Connecting retail demand planning to campaign and store execution

Summary

Retail and consumer goods teams often split performance review, demand planning, marketing activation, store execution, and measurement across disconnected systems, making it difficult to connect analysis with results. The Databricks demo presents a unified application built on data unification, governance, and intelligence, combining point-of-sale, loyalty, supply chain, media spend, inventory, and store-operations signals with role-specific access. Users can move from KPI and forecast-versus-actual analysis to SKU or store what-if planning, audience selection, campaign activation, replenishment, price-tag updates, assigned tasks, and post-campaign measurement. Genie supports natural-language querying, while specialized agents and a coordinating supervisor connect sales insights, demand planning, audience building, in-store operations, and measurement. The stated design goal is a governed, shared workflow that lets teams test decisions, execute them across stores, and evaluate outcomes without losing operational context.

Context

Retail decisions span sales, inventory, transactions, traffic, delivery, demand forecasts, campaign planning, and store execution, but the signals and responsibilities are distributed across disconnected systems and teams. This makes it difficult to align products, categories, locations, and actions or connect analysis with results.

Approach / What changed

The demo uses a unified application built on data unification, governance, and intelligence. It connects retail signals and role-specific access across performance analysis, forecast review, what-if planning, audience activation, store task execution, and campaign measurement. Genie provides natural-language querying, while specialized agents and a supervisor coordinate the workflow across sales insights, demand planning, audience building, in-store operations, and measurement.

Takeaways

• A shared data foundation combines point-of-sale, loyalty, supply chain, media spend, inventory, and store-operations signals so teams can work from consistent views of products, categories, and locations.
• What-if planning lets users adjust a business lever such as a discount and observe projected effects across replenishment, audience reach, units, revenue, and other application KPIs.
• The workflow connects campaign activation to store-level execution and later measurement, including replenishment, price-tag updates, assigned tasks, recovery velocity, ROAS by tactic, and featured-store insights.

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