Hitting New Heights in Applied Big Data | RetailNext

Comprehensive In-Store Analytics


Hitting New Heights in Applied Big Data

George Shaw
George Shaw
Technical Fellow

Recent innovations are helping retailers to understand human behavior in stores at an unprecedented scale.

Blog_NewHeights_BigDataIn a post-NRF 2014 Forbes column, which looked at how big data helps stores to track shoppers, a statistic from the event was cited: “80 percent of the world’s data has been created in the last two years.” Although often overused as a term, the concept of “big data” is still new. And core to the mission of RetailNext is making sense of it, keeping alive the promise and ROI of applied big data 

Just a year and a half ago, Retail TouchPoints reported that RetailNext was monitoring more than 30,000 sensors in stores across 60 retail chains, collecting 75+ petabytes of raw data each year.  Today, we announced some remarkable new heights in big data. The RetailNext platform now:

More than 120 retailers and brands are now using our big data innovations to improve staffing, store layouts, fixtures, or even marketing campaigns, based on what they learn about their shoppers’ behavior. The fact is that brick-and-mortar retailers today have massive appetites for fact-based insights, wanting the power and flexibility of Google Analytics for their stores. And since their physical spaces are teeming with information about how humans behave, retailers are catching on that big data can be applied to answer a plethora of questions. Do women use fitting rooms more than men, and at what time of day? Which window displays drive more traffic? Which personalized promotions are most effective? What are the demographics of showroomers? How was the weather on a particular shopping day?

The challenge for retailers has been in pulling together all the pieces in a meaningful context. The RetailNext platform doesn’t just look at Wi-FI, foot traffic, or sales data alone.  Rather, it collects, correlates, and analyzes numerous sources of disparate data—and that’s what makes the platform so powerful. We provide in-depth views about shoppers while in store, whether engaged physically or though their smartphones.

The ecommerce-style analytics, and the actionable insights they yield, are derived from the most comprehensive set of data sources, including:

Guest Wi-Fi. The platform manages guest-Wi-Fi services and provides a real-time personalization API that integrates shopper data for more targeted marketing.  100,000+ new shoppers opt in daily, offering new ways to influence purchases through one-on-one campaigns while shoppers are in store.  

Heat maps and video cameras. We’re making sense of big data from video recordings for analog and IP cameras, including those used for loss prevention (LP).  Innovations enable retailers to understand in-store shopper activity and movement, with varying levels of detail and granularity.

POS and other data sources. We’re extracting data from all popular POS systems and integrating the transactiondata with other sources, such as traffic counting and LP video, to generate a variety of analytics and help retailers save significantly on capital expenditures. And being hardware-agnostic, the platform seamlessly integrates and correlates data from dozens of other sources such as promotional calendars, staffing systems, and even weather services.

At the end of the day, we’re making useful sense of the most in-store data available to retailers. To learn more about how big data solutions can be applied to brick and mortar challenges, visit the RetailNext product platform page