Due to an ongoing issue, the availability of data assets is currently delayed. Our team is actively working to resolve the issue and restore access as quickly as possible. We appreciate your patience and understanding.
We are aware of incorrect data for the street_dt field in Product Dimensions. Our data teams are prioritizing identifying a resolution. We anticipate that the corrected data will be available in the September 25, 2026 refresh. We apologize for the inconvenience.
We are aware of incorrect data for the street_dt field in Product Dimensions. Our data teams are prioritizing identifying a resolution. We anticipate that the corrected data will be available in the September 25, 2026 refresh. We apologize for the inconvenience.
As of today, September 23, Scintilla’s Store Inventory metrics have transitioned from a legacy data source to the new Enterprise Inventory (EI) data source, meaning that any data for September 23 and onwards will sourced from EI. This aligns Scintilla with the enterprise-standard inventory system, eliminates inconsistent logic within the legacy data source, and improves replenishment metric accuracy. Merchandising already completed this transition for Merch One on Tuesday, June 23. Historical data (June 1, 2025 – September 22, 2026) will go through a restatement process. Restatement work will begin on September 24, 2026 and will not be completed until October 8, 2026. Approximately 60 days' worth of data will be restated daily (with no restatement work performed on Fridays and Saturdays). In completing the historical restatement on October 8, we will be eliminating the temporary variance that existed between Scintilla and Merch One from June 23–September 22, 2026. During restatement period, clients should expect fluctuations to the relevant metrics that will be resolved once we finalize on October 8. They should also expect fluctuations due to changes to logic / formulas for the following fields: Instock % - TY/LY, Traited & Valid Store Count, Traited Item count and related velocity metrics. The following columns will be removed in the UI and null in BI Link from the date of release: Carry opt Cd, Carry Opt Status.
Due to an ongoing issue, the availability of data assets is currently delayed. Our team is actively working to resolve the issue and restore access as quickly as possible. We appreciate your patience and understanding.
On September 18, users may experience delays in receiving Store Inventory (catman) data, potentially resulting in an SLA miss. We are aware of the delay and our team is working to resolve it. We apologize for the inconvenience.
Hierarchy changes are now reflected across the Data Access and Collaboration portfolio. If you have any pre-built reports utilizing hierarchy filters, you may need to clear these filters and recreate them with the new hierarchies.
Please submit requests via a support ticket to get historical UPC Sales to reflect hierarchy changes in Shopper Behavior. You can submit a ticket for these August changes beginning on Monday, August 24.
Hierarchy changes are now reflected across the Data Access and Collaboration portfolio. If you have any pre-built reports utilizing hierarchy filters, you may need to clear these filters and recreate them with the new hierarchies.
Please submit requests via a support ticket to get historical UPC Sales to reflect hierarchy changes in Shopper Behavior. You can submit a ticket for these August changes beginning on Monday, August 24.
Hierarchy changes are now reflected across the Data Access and Collaboration portfolio.
Please submit requests via a support ticket to get the updated item hierarchy. You can submit a ticket for these August changes on or after Tuesday, August 11.
We will restate corrected Markup Markdown Board data this weekend. The restated records will flow through incremental updates to both Data Feeds and Cloud Feeds, which may update values previously received. Please ensure downstream processes are prepared to consume these incrementals. We will update this notice when the restatement is complete.
We are aware of a data issue with RFID Tag Part table. We are working to fix the issue.
Service Advisory: Cloud Feeds, Data feeds Pipeline Delay We are currently experiencing an infrastructure issue impacting Cloud Feeds and Data feeds data pipelines due to a constraint within our platform. As a result, there may be delays in data availability and refreshes. We are actively engaging and working toward resolution. We will provide updates as additional information becomes available.
We have become aware that there is missing store sales data for May 26, 2026. This missing data is impacting Scintilla suppliers and the category advisor persona who rely on headless access (Cloud Feeds, Data Feeds API and Scintilla Media Data Feed). Our team investigated the issue and resolved it as of 11:30 am CT this morning. Report Builder is unaffected and has all sales data for May 26, 2026. Please see the next steps below on how to restore missing sales data. Data Feeds API: The historical file for the affected May 26 store sales data will be made available, and suppliers will need to manually consume it to recover the missing records.
We have become aware that there is missing store sales data for May 26, 2026. This missing data is impacting Scintilla suppliers and the category advisor persona who rely on headless access (Cloud Feeds, Data Feeds API and Scintilla Media Data Feed). Our team investigated the issue and resolved it as of 11:30 am CT this morning. Report Builder is unaffected and has all sales data for May 26, 2026. Please see the next steps below on how to restore missing sales data. Cloud Feeds: The data will be restated and published as an on-demand update in the status table. Suppliers can manually use the status timestamp to read the restated store sales data using CDF. If they do not take manual action today, then it will process automatically tomorrow via the normal CDF process.
Store Sales provides information about all store sales at item, channel, store, or date level, including a report code indicating whether an item sold was a rollback, on clearance, etc.
What Are Considered “Store Sales”?
Store Sales are any sales made via the following service channels:
Buy in Store
Pickup
Delivery
Ship from Store
With the Store Sales data, you can:
Break out store-level sales by service channel
Identify stores that index higher for in-store vs. OPD (online pickup & delivery) customers
Identify top and bottom performing stores across key KPIs
Conduct research and root cause analyses
What Is a Service Channel?
A service channel represents:
How a customer places an order
How that order is fulfilled
How the customer receives the order
Walmart offers a total of seven service channels, four of which are included in Store Sales data:
Buy in Store
Pickup
Delivery
Ship from Store (historical only)
Service Channel Definitions
Buy in Store (BIS)
Traditional brick-and-mortar in-store purchases where customers select and buy items in person.
Pickup (PU)
Orders placed online, fulfilled by the store, and picked up by the customer.
Includes scheduled and unscheduled pickup
Not differentiated in Store Sales data
Delivery (DLV)
Orders placed online, fulfilled by the store, and delivered to the customer’s home.
Includes scheduled, unscheduled, and in-home delivery
Not differentiated in Store Sales data
Ship from Store (SFS)
A discontinued service channel:
Orders placed online with two-day shipping
Fulfilled by stores
Delivered via carriers (e.g., FedEx)
Depending on store availability, SFS could be more efficient than shipping from a Fulfillment Center (FC), but only if the store had capacity.
Related Channels
Ship to Home (S2H) and Ship to Store (S2S) are available in Omni Sales data
Marketplace / 3P data is not available
Metrics Explained
There are a variety of attributes and measures that can be used to analyze Store Sales data. These metrics can be combined to generate deeper insights.
Examples include:
Combining Walmart Item Number, Store Number, and metrics to analyze item performance across stores
Using inventory metrics (e.g., On Hand Quantity, replenishment metrics) to evaluate stock levels and efficiency
Analyzing Sales Amount by Service Channel to determine which channels drive the most sales
Example Use Cases & Recipes
Below are examples of how to use Store Sales data to analyze performance.
Use Case #1: Performance
Create a report to view sales and in-stock performance for all active items in a store during a selected time period.
Relevant Fields
Table Name
Technical Name
Business Name
Store Sales
store_nbr
Store Number
Store Sales
wm_item_nbr
Walmart Item Number
Store Sales
svc_chnl_nm
Service Channel
Store Sales
mds_fam_id
Store Item ID
Store Sales
rpt_cd
Sales By Type
Store Sales
op_cmpny_cd
Operational Company Code
Store Sales
vendor_nbr
Vendor Number
Store Sales
vendor_nm
Vendor Name
Store Sales
wm_yr_wk_nbr
Walmart Year Week Number
Store Sales
sales_amt
Sales Amount
Store Sales
qty
Quantity
Store Sales
aur
Average Unit Retail Amount
Store Sales
scan_cnt
Scan Count
Store Sales
geo_region_cd
Geographic Region Code
Store Sales
bus_dt
Business Date
Use Case #2: View the Inventory Pipeline with Sales and Inventory by Item
Assess whether shelf capacity meets demand, ensure compliance with pack size guidelines, and identify potential inventory issues.
Relevant Fields
Table Name
Technical Name
Business Name
Store Sales
store_nbr
Store Number
Store Sales
wm_item_nbr
Walmart Item Number
Store Sales
svc_chnl_nm
Service Channel
Store Sales
mds_fam_id
Store Item ID
Store Sales
sales_amt
Sales Amount
Store Sales
qty
Quantity
Store Inventory
mds_fam_id
Store Item ID
Store Inventory
ty_in_trnst_qty
Store In Transit Quantity - This Year
Store Inventory
ty_in_whse_qty
Store In Warehouse Quantity - This Year
Store Inventory
ty_on_hand_qty
Store On Hand Quantity - This Year
Store Inventory
ty_on_order_qty
Store On Order Quantity - This Year
Store Inventory
ty_pipeline_qty
Store Pipeline Quantity - This Year
Store Inventory
ty_repl_instock_numerator
Replenishment Instock Numerator - This Year
Store Inventory
ty_repl_instock_denominator
Replenishment Instock Denominator - This Year
Store Inventory
ty_traited_cnt
Traited Store/Item Count - This Year
Store Inventory
ty_repl_store_item_cnt
Valid Store Item Count - This Year
Store Inventory
max_shelf_qty
Max Shelf Quantity
Conclusion
The Store Sales data table provides a comprehensive view of sales across multiple service channels.
With this data, you can:
Break out store-level sales by service channel
Identify top and bottom performing stores
Conduct detailed analysis and root cause investigations
The ability to analyze data at the item, channel, store, and date level, combined with attributes like rollback or clearance status, enables a deeper understanding of sales performance.
These insights can significantly improve decision-making and overall product performance across distribution channels.
