Finding a Perfect Inventory Balance Using Retail Predictive Analytics
Written by: Brad Thomas

Today’s consumer-driven fashion styles change from month to month, while omnichannel shopping habits continue to evolve. To keep up, retailers must balance their inventory levels by stocking stores with just the right amount of the top selling looks and styles. Shoppers have an overwhelming number of options, so it’s crucial that retailers don’t let their hottest items go out of stock at the wrong time. On the flip side, overstocked items can also be costly to your business and tie up space, money, and resources. So how can retailers guarantee that they always have the right products available in-store and online to cash in on the latest trends?

Let retail predictive analytics be your guide

There are all kinds of data points that retailers use to drive business decisions. What styles sold the most? What styles sold the least? What did customers think of certain items? Which stores had the highest volume of shoppers? Which items were sold out or went out of stock? Could those unavailable items have sold more if they were in-stock?

Retailers leverage this kind of historical performance data to improve upon the previous year’s product assortment, but for larger brands it can be a lengthy process involving multiple data systems in a market where supply and demand changes at the drop of a hat.

Predictive analytics tools give retailers the power to answer those same questions almost instantly, using machine learning to quickly identify trends and offer automated recommendations that accelerate decision making. This allows retailers to proactively adjust their inventory levels on the fly using real-time data, while also being able optimize the in-store experience and incorporate customer feedback.

Achieve the perfect inventory balance with predictive analytics

Getting product performance data to work in conjunction with inventory visibility is key to finding the perfect balance for your product assortment. What once took entire teams manually sifting through millions of records and thousands of variables can now be done through machine learning and predictive analytics in an instant, saving time and resources while providing retailers with a strong competitive advantage.

Turning big data into knowledge is a struggle not just in retail, but across all industries. With predictive analytics, you can reduce the guesswork and unnecessary reporting that slows your decision making and future-proof your retail business before next season gets here.

To learn more about how predictive analytics can help retailers find the perfect inventory balance, watch this on-demand webinar featuring retail technology solution experts from PTC and Tech-Clarity.

Retail Analytics Solutions Capabilities
Tags: Augmented Reality Retail and Consumer Products Product Lifecycle Management (PLM)
About the Author Brad Thomas

Bradford Thomas is a product manager in PTC’s Retail Business Unit. Over 25 years, Brad has helped some of the world’s leading retailers and brands develop and implement predictive analytics strategies to improve operational efficiencies and to better understand and market to their customers. His particular expertise is customer analytics, having served as the primary consultant for several frequent-shopper programs and on iconic loyalty campaigns for Pepsi and Marlboro.