At a recent Future Stores Miami conference, I attended a panel discussion focused on practical applications of artificial intelligence (AI) to enhance the in-store experience. One of my key takeaways from that discussionwas that retailers are struggling to find the right opportunities to deploy AI in stores. With AI still a nascent technology, many people across multiple sectors aren’t savvy on what’s even possible by using it. I’d like to offer a few insights to help get some ideas flowing.
The oft-maligned retail landscape is certainly going through a significant evolution. Technology is playing a huge role in that, both good and not so good. The competition from e-Commerce that has drawn shoppers away from brick-and-mortar is forcing innovation of the in-store experience. AI presents a significant opportunity to transform that experience into one that is more satisfying for customers and more profitable for retailers.
The combination of machine learning and data science, both integral to AI’s value, are immensely powerful in the retail environment. These technologies eliminate the need for “gut feel” and guesswork, providing reliable insights into everything from the working preferences of in-store staff to personalization of customer offerings and much more. Let’s consider a few practical opportunities:
Merging AI And Employee Experience
Competition for good workers is tight, and employees’ expectations of their jobs have never been higher. They want an inspirational workplace where they feel motivated to be loyal, productive and engaged. Among many things, that means keeping up with technology. Giving retail teams access to leading-edge tech that uses AI and machine learning will provide them — and you — insights not previously available, increasing productivity and helping morale.
For example, modern workforce management can empower employees with preferred scheduling options and flexible clocking. Worker data is matched to store data, enabling management to optimize labor modeling, budgeting and task management. Some recent research we conducted at REPL Group revealed that over 80% of retail workers claim that more efficient management of stores and better forecasting would improve their job satisfaction. Also, innovative technologies can enhance employee engagement by more accurately providing automated systems to carry out menial tasks, freeing them up for higher value work.And that will directly translate to a better customer experience.
Understanding Competition And How To Fit In
Amazon, the thousand-pound behemoth overshadowing virtually all modern retail, continues to expand consumer choice and further threaten traditional retail growth. To fight back, AI will help retailers leverage data science rather than guesswork to learn what customers will want and when they will want it, so they can stay on top of effective inventory management.
Even with the popularity of online shopping, 55% of our research respondents said they still enjoy and engage in the in-store experience. The obvious advantages of touching exactly what you will get and walking out of the store with it in-hand are still strong incentives. However, in-store shopping brings its share of frustrations, most critically from poor availability of stock and poor customer service. With so much competition from online and brick-and-mortar sellers, AI will help retailers better establish their niche, differentiate their in-store experience and more effectively target the right customers.
Applying Predictive Analytics
Let’s imagine it’s three days before Christmas Eve, and a disruptive snowstorm is due to hit your area tomorrow evening. Will your store be left half-empty? What stock will be most affected? Will staff be able to travel to work? Will customers come out or stay home?
Powerful AI-based algorithms can incorporate data about extreme weather, along with events and promotions, into your inventory software’s forecasting capabilities. The technology will base decisions on solid data, not hunches or whims. You can confidently leverage this data into your contingency planning: more staff on earlier shifts and more snow shovels near the checkout stands.
Predictive analytics are possible through “big data” science that is now readily available. The cost of storing data has gone down to pennies a gigabyte, resulting in massive data accumulation. Machine learning and data science make retail data meaningful, crunching details of historic sales patterns, footfall figures, weather patterns, traffic reports, promotions, item location and even what staff was working when and their level of productivity. All of that enables analysis and modeling that will help predictively drive everything from what to stock to where to place it on the shop floor to discounting strategy. These complex and impactful decisions can be automated across thousands of product lines in hundreds of stores.
By taking these steps toward a more tech-focused retail experience, retailers will see how AI can open the door to more opportunities and increased implementations. Always having the right stock, staff and technology will help modern retailers meet and exceed customer and employee expectations.
Mike Callender, Chairman of REPL, founded the company in 2007 with Chris Love and has guided the company from startup to worldwide success. An enthusiastic, hands-on entrepreneur, he believes that everything can be made to work better. Callender has worked in retail since he was 15 and has extensive experience within the industry. Specializing in business analysis and strategy, team leadership and change management, he’s passionate about innovation and keeping ahead of the game. As chairman and product visionary at REPL, he spends time with customers understanding what they need and immerses himself in all the latest retail trends. An inspirational event speaker, Callender is dedicated to future growth through both acquisitions and the further development of international markets.