Predictive Analytics for Retailers: Insights into Tomorrow’s Shopping Behaviors | RetailNext

Comprehensive In-Store Analytics


Predictive Analytics for Retailers: Insights into Tomorrow’s Shopping Behaviors

Nikitas Magel
Nikitas Magel
Content Marketing Manager

Join the webinar on Tuesday, March 25 at 11am PT to learn how to use predictive analytics to get more out of Big Data

Blog_Predictive-WebinarThe world of physical retail offers an endless amount of valuable information. Retailers gather and store a tremendous amount of data from point-of-sale systems, CRM and loyalty programs, staffing calendars, and other sources. In-store analytics collects, correlates, and measures this data to provide a wealth of information about shopper behavior — in the here and now.

But what if you were able to use this Big Data to make predictions of the future by learning from the past? 

Predictive analytics attempts to do exactly that. Retailers are increasingly embracing it to better understand and anticipate the needs and behaviors of shoppers, gain more precise insights into what customers will buy, and to increase the profitability of their stores.  Advancements in this area will empower retailers and brands to improve operations and marketing, such as targeting new shoppers, forecasting store traffic for staffing, maintaining ideal inventory levels, and anticipating buyer behavior patterns.

With predictive analytics, retailers are now equipped to be proactive and to make decisions based on shopper behaviors before they happen. Better predictions mean more sound strategies for both planning ahead and driving higher conversions.

A report published by Transparency Market Research forecasts the overall market for predictive analytics software to reach $6,546.4 million globally by 2019. The technology is hot and savvy retailers are on board.

However, making proactive decisions with predictive analytics hinges on accurate and comprehensive data—historical and current—gathered from myriad sources, including brick-and-mortar stores. Some examples of this data are shopper traffic patterns, demographics, queue analysis, and conversion rates—the same depth of data collected and measured with in-store analytics.

During our March 25th webinar, we’ll discuss predictive analytics in a way that’s light on the technical, but heavy on the practical. We’ll explain why it’s useful for retailers and how they can apply it to gain deeper, predictive insights into shopper motivation.

Find out more on Tuesday, March 25th from 11:00am to 12:00pm PT during the webinar, “Predictive Analytics for Retailers: Insights into Tomorrow’s Shopping Behaviors.” From it, you will learn:

To register and secure your spot, click here!



  • Retail Reco

    Predictive analytics approach for retailers which generates more accurate predictions of future buys of customers. For retailers of all sizes even when sufficient sale history of our single product is not present.