AI Agents for CPG Commercial Analytics

Traditional BI tools show you what happened—dashboards displaying last month's numbers. You ask questions, wait for reports, and hope someone notices when metrics move. AI-powered agentic analytics inverts this model: AI agents monitor your business continuously, detect meaningful changes, investigate root causes autonomously, and surface insights before you ask.

For CPG commercial teams, this means promotion ROI flagged while events are still running, distribution voids detected within days of occurrence, and competitive share shifts surfaced weeks before quarterly reviews. The difference isn't just faster answers—it's answers you didn't know to ask for, delivered when they can still change outcomes.

AI is the enabler that makes agentic analytics possible. Without AI's ability to learn patterns, detect anomalies, and generate explanations automatically, continuous monitoring at scale would require armies of analysts. AI agents do what humans can't: watch everything, all the time, and surface only what matters.

AI agents are configured with monitoring rules that reflect what matters to your business. For trade promotion, agents track in-flight events against expected lift curves and flag underperformers by Day 3-4 rather than waiting for post-event analysis. For distribution, agents compare authorized stores to selling stores daily and flag new voids immediately. For competitive intelligence, agents monitor share, distribution, and pricing changes and alert when competitors cross meaningful thresholds.

The agents run continuously against your connected data—syndicated feeds refreshing weekly, retailer data updating daily where available, and internal data flowing from TPM and ERP systems. When an agent detects something noteworthy, it generates an alert with context: what changed, how significant it is, what likely caused it, and what action might be warranted.

Tellius connects the full CPG data ecosystem. Syndicated data from Nielsen (Connect, Answers) and Circana (Unify) provides market context—share, distribution, velocity, pricing trends. Retailer-specific platforms including Walmart Luminate, Kroger 84.51°, Target Roundel, and Amazon Retail Analytics provide account-level sales, inventory, and shopper insights.

TPM systems (SAP TPM, Exceedra, Anaplan, or custom) provide promotion event definitions and trade spend. Internal ERP systems provide shipment, inventory, and order data. Panel data from Numerator or NIQ provides shopper behavior and source-of-volume analysis.

The platform harmonizes product hierarchies across these sources automatically—mapping syndicated UPCs to retailer item numbers to internal SKUs—so you can analyze across sources without manual reconciliation projects.

Implementation typically takes 8-12 weeks, with first value arriving earlier. Weeks 1-3 focus on data integration—connecting syndicated feeds, retailer platforms, and internal systems. Weeks 4-6 cover semantic layer configuration—defining your brand hierarchies, competitive sets, and metric definitions so the platform understands your business. Weeks 7-9 involve user enablement and workflow integration. Weeks 10-12 focus on agent configuration and validation.

First value—basic dashboards and query capability—typically arrives around Week 5-6. AI agent monitoring activates progressively as data connections mature. Most teams see meaningful ROI within the first quarter post-deployment.

ROI comes from four sources. First, trade spend improvement: AI identifies losing promotions and enables reallocation to winners, generating 10-15% improvement in trade ROI—on a $300M budget, that's $30-45M in recovered value annually.

Second, distribution recovery: AI finds and flags voids, out-of-stocks, and execution gaps, recovering 2-3% of lost sales—typically $10-20M for mid-sized CPG companies.

Third, time savings: AI automates the data reconciliation and analysis that consumes 60-70% of analyst time, redirecting hundreds of hours toward strategic work rather than data wrangling.

Fourth, decision speed: AI answers "why" questions in seconds rather than weeks, enabling faster response to competitive threats and market changes.Most organizations see payback within 6-9 months.

Multiple functions benefit from different capabilities. Revenue Growth Managers use conversational analytics to query trade promotion performance and AI agents to monitor event ROI. Category Managers use it for assortment analysis, SKU rationalization modeling, and retailer review preparation. Sales and Key Account teams use it for JBP preparation and real-time retailer intelligence during meetings.

Supply Chain and Demand Planning use distribution analytics for void detection, OSA tracking, and demand signal integration. Commercial Leadership uses it for business review preparation and competitive intelligence. Finance uses it for gross-to-net forecasting and trade spend validation.

The platform serves both self-service users (asking questions directly) and power users (configuring agents and building workflows).

Yes. The AI-powered semantic layer understands CPG vocabulary—syndicated metrics (ACV, TDP, velocity), trade terminology (TPR, BOGO, lift, incrementality), retailer names and hierarchies, and your specific brand and category structure. Users ask questions like "What was the ROI on Q3 Walmart beverage promotions?" or "Why did our share decline at Kroger last period?" and get instant answers.

The key is that the semantic layer has been configured with your specific business context—your brand names, your competitive sets, your retailer naming conventions. This isn't a generic LLM that might hallucinate CPG terms; it's a purpose-built AI analytics platform that understands how your data is structured and what questions make sense against it.

You can build CPG analytics capabilities on your data platform, but expect 12-18 months before commercial teams can actually use it. Custom builds require integrating syndicated, retailer, TPM, and internal data with hierarchy harmonization; building semantic layers that understand CPG metrics; creating baseline models for promotion lift and incrementality; developing user interfaces non-technical users can navigate; and maintaining all of this as data sources evolve.

Tellius provides pre-built CPG connectors, semantic understanding of trade and category vocabulary, validated incrementality models, conversational interfaces, and agentic monitoring—deployed in 8-12 weeks instead of 12-18 months. It runs on your existing Snowflake or Databricks infrastructure, so you get purpose-built capability without moving your data.

Build if you have 18+ months runway and dedicated data engineering. Deploy Tellius if you need CPG analytics this year.

Data integration: Can you connect Nielsen/Circana syndicated data, Walmart Luminate, Kroger 84.51°, and major TPM systems? How do you handle hierarchy differences across sources?

‍Semantic understanding: Does the platform understand CPG vocabulary natively (lift, incrementality, ACV, TDP), or does it require extensive configuration to recognize basic metrics?

‍Agentic capability: Can AI agents monitor metrics continuously and alert proactively, or does the platform only answer questions when asked?

‍User experience: Can Revenue Growth Managers and Category Managers ask questions in plain English, or do they need analysts to build reports?

‍Time to value: How long until commercial teams can actually use the platform? What does the implementation timeline look like?

Vendors who demonstrate with your actual data—showing real analysis on your brands at your retailers—are worth serious consideration.

Three things differentiate Tellius from generic analytics platforms. First, semantic understanding of CPG data: the platform knows the difference between syndicated and retailer data, understands trade promotion mechanics, recognizes that ACV and TDP mean specific things, and doesn't require months of configuration to answer basic CPG questions.

Second, pre-built connectors to CPG data sources: Nielsen, Circana, Walmart Luminate, Kroger 84.51°, and major TPM systems connect without custom integration projects. Hierarchy harmonization happens automatically.

Third, CPG-specific analytical models: promotion lift calculation, incrementality modeling, void detection, and share decomposition are built in—not capabilities you need to construct from scratch.

Tellius was designed for CPG commercial analytics. It's not a generic BI tool adapted for CPG; it's a platform that understands your business from day one.