5 Brands Leveraging AI to Give Consumers What They Want This Holiday Season

Share this:

What do shoppers really want this holiday season? As more brands transition to first-party data, advanced technologies like artificial intelligence (AI) and machine learning (ML) are taking the guesswork out of managing supply and improving the shopping experience with more personalized service.

With this holiday season expected to be the largest yet—the National Retail Federation estimates that sales during November and December will grow between 8.5% and 10.5% over 2020—billions of dollars are at stake. After a tumultuous few years, retail brands are ready to rebound. Those brands that can successfully leverage AI and ML to more effectively determine what consumers really want are poised to win market share.

While advanced technologies haven’t changed the fundamental aspects of physical retail—most shoppers still enter stores, search for products, and complete their transactions at the point-of-sale—they have made it easier for brands to understand their audiences and effectively target individuals and niche demographics.

Here are five of the best examples of brands using AI and ML to help consumers navigate their products and determine what shoppers really want this holiday season.

1. Making Complex Tasks Easier

A longtime player in the AI space, Lowe’s is working on a new feature this holiday season that will allow shoppers to hold up their phones to measure the household space needed for large-scale products. Customers who have downloaded the Lowe’s mobile app on their LiDAR-enabled phones can measure rooms in their homes for new carpet or drapes and then order those items directly without stepping foot inside a physical Lowe’s location. The new technology is accurate enough that Lowe’s believes it can eliminate the need for in-person measurements and installer appointments.

2. Understanding What Customers Are Looking For

Wayfair uses AI to better understand what customers are looking for—sometimes before customers themselves even know what they want. The e-commerce retailer is using AI to learn each furniture product’s basic information (like color and dimensions), as well as the design style and other less straightforward factors. Custom AI models are trained to detect nuance, given that design style isn’t always straightforward. For example, a dining chair could be 70% modern and 30% contemporary. Visual search tools are available to help shoppers who are having a hard time describing what they’re looking for through keywords alone. Shoppers with Wayfair’s mobile app can see specific items in their own homes based on the style and physical attributes selected by the company’s algorithm.

3. Selling First, Manufacturing Second

Levi’s is pioneering a new technology that would allow customers to order clothing using their exact body size measurements. The concept of selling first, manufacturing second is one that brands like Nike have also deployed. What makes Levi’s approach unique is the company’s integration of analytical and computer skills into the process. Levi’s designers are now applying computer vision and a “style transfer algorithm” when creating customized apparel.

4. Building Long-Term Relationships

Neiman Marcus Group is using AI and ML to build long-term relationships with high-end shoppers. Rather than manually putting together lookbooks for customers, which can be hard to scale across thousands of store locations, Neiman Marcus is using AI to categorize order attributes like size, color, brand, occasion, and lifestyle. Those attributes are then overlaid with customer data, like purchase history or demographics, and entered into a sophisticated algorithm to develop custom lookbooks and styling advice for individual shoppers.

5. Predicting the Success of Media Investments

It’s been a year of incredible opportunity and challenge for the travel industry. Alaska Airlines has taken the time to refine its approach to digital marketing. The company partnered with Adswerve to implement ML and first-party data to predict which media investments will deliver the best long-term value. It also teamed up with Google to develop a predictive customer lifetime value model, which is being used to better target audiences. This isn’t the first time Alaska has used ML. The company previously implemented ML and AI to optimize routes and improve the predictability of airline traffic.

​​Stephanie Miles is a senior editor at Street Fight.

Stephanie Miles is a journalist who covers personal finance, technology, and real estate. As Street Fight’s senior editor, she is particularly interested in how local merchants and national brands are utilizing hyperlocal technology to reach consumers. She has written for FHM, the Daily News, Working World, Gawker, Cityfile, and Recessionwire.