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IoT and streaming data are revitalizing the brick-and-mortar shopping experience

The cat, er, robot, is finally out of the bag: In-store retail is not only not dead, it’s in a position to revolutionize shopping in much the same way e-commerce did two decades ago. While e-commerce is growing rapidly, 91% of U.S. retail sales still occurred in-store in 2017 according to Statista, with 86% predicted by 2021.

The path to the “store of the future” — which we’ve been envisioning for years — is well underway with industry leaders. At the very least, predictions of an all-online shopping world should be put to rest for a long, long time.

And everybody will win: consumers, employees, consumer goods partners and the retailers themselves.

We can thank the burgeoning IoT industry; specifically, sensors, beacons, robotics and video — all powered by streaming data analytics. Exemplar retailers have taken the lead in this and are actively testing once “futuristic” applications to further streamline operations and deliver better customer experiences today.

Walmart recently reported that it is actively recruiting top-level computer scientists to develop its initiatives, which include the recent news that it will deploy hundreds of autonomous robots to clean the floors at some of its stores in January 2019. The world’s largest retailer is also testing automated (robotic) scanning of shelves to improve in-store merchandising execution and out-of-stock response.

Other soon-to-be-unveiled IoT-based advancements include using computer vision and data analytics within shopping areas to improve inventory tracking, implement dynamic pricing and better understand consumer behavior.

Today, the goals of IoT and streaming data analytic initiatives can generally be divided into three categories:

  1. Driving greater operational excellence;
  2. Delivering personalized customer engagement; and
  3. Offering relevant and localized products and services.

Driving operational excellence through IoT and streaming analytics

Consider one use case already occurring: robots armed with streaming data analytic capabilities that can scan shelves to identify out-of-stock items, shelf schematic compliance and display execution in real time. In fact, Walmart recently announced it has experienced as much as a 50% improvement in out-of-stock labor costs after deploying them. Kroger has also announced experimentation with this technology for similar purposes.

One of the biggest revenue opportunities for bricks and mortar remains mitigation of lost sales through quick replenishment of out of stocks. Keep in mind that out-of-stock items are often not in a distant warehouse, but somewhere else in the store. Also keep in mind that stores will not only frustrate shoppers, miss out on the immediate sale and perhaps leave a negative impression, but also risk losing longer-term business to competitors because of out of stocks. In fact, a recent IHL survey suggested that over 24% of Amazon’s retail revenue comes from consumers who first tried to buy the product at their local stores.

Computer vision insights (using streaming video) are also beneficial for the purposes of informing corporate merchandising teams of in-store compliance (pricing, promotions and placement) in real time, including conditions that may be impacting seasonal sell-through and markdowns. Incremental benefits include data monetization (and disruption of syndicated data providers) by sharing these insights with their supplier partners.

Additionally, connected data and devices within the four walls of brick-and-mortar stores are creating greater operational and cost efficiencies. Just think about refrigeration and freezer units in thousands of grocery stores. There’s not just one big refrigerator; there are several large units in each store (across thousands of locations) to store products such as produce, dairy, deli and meat that may need to be kept at different temperatures. Temperature fluctuations can significantly impact product shelf life and go completely unnoticed by in-store personnel.

IoT and streaming analytics can alert stores to take action in order to ensure freshness and reduce waste. These new technologies can automatically push notifications to in-store associates to inform them that, for example, they lost a half-day’s life on specific dairy products overnight, based upon predetermined business rules contemplating the temperature fluctuation impact on product lifecycles. This advisory can simply instruct the store associate to reduce the price for the day or take other actions to protect freshness and quality.

Delivering personalized customer engagement and improved shopper experiences

IoT and streaming analytics hold great promise to assist merchandising and store operations teams with new insights. These include better understanding of traffic patterns, paths and hot spots within a store. It can look at dwell times: How long are customers spending in the women’s shoes department? How much time are they looking at a display? What is the associated conversion rate after checkout? How long are they waiting to check out?

These technologies and their uses will supply new metrics, insights and actions that retailers have never been able to measure or respond to at this granularity. This is especially meaningful considering tight labor constraints and a desperate need for “actionable intelligence” versus more dashboards or reports.

Emerging technologies such as computer vision (using existing in-store video cameras) are even providing new capabilities to move from prediction to intervention to help mitigate fraud and shrink — a multibillion dollar issue in retail yet today. Whether at the back door, self-checkout lanes, kiosks or in-aisle, IoT and streaming analytics hold much promise to help curtail this bottom-line challenge for all retailers.

Finally, proximity marketing and personalized engagement using mobile applications, Wi-Fi, beacons and RFID technology in-store continue to hold much promise, but remain experimental for most retailers still looking to find the right balance between privacy concerns and the type of customer insights that can be gleaned from in-store technology deployment. North American retailers such as Macy’s, McDonald’s, Kroger and Walmart have been experimenting with this, as is Carrefour, one of the world’s largest hypermarket chains in Europe.

Providing relevant and localized products and services

While the initial benefits of IoT and streaming analytics may have been focused primarily on customer engagement and customer experiential use cases, it seems the use cases for in-store merchandising are also demonstrating solid early results.

Dynamic pricing is yet another nascent e-commerce analytic capability that can be carried over to brick-and-mortar stores. While electronic shelf labels (ESLs) have been around for a couple of decades, the high cost to implement digital shelf tags has limited widespread adoption. However, the transparency of competitive pricing and the growing use of new analytic capabilities to instantly change prices on hundreds (or thousands) of items on e-commerce platforms also provides an opportunity for retailers to push this capability to individual stores using ESLs.

Kroger, America’s largest grocery chain, has just begun to roll out Kroger Edge (Enhanced Display for Grocery Environment), which connects IoT sensors to the retailer’s cloud-based storage, beams real-time data from every aisle and digitally displays pricing, nutritional information, video ads and coupons. This allows corporate and store managers to instantly change prices and initiate flash sales events on individual items. It also saves labor costs, because employees on the floor don’t have to change prices on hundreds of item tags by hand.

Light — and profit — at the end of the tunnel

If it works as designed, Kroger Edge will also be environmentally friendly and cost-efficient. That’s because Edge uses low-voltage LED lighting and will eventually run on renewable energy sources. The need for extremely bright and costly fluorescent or incandescent lighting is basically for customers to read paper tags, but with next-generation ESLs, stores can decrease the brightness of the bulbs.

Suffice to say (and to borrow from Mark Twain), reports of the death of brick-and-mortar shopping was an exaggeration. More than a few “experts” have predicted that Alibaba and Amazon would end the in-store retail experience for good. And while it’s true that e-commerce has had a deleterious effect on bricks and mortar, the retail industry is nothing if not resilient and competitive. Retail executives will always search for innovative ways to increase revenue — even if it means reimagining the in-store shopping experience. IoT and streaming analytics are driving that new imagination.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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