In this article, learn about:
What is Store of the Community
Why does Store of the Community Matter to suppliers
How to create a Store of the Community dataset
How suppliers use demographic data
Store of the Community (SOTC) reflects Walmart’s belief that each store should represent the community it serves. Rather than following a uniform approach, Walmart adapts each store’s assortment, layout, and promotional activity to align with the local customer base.
Demographics in retail involve analyzing customer characteristics to inform product offerings. This creates stores that feel familiar and relevant to the people who shop there. For example, a store in Northwest Arkansas may highlight University of Arkansas merchandise, while a coastal Florida store may highlight Florida State University. The same principle applies across all regions, ensuring that every Walmart store reflects the lifestyles, preferences, and values of its community.
Why It Matters to Suppliers
For suppliers, understanding the Store of the Community model is essential to maintaining sales and inventory efficiency. Two stores may carry the same products, yet one may outperform the other due to customer demographics, store execution, or how well the product resonates with local shoppers.
Monitoring these performance trends helps suppliers make informed decisions about which stores to prioritize, where to test new items, and how to adjust distribution. By studying how different stores perform, suppliers can strengthen product placement, reduce markdowns, and ensure that assortments better match local demand.
Related Reading: What is Supply Planning?
What Are Store Traits?
In Walmart’s data systems, traits are characteristics or attributes assigned to each store that describe its customer base, location, and operations. These traits are part of the data used to understand what makes each store unique and are a core component of the Store of the Community model.
Traits can include both demographic and operational information. For example:
Community type (urban, suburban, or rural)
Proximity to colleges or universities
Average household income or size in the store’s area
Predominant age range of shoppers
Regional characteristics (coastal, agricultural, tourist-driven, etc.)
Seasonal shopping behaviors
By analyzing traits, suppliers can see patterns in how specific community factors influence product performance. A store located in a retirement community might sell more healthcare and leisure items, while a store near a major university might show higher demand for grab-and-go snacks or low-cost essentials.
These insights help suppliers align their assortments, pack sizes, and promotions with real-world customer behavior.
How Can Suppliers Use Store Demographics?
Suppliers can maximize store demographic information by following these steps:
Start by performing an item-store level point-of-sales analysis to understand which stores are overperforming (or underperforming if considering minimizing store count to maximize inventory efficiency and strengthen sales velocity).
Once you have a list of stores you want to review traits for, you can access information about each store utilizing the Store of the Community report in Scintilla.
How to Create a Store of the Community Dataset
Suppliers can access store-level trait data through Walmart’s Scintilla platform to analyze which factors contribute to performance differences.
Step 1: Create a custom dataset
Go to Scintilla > Dataset > Select Custom Dataset.
Step 2: Choose data tables
Select the following tables to begin:
Store Info
Traits
Additional tables such as Item Velocity, Markup/Markdown, Store Sales, and Store Returns can provide a more complete picture of performance.
Step 3: Apply filters
Use the Trait Number filter and change the condition to “has any of.” Then click Advanced and search for “STORE OF” to filter directly to Store of the Community traits.
From here, select up to 30 traits at a time, such as:
Store name
Store number
Store address line 1
Store city
State
If needed, apply a date filter to view traits for a specific time period. Setting the filter to WMT Week-to-Date will show the most current store traits.
Related Resource: Walmart Fiscal Year End 2027 Calendar
Step 4: Create the report
Once your filters are set, click Create Report. Add a name and description, choose your file format (.xlsx or .csv), and decide if you want the report to run once or continuously.
You can also schedule the report and receive an email notification once it’s complete. After it runs, the report will appear in My Reports. When the status changes from Running to Complete, it can be downloaded and opened in your chosen format.
Tip: Many suppliers run Store of the Community reports weekly or monthly to track demographic shifts and maintain alignment with Walmart’s localized strategy.
Related Reading: How to Pull Reports in Scintilla
What Suppliers Do with Store Demographic Data
Once the Store of the Community dataset is created, suppliers can use the results to better understand their product performance at the store level.
Identify high-performing stores: Cross-reference POS data to see which stores outperform category averages.
Study underperforming stores: Determine if sales gaps are linked to demographics, store traffic, or shelf visibility.
Adjust distribution: Reallocate product quantities to stores that show higher demand or stronger alignment with the target customer.
Refine modular and marketing strategies: Use demographic insights to support merchandising plans or targeted promotional efforts.
For example, a supplier who finds strong sales in stores near college campuses may decide to expand similar product lines to other college towns or run student-focused promotions.
Connecting Store of the Community to Broader Analysis
Suppliers can combine Store of the Community data with other reporting tools like point-of-sale (POS) and Demand Forecasting for a more complete view of performance.
POS analysis shows what is selling.
Store of the Community shows why it’s selling.
Together, these insights help suppliers understand not only which stores are driving growth, but also the customer behaviors behind those results.
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