1. Data architecture: The winners stopped running a Ferrari on a dirt road. They moved from siloed data to federated architecture, real-time inventory and customer context across all channels. 2. Proprietary experiences: They didn't just expose their catalog to AI agents. They built reasons for customers to stay in their ecosystem. Virtual try-ons, loyalty perks, things no third-party agent can replicate. 3. Human plus agent design: They let AI handle the transactional, returns, stock checks. Freed their people for the transformational, styling, complex consulting. The executives who understood this kept their jobs. The ones still running "AI projects" in 2026 are discovering that the honeymoon phase ended a year ago. Which side of that line is your company on? I'm advising companies on AI strategy and exploring my next executive role (CDO, CPO, VP of Product/AI). If you're building something interesting, or hiring, DMs are open. #RetailTech #AI #DataStrategy #DigitalTransformation #Leadership

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Real transformation happens when we shift from building dashboards to solving real problems for real roles. From requests → to outcomes. From data delivery → to decision support. The future of data teams isn’t being a report factory. It’s becoming partners who deeply understand business personas, workflows, and moments that matter — and design solutions around them. This is the evolution data teams need: Less “dashboard concierge,” more “people-first problem solving.”

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THE QUIET REVOLUTION HAPPENING IN RETAIL Some of the most interesting conversations in retail are between data scientists and merchandisers figuring out why their AI models think flip-flops will outsell boots in December. (Spoiler: The AI was right. Holiday travel patterns were shifting significantly.) Here’s what I’m seeing across the industry: → According to Salesforce’s latest Connected Shoppers Report, 76% of retailers are increasing AI investment¹, but most are still thinking “tools” not “agents” → The gap between AI pilots and scaled implementation is where competitive advantage lives → Customer expectations are shifting faster than our ability to deploy solutions as product discovery and the path to purchase continues to transform to natural language The fascinating part? The retailers winning aren’t necessarily the ones with the biggest AI budgets. They’re the ones asking more profound questions. Instead of “How do we use AI?” they’re asking “How do we think like our customers’ future selves and what does future engagement look like from them i.e. human instructed, agent executed?” Personal observation: After 30+ years in the publishing and advertising, I’ve never seen a technology shift where the learning curve rewards curiosity over experience quite like this one. The real opportunity isn’t replacing human judgment—it’s augmenting human intuition with AI precision. This current AI phase we’re in is all about increased productivity and using AI assistants to 10x our output. The next phase we’re entering into is about using Agentic AI to power growth and opportunities. The case studies in retail and retail media for Agentic AI are numerous and when you couple these case studies to context engineering, LLM’s and vibe coding apps you can build front end protoypes to problem solve in hours. What problems are you solving using the above combination in retail media? #RetailAI #CustomerInsights #RetailInnovation #AIStrategy #RetailLeadership #ContextEngineering #AI #VibeCoding #SearchisShifting #RetailMedia #RetailMediaNetworks #AIAgents #AgenticAI

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The walled garden around Walmart's DSP just got a new gate. They've loosened their exclusive partnership with The Trade Desk. This is a bigger deal than it sounds. For years, you played in their sandbox, with their rules. Now? ✓ Brands have broader programmatic flexibility. ✓ More holistic use of Walmart's incredible audiences. But freedom has a cost. The new bottleneck isn't strategy. It's plumbing. Moving data and audiences between platforms is technically messy. Prone to error. Full of friction. Clean data pipes are now a competitive edge. Full stop. This is where we live. My team at Trellis has been deep in Walmart's API changes, building automated workflows to turn this new programmatic chaos into a clear, operational advantage for our clients. Your move: Is your data house in order?

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Lululemon - FY 24 Highlights: > Net revenue increased +10% YOY to $10.6bn - 2024 was a 53 week year, excluding the revenue from the 53rd week, revenue increased +8% YOY, >> Americas revenue increased +4% >> International revenue increased +34% > Comparable sales (excludes 53rd week) increased +4%, > Gross profit increased 12% to $6.3bn, by Andy Gooday

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Walmart wants 50% of its $650B in revenue to come from online within 5 years. To make that happen, it’s going all-in on AI. Here’s what they’re doing: Hired Daniel Danker (former Instacart Product Chief) as EVP of AI Acceleration, reporting directly to CEO Doug McMillon. Unveiled a company-wide AI framework built around 4 “super agents”: Sparky – for customers Marty – for suppliers & advertisers Associate – for store teams Developer – for engineers All built on Anthropic’s open-source Model Context Protocol (MCP) And it’s already working: Store managers saving 1 hour per shift on planning Customer service resolution time down 40% Faster inventory and trend detection across digital + physical Impressive operationalization at scale - across 4,700+ stores, 2.1M employees, and a growing eCommerce ecosystem.

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It's okay to finish last and not know your path. Early retail taught a key lesson: big department stores were fading. Specialty stores like The Gap rose by focusing on specifics. It highlights how quickly industries evolve. Who could have predicted GameStop's massive growth? It reinforces the need to stay open to unexpected opportunities. #CareerJourney #RetailInsights #BusinessStrategy #Opportunity #GameStop

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