Offline retail in 2023: how retailers can compete with marketplaces

The eCommerce segment was developing even before the pandemic, while the spring of 2020 was a serious catalyst. The share of online sales in overall retail revenue of the US market increased from 11% in 2019 to 15% in 2023. And this number will increase even further, with online sales expected to grow by up to 10% YoY in the coming years.

Online marketplaces are growing fast but keep their innovative technologies and algorithms private from classic retailers. Therefore, traditional retail chains and brands face a challenge: how not to become an outsourced supplier of goods for marketplaces but to compete with online giants on an equal footing.

Of course, most consumers still want to communicate with real people and get emotions from the offline shopping experience. However, showrooming (when a customer comes to the offline store, interacts with the products, and leaves to buy them online) is a common occurrence. Therefore, the skillful management of offline resources becomes a competitive advantage. And here, modern technologies come to the aid of traditional retailers.

To stay competitive, retailers already use solutions in predictive analytics, targeted promotions, dynamic pricing, assortment management, and big data-based order logistics planning. But there are other IT solutions that have yet to be so popular but are worth researching and implementing.

Automation and space management systems

Shelf occupancy control

Automatically signals changes in the calculation. Thanks to it, the retailer understands what and when to put, the supplier monitors sales dynamics, and the consumer is relieved of the uncomfortable feeling of encountering empty shelves.

Analysis of consumer behavior around the shelf space

Tracks attention and areas where customers stay the longest. Such measurements were made before using subjective research methods: surveys or putting special glasses on focus group members.

Modern attention control sensors have replaced this limited control method. With their help, you can optimize the layout, so the buyer is as comfortable inside the shelf space as possible. There are also attempts at combining this information with transactional and online data for synergy in personalized offers.

Monitoring the customer interaction with the store space

Analyses what the buyer puts in the cart and how much it costs. This solution allows you to maintain a store without cash desks and with an automatic checkout at the exit. Example: the sensational AmazonGo became possible thanks to sensor fusion, deep learning, and computer vision technologies.

In recent years, there was a breakthrough in computer vision: now, many algorithms provide unimaginable recognition quality, which allowed for the following solution.

Centralized content management and personalization system with feedback tracking

Some retailers already use targeted digital signage, switching content depending on gender, age, trajectory, attention, contact time, and even the emotions of the customer passing by. Such personalized offers increase both sales and consumer satisfaction, and for selling advertising space, it completely blurs the line between online and offline.

While retailers now offer advertisers only screen time, this system provides advertisers with quality touchpoints to consumers at the point of purchase, which is very similar to online targeting. It makes it possible to show ads accurately to the target audience and evaluate feedback and campaign effectiveness.

Implementation of this solution in Europe and the Middle East showed that DISPL's Digital Signage with feedback tracking results in:

• upsell of advertised goods by up to 20%;

• increasing the attractiveness of advertising space, increasing advertisement cost by up to two times;

• 2.5x increase in relevant customer interactions.

Use of interactive scenarios

Targeting in promotional campaigns

One of the practical yet funny scenarios can be emotion detection in advertising campaigns. Coca-Cola used software for a campaign at a food court. Screens at points of sale prompted customers to smile to get a promo code for a discount on soft drinks. Not only did this promotion increase sales of Coca-Cola, but it also increased customers' positive emotional connection with the brand.

Screens in fitting rooms

H&M Group is piloting tech-enabled in-store shopping experiences in the United States. One of the standout features is the introduction of smart mirrors in fitting rooms. These mirrors can recognize products that customers bring into the room, including details like item, size, and color. This technology then offers personalized product and styling recommendations to the customer. The aim is to create a seamless and personalized shopping experience, from the fitting room to checkout, enhancing customer engagement and satisfaction.

Taking digital interaction into offline

Nike introduced an interactive screen for on-the-spot T-shirt customization and self-checkouts. The initiative was a hit: within just nine days, 705 custom T-shirts were ordered, and self-service checkouts accounted for 20% of sales, reducing queues and enhancing customer experience. This showcases how technology can elevate retail engagement and efficiency.

Addressing privacy concerns

Certainly, addressing privacy concerns is crucial when implementing advanced audience analytics solutions in retail environments. It's important to note that such technologies are designed with privacy as a cornerstone. For instance, DISPL's analytics algorithm operates "at the edge," meaning all data processing is done on the device itself. No images or videos from the camera are transmitted elsewhere, ensuring that the data collected is anonymized and non-personal. This edge computing approach complies with privacy regulations like GDPR and CCPA. It ensures the analytics don't "remember" faces, only capturing generalized metrics such as age, gender, and emotion for analytical purposes. Therefore, while these systems are highly intelligent, they are not collecting personal data, thereby maintaining customer privacy.

Conclusion

The rise of eCommerce poses challenges but also opens doors for traditional retailers. Advanced technologies like predictive analytics and audience analytics solutions offer a competitive edge. These tools enhance customer experience and adhere to stringent privacy standards. As the lines between online and offline shopping blur, the future of retail lies in embracing these intelligent, secure technologies to deliver a seamless and personalized customer experience.