0%
Revenue profit increase
0
Designer — sole owner
0s
Ticket listings affected
0x
Platforms validated
01 — Problem

Users couldn't tell a
good deal from a bad one

On MegaSeats, users browse dozens — sometimes hundreds — of ticket listings that look almost identical. Price, section, row, fees, availability all vary, but visually everything reads the same. No signal cuts through. Users hesitate. They leave.

🔍

Visual flatness

All listings appeared equally weighted — no visual hierarchy to distinguish high-value options from the rest.

🧠

Analysis paralysis

Too many similar choices with no guidance caused users to freeze before add-to-cart — a critical funnel drop-off point.

⚠️

Broken trust signals

"Best Deal" labels were overused and applied to too many listings, destroying credibility and diluting any real signal.

💸

Lost conversion

High-intent users — ready to buy — were hesitating at selection. The product wasn't meeting them where they were.

Before: Overuse of "Best Deal" label — signal collapse
All Tickets
Best Seats
Best Deal
Section 104, Row 12 2 tickets · $42 fees
Best Deal $89
Section 108, Row 5 4 tickets · $38 fees
Best Deal $97
Section 201, Row 8 2 tickets · $55 fees
Best Deal $112
Section 310, Row 2 2 tickets · $60 fees
Best Deal $134
⚠️ When everything is labeled "Best Deal," nothing is. Signal becomes noise, and users disengage.
02 — Process

Discovery to delivery —
end-to-end ownership

As the sole designer, I drove every phase of this initiative: from diagnosing the real problem to designing the test, running iterations, and measuring impact. Here's how the process unfolded.

01
Discovery
Behavioral analysis & competitive audit
Partnered with product to analyze engagement patterns — specifically where users slowed down or dropped off before add-to-cart. Found clear hesitation correlated with dense, undifferentiated listings. Ran a competitive audit of StubHub, Ticketmaster, and SeatGeek to understand the expectation landscape.
Funnel analysis Competitive audit Behavioral review
02
Definition
Hypothesis framing with product & engineering
Cross-functional alignment sessions to understand how "value" was being defined internally. Confirmed that while pricing logic existed in the backend, none of it was surfacing meaningfully in the UI — a gap between data and perception. Framed the core hypothesis: targeted value signals would reduce decision friction and increase conversion.
XFN alignment Hypothesis writing Success metrics
03
Exploration
Concept testing: language, placement & frequency
Tested multiple label concepts — "Best Deal," "Top Value," "Recommended," "Hot Deal" — with variations in prominence, placement, and how selectively they were applied. Key early finding: high application frequency destroyed trust. Shifted focus from labeling volume to signal credibility.
★ Best Deal
Tested — dropped
✓ Recommended
Tested — dropped
↑ Top Value
Tested — dropped
🔥 Hot Deal
Selected ✓
Label variants Placement testing Trust research Frequency modeling
04
Design
Visual system + filter integration
Designed the badge with strong contrast, strategic placement aligned with natural eye movement, and restrained application criteria. Then pushed further — integrated "Hot Deals" into the sort/filter system so users could actively hunt for value, not just passively discover it. Turned a decoration into a discoverability system.
Visual design Filter UX Interaction design Visual hierarchy
05
Validation
A/B test rollout & iteration
Ran controlled A/B test on MegaSeats as the primary validation environment before any rollout to TicketNetwork. Monitored conversion rate (event page → checkout), ticket selection confidence proxies, and add-to-cart velocity. Iterated based on live data — badge criteria, copy, and application thresholds all evolved through the test cycle.
A/B testing Conversion tracking Live iteration
03 — Solution

A value signal system,
not just a badge

The solution centered on a disciplined redesign of how deals are surfaced — combining a credible label with filter-level discoverability. Restraint was the design principle: fewer, stronger signals over more noise.

💡 The shift from "Best" to "Hot" wasn't just copy — it was a trust reframe. "Best" overpromises in a dynamic pricing system. "Hot" is timely, contextual, and honest — it signals urgency without claiming objectivity.
Design exploration: Badge variants & contextual tooltip system
Hot Deal badge variants and contextual value tooltip showing '406 tickets sold in the last hour' and '4 tickets from $262 — 20% cheaper than similar seats'

Three badge states tested (filled, outlined, ghost) alongside a contextual tooltip surfacing real-time demand and relative value signals — giving users both urgency and confidence.

After: Selective "Hot Deal" — high signal, low noise
All Tickets
🔥 Hot Deals
Best Seats
Under $100
Section 104, Row 12 2 tickets · $42 fees
Hot Deal $89
Section 108, Row 5 4 tickets · $38 fees
$97
Section 201, Row 8 2 tickets · $55 fees
$112
Section 310, Row 2 2 tickets · $32 fees
Hot Deal $118
✓ 2 of 4 listings labeled — scarcity maintained, signal trusted. Users know exactly where the value is.

Key design decisions

Contextual value, not universal

A "Hot Deal" is relative to similar inventory in that event — not a global standard. This kept the label honest across constantly-shifting pricing.

Selective application

Set a threshold: no more than ~20% of listings could carry the badge. Scarcity preserves signal strength. More than that and it's noise again.

Filter integration

Turned "Hot Deals" into an active discovery tool — not just a passive badge. High-intent users could filter directly, creating a fast lane to checkout.

Eye-movement aligned placement

Positioned to appear in the natural scan path — left of price, where users' eyes were already landing when comparing options.

Extending the signal to the seat map

The badge didn't stop at the ticket list. Hot Deal sections were mirrored directly onto the interactive seat map — so when a user hovered or selected a flagged listing, the corresponding section lit up with the same 🔥 marker. This closed the loop between the list and the visual map, reinforcing the signal at every touchpoint in the selection flow.

Live product: Hot Deal badges mirrored on seat map — section-level signal
MegaSeats seat map for Justin Timberlake at Honda Center showing Hot Deal badge markers overlaid on corresponding sections in the interactive map view
01
List-to-map sync — selecting or hovering a Hot Deal ticket in the list highlights the exact section on the map with the same 🔥 marker, so users never lose spatial context.
02
Section-level badging — rather than cluttering every seat, only sections with active Hot Deal listings carry the marker, preserving the map's scannability.
03
Consistent visual language — the same flame icon and red accent used in the list badge appears on the map, building a unified system rather than two disconnected UI patterns.
04 — Results

Perception changed.
Revenue followed.

By shifting from noisy, overused labels to a disciplined value-signal system, conversion improved meaningfully — proving that the bottleneck wasn't inventory, pricing, or intent. It was perception.

+35%
Revenue profit increase without changing prices — purely through improved perception and decision confidence.
2x faster
Ticket selection velocity improved. High-intent users moved to checkout with less hesitation.
Validated via add-to-cart time tracking
Repeatable
Delivered a reusable A/B testing framework for future merchandising experiments at TicketNetwork.
Scaled beyond single initiative

Conversion funnel improvement

Control conversion: Browse 82%, Select 41%, Checkout 19%. Hot Deals variant: Browse 83%, Select 56%, Checkout 27%.
📊 The biggest lift happened at the selection stage — exactly where the friction was. This validated the core hypothesis that the problem was decision-making, not intent.

Beyond the numbers

Validated

MegaSeats as a test bed

Proved MegaSeats as an effective, lower-risk testing environment before rolling changes up to TicketNetwork — faster learnings, lower exposure.

Delivered

Scalable A/B framework

Walked away with a documented, repeatable experiment framework applicable to future merchandising and pricing initiatives.

Demonstrated

Design as business strategy

Revenue grew without touching pricing or inventory — demonstrating that design decisions directly impact business outcomes at scale.

Strengthened

XFN trust

Led end-to-end with full ownership. Built credibility with product and engineering as a design partner who thinks in systems and ships results.

05 — Takeaways

What I'd carry
into every project

Restraint is a design decision
The instinct is to add more signals. The discipline is to add fewer, better ones. Every label you remove makes the ones you keep more powerful.
Connect data to perception — that's the gap
The backend had the information. Users just couldn't feel it. Design's job was to close the gap between what's technically true and what's perceptually clear.
Features scale when they're systems
Integrating "Hot Deals" into filtering turned a one-off badge into a platform capability — discoverable, intentional, and extensible to future experiments.