Top 6 Retail Analytics Trends of 2017

By: Jeff Huckaby, Segment Director, Retail and Consumer Goods, Tableau Software [NASDAQ: DATA]

Jeff Huckaby, Segment Director, Retail and Consumer Goods, Tableau Software [NASDAQ: DATA]

Business intelligence norms are evolving across the retail industry, and leading retailers are prioritizing analytics initiatives as a result. More retail and consumer-goods companies are opening up their data to executives and front-line employees. As a result, the call for faster, simpler, and mobile-friendly tools is growing.

"The enterprise will pair these virtual reality cases with embedded data analytics to optimize revenue and profitably"

Here are six trends in retail and consumer-goods analytics we will see this year.

1. Advanced Analytics Is No Longer Just for Analysts

With the self-service boom, non-analysts throughout retail organizations are becoming increasingly data-savvy. Store managers and bookkeepers alike are digging deeper into data, thanks to interactive visualizations that allow them to ask and answer their own questions at the speed of thought.

Most big-box vendors are also leveraging advanced predictive analysis to allocate labor during peak times and provide quality customer care. Advanced analytics functions such as clustering and outlier detection help store employees make data-driven decisions. The resulting insights empower employees to choose the most efficient store layout, enhance the shopping experience, and, ultimately, increase the bottom line.

2. Mobile Analytics Is Fully Realized

For retailers, finding actionable insights in the field with a mobile device is no longer just a pipe dream. Instead of interfacing via legacy business intelligence systems, modern mobile analytics lives at the core of decision-making for major brick-and-mortar stores and their distribution centers.

More than ever, retailers are leveraging their in-store Wi-Fi investments to empower cashiers and distribution associates with analytics in hand. Working with live mobile data on tablets on a daily or even hourly basis is the new normal. Merchants, regional managers, loss-prevention associates, and even vendors have all ditched their stacks of spreadsheets to instead collaborate using interactive visualizations on their mobile devices. This model enables them to make on-the-fly decisions about inventory, omnichannel supply chain, and operational efficiency.

3. The Internet of Things Starts to Improve Data Accuracy

This year, an influx of beacons, Wi-Fi based sensors, and Radio Frequency Identification (RFID) tags will help track items throughout the supply chain and improve accuracy for in-store inventory levels.

To entice purchases, companies are exposing live IoT data about product counts both in-store and online. They are sharing the exact location of the product, down to the isle and bin at a specific store. Major brick-and-mortar stores are also utilizing improved IoT data to understand shopper behavior. Mobile data helps retailers see which in-store marketing techniques work best, and which walking pathways shoppers use the most. Marketing teams then use this information to determine which visual breadcrumbs and shopping routes result in increased sales, and can use this data to market digitally to customers.

4. Omnichannel Data Integration Gets Exciting

Successful retailers must be able to see and understand, in one holistic view, commerce-channel data, supply-chain data, and customer data. This is the promise of omnichannel.

Working across different channels and data sources can seem tedious and even impossible. In 2017, we’ll see many new players in the data integration space. With the rise of sophisticated tools and the addition of new data sources, companies will stop trying to gather every byte of data in the same place. Retailers will connect to data sets where they live. They will combine, blend, or join other data sets with more agile tools and methods.

By analyzing the trends with data from multiple sources, the team can set operational and promotional strategies, and continue to improve efficiency and performance.

5. Robots Bring Big Opportunities to Retail Data

For years, major retailers have employed robotics in distribution centers, but in 2017 robots will take center stage as part of the in-store experience. We will see machines, robots, and artificial intelligence begin to help retailers with routine tasks such as taking physical inventory, offering promotions, and even taking surveys and orders. These robots will begin to serve as new data touchpoints, gathering vital information about customer behaviors and interactions that companies can eventually leverage for insights.

Retailers will continue to work to extend loyalty way past point of purchase, and customer-service data gathered from robots will be one of the differentiating factors between success and failure. As social robots encourage customers to interact, they will offer additional value such as advice, recommendations, reviews, and real-time information, creating a more authentic relationship between shoppers and retailers.

6. Augmented and Virtual Reality Add More to Insight to Retailer Analytics

Ever wonder what a new couch would look like in your living room? In 2017, customers will be able to harness Augmented Reality (AR) and Virtual Reality (VR) to imagine potential purchases in their own lives. Taking guesswork out of a purchase cycle will likely improve sales, increase customer satisfaction rates, and minimize costly returns. Adding analytics to the mix, retailers can use data to provide customers with real-time inventory, visualized on store location maps, to show where products currently exist in-store.

Merchandisers will also leverage AR and VR to visualize in-store scenarios. For example, instead of spending hours and dollars physically creating product plans for shelves and store layouts, retailers will review various arrangements and alternates via VR.

The enterprise will pair these VR cases with embedded data analytics to optimize revenue and profit. Retailers will go through mock trials of stocking shelves with virtual products, and also use data to predict the outcome of each scenario.