AI-Powered Shopping
Case Study — 2026
DealScout AI
— Smart Local Savings
Designing an AI-powered app that scans nearby stores' websites to automatically locate real-time physical coupons, deals, and promotions based on your GPS coordinates.
- Role
- Lead Product Designer
- Project Type
- Mobile & Web App
- Duration
- 10 Weeks (End-to-End)
- Platforms
- iOS, Android, Responsive Web
94103, San Francisco
Scanned Today Near You
Whole Foods • 0.4 mi away
30% Off All Organic Produce
Scanned 12m ago by AI
Target • 1.2 mi away
Buy 2 Get 1 Free Smart Bulbs
Scanned 1h ago by AI
01 — The Scout Method
Simplifying local savings through automated discovery.
The Concept
DealScout AI changes how shoppers save money. Instead of requiring users to search dozens of store loyalty programs, digital flyers, and sketchy database coupon sites manually, our AI does the legwork automatically. Simply entering a zip code triggers an on-demand scrape of local retail sites, extracting, organizing, and plotting active, verified deals directly onto a local map.
Key Features
- Location-Based Search
- AI Real-Time Scraper
- Favorite Store Watchlist
- Savings Coupon Tracker
- Smart Push Notifications
Target Audience
Our strategy focused on three high-leverage user segments:
Budget-Conscious Shoppers
Everyday grocery buyers hunting for micro-savings.
Savvy Tech Deal Hunters
Digital-native users optimized to stack discounts.
Busy Families
Stretched for time, shopping for volume, requiring automated help.
02 — Competitive Analysis
Identifying gaps in a fragmented coupon market.
Most digital deal products center strictly around desktop browser extensions (e-commerce only) or digitized, non-curated paper flyer sheets. We audited direct players to spot our opportunity space.
Honey (PayPal)
Online / Automated
Core Strength
Auto-applies coupons at digital e-comm checkouts.
Critical Weakness
Strictly online desktop stores. Zero physical / local store capabilities.
RetailMeNot
Online / Manual
Core Strength
Large crowdsourced coupon and code base.
Critical Weakness
Outdated UI, frequently expired community codes, zero AI verification.
Ibotta
Local / Manual
Core Strength
Generous rebate model with massive retailer support.
Critical Weakness
High-friction manual receipt photo scanning; limited real-time local search.
Flipp
Local / Manual
Core Strength
High-fidelity digital flyers and circulars catalogs.
Critical Weakness
No smart personalization, heavy visual clutter, no automated extraction.
Google Maps Deals
Local / Underused
Core Strength
Excellent visual localized map integration.
Critical Weakness
Not focus-built for discounts; reliant on passive merchant input.
The Strategic Opportunity
Owning the Automated Local Quadrant
As visualized in the mapping matrix, existing local deal products still rely heavily on manual scanning, flyer-flipping, or physical receipts. Conversely, automated AI scraping products cater purely to online checkouts. DealScout AI occupies the unaddressed whitespace: bringing state-of-the-art web-scraping agents down to the local high street level.
Real-time AI Scraping
We query live websites upon search, bypassing stale databases.
Hyper-Local Focus
Localized map searches prioritized by walking or driving distance.
Automated Verification
No dead promo codes. Deals are system-verified directly at checkout source.
03 — User Research & Metrics
Listening to shoppers in the wild.
To build trust, we needed deep insights. We organized quantitative surveys with 500 grocery shoppers alongside 12 immersive, qualitative structured user interviews.
Surveying 500 Shoppers
Regularly search for deals before purchasing
78%
Find searching for coupon codes too time-consuming
62%
Have missed deals due to simple lack of awareness
54%
Would switch stores for a competitor with better deals
41%
Would use an app automating live local deal search
89%
Survey Takeaways
An overwhelming demand for localized convenience
The numbers made the problem clear. Over 89% of surveyed shoppers expressed strong intent to use a localized deal-finding platform, with 62% validating our core premise: manually scraping the internet for physical coupons is exhausting, outdated, and ultimately ignored due to sheer friction.
89%
Massive Target Addressability
Nine out of ten shoppers actively want automation to resolve high-street retail discount searching.
03B — Voices of Our Users
Qualitative User Insights
Selected quotes from 12 in-depth shopper interviews that shaped our UX features.
"I spend 20 minutes before each grocery trip looking for coupons across 5 different apps. By the time I get to checkout, half have expired."
"I never know if the coupon I found online actually works in-store. It's embarrassing to hold up the line trying code after code."
"I wish I could just see what deals are near me right now on a map. If I am driving past a store, notify me if there's a deal on today."
03C — The Synthesis
Synthesizing the Clusters
We grouped the data and user interviews into 5 distinct opportunity themes that directly shaped our product architecture.
- 01
Time is the biggest barrier
Friction is the ultimate discount killer. Users abandon deal hunting entirely when it takes longer than 5-10 minutes. Automation must be the starting default experience.
- 02
Local/physical stores are underserved
Online e-commerce code application is largely solved by browser extensions, but localized physical grocery stores and retail remain highly underserved.
- 03
Location awareness is key
Shoppers want geographic context. A high discount rate is only helpful if the destination store is situated directly on the commuter path.
- 04
Trust and real-time verification
A single failed promo code at the checkout desk destroys user trust instantly. All deals surfaced by the scraper must be auto-verified through AI validation.
- 05
App Consolidation
Shoppers are exhausted by carrying 15 separate brand-specific digital loyalty cards. They want a single central index that spans all adjacent brands.
04 — Structural Skeleton
Low Fidelity Wireframes
Establishing core user flows, navigation hierarchies, and spatial layouts on mobile and desktop before applying brand elements and high-fidelity styling.
05 — Visual Direction & Branding
Color Exploration & Systems
Exploring three color spaces to align the experience with DealScout's value proposition of automated local savings and intelligent curation.
Option A
Fresh & Energetic
A youthful, warm, and highly action-oriented direction aimed at modern consumers who value speed and dynamic grocery promotions.
Option B
Trust & Growth
A solid, trustworthy direction built around standard savings greens. Instills a sense of stability, verified coupons, and local growth.
Option C
Smart & Premium
An AI-forward, highly modern palette communicating precision automation, advanced scanning agents, and curated premium high-street savings.
System Overview
Option C — Expanded Design System
Detailed specifications of typography styles, interactive states, UI primitives, and forms in the selected smart premium identity.
Typography Scale — Fraunces & Manrope
Display Heading
Fraunces Semibold • 40px / 1.1
Section Header Style
Fraunces Regular • 24px / 1.3
Body copy renders in Manrope — a clean, highly-legible grotesque optimized for dense deal listings, price labels, and map annotations.
Manrope Regular • 16px / 1.5
Buttons & Form Elements
Card Style & Glyphs
06 — Final UI Design
Bringing the experience to life with the Smart & Premium palette.
Using Electric Indigo as the primary functional anchor, Vibrant Cyan for verification status, and Warm Coral for immediate, high-value savings. All framed in premium, clean structural mockups.
DealScout AI
Smart savings. Found for you.
Splash
Find Deals Near You
DealScout scans local digital storefronts automatically based on your location coordinates.
Onboarding — Find Deals
AI Scans Every Coupon
Autonomous web-agents scrape and parse local digital flyers, verifying checkout codes live in seconds.
Onboarding — AI Scanning
Never Miss a Deal
Bookmark your go-to stores and receive smart push notifications the moment fresh savings drop.
Get Started
Onboarding — Get Started
Good morning,
Sarah Chen
Search deals near you...
Deals Near You
$5 OFF
Organic Produce
Whole Foods • 0.3mi
20% OFF
Home Essentials
Target • 0.6mi
Popular Stores
Home
Whole Foods Market
0.3 mi away
Illustrative sample content
Save $3.00
On fresh berries and select seasonal items
Buy 1 Get 1 Free
Organic kombucha all flavors 16oz
Store Detail
Sarah Chen
Member since March 2026
47
Coupons Used
$312
Total Saved
12
Saved Stores
My Favorite Stores
Account & Favorites
07 — Desktop Experience
Sizing up for complex localized dashboards.
Responsive layouts translate structural phone sketches into a cohesive desktop master-detail viewport, keeping the high-street context central to the workspace.
Mockup A — Home & Radar Map Interface
DealScout AI
Active Local Radar
Scanning store flyers within 1.5 miles of you
Filter by item name (e.g. coffee, berries)...
Whole Foods Market
$5.00 Off Organic Berries
0.4 mi away
Target Stores Downtown
Buy 2 Get 1 Free LED Bulbs
0.7 mi away
Trader Joe's
20% Off Sourdough Bread Loaf
0.2 mi away
GPS Active RadarMockup B — Store Details & Flyer Matrix
DealScout AI
Logged in as Sarah ChenStore Visual — Placeholder
Whole Foods Market
Organic grocer & local fresh goods
Store Details
- Category: Organic Grocery
- Deals tracked: Updated live by AI scanner
- 0.3 mi from your location
Available Promotions Today
Scanned and verified by AI check agents 12 minutes ago
$3.00 OFF
Seasonal Berries & Strawberries Organic
BOGO FREE
Fresh Kombucha All Brands 16oz Bottle
30% OFF
Artisanal Cheese Selection
08 — Impact & Reflection
Quantifying high-street scanner value.
Usability tests and live-environment trials confirmed that bridging automated digital agents with real physical geolocations creates massive consumer convenience.
Illustrative target metrics — conceptual project, not measured data.
47%
Target: Increase in coupon redemption rate
Illustrative design goal (vs. manual flyer searching), not measured usage data.
3.2x
Target: More store visits from AI recommendations
Illustrative goal — would require real opt-in location data to validate in a shipped product.
4.5★+
Hypothetical App Store launch target
Hypothetical launch target — DealScout AI is a concept project; it has not shipped and has no real App Store listing or reviews.
89%
Target: User retention rate after 30 days
Illustrative design goal, not measured retention data.
Designing AI-First Curation
Building an interface driven entirely by autonomous agents taught us that transparency is the absolute trust baseline. In initial runs, users felt uneasy seeing automated pins crop up without explicit sources. Surfacing the 'Scanned live 4m ago' indicator alongside exact store checkout URLs completely resolved this concern.
Competitive whitespace insights
While massive organizations have e-commerce discount code extension coverage secured, the localized brick-and-mortar storefront remains incredibly fragmented. Progressive disclosure patterns allowed us to tuck away the technical scanning code lists, offering a clean, simple map interface.
'DealScout AI' is a fictional product created for portfolio demonstration purposes. Real retailer names referenced as example deal partners (such as Whole Foods, Target, Costco, and others) are trademarks of their respective owners and are not affiliated with or endorsing this concept project. Deals, ratings, and reviews shown are illustrative sample content, not real data.