Artificial intelligence and machine learning have important roles to play in virtually every market and industry vertical. The US$26 trillion global retail industry is no exception. Here, savvy retailers are beginning to use AI and ML to anticipate customer needs, creating experiences that are relevant and that drive conversions across digital channels.
Given the upside potential, it’s not surprising that retailers are taking a hard look at AI and ML. In fact, by 2022, retailers are projected to spend an estimated $7.3 billion on AI each year. So where exactly is all of that investment going? Let’s take a look at some of the specific areas in which retailers are using artificial intelligence to boost their bottom line.
Supply chain planning. AI is completely transforming the supply chain by giving retailers insights into how their supply chain is working, including how to make the improvements necessary to eliminate waste and costly overhead. According to a study by CapGemini, for example, applying AI to the supply chain could save retailers $340 billion in operational expenses.
Demand forecasting. Being able to anticipate demand quickly and accurately is essential in retail. That’s why many of the world’s largest retailers are using AI to analyze massive data sets so that they can anticipate trends or anomalies that affect demand. French retailer Intermarché is a case in point. In a pilot project, the supermarket chain was able to achieve a forecasting reliability rate of 95 percent—15 percent higher than they’d previously managed using traditional methods.
Customer intelligence. Retailers are using AI and ML to better understand and anticipate their customer needs by aggregating and analyzing data from all of the systems, applications, and channels they use. Some are even acquiring third-party data. As a result, they are able to deliver much more personalized customer experiences, which can ultimately help drive more sales. One potential tradeoff to this approach is that relevance comes at the cost of customer privacy. But it doesn’t have to be that way.
Marketing, advertising, and campaign management. Another retail application of AI and ML is targeting the right customers at just the right time to boost customer engagement and increase company revenue. Online eyewear retailer Readers.com is a great example. After applying AI software to their Facebook and Instagram ads to try to save money and improve ad conversion, they achieved a 15 percent higher return on advertising spend and hundreds of more conversions, all within just a matter of weeks.
Store operations. AI and ML have a variety of applications in day-to-day store operations. Walmart recently debuted its store of the future, for example, where it will use AI to track inventory levels. In that way, the company can notify staff when shelves need to be restocked or if perishable items have sat too long and need to be replaced.
Pricing and promotion. For the many ways that AI and ML can help retailers boost their bottom line, pricing and promotion are among the most interesting. Retailers can use algorithms to understand when offering a discount is and isn’t necessary to get a sale. At the same time, retailers can use AI and ML systems to give shoppers prices they perceive to be fair for the products that are most important to them, thus helping to drive ongoing purchases.
As a retailer, tapping into the benefits that AI and ML can enable is critical. Of course, the key to doing so safely and securely is to ensure that the AI solution you partner with brings you peace of mind. And the key to doing that is to find an AI system that surfaces insights into your customers’ behaviors rather than simply serving up the personally identifiable information (PII) that can get you into trouble.
The importance of good data stewardship
While AI and ML are already transforming the retail industry, that trend is only going to accelerate in the months and years ahead. But to reap the benefits that these technologies can bring, you need to take the right precautions.
First and foremost, this includes ensuring that any AI solution you use offers good data stewardship. Our Trusted Signals Exchange, for example, extracts data (or what we call signals) from de-identified cross-industry datasets. Those signals are encrypted and aren’t shared, and PII is never used. In this way, we provide the data stewardship necessary to ensure that our users never have to worry about running afoul of regulatory requirements or exposing their customers to unnecessary risks. For retailers looking to benefit from the insights contained in vast datasets, it’s a win-win.