Store Sales
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, and Ship from Store.
With the Store Sales data, you can break out your store-level sales by service channel, allowing you to identify stores that index higher for in-store customers versus online pickup and delivery (OPD) customers. Combined with other data, Store Sales data can also help identify top and bottom performing stores across critical store KPIs, conduct research and root cause analyses, and more.
Read on for a brief explanation of what a service channel is and a quick overview of each store-related channel.
What Is a Service Channel?
A service channel represents how a customer places an order, how that order is fulfilled, and how the customer receives the order.
Walmart offers a total of seven service channels, four of which are included in the Store Sales data: Buy in Store, Pickup, Delivery, and Ship from Store (historical only).
Read on for a brief description of each service channel included in the Store Sales data.
- Buy in Store (BIS) orders are the traditional brick-and-mortar in-store purchases. The customer selects and purchases their items in the store.
- Pickup (PU) orders are placed online, fulfilled by the store, and picked up at the store by the customer. Both types of Pickup orders (scheduled and unscheduled) are included in this service channel and will not be differentiated in the Store Sales data.
- Delivery (DLV) orders are placed online, fulfilled by the store, and delivered by a driver to the customer’s home. All types of Delivery orders (scheduled, unscheduled, and in-home) are included in this service channel and will not be differentiated in the Store Sales data.
- Ship from Store (SFS) is a discontinued service channel in which orders are placed online with two-day shipping, fulfilled from the store, and delivered via carrier (e.g., FedEx) to the customer at their home. Depending on store availability, SFS can be more efficient than shipping from a Fulfillment Center (FC); however, it can only be used if the store is set up for SFS and has the capacity to fulfill the order.
The Ship to Home (S2H) and Ship to Store (S2S) channels are available in the Omni Sales data (link will open in new tab). 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. Many of these metrics can be used together to gain comprehensive insights. Examples include (but are not limited to):
- By combining the Walmart Item Number, Store Number, and your desired metrics, you can analyze the performance of specific items across different stores.
- Adding Store Item Measures (Inventory) metrics like On Hand Quantity and Replenishment metrics can help you understand inventory levels and stock replenishment needs.
- Store Fulfillment (OPD) metrics such as First Time Pick Rate and Nil Picks provide insights into the fulfillment process and potential issues with item availability on shelves.
- Analyzing Sales Amount across different Service Channels can help in identifying which channels are driving the most sales and where improvements can be made.
Sample Use Cases
Below are some examples of how you could use the Store Sales data to dive into your sales metrics.
Use Case #1: Year Over Year (YOY) Item Sales
You can create a report to show a simple summary of this year versus last year sales. You can also include other dimensions to gain further insight; for example, you could include Store Number if you’d like to see performance for the item broken down by individual stores.
| Table Name | Technical Name | Business Name |
|---|---|---|
| Store Sales | bus_dt | Business Date |
| Store Sales | ly_sales_amt | POS Sales-Last Year |
| Store Sales | mds_fam_id | Store Item ID |
| Store Sales | rpt_cd | Sales By Type |
| Store Sales | store_nbr | Store Number |
| Store Sales | svc_chnl_nm | Service Channel |
| Store Sales | ty_sales_amt | POS Sales-This Year |
| Store Sales | wm_item_nbr | Walmart Item Number |
| Store Sales | wm_yr_wk_nbr | Walmart Year Week Number |
Use Case #2: Performance
You can create a report to get a snapshot view of the sales and performance for all your active items by store during the time period of your choice.
| Table Name | Technical Name | Business Name |
|---|---|---|
| Store Sales | ly_aur | Last Year AUR |
| Store Sales | ly_qty | POS Quantity - Last Year |
| Store Sales | ly_sales_amt | POS Sales-Last Year |
| Store Sales | ly_scan_cnt | Scan Count - Last Year |
| Store Sales | mds_fam_id | Store Item ID |
| Store Sales | store_nbr | Store Number |
| Store Sales | svc_chnl_nm | Service Channel |
| Store Sales | ty_aur | This Year AUR |
| Store Sales | ty_qty | POS Quantity - This Year |
| Store Sales | ty_sales_amt | POS Sales-This Year |
| Store Sales | wm_item_nbr | Walmart Item Number |
| Store Sales | wm_yr_wk_nbr | Walmart Year Week Number |
| Store Dimensions | store_nbr | Store Number |
| Item Dimensions | wm_item_nbr | Walmart Item Number |
Use Case #3: At the Store, But Not on the Shelf
Create and monitor this report over a period of time – very little is actionable based on a single day’s data, but patterns leading to actionable insight can emerge over the course of a week or two.
At traited stores where product is onhand, but nil picks are high, consider the following:
- Is shelf capacity at that store large enough to hold an entire day’s worth of demand for the item?
- Does my warehouse pack size comply with the “pack and a half” rule (or whatever guidance has been provided)?
- If I’m not seeing any sales, is there a potential PI issue at the store?
| Table Name | Technical Name | Business Name |
|---|---|---|
| Store Sales | ly_qty | POS Quantity - Last Year |
| Store Sales | ly_scan_cnt | Scan Count - Last Year |
| Store Sales | mds_fam_id | Store Item ID |
| Store Sales | store_nbr | Store Number |
| Store Sales | svc_chnl_nm | Service Channel |
| Store Sales | ty_qty | POS Quantity - This Year |
| Store Sales | ty_sales_amt | POS Sales-This Year |
| Store Sales | wm_item_nbr | Walmart Item Number |
| Store Sales | wm_yr_wk_nbr | Walmart Year Week Number |
| Store Inventory | bkrm_adj_qty | Backroom Adjustment Quantity |
| Store Inventory | ly_in_whse_qty | Store In Warehouse Quantity - Last Year |
| Store Inventory | ly_on_hand_qty | Store On Hand Quantity - Last Year |
| Store Inventory | ly_pipeline_qty | Store Pipeline Quantity - Last Year |
| Store Inventory | ly_repl_instock_denominator | Replenishment Instock Denominator - Last Year |
Use Case #4: Track Shelf Availability at Item-Store Level Based on OPD Data
Tracking shelf availability at the item-store level based on OPD data involves assessing whether the shelf capacity at a store is sufficient to meet a day’s demand for an item and ensuring compliance with warehouse pack size guidelines. This helps identify potential issues such as high nil picks despite product availability, which could indicate problem like PI issues at the store.
| Table Name | Technical Name | Business Name |
|---|---|---|
| Store Sales | ly_scan_cnt | Scan Count - Last Year |
| Store Sales | mds_fam_id | Store Item ID |
| Store Sales | rpt_cd | Sales By Type |
| Store Sales | store_nbr | Store Number |
| Store Sales | svc_chnl_nm | Service Channel |
| Store Sales | ty_qty | POS Quantity - This Year |
| Store Sales | ty_scan_cnt | Scan Count - This Year |
| Store Sales | wm_item_nbr | Walmart Item Number |
| Store Fulfillment | catlg_item_id | Catalog Item ID / eComm Prod ID |
| Store Fulfillment | cust_order_amt | Placed Sales |
| Store Fulfillment | cust_order_qty | Customer Order Quantity |
| Store Fulfillment | ftpr | First Time Pick Rate |
| Store Fulfillment | ftpr_dnmntr | First Time Pick Rate Denominator |
| Store Fulfillment | ftpr_nmrtr | First Time Pick Rate Numerator |
| Store Fulfillment | ftpr_qty | First Time Pick Rate Quantity |
| Store Fulfillment | nil_pick_qty | Nil Picks |
| Store Fulfillment | postsub_rate | Post Substitute Rate |
| Store Fulfillment | postsub_rate_dnmntr | Post Substitute Rate Denominator |
| Store Fulfillment | postsub_rate_nmrtr | Post Substitute Rate Numerator |
| Store Fulfillment | presub_qty | Pre Substitute Quantity |
| Store Fulfillment | presub_rate | Pre Substitute Rate |
| Store Fulfillment | presub_rate_dnmntr | Pre Substitute Rate Denominator |
| Store Fulfillment | presub_rate_nmrtr | Pre Substitute Rate Numerator |
| Store Fulfillment | schdl_nil_pick_qty | Scheduled Nil Pick Quantity |
| Store Fulfillment | schdl_nil_pick_rate | Scheduled Nil Pick Rate |
| Store Fulfillment | schdl_nil_pick_rate_dnmntr | Scheduled Nil Pick Rate Denominator |
| Store Fulfillment | schdl_nil_pick_rate_nmrtr | Scheduled Nil Pick Rate Numerator |
| Store Fulfillment | unschdl_nil_pick_qty | Unscheduled Nil Pick Quantity |
| Store Fulfillment | unschdl_nil_pick_rate | Unscheduled Nil Pick Rate |
| Store Fulfillment | unschdl_nil_pick_rate_dnmtr | Unscheduled Nil Pick Rate Denominator |
| Store Fulfillment | unschdl_nil_pick_rate_nmtr | Unscheduled Nil Pick Rate Numerator |
Conclusion
In conclusion, the Store Sales Data Table provides a comprehensive tool for collecting and analyzing sales data across various service channels. By leveraging this data, you can break out store-level sales by service channel, identify top and bottom performing stores, and conduct in-depth research and root cause analyses. The ability to view sales metrics at the item, channel, store, or date level, along with specific attributes like rollback or clearance status, allows for a nuanced understanding of sales performance. Utilizing these insights can significantly enhance decision-making processes and improve overall product performance across different distribution channels.
