Forecasting inventory needs with not-so-obvious signals


If you’re an online retailer, you probably employ a series of data points to decide how many units of a given product to order. In other words, you use fairly straightforward things like the current inventory levels for the product, historical sales volume and seasonal trends to dictate your expected inventory needs.

However, I wonder if there are other, not-so-obvious signals that can lead to better inventory forecasting. For instance, you could look at:

– How many customers have the product in their cart, but haven’t bought it yet
– How many people added the product to their wish list
– How many users sent the product info to someone else via a sharing feature

In each case, higher numbers would suggest that more sales are on the horizon, so you might consider adding more inventory in anticipation of that. Granted, I’m no expert on inventory matters. But I’m pretty sure there’s a lot of data in the hands of online retailers that could make the task of managing their inventory quite a bit easier — while reducing the number of “out of stock” messages that customers see.