AI-Powered Retailer Analytics for CPG: Walk Into Every Meeting With Better Data Than the Buyer

Retailer analytics focuses on account-level performance—how your brands and categories perform at specific retailers, what drives performance differences across accounts, and what opportunities exist at each retailer. It's the lens that sales and key account teams need.

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Brand analytics focuses on brand performance across the market—national share, competitive dynamics, portfolio health. Category analytics focuses on category performance and assortment optimization. Both typically aggregate across retailers or provide limited account breakouts.

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Retailer analytics disaggregates to the account level. "We have 15% share nationally" becomes "We have 18% share at Walmart, 14% at Kroger, and 11% at Target—here's why, and here's the opportunity at each."

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AI enables retailer analytics at scale by automating the account-specific analysis that previously required manual work for each retailer. What took weeks of data assembly and custom analysis now happens on demand through natural language queries.

Retailer-level analytics matters because retail performance varies dramatically by account—and the reasons for that variation drive different actions.

A brand might have strong national share but underperform at specific retailers due to distribution gaps, poor shelf positioning, promotional under-investment, or competitive intensity. National averages hide these account-specific dynamics.

Key account teams need account-specific insights to: prepare for retailer meetings with relevant data, identify growth opportunities at each account, explain performance differences to leadership, and negotiate from a position of knowledge rather than guessing.

Without retailer-level analytics, commercial teams operate with incomplete information—using national data to have retailer-specific conversations.

Joint Business Planning is a collaborative process between CPG manufacturers and retailers to align on shared growth objectives, typically conducted annually with quarterly reviews. JBP conversations cover assortment, promotion, pricing, shelf strategy, and new item placement.

JBP requires specialized analytics because every discussion point needs retailer-specific data. "How should we optimize assortment at Kroger?" requires Kroger-specific performance, shopper insights, and competitive dynamics—not national averages.

Effective JBP analytics includes: account-specific performance trends, retailer-level competitive share, shopper demographics and behavior at that retailer, promotion effectiveness at that account, distribution and availability metrics, and category growth opportunities.

Preparing this analysis manually takes weeks. AI-powered retailer analytics generates it on demand.

Retailer data platforms provide account-specific data that syndicated sources can't match. Walmart Luminate offers store-level sales, inventory, shopper demographics, and search behavior. Kroger 84.51° provides household-level purchase data, loyalty insights, and targeting capabilities. Target Roundel, Amazon Retail Analytics, and other platforms offer similar retailer-specific intelligence.

These platforms are essential inputs to retailer analytics—they provide the granular, retailer-specific data that makes account-level analysis meaningful. But they're also siloed: each platform requires separate login, separate data pulls, and separate analysis.

AI-powered retailer analytics integrates these platforms with syndicated data and internal data, creating unified account views. Instead of logging into five platforms to understand Walmart performance, you get one integrated view that combines all relevant data sources.

Key Account Managers and National Account Directors use retailer analytics to prepare for retailer meetings, identify growth opportunities, and track account performance. Their questions include "How is our brand performing versus category at Kroger?" and "What's driving the gap between our Walmart and Target performance?"

Sales Leadership uses retailer analytics for account portfolio management and resource allocation. They ask "Which accounts have the most untapped opportunity?" and "Where should we focus investment for maximum return?"

Category Managers supporting retail partnerships use retailer analytics to provide category-level recommendations. They ask "How is the category performing at this retailer versus market?" and "What assortment changes would grow the category?"

Trade Marketing uses retailer analytics to optimize promotional investment by account. They ask "Which promotional tactics work best at Walmart versus Kroger?" and "Where should we shift trade spend?"

Syndicated and retailer data often tell different stories because they use different methodologies. Syndicated data (Nielsen, Circana) projects store-level data to market totals using sampling and projection. Retailer data represents actual transactions at that retailer but may use different product hierarchies and time periods.

AI automates reconciliation by learning the mappings between data sources and flagging discrepancies that require investigation. When retailer data shows 12% share and syndicated shows 14%, AI identifies whether the gap is methodological or signals a real issue worth investigating.

Reconciliation requires: aligning product hierarchies (mapping retailer SKUs to syndicated UPCs), aligning time periods (matching retailer weeks to syndicated weeks), understanding methodological differences (projected versus actual), and identifying which source is more reliable for which purpose. AI handles these mappings automatically rather than requiring manual reconciliation for each analysis.

Sell-in measures shipments from manufacturer to retailer—what you sold to the retailer. Sell-through measures sales from retailer to consumer—what actually sold off the shelf.

Both perspectives matter for retailer analytics. Sell-in reflects your shipping volume and revenue. Sell-through reflects consumer demand and retailer inventory health. The gap between sell-in and sell-through is retailer inventory—building inventory (sell-in > sell-through) or depleting it (sell-through > sell-in).

Healthy retailer relationships require sell-in and sell-through alignment. If you're shipping more than the retailer is selling, inventory builds and future orders decline. If sell-through exceeds sell-in, you're leaving sales on the table due to availability gaps.

AI connects sell-in and sell-through data to identify misalignment before it becomes a problem—flagging accounts where inventory is building or where demand is outpacing supply.

Comprehensive retailer analytics requires four data categories. First, syndicated market data (Nielsen, Circana) for market context—category trends, competitive share, national benchmarks to compare against retailer-specific performance.

Second, retailer-specific data platforms (Walmart Luminate, Kroger 84.51°, Target Roundel) for account-level sales, inventory, shopper insights, and execution metrics unique to each retailer.

Third, internal data including shipments (sell-in), trade spend, promotional calendars, and account plans—connecting your actions to retailer results.

Fourth, panel data (Numerator, NIQ) for shopper behavior insights—understanding who shops at each retailer, how their baskets differ, and how purchase behavior varies by account.

The integration challenge is harmonizing these sources into coherent account views—different hierarchies, different time periods, different definitions. AI-powered platforms automate this harmonization.

JBP preparation traditionally requires weeks of manual work: pulling syndicated data for the specific account, reconciling with retailer data, building competitive analysis, sizing opportunities, and creating presentation materials. Most teams have templated approaches but still spend 60-80 hours per major account.

AI reduces JBP prep from weeks to hours by automating the data assembly and initial analysis. Account-specific performance views, competitive dynamics, opportunity sizing, and shopper insights are generated on demand rather than manually compiled.

The key is having the right questions ready. AI can answer "How did our share at this retailer change versus last year?" instantly—but you need to know that's the question to ask. Experienced key account managers typically develop a standard JBP question set, then let AI generate the answers.

Retailer meetings typically probe five areas. Performance questions: "How are you performing in my stores?" "Why did your share decline?" "What's driving your growth (or lack of it)?"

Opportunity questions: "What's the incremental opportunity if I add your new SKU?" "What space are you requesting and why?" "How will your promotion perform?"

Category questions: "How can you help me grow the category?" "What trends should I be watching?" "What's your competitor doing?"

Shopper questions: "Who's buying your products in my stores?" "How do your shoppers differ from category shoppers?" "What basket affinity do your products have?"

Competitive questions: "What are your competitors doing?" "How do you plan to respond to [competitor action]?" "Why are you losing share to [competitor]?"

AI-powered retailer analytics enables real-time answers to all five categories—no more "I'll get back to you."

Performance differences across retailers stem from multiple factors: distribution (you may have more/fewer SKUs authorized), velocity (products may sell faster/slower at different retailers), promotion (promotional support and effectiveness varies), competition (competitive intensity differs by account), and shopper base (retailer shoppers have different demographics and preferences).

AI automates cross-account decomposition, showing exactly what drives performance differences. "The 6-point share gap versus Target decomposes as: +3 points from distribution (2 additional SKUs), +2 points from velocity (15% higher turns), +1 point from promotional lift (25% more promotional weeks)."

This decomposition happens instantly rather than requiring days of manual analysis. When leadership asks "Why are we doing better at Walmart than Target?", AI provides the quantified answer immediately.

Retailer-specific opportunities fall into four categories. Distribution opportunities: SKUs you sell elsewhere that aren't authorized at this retailer, or authorized SKUs with store-level voids. Velocity opportunities: products selling below their potential due to shelf position, pricing, or promotional support. Share opportunities: categories or segments where you underindex at this retailer versus market. New item opportunities: launches performing well elsewhere that could be introduced at this account.

AI identifies opportunities by comparing account performance to benchmarks—your performance at other retailers, competitor performance at this retailer, category performance versus your brand performance. Gaps between current state and benchmark represent quantifiable opportunity.

Prioritization considers opportunity size (revenue potential), feasibility (retailer receptivity, competitive barriers), and strategic fit (brand priorities, relationship dynamics). AI ranks opportunities automatically rather than requiring manual analysis for each account.

Retailers want numbers. "You should add our SKU" isn't compelling. "Adding this SKU would generate $2.4M incremental category sales based on performance at comparable retailers" is compelling.

AI automates opportunity sizing by maintaining comparable databases and applying sizing methodologies consistently. Ask "What's the opportunity from adding our new premium SKU at Target?" and get a sized estimate with supporting analysis.

Opportunity sizing requires: establishing the comparable basis (similar retailers, similar stores, similar markets), measuring performance at the comparable (velocity, distribution, share), calculating the gap between current and comparable, and adjusting for retailer-specific factors (shopper differences, competitive intensity, space constraints). AI handles these calculations automatically.

Promotional effectiveness varies dramatically by retailer. The same 20% Temporary Price Reduction (TPR) might generate 40% lift at one retailer and 15% at another due to shopper responsiveness, competitive promotional intensity, execution compliance, and retailer promotional ecosystem.

Comparing across retailers requires: measuring lift and ROI by retailer using consistent methodology, controlling for promotional variables (tactic type, depth, timing), understanding retailer-specific factors that affect response, and identifying best practices from high-performing accounts.

AI enables cross-retailer promotional comparison by calculating lift and ROI consistently across accounts. "Buy One Get One (BOGO) promotions generate 35% average lift at Kroger versus 22% at Safeway. The gap is primarily explained by higher competitive promotional intensity at Safeway."

Category captains serve as the retailer's category advisor—providing recommendations on total category assortment, shelf layout, and promotional strategy. This requires retailer-neutral analysis that benefits the category, not just your brands.

Category captain analytics must demonstrate objectivity. Recommendations should be grounded in data that shows category benefit, not just brand benefit. This means analyzing total category performance, all-brand assortment productivity, competitive dynamics, and shopper needs—then making recommendations that serve category growth.

AI supports category captain work by: generating total-category analytics (not just your brands), identifying white space opportunities regardless of which supplier fills them, modeling assortment scenarios objectively, and producing retailer-ready insights that demonstrate analytical rigor.

Compliance conversations go poorly when they feel accusatory. Productive approach: frame as partnership for shared success, bring specific store data (not just assertions), quantify the mutual loss (your missed ROI + their missed category sales), and propose solutions rather than just complaints.

Example: "Our BOGO saw 45% lift at stores with verified display execution but only 8% lift at stores without—that's a gap of about $400K in category sales we both missed. Here's the list of 200 stores where our field team couldn't verify displays. Can we review your execution process for our next event?"

Data-backed conversations shift from blame-casting to problem-solving—and demonstrate that you're investing in analytical capability that benefits the retailer too.

Most CPG companies have more accounts than analysts. Key accounts get deep analytical support; mid-tier accounts get periodic attention; tail accounts get minimal coverage. This creates blind spots where performance issues or opportunities go unnoticed.

AI extends analytics coverage by automating the monitoring and alerting that previously required manual attention. Agentic workflows track performance across all accounts continuously, flagging issues and opportunities regardless of account tier.

This doesn't eliminate the need for strategic analysts—but it shifts their role from data assembly to insight interpretation and action recommendation. The analyst who spent three weeks preparing Kroger JBP materials can instead interpret AI-generated insights and develop strategic recommendations.

Retailers care deeply about their shoppers—understanding who shops at their stores, what they buy, and what drives their behavior. Bringing shopper insights to retailer conversations demonstrates value beyond just selling your products.

Relevant shopper insights include: how your category shoppers compare to average store shoppers, what basket affinities exist with your products, how shopping trips involving your products differ from average trips, and what shopper segments are over/under-developed at this retailer.

AI integrates panel data and loyalty insights to generate retailer-specific shopper views. "Your store's category shoppers over-index on premium products but you're under-distributed in premium—that's a $5M gap we can close together."

Category analytics provides the foundation for retailer conversations, but retailer-specific strategy requires account-level customization. Category performance varies by retailer—a category growing 5% nationally might be growing 8% at Walmart and declining 2% at Target.

The connection works in both directions: category insights inform retailer strategy (what assortment changes would benefit this retailer's category?), and retailer insights inform category strategy (what's working at top-performing accounts that could transfer elsewhere?).

AI connects category analytics to retailer strategy by generating account-specific category views—showing how national category trends manifest at each retailer, where this retailer over or underperforms category benchmarks, and what account-specific actions would improve category performance.

Yes. Accelerated deployment for a single priority account can deliver JBP-ready insights in 4-5 weeks by focusing integration effort on one retailer data source (e.g., Walmart Luminate) plus syndicated data. This creates a working proof of concept for your most important account while broader multi-retailer integration continues in parallel.

Teams facing imminent JBP deadlines often pursue this approach—getting Walmart insights operational before expanding to Kroger, Target, and other accounts. The trade-off is sequenced value delivery: you get one account working quickly rather than waiting 10-12 weeks for comprehensive multi-retailer coverage.

The single-account approach also validates the platform's fit for your team. If key account managers find value in Walmart analytics, you have internal champions for the broader rollout. If issues emerge, you've discovered them before committing to full deployment.

Retailer analytics complements rather than replaces existing systems. Retailer data platforms (Walmart Luminate, Kroger 84.51°) remain sources for account-specific data. Syndicated platforms (Nielsen Connect, Circana Unify) remain sources for market context. CRM and account planning systems continue managing relationship workflows.

Integration works through data flows: retailer analytics pulls from these sources to create unified account views. Outputs can feed existing workflows—account insights pushed to CRM, JBP materials generated in presentation formats, alerts routed to account teams.

The goal is creating the unified account intelligence that doesn't exist in any single source—not replacing the sources themselves.

Real-time AI query capability transforms retailer dynamics. When the buyer asks "What's driving your share decline in my stores?", query live and answer immediately: "Share decline is primarily velocity-driven—we're 15% below category velocity at your banner versus 5% above at comparable retailers. Promotional intensity gap appears to be the driver—we have 30% fewer promoted weeks here than at similar accounts."

When they ask "Why should I authorize your new SKU?", query: "At retailers with similar shopper demographics, this SKU generates $2,400/week velocity and 85% incrementality versus portfolio. The opportunity at your store base is approximately $3M annually."

The difference between these in-meeting answers and "I'll get back to you next week" is often the difference between winning and losing the recommendation. Key account managers using conversational analytics in meetings report qualitative shifts in retailer perception—buyers recognize analytical capability and treat manufacturers as more strategic partners.

Every "I'll get back to you" moment costs credibility that compounds over time. Retailers interpret analytical gaps as lack of preparation, lack of investment in the relationship, or lack of sophistication. These perceptions affect shelf decisions, promotional calendar positioning, and new item authorization.

More directly, follow-up responses arrive after the conversation has moved on—the retailer has already made their decision, often unfavorably. The hardest ROI to quantify is the deals that never happened because the retailer concluded you weren't analytically capable enough to be a strategic partner.

Teams that can answer questions in real-time report qualitative improvements in retailer relationships that translate to better outcomes in negotiations. One key account director described the shift: "We went from being a vendor they manage to a partner they consult."

AI-powered retailer analytics delivers ROI across three dimensions: time savings, negotiation outcomes, and continuous intelligence.

Time savings: JBP prep drops from 3-4 weeks to days—a 75%+ reduction. For teams managing 10+ major accounts with quarterly reviews, this frees hundreds of hours annually for strategic work instead of data assembly.

Negotiation outcomes: Teams report 15-25% better outcomes in JBP negotiations when they arrive with retailer-specific incrementality data, shopper insights the retailer hasn't seen, and real-time query capability. On a $50M account, 20% improvement translates to $10M+ in annual value.

Continuous intelligence: Agentic monitoring surfaces share erosion, distribution losses, and competitive gains in real-time rather than waiting for quarterly reviews—catching issues weeks earlier when intervention is still possible.

The best AI platform for retailer analytics needs five capabilities most tools lack.

First, multi-retailer data integration: seamless connection to Walmart Luminate, Kroger 84.51°, Target Roundel, and other retailer platforms alongside syndicated data—with automated hierarchy harmonization across sources.

Second, account-level analytics: performance decomposition, opportunity identification, and competitive analysis at the individual retailer level—not just national or channel aggregates.

Third, cross-account comparison: the ability to explain why performance differs across retailers and identify best practices that can transfer between accounts.

Fourth, conversational access: key account managers asking questions in plain English and getting instant retailer-specific answers, without building reports or navigating complex interfaces.

Fifth, JBP-ready outputs: insights that translate directly into retailer presentations and business review materials, not raw data requiring additional processing.

Tellius is purpose-built for CPG retailer analytics and delivers all five capabilities.

AI can generate automatically: performance summaries (share, velocity, distribution trends versus prior periods), decomposition analysis (why performance changed), opportunity sizing (what adding SKU X would deliver based on comparable performance), competitive positioning (how you compare to competitors at this account), and shopper profiles (who shops at this retailer and how they differ).

Human strategic input is still required for: negotiation strategy (what to ask for and what to concede), relationship management (how to frame requests given relationship history), prioritization (which opportunities to pursue given limited retailer receptivity), creative positioning (how to tell a compelling story, not just present data), and commitments (what you're willing to invest to capture opportunities).

The AI handles the analytical heavy lifting that consumes weeks. The human brings judgment about what to do with the analysis. The combination—instant analytics plus experienced account management—creates the advantage.

AI-powered account decomposition can extend to whatever granularity your data supports. Most retailer data platforms (Walmart Luminate, Kroger 84.51°) provide store-level data, enabling decomposition at any aggregation: total account, region, division, market, or individual store.

Regional decomposition is valuable for understanding geographic variation within an account. "Your share is 2 points below account average in the Southeast region—decomposition shows this is entirely distribution-driven; you're missing 3 SKUs that are authorized elsewhere."

Store-level decomposition identifies specific locations driving account-level trends. "Share decline at this account is concentrated in 200 stores representing 15% of volume. These stores share two characteristics: they're in urban centers and they have a specific competitor display program we don't have."

The limitation is typically data availability, not analytical capability. Where store-level data exists, AI can decompose at store level.

Retailer data platforms update frequently—new data elements, changed API structures, and modified access requirements. Walmart Luminate alone makes significant changes 3-4 times per year. Each change requires engineering time to understand the update, modify integration code, validate that historical data still aligns, and redeploy.

With 5-10 major retailer platforms to maintain, API changes become a continuous engineering obligation. Your data engineering team signed up for a project; they inherited an ongoing maintenance commitment. And because these changes happen without warning, they interrupt whatever else engineering had planned.

Purpose-built platforms absorb this maintenance as part of the service—when Kroger 84.51° changes their data structure, the platform vendor updates their integration, not your team. The hidden cost of building in-house is often this perpetual maintenance, not the initial build.

Vendors typically show polished demos with sample data, highlighting features you may never use. What you should demand instead: your actual retailer data connected during the demo (not mocked up), your actual team members asking their actual questions (not scripted queries), and live troubleshooting when something doesn't work as expected (not "we'll follow up on that").

Ask to see your Walmart Luminate data reconciled against your Nielsen data for the same period—watch how the platform handles discrepancies. Ask a key account manager to query something they actually need for an upcoming retailer meeting. If the demo environment can't handle real-world messiness, neither will the production platform.

The demo should feel like a working session, not a presentation. Vendors confident in their platform welcome this approach. Vendors who insist on scripted demos with generic data may be hiding capability gaps.

Start with your highest-stakes account—typically a top-3 retailer where JBP conversations are frequent and consequential. This is counterintuitive (shouldn't you practice on lower-stakes accounts first?) but strategically correct.

First, high-stakes accounts have the best data—Walmart Luminate and Kroger 84.51° provide richer intelligence than smaller retailer portals, making early wins easier. Second, high-stakes accounts justify investment—demonstrating value at your biggest account makes the ROI case self-evident. Third, high-stakes accounts create organizational pull—when the Walmart team has capabilities the Target team lacks, the Target team demands access.

The risk of "practicing" on low-stakes accounts is investing months without generating visible value that drives broader adoption. Start where success matters most, and let that success pull the platform into additional accounts.

Retailer alignment requires three validation approaches. First, metric comparison: pull the same metrics from your analytics platform and directly from the retailer's portal (Walmart Luminate, Kroger 84.51°) for the same time period—they should match within expected methodological variance (typically 2-5%).

Second, insight validation: share a preliminary finding with your retail buyer and observe their reaction—do they recognize the pattern you're describing? Third, recommendation validation: after presenting an opportunity, track whether the retailer engages or dismisses it. If your "high-opportunity" recommendations consistently get rejected, either your opportunity sizing is off or your recommendations don't align with retailer priorities.

Validation builds credibility for future conversations. The first few JBP meetings with new analytics capabilities are as much about establishing trust as driving outcomes. Once the retailer trusts your numbers match theirs, conversations become more productive.