Target is harnessing artificial intelligence to enhance its back-to-school shopping experience, aiming to make it more personalised and efficient for customers. According to Brad Thompson, Target’s Senior Vice President of Technology, the retailer is leveraging AI to support the creation of wish lists, a feature that has proven to increase customer demand by about 45% within this category.
AI-driven Recommendations and Widgets
Thompson explained that AI is being used to offer tailored product recommendations as shoppers compile their wish lists. These suggestions are informed by data on customers’ previous purchases and patterns observed from other buyers, as well as specific school requirements and teacher needs. This approach is intended to streamline the decision-making process for customers.
Additionally, Target has implemented a “next best action widget” on its e-commerce platform. This tool, which is adjusted according to seasonal demands, suggests different actions to customers, such as initiating a wish list, based on gathered browsing data.
Innovative Back-to-School Initiatives
Target launched a range of new initiatives in June to capitalise on the back-to-school period. These include special offers for its Target Circle 360 subscription, school uniform options under a private label, and a limited-time partnership with LoveShackFancy. This seasonal shopping phase is significant for retailers, with expectations for record spending levels; K-12 expenses are anticipated to reach $43.3 billion, while college-related spending could soar to $103.5 billion, based on a National Retail Federation and Prosper Insights & Analytics survey.
Personalised Shopping Experience
Thompson highlighted the back-to-school season as an opportunity for Target to trial various innovative approaches, such as varied homepage experiences tailored through data insights. AI is gradually being used to scale content personalisation, with plans for hyper-personalised web page layouts in the future. This involves dynamically configuring different parts of the website to appeal to individual users based on AI-driven insights.
Reporting based on coverage first published by retaildive.com.