Next-Gen Retail Automation Powered by Deep Learning

Michael Deane
Guest Contributor

Retail is being transformed by automation paired with deep learning and other advanced technologies, and the changes are pleasing shoppers and shopkeepers alike.

Disruptive technologies have been transforming different industries, and retail is one of them. An increasing number of retailers leverage the power of artificial intelligence (AI), automation and deep learning in an attempt to cut overhead costs, boost efficiency, perform tasks faster and improve customer satisfaction.

Automation is a widely adopted technology and one of its many benefits is the fact it can be used throughout the product and service cycle. But it’s the synergy of automation and deep learning that allows retailers to step up their game and dramatically improve their performance.

Below are some of the most notable ways automation powered by deep learning will dominate retail in the future.


Personalized customer experience has become somewhat of a staple in both retail and marketing. Your target audience will ignore your newsletters and emails if they’re obviously written in a one-size-fits-all manner with the same copy for every customer in your CRM. Shoppers need to see you’re talking to them directly and addressing their own particular needs.

According to Salesforce, 57 percent of consumers are ready to share their personal data in exchange for personalized discounts and offers, while 52 percent of consumers are willing to share their data so that they can get personalized recommendations.

Chatbots, which many companies already use, are an excellent example of how automation and deep learning work together to improve shopper satisfaction. With their help, shoppers can get the information they need without waiting for a sales rep to pick up their call, thus speeding up your sales cycle.

While many of these algorithms are still pretty generic, the future of chatbots lies in AI and deep learning, as the idea is to implement advanced technologies which will enable them to learn and expand their capabilities, allowing them to have more complex conversations with shoppers and provide answers that are not predefined. Also, they will be able to collect personal customer data, memorize it, and provide better feedback in the future.

However, it’s worth mentioning that there are different kinds of bots which aren’t used at the front end and which don’t interact with shoppers and customers directly – Robotic Process Automation, or RPA, bots. Their role is to streamline different processes and eliminate human interference, thus reducing the possibility of an error and completing tasks much more efficiently, both of which result in a more positive customer experience.

Enhanced Customer Service

Physical stores can tap into the power of automation and artificial intelligence too. Smart humanoid robots used as customer service greeters can give customers directions and help them find the items they need.

Pepper is a robot developed for Japan’s SoftBank and it has already gained remendous popularity. Not only is this shop assistant of the future capable of interacting with shoppers, but it can also perceive human emotions, allowing it to provide more personalized and authentic engagement.

Apart from Japan, this AI-powered robot has had success in the U.S. too: the b88ta store in Palo Alto launched a Pepper pilot and saw a 70 percent increase in its foot traffic. The Ave also gave Pepper a try, and the retailer reported a whopping 98 percent increase in customer interactions.

The only problem so far is that Pepper is expensive, rendering it unattainable for many retailers. But, if it turns out that its use can deliver a sustainable increase in sales, many companies will definitely consider such an investment.

Sophisticated CRM

Customer relationship management systems are essential for creating an effective marketing strategy, shortening the sales cycle, and automating outreach. CRM tools collect and analyze huge volumes of relevant data, thus helping retailers nurture their customers and build loyalty. With the help of these systems, retailers can better predict their sales, drive revenue, make informed decisions, and craft better offers.

The latest trend is using video analytics software – like the solutions provided by RetailNext – for improving customer service. Namely, the visual data shows how customers interact with products and provides an insight into the path each visitor takes through the store, which is helpful for improving the store layout and increasing shopper engagement. However, this method shows how visitors navigate the store, but it can’t tell what they are thinking; similarly, it keeps track of how much time shoppers spend in-store, and while it presents data-driven insights, it doesn’t completely answer an important question – Why do some shoppers leave without converting?

Similarly, e-commerce businesses can also monitor how their customers navigate a site, where they spend the most time, and how long they stick around. And, again though, without direct customer feedback, they can’t figure out why some of their visitors leave their online store without converting.

It’s clear that connecting your CRM with an AI platform is essential for getting the whole picture. By addressing your shoppers’ questions and complaints in a timely manner, it’s possible to prevent customer churn. AI-powered CRM tools can process large quantities of customer information and help retailers with key takeaway points. It’s essential to automate your interaction with your customers and obtain as much information about them as possible, as that will allow you to improve customer satisfaction, drive more sales, and increase retention.

More Efficient Delivery

Customers have become more demanding when it comes to the delivery of items they bought online. They want their new phone, shoes, or watch right away, and if you aren’t efficient enough, next time they’ll make a purchase somewhere else.

IBM launched its Watson Supply Chain which uses cognitive learning to help retailers optimize their supply chain performance and prevent different risks and disruptions. This advanced technology uses data from different sources, including social media, weather forecast services, and news feeds to predict possible chain disruptions ahead of time, so that retailers can find an alternative solution for delivering their orders on time. It also provides accurate, real-time information about goods and shipments, thus providing much-needed visibility, the lack of which was named as one of the biggest challenges by 84 percent of CSCOs.

TransVoyant is another machine learning-based technology which optimizes the supply chain. It can collect an impressive amount of data – trillions of events on a daily basis – using different sensors, radar data, cameras, smartphones, and satellites. Moreover, its high-tech algorithm is fed by information such as the real-time movement of shipments, weather conditions, traffic congestions, or natural catastrophes so that it can estimate when goods will be at their destination.

Automation paired with deep learning and other advanced technologies will transform the retail industry in the future, and these examples are just the beginning.

About the writer: Michael Deane is a marketing executive and account manager, juggling dozens of clients on a daily basis. He is one of the editors of Qeedle, a small business hub.

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