Grocery shopping, even in 2026, remains a mentally demanding and inefficient task, especially for people managing busy lives.
Through interviews and contextual observation, several key issues emerged:
1. Shoppers rely heavily on memory, leading to missed items and unnecessary purchases
2. Even when using digital lists, users experience frequent friction, interacting with their phones every 2–4 minutes
3. 60% of users switch between apps while shopping, breaking flow
4. Users consistently pause before checkout to mentally verify their list
Existing tools (like Notes apps) are not designed for grocery shopping workflows, leading to inefficiency, stress, and lack of confidence.
Key Behavioral Gaps:
1. Digital lists do not match how users mentally organize stores
2. Tools increase interaction overhead inside the store
The project focused on improving the in-store grocery shopping experience without introducing a complex, multi-purpose system. Instead of redesigning how people plan groceries across multiple platforms, I narrowed the scope to weekly shoppers who rely on simple lists, particularly students and early professionals.
The goal was to work within this familiar behavior and reduce the biggest pain points - disorganized lists, frequent phone interaction, and lack of confidence at checkout. By centering the experience around a single shopping trip, I aimed to create a system that supports users before, during, and after the aisle, while remaining lightweight, intuitive, and entirely offline.
Planning Phase
• Goal: Create a clear, structured understanding of what to buy
Buying Phase (In-Store Experience)
• Goal: Minimize friction while navigating the store
Confirmation Phase (Pre-Checkout)
• Goal: Ensure confidence before completing the trip
One key behavior I observed was that users don't plan lists structurally, they dump items as they remember them. Most participants used apps like Apple Notes, where items are added in a free-form way with no automatic organization.
This creates a disconnect:
the way a list is created is very different from how it is used in-store.
During planning, users are focused purely on recall, not structure. They want to quickly capture everything before they forget, without worrying about categories or order.
Interestingly, tools like Apple Reminders attempt to solve this by categorizing items as the user types. However, this introduces a new problem - premature structuring. As the list grows, items get reorganized in real-time, making it harder for users to verify whether they've added everything they need.
Provide structured, intelligent organization on demand, not during input. Align items with real-world store layout, rather than abstract categories.
While observing users in-store, a clear pattern emerged: even when people had a list, the experience was far from seamless. Users repeatedly took out their phones, checked items, locked the screen, and then repeated the cycle every few minutes. This constant interaction broke their flow and made the experience feel fragmented.
Another consistent behavior was revisiting aisles. Because lists were not organized according to the store's physical layout, users often missed items and had to backtrack, adding both time and cognitive effort to the trip.
Additionally, many users switched between multiple apps (notes, messages, reminders), increasing context switching and making it harder to stay focused on the task.
This highlighted a key mismatch:
current tools support storing items, but not navigating the store efficiently.
The problem isn't access to the list - it's the cost of interacting with it during movement.
While the current system focuses on optimizing a single shopping trip, the next iteration expands toward making the experience more adaptive, reusable, and personalized over time.
Master List
A persistent layer where frequently purchased items are stored and reused across trips. Rather than rebuilding a list from scratch each week, users can pull from a running inventory of staples, reducing recall effort and making list creation nearly automatic.
Leveraging Previous Shopping Data
By learning from past lists, the system can surface items a user is likely to need but may have forgotten. This shifts the experience from purely manual recall to assisted planning, catching gaps before the user reaches the store.
Custom Grocery Categories
Enabling users to define their own categories based on how their preferred store is organized. Since no two stores are laid out the same way, user-defined groupings improve route accuracy, reduce backtracking, and build trust in the system's recommendations.