Karen Krivaa, VP Marketing, GigaSpaces
Black Friday is traditionally the biggest holiday shopping day of the year in the United States, and Cyber Monday is closing fast in terms of volume. In 2016, more people shopped online than they did on stores over the holiday weekend, which typically spans from Thanksgiving through Cyber Monday. In just the first two days of that weekend, shoppers spent more than $5 billion and $36.5 billion over the entire weekend. This year is promising to be even bigger; Adobe predicts that online sales will top $100 billion throughout the holiday shopping season, while Salesforce is forecasting that 40 percent of all purchases made will be mobile, potentially beating last year’s $1 billion in purchases on mobile.
For IT and marketing leaders, Black Friday and Cyber Monday used to be about “surviving the day.” Over the years, a wide number of retailers have hosted post-holiday earnings calls explaining that projected earnings missed because of an outage on these critical shopping days. Today, however, it’s not only about “surviving.” It’s about thriving.
To capitalize on the holiday, retailers are relying on Artificial Intelligence (AI) to boost sales and improve customer experiences. This is manifesting as marketing looks to scale both acquisition and retention efforts:
In the past, consumers would comb through circulars, hunt for deals, and plan their Black Friday based on where they thought the best offers would be. Today, the offers are coming right to them, personalized for their tastes and interests.
Thanks to machine learning and automation, we have now entered the age of mass personalization. To be fair, this concept has been around for several years now, but typically meant creating a handful of customer segments and personalizing an email with their name and possibly a few products.
Now, however, marketers have access to the technology that can create messaging, special offers, and even a customized shopping experience for each specific consumer. Using machine learning to comb through hundreds of thousands, if not millions, of pieces of consumer data, retailers are developing models that predict buyers’ behaviors. That means analyzing each individual consumer’s purchasing patterns—where they shop, when they shop, what they buy, how much they spend, etc.—and then using that analysis to create personalized, individualized offers for each customer. Because of the highly customized nature of these campaigns, typically conducted through email and social media, retailers are likely expecting a significant ROI boost in their holiday campaigns, improving conversions and increasing the efficiency of their ad spend.
The best way to increase sales—over the holidays and throughout the year—is to increase the number of repeat customers a business has. And to do that, it’s important to identify when a customer is potentially unhappy, either because of a bad experience or otherwise. But when selling online to millions of customers, it can be a real challenge to identify when that happens before it’s too late. Machine learning can play a key role in keeping customers coming back by predicting when a customer may be dissatisfied and offering timely incentives to retain their business. Check out this interesting case study on the Amazon Web Services AI blog for a great example of this. Retailers should be able to use their own proprietary customer data to identify potentially unhappy customers, and then use their own purchasing habits—and potentially discounts based on those habits—to predict the likelihood of retaining that customer.
This holiday season, retailers of all shapes and sizes are looking to bolster their approaches to obtaining and retaining customers. The days of “let’s make it through Black Friday” have been replaced with “let’s make it through Black Friday and provide the best customer experience this season.” Many of the best known retailers are successfully incorporating new technologies such as machine learning and AI to accomplish this goal.