Target is using AI to push shoppers from casual browsing into curated wish lists — and the retailer says those lists are associated with roughly 45% higher demand in the category. For marketers and merchandisers, that’s not an abstract efficiency win: it’s a concrete lever to increase conversion and basket value during one of retail’s busiest seasons.
Behind the scenes, Target combines recommendation models with a contextual prompt system that suggests the “next best action” for each shopper. The result is a short loop: spot intent, recommend items, nudge the user to save a list — and capture a higher-value interaction while traffic is high.
What Target is rolling out
Target blends two AI-driven tactics. First, as shoppers start assembling wish lists, algorithmic product recommendations surface so customers don’t have to remember every needed item. According to Brad Thompson, Target’s senior vice president of technology, recommendations use signals such as similar shoppers’ purchases, a guest’s purchase history and contextual cues like school-specific supply needs.
Second, the site runs a “next best action” widget that prompts a specific behavior during a browsing session. During back-to-school season that prompt often encourages wish-list creation; at other times it can surface seasonal offers or membership prompts tied to Target Circle 360 and other initiatives.
Target is also using back-to-school traffic as a testing ground for larger personalization experiments. The company A/B tests variant homepages and product pages that adapt to different visitor segments. Thompson outlined a roadmap toward “one-on-one” page layouts — dynamically rearranged page content that AI selects to match an individual’s intent and likely next step.
Why this matters for retailers and marketers
Back-to-school is a predictable but high-stakes seasonal spike: the National Retail Federation and Prosper Insights & Analytics project substantial K–12 and college spending for the period. For large retailers, incremental percentage gains in conversion or average order value scale to significant revenue.
Target’s work illustrates three practical shifts. First, personalization is expanding beyond product suggestions to recommend actions — telling a shopper what to do next can produce measurable value. Second, seasonal surges are effective labs for experimentation: high traffic tightens experiment windows and speeds iteration. Third, moving from item-level recommendations to layout-level personalization requires coordination across data science, UX and merchandising and a robust experimentation framework to avoid degrading the experience.
Practical implications and what to watch
If you’re building similar capabilities, prioritize measurement and operational guardrails. Track conversion and average order value alongside downstream signals such as returns and inventory churn, and monitor membership adoption when prompts push paid or loyalty options. Keep privacy and transparency front of mind when using first-party data to personalize experiences.
Technically, session-level context and fast inference are necessary for timely “next best action” prompts. Teams should also document seasonal experiments so successful features can be productized outside peak windows rather than lost after the campaign ends.
For now, Target’s approach is pragmatic: apply recommendation logic to reduce shopper friction and convert surges into higher-value interactions. The key indicators to watch next are whether these features sustain lift outside the seasonal spike and how Target quantifies long-term customer value from AI-driven personalization.
