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Using Data Infrastructure and Management as a Forecasting Tool

Blog Post
We’ll discuss 3 data collection and management strategies you can adopt to better forecast your sales and prepare your inventory for customer demand.
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Customer demand is volatile, which makes it difficult to predict product sales and stock the right amount of inventory. If you estimate too high, you can wind up with large amounts of overstock and wasted warehouse space. But if you shoot too low, your customers will end up dealing with backorders and long fulfillment cycles.  

To reach the sweet spot — where you consistently have the right amount of inventory for every category — you need to accurately forecast customer demand. And that takes data.  

Below we’ll discuss 3 data collection and management strategies you can adopt to better forecast your sales and prepare your inventory for customer demand.  

Connect Data From Multiple Channels 

When you sell products via multiple channels, like brick-and-mortar stores and eCommerce sites, it’s fairly easy to review sales data to see what’s being purchased. However, if your sales data is segmented by channel (i.e. your physical locations, eCommerce store, and social media profiles), it can be difficult to see the big picture.  

Alternatively, by collecting and storing data in a single platform you can query information however you need to. For example, you can pinpoint your most popular products by pulling data points together from every physical storefront, eCommerce site, and social media channel.  

Similarly, collecting and visualizing your data holistically can help you see where products are in high demand — both from in-person and online shoppers. This not only helps you predict sales, but it also enables you to determine the best way to spread inventory across your warehouses and storefronts for fast and easy fulfillment.  

For instance, if you find that a lot of inventory is being shipped to people in San Francisco, you can store more inventory in that region to shorten order cycles and shrink shipping costs. You might even decide to stock some of that inventory in your local stores to capitalize on the “ship from store” fulfillment option while bulking up the stockrooms of your popular stores.  

Segment Data for Channel and Location-Specific Insights 

While having the ability to look at your data holistically is important, it’s also valuable to get targeted insights for individual channels and brick-and-mortar locations. The reason being: doing so makes it easier to plan inventory availability and map out the best fulfillment route for each online customer order.  

For example, by exclusively viewing the data of your online shoppers, your order management and fulfillment teams can strategically select a fulfillment location, carrier, and route that are the fastest and most cost-effective option for each delivery. At the same time, you can view the data by location or channel to identify issues and find ways to optimize order fulfillment for a specific segment of customers.  

Use Past Data to Predict Future Trends  

Demand can change rapidly throughout the year and from year to year. And while certain factors like major economic booms and busts and natural disasters can be difficult to predict, there are recurring events that affect customer demand like clockwork. For instance, at the end of each year, retailers fulfill more orders as holidays like Black Friday, Thanksgiving, Christmas, Hannukah, and New Year’s Eve approach.  

As a result, having a method to pull old data from previous years is helpful, as it gives you a baseline to predict the current year’s demand, and therefore make smarter decisions about stocking inventory and fulfilling orders.  

Predicting sales and planning inventory doesn’t have to be a guessing game. In fact, by collecting, managing, and using data strategically, you can easily get a clear picture of future customer demand. And using the strategies discussed above — to combine multiple data sources for big picture insights, segment data for precise insights, and revisit past data to predict future trends — you’ll have a solid foundation to start with.