Taran S.

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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Leadership in AI sits at the intersection of business ambition and team capability. Influencing upward requires clarity and conviction. Enabling downward requires protection, context, and trust. Balancing executive expectations while creating space for teams to build sustainably is a constant discipline, not a one-time decision. Appreciate the thoughtful conversation around this very real leadership tension Kausik Kumar. #AILeadership #AI #Analytics #TeamBuilding

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Algorithmic Pricing is one of retail’s biggest sales opportunities - and also one of the fastest ways to lose customers. Consumer Reports just found that Instacart’s AI pricing tests showed 20%+ price swings on identical items and ~7% basket differences across shoppers. That’s ~$1,200 a year in variance for a typical household! The paradox of algorithmic pricing: 🚀 Massive upside – Surfaces discounts on slow moving or out-of-season items – Expands margin in a world clawing for 2–3% YOY sales growth – Enables pricing with precision, not gut feel ⚠️ Massive downside – Zero transparency = Brand erosion – Value perception can collapse overnight – Trust (once broken) rarely comes back AI pricing isn’t inherently “bad". But when customers feel the algorithm is working against them, not for them, the brand loses long before the margin lift pays off. The retailers who will win? Those who use pricing algorithms transparently to create value - not quietly extract it. #retail #ecommerce #pricingstrategy #AI #customertrust https://lnkd.in/gQDduYYi

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We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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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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𝗥𝗲𝘁𝗮𝗶𝗹 𝗔𝗜 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗽𝗿𝗼𝗼𝗳 𝗼𝗳 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗼 𝗽𝗿𝗼𝗼𝗳 𝗼𝗳 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹. NVIDIA's 2026 State of AI in Retail and CPG makes this clear. The industry has crossed a threshold: 91% engaged with AI, 58% actively deploying (up from 42% last year), 89% reporting revenue impact, 95% seeing cost reductions. The technology is working. 92% of executives are increasing budgets. 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗵𝗲𝗮𝗱𝗹𝗶𝗻𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗱𝗼𝗻'𝘁 𝘁𝗲𝗹𝗹 𝘆𝗼𝘂: 𝘁𝗵𝗲 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝗵𝗮𝘀 𝗳𝗹𝗶𝗽𝗽𝗲𝗱. Training data as a top challenge dropped from 27% to 13% in a single year. In its place, talent surged from 31% to 46% - now the #1 barrier to implementation. The industry spent years fixing its data problem and ran straight into a people problem. And the execution gap is already visible. 47% are exploring agentic AI, but only 20% have agents in production. Productivity gains hit 54% against a 29% initial target. The technology is outperforming expectations, but organizations can't absorb the output fast enough. This is where retail has a structural blind spot. Marketing runs its AI pilots. Supply chain runs its own. The CIO manages infrastructure. Nobody owns the layer where these investments either compound or cancel each other out. The question has shifted from whether to invest to how to deploy effectively across the value chain. 𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗴𝗮𝗽 𝗶𝗻 𝗿𝗲𝘁𝗮𝗶𝗹 𝘄𝗼𝗻'𝘁 𝗯𝗲 𝘄𝗵𝗼 𝗵𝗮𝘀 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜. 𝗜𝘁 𝘄𝗶𝗹𝗹 𝗯𝗲 𝘄𝗵𝗼 𝗯𝘂𝗶𝗹𝘁 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝗶𝘁. Source: NVIDIA State of AI in Retail and CPG: 2026 Trends #RetailAI #AgenticAI #RetailTech

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The Retail Black Hole Every retailer has a product that shines brighter than the rest. It drives volume. It props up marketing. It makes the numbers look safe. But stars collapse into black holes. And that’s what happens when a hero SKU gains too much gravity. It bends everything around it, buying decisions, range planning, promotions, until the business is trapped in its orbit. The problem is, these products don’t die overnight. They fade. Slowly. Customers still buy them, but the perceived value shifts, what once felt exciting now feels like habit. Yet because the line once carried the business, leadership insists on keeping it alive. Cash, shelf space, and energy keep pouring into something that quietly stopped earning its keep years ago. And what do most leaders have to guide them? A spreadsheet. A neat line from first sale to last. Historical sales data that gives you no sense of customer perception, market signals, or timing. By the time the chart shows the dip, the brand has already paid the price. It’s rare to find a solution that can understand your business, your people, your thinking, and analyse it down to the most granular detail. Applying the right solution to each individual scenario isn’t just hard, it’s outside human capability. Even in traditional computing, this level of modelling has been so computationally expensive it was only available to the biggest players. But think differently: LLMs, whether Claude, ChatGPT, Gemini, or Grok, can now act as a digital resemblance of your business. You can run scenarios, test assumptions, and augment your team’s judgement with a system that sees connections humans miss. Instead of asking Excel to draw a line, you ask an LLM to paint a picture: – Reviews revealing the early fatigue no sales graph shows. – Social chatter signalling your hero line peaked as a fad, not a staple. – Category data proving your star SKU is cannibalising others. – Customer service logs highlighting frustration long before revenue drops. Individually, these data points are noise. Together, stitched by an LLMOps, they become a story of what’s really happening, and where your next star will emerge. Scaling isn’t about milking one product until the lights go out. It’s about building a universe where no single black hole can pull your business under. And that requires tools that think beyond what humans or spreadsheets alone ever could. #Retail #LLMOps #ScalingChallenges #PracticalSolutions Excited by opportunities like this? Dubai | Doha | Kuwait | Riyadh | London Mid-Market | PE-Backed | Founder-Led Fractional CIO/CDO | Strategy | Execution Clarity | Traction

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Walmart, in its recently published Retail Rewired report, highlights an interesting trend - increased trust of customers in AI-based recommendations. In fact, it is already at par with the social media influencers: 47% of research respondents say they would trust a digital assistant or agent to choose and purchase household essentials for them within a set budget. This signals an upcoming fundamental shift in marketing, opening doors for AI-powered experiences that are predictive. But there is a fine line: although customers start feeling positive about a digital assistant or agent knowing their preferences so well it suggests purchases before they consider them, over 30% feel negative towards such function - think about it, adepts of hyper-personalization.

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Excited to be joining Roberto Robles and Robbie Stein from Databricks at the CPG Forum in Bentonville to discuss the future of data collaboration between retailers and CPGs. We’ll share how Walmart Data Ventures is enabling secure, scalable data access and collaboration to help suppliers and partners make faster, smarter decisions. If you’re attending, I’d love to connect.

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The AI tool is fine. The data feeding it may not be. Demand forecasting models running on incomplete inventory data produce confidently wrong forecasts. Dynamic pricing without real-time ASN coverage will optimize against a stock position that no longer exists. The failure mode I see repeatedly: ML-driven inventory tools deployed on top of EDI networks with partial supplier coverage, manual exception handling, and 24-48 hour data latency. The model performs exactly as designed, but the inputs create a problem. Before evaluating AI vendors, audit the data layer first: ASN compliance rates across your supplier network, ERP/WMS integration latency, how many trading partners still require manual intervention to process. Those numbers will tell you more about AI readiness than any vendor demo. Read the full article: https://lnkd.in/gfw-Ue4u

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The most expensive mistake in data? Buying a platform you can’t leave. We’ve seen it happen too many times—shiny demos, big promises, and 18 months later… you’re replatforming. Again. The 2025 Data Platform Buyer’s Guide is the playbook vendors hope you never find. Inside:  • The 10-minute test to see if you’re even ready    (https://lnkd.in/g4F-Cwh2)  • 3 words that make bad vendors sweat  • How to hijack a demo and get the truth The questions that kill bad deals 💡 One read now could save you millions later. 🔗 [https://lnkd.in/g2-BUjkv] #dataplatform #buyersguide #dataanalytics

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