When deciding what inventory to buy in eCommerce there are a number of factors to consider. In fact, there are so many that often a lot of key data is ignored which could lead to an uninformed buying decision.
As part of his predictions for data interpretation and segmentation, Jonathan Bellwood spoke about how data can be used to inform buying in eCommerce. The aim of this post is to highlight how it can be done using three levels.
Level 1 - Historical data
A lot of eCommerce companies just use historical data to reach a decision on what inventory to purchase. For example, they look at what stock they currently have in the warehouse and how much they sold over a period of time. For instance, to work out what to buy in October, they may look at what sold well in October last year.
From here, the decision maker will have an idea of what inventory they are going to buy so they will next look into where they can source it. They might consider two suppliers, one who offers a quicker delivery but charges more against another that takes longer to deliver but is ultimately cheaper.
This is usually the last step in the buying process, but there is so much data that hasn’t been interpreted and so the decision is not fully informed.
Level 2 - Historical data + days in stock
The next level takes into account the number of days a product is in stock. This is best obtained using a warehouse management system as the stock levels should be 100% accurate. Let’s look at the example of Product A and Product B.
Over a period of 8 weeks (you could choose to measure in days or months also), 200 units of Product A were sold whereas Product B was only in stock for three weeks but sold out of its 80 units during that time.
Assuming eCommerce companies are using just historical data as above, they would conclude that Product A sold much better than Product B over the 8 week period since more units were sold. A bigger problem occurs when they look back at sales over the last month, say, for ordering top-ups and see that Product A sold well but Product B wasn't even in stock during those last four weeks and, therefore, could not be sold.
Level 3 - Historical data + days in stock + website data
Combining levels 1 and 2 will provide you with a more accurate picture of what inventory to buy, but to make the most informed decision possible you must go one step further. This involves analysing your eCommerce website data with regards to clicks and conversion.
Assuming that Product A and Product B are listed on separate pages, companies should monitor which of the two products receive the most clicks and the highest conversion when the item is in stock. For example, Product A might have lots of clicks but a low conversion as it only sold at an average rate over 8 weeks. However, while Product B was in stock, the conversion would likely be very high considering it sold out quickly. If the number of clicks on the page remained high after the item was out of stock, this would suggest a consistent demand.
This rich data should then be combined with the information from levels 1 and 2 to derive the best process for buying inventory in eCommerce.
By using this method and assuming that it received the highest conversion online, it would be sensible to buy more of Product B considering it sold out quickly when it was in stock.
>> Look into historical data to see what is in stock, what has been sold, and which suppliers can offer the best deal.
>> Compare how the items sold on the days they were in stock.
>> Analyse website data to find out which items achieved a higher conversion when they were available to buy online.
>> Combine all of this data to make the most informed inventory buying decision.