This module is for loading daily sales data across stores and cleaning it up. Often heavy discounts are given on products to bump up sales and liquidate excess inventory. Such sales should not be considered for forecasting, as it distorts real demand. This module cleans up such sales for better analysis of data. Also, having right product sizes at stores is important for good sales. This module also finds out which sizes are not selling well at stores and cleans up that sales data.
If wrong data has been uploaded by mistake, then the Data Deletion feature can be used to delete the data. This data will be lost for-ever, so ensure that a backup has been taken before deletion.
|Start Date||Only data between start-date and end-date (both dates included) will be considered for analysis. You can also use to analyze data for last season|
|End Date||Only data b/w start-date and end-date will be considered for analysis, for up to 1 year. You can also use to analyze data for last season|
|Max Liquidation Cleanup %||How much revenue % you are willing to forgo (at a partner+category level) while doing liquidation sales data cleanup. For e.g., in EBO+Jeans, remove my bottom 10% revenue sales data. . Keeping it at 0% does not clean any sales data.|
|Exit Sizes Revenue Contribution %||If revenue contribution in a store for a size is below this %, then it is considered as an exit size. For e.g., for each store+category, remove all sizes which contribute to bottom 3% revenue. The same process is done at a partner+category level also, i.e. do not procure those sizes for a partner, which contribute to bottom 3% revenue. Keeping it at 0% does not clean any sales data|
Please see the File Formats page for file formats