Log workspaces analyze retail inventory use case

content/en/logs/workspaces/use_cases/analyze_retail_inventory.md

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+---
+title: Analyze Retail Inventory
+further_reading:
+- link: "/logs/workspaces/"
+  tag: "Documentation"
+  text: "Learn more about Log Workspaces"
+---
+
+## Use case
+
+
+Use Log Workspaces to perform a detailed analysis of products with low stock and high recent sales various different regions or countries. This guide walks you through the steps to:
+- Join inventory, sales, and location data in a workspace
+- Filter for products with low stock and high demand
+- Visualize sales of each of these products by country
+
+This process helps retailers prioritize restocking in geographic regions where demand is high, ensuring products in high demand remain available to customers.
+
+## Setup
+
+This guide assumes that you are:
+- Already submitting logs to Datadog for a similar use case.
+- Able to [create a workspace][1] and add cells. 
+
+### 1. Bring in your data source
+
+To get started, bring in the logs from the service(s) you want to analyze.
+1. [Create a Workspace][1].
+1. Select **Logs Query** as your data source.
+
+### 2. Query for inventory and sales data
+
+To search for inventory data, set up your logs query to filter for your specific service, index, or attributes that contain this data.
+
+{{< img src="/logs/workspace/use_cases/analyze_retail_inventory/query_inventory_sales_data.png" alt="Example workspace query to find logs from service:product-inventory" style="width:100%;" >}}
+
+You can add any additional filters, facets, or attributes to narrow your search based on your requirements and what is available in your logs.
+
+Add a **Data source** cell, and search for your sales data in the same manner.
+
+
+### 3. Join inventory and sales data
+
+Analyze products with low stock and recent high sales by joining inventory and sales logs with an Analysis cell using SQL.
+
+1. Add an [Analysis cell][3] to your workspace.
+1. Create a SQL query to join the inventory and sales data to identify products with low stock and recently high sales.
+1. Run this SQL query.
+    ```sql
+    SELECT 
+      inventory.product_id,
+      inventory.store_id,
+      inventory.stock_level,
+      inventory.threshold_level,
+      sales.quantity_sold,
+    FROM
+      inventory_data AS inventory
+    JOIN
+      sales_data AS sales
+      ON inventory.product_id = sales.product_id
+      AND inventory.store_id = sales.store_id
+    WHERE 
+      inventory.stock_level < inventory.threshold_level
+      AND sales.quantity_sold > 30;
+    ```
+    {{< img src="/logs/workspace/use_cases/analyze_retail_inventory/join_inventory_sales_data.png" alt="SQL query to join the inventory and sales data to identify products with low stock and recently high sales" style="width:100%;" >}}
+
+This analysis helps identify products with limited availability despite recent sales, indicating potential need for restocking. Additionaly, you can reveal other patterns, such as products with unexpected spikes in sales.
+
+### 4. Analyze by location
+
+You can further analyze your product sales by location. After identifying products with low stock and high recent sales, determine the countries where these products are most popular or experience the highest sales. 
+
+Achieve this by joining your inventory and sales data with a [Reference Table][5] that includes store location information. Use the store ID to assess in which locations specific products are most popular.
+1. **Add a Data Source Cell**: Select the Reference Table.
+2. **Select the Reference Table**: Ensure you choose the correct table containing store location information.
+  {{< img src="/logs/workspace/use_cases/analyze_retail_inventory/analyze_by_location.png" alt="Data source cell with the Reference table selected in Data source dropdown field" style="width:100%;" >}}
+3. Write a SQL query to join the inventory and sales data, identifying products with low stock and recent high sales.
+  ```sql
+  SELECT
+    lsa.product_id,
+    location.store_country,
+    sum(lsa.quantity_sold)
+  FROM
+    low_stock_analysis AS lsa
+  JOIN
+    store_location_data AS location
+    ON lsa.store_id = location.store_id
+  GROUP BY lsa.product_id, location.store_country
+  ```
+
+For each product, this query sums the total quantity sold in each country and groups them in a way that will visualize by product, what were the quantities sold in each country. 
+
+### 5. Visualize product sales by country
+
+Create a visualization to display product sales distribution by country. 
+1. Add a [Visualization cell][4].
+1. Select your location analysis as the source dataset.
+1. Select **Pie Chart** from the "Visualize as" dropdown.
+1. Group by product and country to view total quantities sold for each product across different regions.
+  {{< img src="/logs/workspace/use_cases/analyze_retail_inventory/visualize_product_sales_by_country.png" alt="Group by product and country to see total quantities sold for each product across different regions" style="width:100%;" >}}
+  {{< img src="/logs/workspace/use_cases/analyze_retail_inventory/visualize_pie_chart.png" alt="Pie chart of example inventory product by country" style="width:100%;" >}}
+
+## Further reading
+
+{{< partial name="whats-next/whats-next.html" >}}
+
+[1]: /logs/workspaces/#create-a-workspace-and-add-a-data-source
+[2]: /logs/workspaces/
+[3]: /logs/workspaces/#analysis-cell
+[4]: /logs/workspaces/#visualization-cell
+[5]: /integrations/guide/reference-tables/