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This is how e-commerce companies should be buying inventory

Jonathan Bellwood
  • 05 April 2018
  • 5 min read


When deciding what inventory to buy in e-commerce 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.

This post looks at three approaches that e-commerce companies take to making decisions about which inventory to buy. Each approach is incrementally more complex, but with this additional complexity you will be able to make more intelligent, informed purchases. 

Approach 1 - Just looking at 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 here that hasn’t been interpreted. Any purchase decisions made purely on the back of historical data are therefore not fully informed.


Approach 2 - Combining historical data with 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  (WMS) 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 also choose to measure in days or months), 200 units of Product A were sold whereas Product B was only in stock for three weeks but sold all of its 80 units during that time.


Assuming that e-commerce companies are using just historical data, 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.

This approach gives a slightly better idea about what inventory is needed in the warehouse, but it still doesn't provide full visibility. This makes it easy to make the wrong decisions.


Approach 3 - Bringing in clicks and conversions

Combining the first two approaches 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 e-commerce click and conversion data.

Assuming that Products A and B are listed on separate pages, companies should monitor which of the two products receive the most clicks and achieve the highest conversion when the item is in stock.


For example, Product A might have received lots of clicks but also received 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 the first two approaches to derive the best process for buying e-commerce inventory.

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.


Key Takeaways

  •  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.

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