When a shopper walks into a retail store or visits a retailer’s website, data science isn’t likely top-of-mind. But everything — whether it’s physical or online — is strategically placed to optimize the customer experience and maximize sales for the retailer, or at least it should be. From store location, product placement, items on sale, which employees are working, etc., there is strategy behind these seemingly simple things to ensure the retailer makes the most out of every aspect of its operation. And the early adopters are turning to artificial intelligence (AI) to solve their most pressing data-driven problems.
Here are six ways retailers are putting AI to work.
New Store Optimization
Looking at historical data such as sales, demographics, distance from competitors, nearby events and more allows retailers to be strategic about where and when to open a new location. AI applications that learn from this data can do more than just create and sort a list of best locations to open a store; they can actually provide retailers with an understanding of why, based on identifying the most important “drivers”/variables that contribute to new store success.
One of Nutonian’s (disclaimer: my employer) customers, for example, estimates that the difference between building a new store in a “good” location versus an “average” location equates to roughly $30 million in extra revenue per store per year. Based on sales data per store and per product, demographics and proximity to competitors, this retailer can determine exactly where to build their next store — and even the type of store (large brick-and-mortar, outlet) it should be.
The same theory can be applied to closing poorly performing stores. By looking at and analyzing the aforementioned data with AI, this company discovered that it is saving $10 to $15 million annually by not building a store in a “bad” location.
It’s Black Friday, your store is packed with customers, but you are dramatically understaffed despite feeling prepared for the busiest shopping day of the year. The reality is that unless companies are making business decisions — in this case, properly staffing — based on accurate data, a store can be left with either skyrocketing operating costs or poor customer experience.
It goes without saying that retailers can expect heavy traffic during the holidays, but how do retail companies determine precisely how to staff stores? Should they add two more employees during this time? Five? Furthermore, what about staffing different locations?
Predictive models built with information such as historical traffic, sales and marketing efforts during certain times show retailers how to dynamically staff stores based on expected traffic. The result is lower costs for the retailer and a better store experience for the customer — a win-win.
Supply Chain Optimization
Inventory management is a huge challenge for retail companies. An excess of supplies leads to low turnover and decreased profitability. Yet stock-outs result in backorders, lost sales and dissatisfied customers. It’s a difficult balance that has significant impact on retailers’ revenue streams.
Retailers require forecasting models that show what items will be needed when and where — i.e. based on this time of year and this store location, here are the products you should stock to keep inventory and costs low, while maximizing sales.
Retail companies can achieve this by plugging data such as past sales for different products, events, marketing campaigns, etc. into AI apps that build predictive models to give retailers answers — prescriptive measures to ensure shipping and delivery logistics are optimized, and operational funds are allocated effectively.
Marketing is important for any business, and knowing your company is making the best use of its marketing investments is critical. It’s also difficult to measure ROI.
Running campaigns is integral to help grow, engage and convert audience, but it’s not always easy to identify which campaigns are successful and under what conditions.
By looking at historical sales, marketing campaigns, web site discounts, events and competitor events, retail companies can discover through data modeling applications that Campaign "X" converted the highest number of customers via email blasts when distributed in the evening with specific key words used.
With this level of insight, retailers can use results to optimize marketing spend and know they are allocating marketing resources effectively.
Accurate sales forecasting affects all facets of a company and its operations — revenues, resource and product planning, investor relations, etc. — and without a clear understanding of what’s driving ROI, retailers can’t effectively manage working capital requirements.
Analyzing and building predictive models from data such as historical sales for different products, marketing, event schedules and weather provides companies with a clear picture of the road ahead, explain why it’s going to happen, and detail how retailers can optimize their desired outcome.
Improved Hiring Process
Employee turnover can cost companies thousands of dollars, so it’s critical to minimize costs by making personnel decisions before time and money are wasted. During the hiring process, retailers plug data such as historical employee performance and attributes (i.e. background, previous sales experience, previous jobs, focus, etc.) into modeling apps to gain a sorted list of best potential candidates, and a list of growth areas for current candidates.
Retailers have all this data and more available to them. It’s just a matter of knowing how to use it and learn from it. Savvy retailers are using AI apps to create predictive models that will give them competitive insight, increased sales and improved customer experiences.
Scott Howser is the SVP of Products and Marketing at Nutonian. Prior to joining Nutonian, Howser served as vice president of products and marketing at Hadapt. Howser also served as vice president of product marketing at Vertica, an HP Company, where he was responsible for the company’s product messaging, corporate branding, and establishing best practices for deployment and solutions architectures. Scott earned an M.B.A. from the University of Notre Dame, an M.S.I.S.M from Loyola University Chicago, and a bachelor's degree from Ohio Dominican University.