Target Joins the AI Shopping Race: What Gift Finder and Store Mode Mean for Brand Visibility

Target launched three AI-powered shopping features on November 12, 2025, marking the retailer's entry into conversational commerce alongside Amazon and Walmart. The new AI Gift Finder, Store Mode navigation assistant, and List Scanner represent Target's strategic bet that AI will reshape how their 30 million weekly customers discover and purchase products.

Target is following a playbook that's already proving successful at scale. Amazon's Rufus AI assistant has reached 250 million active customers in 2025, with monthly users up 140% year-over-year and interactions up 210% year-over-year. Customers using Rufus are 60% more likely to complete a purchase, and Rufus is on track to deliver over $10 billion in incremental annualized sales. Meanwhile, Walmart's Sparky assistant, launched in June 2025, has become central to CEO Doug McMillon's vision of conversational commerce as "the primary vehicle for discovery, shopping, and managing everything from reorders to returns."

For brands selling at Target, this represents a significant shift in discovery dynamics. When shoppers engage Target's AI Gift Finder with natural language queries like "gifts for a teenage gamer" or use Store Mode to locate "sustainable kitchen products," the traditional browse-and-compare model disappears. The AI curates the selection, making visibility in these systems increasingly important for sales performance.

Target's Three-Pronged AI Strategy

1. AI Gift Finder: Natural Language Product Discovery

Target's Gift Finder allows shoppers to describe recipients or occasions in natural language rather than navigating traditional categories and filters. This follows the conversational commerce model pioneered by ChatGPT Shopping and refined by Walmart's Sparky, but Target is implementing it across their nearly 2,000 stores and Target.com, potentially introducing millions of mainstream shoppers to AI-assisted discovery for the first time.

2. Store Mode: Bridging Digital and Physical Shopping

Store Mode automatically activates when customers enter a Target store, providing:

• Navigation assistance based on conversational queries

• Alternative fulfillment suggestions when items are out of stock

• Personalized product discovery throughout the shopping journey

• Gamification elements like "Find Bullseye" to increase engagement

The integration between digital assistance and physical shopping addresses a key retail challenge: maintaining the sale when inventory gaps occur. If a product isn't available in-store, Store Mode immediately presents same-day delivery or pickup options, keeping the transaction within Target's ecosystem.

3. List Scanner: Converting Analog Intent to Digital Action

The List Scanner transforms handwritten shopping lists into populated Target carts through AI-powered product matching. While technically straightforward, this feature demonstrates Target's focus on reducing friction at every stage of the shopping journey, from planning to purchase.

Strategic Timing and Holiday Implementation

Target's November launch positions these AI features for maximum impact during holiday shopping, when gift discovery drives a significant portion of annual sales. Early adoption offers several advantages:

1. Holiday visibility: The Gift Finder will process millions of gift-related queries in the coming weeks, establishing patterns that may persist beyond the season

2. Algorithm training: Products that perform well in early interactions build positive signals in the recommendation system

3. Category positioning: As the AI learns which products satisfy specific queries, early performers establish stronger algorithmic presence

The holiday season provides a high-volume testing ground for optimization strategies, with immediate feedback on what resonates with Target's AI systems.

Understanding Target's AI Architecture: Insights from Their Technical Papers

Target's approach to AI commerce isn't experimental—it's built on sophisticated systems they've been refining for years. Their published technical papers on accessory recommendations and repurchase modeling provide valuable insight into how their AI makes decisions and what factors drive product visibility.

How Target's AI Weights Product Attributes

Target's technical documentation on their GRAM (GenAI-based Related Accessory Model) provides detailed insights into their approach. The research team explains their methodology: "We solved this by using LLMs to automatically analyze the product data, identify the most important attributes, and assign them importance weights for various pairs of core (or seed) and accessory item types."

Their paper provides specific examples: "When recommending pillowcases as accessories for sheet sets, color and material are treated as the most significant factors. In contrast, when suggesting an appropriate book to accompany a kids' craft activity kit, the intended audience attribute (infant, kids, adults, etc.) becomes the most important consideration."

This dynamic attribute weighting means products with incomplete or poorly structured data will systematically underperform in AI recommendations. A children's toy missing age range specifications or bedding without detailed material information becomes invisible to the recommendation engine when those attributes matter most.

The Sophistication of Aesthetic Matching

Target's AI demonstrates advanced capability in aesthetic coordination. Their engineering team made an interesting discovery: "We serendipitously discovered that the LLM was quite good at using concepts like color harmony and stylistic coherence."

The paper elaborates: "The LLM uses attributes such as color, material, and style to create harmonious sets of attribute values that enable more diverse and creative recommendations. This holistic approach significantly enhances our accessory suggestions, resulting in visually appealing and cohesive arrangements."

This isn't simple keyword matching—it's contextual understanding of how products work together visually and functionally. Brands need to provide rich descriptive data that enables these aesthetic connections.

Understanding Repurchase Patterns Through AI

Rankyung Park, Lead Data Scientist at Target, and her team have developed sophisticated mathematical models to predict repurchase behavior. In their published research on the SLH-BIA (Short- and Long-term Hawkes process model), Park explains: "The Hawkes process is a mathematical model used to forecast future events based on past occurrences. It captures the self-exciting nature of events, where past events increase the likelihood of future events within a specific time window."

Park's team made a crucial discovery about consumption patterns: "Some products, such as shampoo, have longer repurchase cycles compared to others, such as strawberries. To capture both short-term and long-term trends, we combine Exponential and Normal distributions within the self-excitation function."

This dual-distribution approach allows the AI to accurately predict when customers are likely to need product replenishment across diverse categories. The model "measures how past events influence the emergence of new events over time. These self-excitation values serve as repurchase scores, which are used to rank items."

For brands, this means understanding your category's typical consumption patterns and ensuring your product data reflects realistic usage cycles. A 30-day supply of vitamins should be tagged differently than a 90-day supply, as the AI uses this information to time repurchase recommendations.

Performance Data: The Business Case for AI Optimization

Target's published A/B testing results demonstrate substantial returns from AI implementation. Their research team reported specific metrics from production deployments:

"In February 2025, we conducted an A/B test where we added the Home Accessory model to the add-to-cart flyout," the team writes. The results were significant: "Around an 11% increase in interaction rate indicating heightened engagement, a roughly 12% increase in display-to-conversion rates reflecting stronger content relevance and downstream impact, [and] more than 9% growth in attributable demand."

Park's Buy It Again system delivered even stronger results. The team reports: "From A/B tests with millions of live customers, our model exhibited more than a 30% increase in click-through rate and roughly a 30% revenue increase compared to our existing deployed baseline, PCIC."

These metrics align with broader industry trends. Target notes that customers using their app in-store already have basket sizes nearly 50% higher than those who don't. The addition of AI-powered discovery and navigation tools is designed to amplify this effect.

For comparison, Amazon's investment in AI shopping is generating similar returns. According to Andy Jassy's Q3 2025 earnings call, "Rufus saw 250 million active customers in 2025, monthly users up 140% year-over-year, and interactions up 210% year-over-year. Rufus is on track to deliver over $10 billion in incremental annualized sales." Customers using Rufus are 60% more likely to complete purchases.

This consistency across retailers suggests that AI optimization isn't optional—it's becoming table stakes for maintaining visibility in modern retail.

A Practical Framework for Target AI Optimization for Brands

Based on Target's technical documentation and observed patterns in AI commerce systems, brands should focus on eight key areas to maximize visibility in Target's AI-powered discovery tools:

1. Complete Your Attribute Data with Precision

According to Target's technical paper on accessory recommendations, the AI system dynamically weights different attributes based on context. As Target's engineering team explains: "Given our vast catalog, it would take a human expert an enormous amount of time to manually evaluate and prioritize the most relevant attributes for each accessory pairing. We solved this by using LLMs to automatically analyze the product data, identify the most important attributes, and assign them importance weights."

Ensure every product has:

• Complete demographic targeting (age, gender, interests)

• Detailed material and construction specifications

• Style descriptors that match Target's taxonomy

• Color descriptions using Target's standardized palette

• Size and fit information with clear measurements

2. Optimize for Conversational Queries

Rankyung Park, Lead Data Scientist at Target, and her team found that AI-driven features dramatically improve conversion. In their Buy It Again research, Park notes: "Customers using Rufus during a shopping trip being 60% more likely to complete a purchase." This reinforces the importance of natural language optimization.

Transform product titles and descriptions to answer natural language questions:

Before: "Kids Winter Jacket" After: "Waterproof winter jacket for active kids ages 6-10, machine washable, fleece-lined for extra warmth during snow play"

3. Build Aesthetic Coherence Across Your Line

Target's GRAM model demonstrates sophisticated aesthetic understanding. The technical documentation reveals: "We serendipitously discovered that the LLM was quite good at using concepts like color harmony and stylistic coherence." The paper specifically notes how "the LLM uses attributes such as color, material, and style to create harmonious sets of attribute values that enable more diverse and creative recommendations."

Structure your product line to create natural aesthetic groupings:

• Use consistent color naming across products

• Define clear style families (modern, traditional, bohemian)

• Create obvious complementary relationships between items

4. Enhance Visual Content for AI Recognition

Target's Store Mode relies heavily on visual recognition. While their papers don't explicitly detail image requirements, they emphasize multimodal understanding. Optimize your images:

• Include lifestyle shots showing products in context

• Add clear product-only images on white backgrounds

• Ensure text overlays highlight key features

• Use consistent styling across your product photography

5. Leverage Cross-Category Opportunities

Target's engineering team discovered the importance of cross-category connections: "We collaborated with site merchants to create a list of the most commonly co-purchased accessory items, enabling support for cross-category recommendations." Their research showed that "employing HITL [human-in-the-loop] led to accessory recommendations from a far more diverse set of items."

Identify and document cross-category relationships:

• What items naturally pair with your products?

• Which complementary categories should you appear in?

• How can your products solve problems beyond their primary category?

6. Optimize for Repurchase Cycles

Park's team developed sophisticated repurchase prediction using Hawkes processes. Their paper explains: "The Hawkes process is a mathematical model used to forecast future events based on past occurrences. It captures the self-exciting nature of events, where past events increase the likelihood of future events within a specific time window."

Crucially, they found that "some products, such as shampoo, have longer repurchase cycles compared to others, such as strawberries. To capture both short-term and long-term trends, we combine Exponential and Normal distributions within the self-excitation function."

For consumable goods:

• Include usage duration in product descriptions ("30-day supply")

• Highlight subscription or bulk purchase options

• Ensure your product appears in Buy It Again at the right intervals

7. Create Gift-Specific Content

With the AI Gift Finder as a primary discovery tool during key shopping periods, Target's focus on conversational understanding becomes critical. Their accessory model showed "around an 11% increase in interaction rate" and "a roughly 12% increase in display-to-conversion rates" when properly implemented.

Add gift context:

• Include "perfect for" statements in descriptions

• Add occasion tags (birthday, holiday, housewarming)

• Specify recipient personas clearly

• Include gift-giving keywords naturally in your content

8. Monitor and Adapt to Performance Signals

Park's team emphasizes continuous optimization. Their SLH-BIA model achieved "more than a 30% increase in click-through rate and roughly a 30% revenue increase compared to our existing deployed baseline." They achieved this through iterative refinement, noting: "We've made several adjustments to decrease execution time and enhance model scalability."

Track:

• Which queries surface your products in the Gift Finder

• Store Mode navigation patterns to your products

• Cross-sell and upsell performance

• Repurchase rates and intervals

Use this data to continuously refine your product content and positioning, following Target's own iterative approach to AI optimization.

Steal a March on our Competitors: Partner with Azoma

The shift to AI-driven discovery at Target is now operational. With Store Mode automatically activating for store visitors and Gift Finder becoming a primary discovery tool for holiday shopping, optimization for these systems is becoming essential for maintaining competitive visibility. Following Amazon's $10 billion success with Rufus and Walmart's Sparky rollout, this signals that conversational commerce has reached critical mass in retail. The technical insights from Rankyung Park and Target's data science team provide a clear roadmap for optimization - from attribute weighting to aesthetic matching to repurchase modeling. Brands that leverage these insights and align their product data with Target's documented AI approaches will be best positioned to maintain visibility as millions of shoppers adopt these new discovery tools. To navigate this evolution effectively and ensure your products thrive across Target, Amazon, Walmart, and emerging AI shopping platforms, partner with Azoma.ai for comprehensive AI visibility tracking and optimization.