April 16, 2019
With more and more small-to-medium-sized businesses joining the marketplace, the demand for reliable predictive analytics that can forecast customer demands and reduce churn is expected to grow by more than 22 percent over the next few years.
Because smaller retail businesses operate on narrower margins, the need to control supply chains and accurately predict future outcomes and customer requirements is even more important for them than for their larger competitors, driving smaller retailers, as well as larger ones, to invest in predictive analytics that can mitigate risk, optimize inventory, and personalize the customer’s retail journey.
Of course, large retailers have been utilizing machine learning technology for years. Amazon rolled out predictive stocking in their warehouses all the way back in 2014, and in terms of how far AI technology has come, that’s almost a lifetime ago.
Tech giants like Amazon understand the benefits that come with predictive analytics, and smaller companies — for whom operating within tight margins is a vital part of staying competitive and staying afloat — are beginning to come around, with an enormous spike in growth expected between now and 2024.
Predictive analytics mean better margins and better customer experiences
Big or small, retailers are finding that predictive analytics can mean the difference between a strong revenue stream and a dwindling sales pool. Retail relies on always being one step ahead of the customer. Before the customer comes to the store to buy a pair of shoes or a gallon of milk, the store needs to be ready so that the item is on the shelf when they get there. That’s where predictive analytics come in.
Using machine learning technology to provide accurate insights into customer purchasing habits in real-time, predictive analytics can offer retailers a way to optimize their inventory that reduces the risk of stock outs and markdowns while also intelligently fulfilling omnichannel demand. More than just inventory management, predictive retail analytics help to optimize pricing for the best margins and to personalize the customer’s retail journey before it even begins.
Machine learning technology offers data-driven insights
Every day, retailers face countless difficult decisions. Not just what to stock and how much inventory to keep on hand, but how to price it, where to display it for best results, and much more. Predictive analytics don’t necessarily take these decisions out of the hands of business owners and sales managers, but they do give retailers a powerful new tool to help make complex decisions relating to their business, their inventory, their customers, and their overhead, all using data-driven insights that accurately predict customer behavior.
Machine learning has come a long way in the last few years, and today’s predictive analytics can help retail businesses stand out in any number of ways. Who knows what tomorrow’s AI solutions might be capable of?
Modern customers interact with innumerable devices every day. Not just smart phones, tablets, and computers, now there are smart home and personal management devices like Siri and Alexa. Customers are being conditioned to expect a similar responsiveness from their retail experiences, and those companies that can’t keep up will fall victim to customer churn as shoppers look elsewhere for the personalized and targeted retail journey they demand.
Predictive analytics provide solutions that retailers need
Predictive analytics help retail clients become truly customer-driven by leveraging a combination of machine learning and professional scientists from all over the world to create predictive analytics solutions that work. By helping fashion retailers close the gap between supply and demand, we at Evo have delivered more than $200 million in increased margin already, and we’re just getting started.
Our predictive pricing models help retailers set pricing that will move inventory, drive customer visits, and improve their bottom line. By monitoring everything from competitor pricing to customer behavior, demand, and feedback, these constantly-evolving solutions can provide a steady stream of pricing suggestions that help retailers control their margins, stay competitive, and boost sales.
Using this “crystal ball,” retailers can peer into the future of their inventory with predictive supply chain analytics. Retailers no longer have to rely on what happened last year, last month, or last week. Machine learning algorithms can unsilo valuable customer data to provide actionable insights affecting business inventory, forecasting sales, and helping retailers to staff, stock, and supply stores across their entire company.
Evo has already applied these tools to retailers throughout the fashion sector with great results, and we’re eager to share those same results with retailers in other industries. Together, we believe that we can help make retail smarter, better, and more rewarding, for businesses and customers alike.
About the author
Ben Thomas is the Chief Writer and Brand Strategist at Evo, with a core focus on emerging technologies, Big Data, and the Internet of Things (IoT).
He loves to engage audiences about the frontiers of science, culture and technology — and the ways these all come together.