Whole Foods

Whole Foods Market Integration Documentation


Whole Foods Market data provides detailed insights into product performance, sales trends, and distribution metrics across various categories and locations. Integrating this data into Daasity's Unified Retail Schema (URS) allows for comprehensive analysis and reporting, helping businesses make informed decisions and optimize their strategies.

Core Use Cases

Store-level Performance

Analyze product performance at individual store levels. Identify which stores are driving sales, track sales trends, and understand the factors influencing store performance. This information is crucial for making strategic decisions about inventory distribution, promotions, and store-level marketing efforts.

Out-of-Stock and Void Reporting

Monitor product availability across stores, identify products that are frequently out of stock or have voids, address supply chain issues to optimize stock levels and reduce lost sales opportunities.

Promotional Effectiveness

Evaluating promotional effectiveness involves analyzing the impact of marketing campaigns and promotions on sales. Determine which promotions drive sales, compare baseline sales with incremental sales during promotional periods, and optimize future promotional strategies to maximize return on investment.

New Item Performance Tracker

Tracking the performance of new items is essential for understanding their market acceptance and sales trends. Monitor sales and distribution trends for new products and make informed decisions about product launches, marketing strategies, and inventory management.


Here are the key metrics tracked for Whole Foods Market data:

  • Net Dollar Sales: Total revenue generated from sales.

  • Unit Sales: Total number of units sold.

  • Return Amount: Total dollar value of returned products.

  • Return Quantity: Total number of returned units.

  • % Returns: The percentage of returns calculated as Return Amount divided by Gross Sales.

  • Gross Sales: Total sales revenue, excluding returns (Net Sales + Return Amount).

  • Total Distribution Points: The total count of unique distribution placements, calculated as the count distinct of store location ID and product ID.

  • Stores Selling: The number of stores that sold a specific product.

  • Avg # Items per SS: Average number of items sold per store selling.

  • Avg Price (ARP): Average retail price per unit.

Comparison Periods

  • YoY (Year-over-Year): Comparing the current period's metrics to the same period in the previous year.

  • PoP (Period-over-Period): Comparing the current period's metrics to the previous period.

Data Model

  • Date Granularity: Data is aggregated at a weekly level.

  • URMS Aggregations: The data can be aggregated for total Whole Foods or at the regional level, providing insights at different levels of granularity.


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