Why are TSV files used instead of CSV ?
CSV files run into problems when there are special characters like comma(,) or quotes (“) in the data. TSV files are able to handle special characters very gracefully.
How to use the tool for new stores or new categories?
IRIS is a prediction tool that uses historical data. So, without historical data it won’t be able to project for any new store or category. But there is a workaround.
In the same way we do these projections manually, it can be done through the tool also. For example, if we are opening a new Store B, then based on your experience find an existing similar Store A. Now add dummy data for Store B based on Store A’s historical data. Based on this data the tool will be able to provide output for the Store B.
Why customer returns data is not considered?
In offline, the customer physically tried, tested a product before buying. The no. of returns are relatively low. Hence the sales data is far more meaningful for analysis as opposed to returns data. In online, the customer can return the product or multiple reasons –
- The physical product was not the same as shown in catalog.
- It wasn’t of right size
- Product got damaged during transit
- Payment problems
- Customer was not available during delivery
All of these reasons do not give any clue as to whether the product was desirable or not, from a consumer PoV.
Hence the system algorithms focus on what consumers opted to buy as opposed to what they returned.
Sales Data Cleanup
I see lesser “Percentage of Revenue” cleaned
Say you gave 10% value for the Max Liquidation Cleanup Cutoff % , but now you see only 8% as Percentage of Revenue Cleaned. The reason for this is that the system intelligently buckets similarly discounted data, and either includes all or none (as they are similar). This is illustrated below
|Bucket||Discount %||% of Revenue||Total % of |
|bucket 1||50-60 %||3%||3%||Yes|
So, in the above example, there isn’t much different really in giving 35% discount or 40% discount (they are similarly discounted). So either all such sales should be either cleaned up together, or not cleaned up. In order not to breach the 10% criteria, the system chooses not to cleanup such data. Hence you would see only 8% revenue cleaned up
How to avoid allocation of old season styles?
Simply remove them from whstock (Warehouse Stock) file, before uploading it
How to prevent broken styles during allocation?
The system tries its best to do allocation in a manner where only non-broken styles go first to the stores. If the stores still remain empty, then it tries to allocated styles which have good quantity left in the warehouse, but have sizes missing.
If you don’t want your first allocation to contain any broken styles, you can simply remove these from the output.
How to handle dead stock pull back from stores?
You can find store wise dead stock, based on your own Excel exercise. We do not suggest anything here, as different brands may have different approaches to define dead-stock and how to handle such dead stock.
Once you’ve identified the dead stock, you can simply subtract it from storestock (Store Stock) file, before uploading it, so that the system can allocate more inventory to that store.
How to handle goods in transit?
There could be inventory in transit for the stores, or for the warehouse. Simply add such inventory to storestock (Store Stock) and warehousestock (Warehouse Stock) data files respectively, before uploading them
In this way, the system can consider such goods in transit as part of the existing store inventory, or warehouse inventory, and do distribution calculations accordingly.
Merchandising for E-Commerce
How can I create a planogram for online channels?
As online channels do not have any display constraints, creating a planogram is not based on physical constraint but based on forward inventory that the business wants to carry. So for example a subcategory of polo T-shirt, if you project to sell 300 pieces per month on the channel and you want to keep a forward cover of 3 months inventory with the channel.
Then your planogram will be 300*3 = 900 pieces.
As a brand if you plan to keep 50 pieces in each style, this will become the size set for polo T-shirt.
How can I use the tool to do online distribution?
If you have multiple online channels then you can consider them as different stores and based on the channels performance you can distribute your warehouse inventory.