# Walmart Luminate Analytics MCP MCP > Walmart Luminate Analytics connects your AI agent directly to enterprise retail data from Walmart. It lets you analyze shopper paths, find what products people buy together, track how well channels perform, and audit store inventory health. Stop guessing about sales; start basing decisions on deep-dive market basket insights. ## Overview - **Category:** industry-titans - **Price:** Free - **Tags:** omnichannel-data, shopper-behavior, market-basket-analysis, big-data, retail-insights, funnel-reporting ## Description This MCP gives your agent a direct line into massive datasets covering everything that happens in Walmart's retail ecosystem. You can analyze exactly where shoppers drop off during the buying funnel or figure out which product bundles move the most units. It pulls together shopper behavior data with financial metrics, allowing you to see how inventory gaps impact revenue and what loyal customers are actually spending on. Because this data touches sales engines and financials, every single tool call produces a cryptographically signed audit trail, so you know exactly where the numbers came from and that they haven't been altered between sources or tools. You use your AI client to ask complex questions—like 'Why did cart abandonment spike last week?'—and it pulls together the shopper behavior data with channel performance metrics to give you a single answer. ## Tools ### luminate_category_trends Analyzes how sales are trending across different product categories within Walmart. ### luminate_channel_performance Compares and evaluates performance metrics for various selling channels (e.g., app, web, physical store). ### luminate_conversion_rates Verifies whether specific product groups are successfully converting from views to purchases. ### luminate_get_financial_report Pulls safe, logical financial data points to check against predefined budget limits or revenue targets. ### luminate_loyalty_metrics Extracts specific customer loyalty data used for tracking overall member value and engagement. ### luminate_market_basket Executes analysis to find strong relationships between different product groups purchased together. ### luminate_shopper_behavior Retrieves deep analytics tracking how shoppers interact with the site or store environment, from entry to exit. ### luminate_store_inventory_health Verifies current physical stock levels in specific stores against historical demand patterns. ## Prompt Examples **Prompt:** ``` Cross-reference recent shoppers identifying major funnel exit rates. ``` **Response:** ``` Report parsed properly indicating cart dropouts significantly higher strictly on early checkouts. ``` **Prompt:** ``` Find the top 3 items frequently bought together with our organic coffee blend. ``` **Response:** ``` Extracting Market Basket Affinities... Customers purchasing your organic coffee blend also commonly buy: Almond Milk (42%), Brown Sugar (18%), and Paper Filters (15%). ``` **Prompt:** ``` Summarize the shopper retention rate for the last 90 days. ``` **Response:** ``` Analyzed Shopper Behavior over the last 90 days. You have a 68% retention rate among returning customers with an average of 2.4 purchases per cohort. ``` ## Capabilities ### Track purchasing patterns Gets detailed reports on how shoppers move through the sales funnel, pinpointing exactly where people abandon their carts. ### Identify product bundles Analyzes purchase history to show you which items are bought together most often, helping build better cross-sell strategies. ### Gauge channel performance Compares sales and user metrics across different selling channels—online versus physical store—to find bottlenecks. ### Assess operational stock levels Checks the real-time inventory status in specific physical locations to prevent lost sales due to stockouts. ### Report on customer loyalty value Extracts first-party data detailing how valuable and engaged your most loyal customers remain over time. ## Use Cases ### Why did Q3 revenue dip? An agent analyzes the gap between shopper behavior and financial performance. It finds that while cart abandonment rates (from `luminate_shopper_behavior`) are stable, the overall average order value has dropped because store inventory health reports (`luminate_store_inventory_health`) show key high-margin items are consistently out of stock. ### How do we increase our coffee sales? You ask your agent to analyze market basket affinities. It identifies that customers who buy organic coffee also frequently purchase paper filters and specific almond milks, allowing the team to create a targeted bundle deal. ### Are we wasting money on digital ads? Your agent runs `luminate_channel_performance` reports and compares them with conversion rates. It shows that while web traffic is high, poor site navigation prevents users from completing purchases efficiently. ### Who are our most valuable customers right now? You run the `luminate_loyalty_metrics` tool to segment your user base. The resulting data highlights that a small group of long-term members drives 60% of the revenue, informing where marketing efforts should focus. ## Benefits - Stop relying on guesswork. Using `luminate_shopper_behavior` and `luminate_market_basket`, you'll see the real flow of customer interest, not just the final sale count. - Improve your spending decisions by pulling financial data with `luminate_get_financial_report`. Your agent can check if a proposed marketing campaign stays within budget. - Identify lost revenue opportunities. By cross-referencing shopper behavior with inventory health using `luminate_store_inventory_health`, you flag stock gaps before they become problems. - Focus on the right customers. `luminate_loyalty_metrics` helps you shift your focus from one-time purchases to maximizing long-term customer value, which is key for growth. - Understand how channels interact. Use `luminate_channel_performance` alongside conversion rates to see if poor web experience is tanking in-store sales. ## How It Works The bottom line is that you get a unified view of retail performance, combining behavioral science with hard financial data in one place. 1. You ask your AI client to compare shopper behavior metrics with specific product affinities, outlining the exact business question. 2. The MCP executes multiple tool calls, pulling data for market basket analysis and conversion rates from Walmart's system via secure API connections. 3. Your agent compiles the results into an easily readable report showing actionable insights, like which products need better bundling or where to move inventory. ## Frequently Asked Questions **Can this integration edit my item prices or titles?** No. The `walmart-luminate-mcp` works as a Read-Only analytics collector directly. If you seek editing arrays, combine this setup natively with `walmart-marketplace-mcp`. **Is Luminate data real-time or delayed?** Luminate insights provide highly accurate aggregated models but generally operate on a 24-48 hour processing delay to ensure large-scale data integrity across all US stores. **Can I see what other products customers buy alongside mine?** Yes. The Market Basket Affinity algorithms correlate transactions, showing you exact percentages of cross-category items frequently purchased with your SKUs. **When I run `luminate_store_inventory_health`, does it provide real-time stock counts for physical stores?** It provides a near real-time snapshot of inventory. The data reflects current store matrices, helping you spot bottlenecks immediately after the last reported update cycle. **Does `luminate_channel_performance` allow me to compare sales metrics between online and physical stores?** Yes, it aggregates performance across multiple channels. You can analyze how different touchpoints contribute to overall revenue goals, giving a complete view of your customer journey. **If I query `luminate_category_trends` frequently, are there any rate limits I should worry about?** The MCP handles throttling automatically. If you exceed API quotas, the system will pause calls and notify your agent. We recommend batching related trend checks for best results. **How does `luminate_get_financial_report` protect my sensitive financial data during analysis?** Your credentials pass through a zero-trust proxy, meaning keys are only used in transit and never stored on disk. The MCP is read-only; it can't modify any financial limits. **Can I use `luminate_conversion_rates` to check conversion rates for specific product groups?** You absolutely can filter by SKU arrays or product groupings. This lets you verify if distinct, targeted product lines are performing better than general site averages.