Data Requirements for Revenue Forecast
- Ioana Kocurova
To make sure that your model will run, the accelerator needs to have at least 2 years of transaction history. You need to gather data for each required column listed in this table.
Ensure that you set the data filter in “Filter” to get complete periods (and not e.g. half a week at the beginning or the end of the scope).
LEARN MORE: If you want to know more and understand the importance of data quality and data readiness, click here.
Understanding Each Column
Date → This is the date of each transaction. It should be defined as a dimension in your data source or datamart.
Product→ This is typically the Product ID. It identifies the product involved in each transaction.
Revenue → This is the total revenue from each transaction, calculated as the end-customer price multiplied by the quantity sold. Avoid negative or null values.
Quantity → This is the number of units sold in each transaction. Avoid negative or null values. Using the log of the quantity can help if you have a wide range of quantities.
Store → Although required, this column is not used in the current version and will be removed in the next version.
Optional Columns
Revenue at List Price → This is the list price multiplied by the quantity. It helps calculate discount rates and is recommended if available.
Product Categorical Features→ These are categorical attributes related to the product that might influence sales, such as Product Category, Competitor Name, Product Life Cycle, and Promotion Tags.
Product Numerical Features → These are numerical attributes that can be averaged over time, like product ratings or average sales price.
Customer Categorical Features → These are categorical attributes related to the customer that might influence sales, like Customer Segment or Loyalty Status.