Maxx Mode

AI-powered product discovery for TJ Maxx

Duration

3 Months
March 2026 - June 2026

Role

Researcher,
UI/UX Designer

Tools

Figma, Figjam, Balsamiq, Mirro, Google Forms, Canva

The Problem

Dedicated Treasure Hunt Shoppers are TJ Maxx shoppers who visit at least once a month and regularly search for specific items, good deals, or unexpected finds. However, the unpredictable nature of off-price retail makes it difficult for shoppers to know what products are available, where to look, or whether it is worth continuing their search.

Because inventory can vary by store, location, and timing, shoppers often rely on repeated visits, memory, social media finds, and in-store searching to locate desired items. This can lead to frustration, missed finds, and lower confidence in purchase decisions, reducing the sense of reward that makes treasure-hunt shopping enjoyable.

The Research

To understand how shoppers navigate TJ Maxx, we used three research methods: field studies, interviews, and surveys. Each method helped us examine the shopping experience from a different angle: what shoppers do in-store, how they describe their motivations and frustrations, and how common those patterns were across a broader audience.

The Methodologies

Field Studies

8 Shoppers observed in TJ Maxx

We observed shoppers in context to understand real-time navigation behaviors, browsing patterns, and emotional responses during the shopping experience.

Interviews

6 semi-structured interviews

We interviewed recent TJ Maxx shoppers to explore visit drivers, emotional shifts, success criteria, and moments of frustration.

Surveys

71 total responses

We used survey data to quantify patterns around shopping motivation, search behavior, phone use, social influence, shopping challenges, and definitions of success.

Selected Finding Visualizations

1/3

Key Insights

Treasure hunt shopping blends goal-oriented and exploratory behavior

Rather than approaching TJ Maxx with a single shopping mindset, shoppers often balance specific objectives with opportunities for discovery. Shoppers may enter the store with a general category in mind, such as skincare, home decor, or clothing, while remaining open to unexpected finds. Even after locating an intended item, shoppers frequently continue exploring, reflecting a shift between goal-oriented and exploratory behavior.

Shoppers use tools and comparisons to reduce uncertainty in-store and purchase confidence is built through repeated validation

The findings suggest that digital and social tools play an important role in helping shoppers make purchase decisions at TJ Maxx. Shoppers often use their phones in-store to check reviews, compare prices, look at social media, and seek outside opinions to validate whether an item is right for them or represents a good deal.

Social media extends the treasure hunt beyond the store and motivates visits

Many shoppers said haul videos, trending products, and friends’ posts inspired them to visit TJ Maxx. Rather than shopping only out of immediate need, they were often motivated by curiosity, anticipation, and the chance to find something they had seen online. This suggests that social media extends the treasure hunt experience by building excitement and encouraging shoppers to seek discoveries in-store.

Shoppers define success emotionally, placing high value on unexpected discoveries

While browsing and discovery create engagement, purchase remains the key measure of success. Unlike purely hedonic shopping, where the experience itself may be the reward, many TJ Maxx shoppers judge a trip by whether their exploration leads to finding and purchasing a unique, high-value item.

Design Iterations

For the digital recommendation phase, each team member explored a solution direction. My concept was AI Shelf Scan, a feature that helps shoppers locate desired items or find similar in-stock alternatives by scanning store shelves.

Draft 1

The first sketch explored the end-to-end AI Shelf Scan flow, from searching for a desired trending item to scanning the shelf and receiving similar in-stock recommendations when the exact product is not found.

Draft 2

The second draft focused on improving scannability and clarifying the full interaction flow. I refined the result cards to make product information, match percentages, ratings, and shelf-location actions easier to compare at a glance, while also fleshing out key screens such as the shelf scan, similar-match preview, highlighted shelf location, and all-matches view.

Final Design

Based on our research, I designed AI Shelf Scan as a lightweight digital tool that supports the behaviors shoppers already use in-store, rather than replacing the treasure hunt experience. Field study findings showed that shoppers often rely on their phones, product comparisons, and reviews to build confidence before making a purchase. This feature extends that behavior by helping shoppers make sense of the products physically available in front of them.

With this AI shelf scan app, shoppers can enter a desired product, brand, ingredient, or quality, then scan a shelf to identify nearby in-stock options. Each result includes a match percentage, key product attributes, ratings, reviews, and shelf-location guidance. When a shopper is looking for a specific product that isn't available, the tool also surfaces similar alternatives ranked by how closely they fit the shopper's preferences. The goal is to reduce friction during comparison and decision-making while preserving the in-person exploratory nature of TJ Maxx shopping.