FaceValue
Product strategy, UX, brand & agentic delivery
Founder
A founder-led concert intelligence platform with 14M+ organic social views, 686K+ interactions and shares, and press citations for its pricing analysis.
I founded FaceValue, a Toronto concert-ticket intelligence product and media brand. It has earned 14M+ organic social views, more than 686K interactions and shares, 217K website impressions, and a 78% average newsletter open rate. Its original pricing analysis has become a source for Do Not Pass Go, Exclaim!, and 99scenes.
I lead the product strategy, research, UX, brand, content, data model, and day-to-day workflows. I also direct the agentic development process used to build and run much of the system.
Visit FaceValue ↗ · Open the Concert Tracker ↗ · Instagram ↗ · TikTok ↗
Making ticket prices easier to act on
Ticket buyers are asked to make expensive decisions with incomplete context. The original price is hard to find, inventory changes quickly, and every onsale is framed as urgent. FaceValue turns that uncertainty into a direct recommendation: Buy, Wait, or Tossup. Each recommendation is grounded in original face-value prices, current listings, venue sections, sales pace, and historical patterns.
The Concert Tracker brings discovery and decision support together. Fans can find newly announced shows, compare current prices with face value, see recommendations by section, mark shows as Interested or Going, and set a personal price target. I designed the experience around progressive disclosure: a clear recommendation first, section-level evidence second, and personal controls when the user is ready to track a show.
Concert Tracker home with upcoming shows, current deals, and Buy or Wait verdicts
The public product connects discovery, recommendation evidence, and personal price monitoring in one system.
Behind the tracker
FaceValue is not maintained by manually posting isolated updates. I designed an internal system that discovers concerts, tracks onboarding and data quality, compares primary and resale prices, proposes qualified deals, and routes exceptions for review.
The review feed brings price movement, inventory, historical pace, recommendation confidence, and publishing state into one decision surface. Automation handles collection and repetitive production; I retain human approval over recommendations and what reaches the audience.
Once I approve a deal, FaceValue can create a branded social card and send a subscriber alert from the same verified data. I do not need to re-enter the same information for the tracker, social channels, and email.
Internal queue for concert discovery, onboarding, and data checks
Automation handles the repetitive work while I review the decisions and audience-facing output.
Working with AI as an implementation partner
I use Claude and Codex as implementation partners. I break work into clear tasks, document the relevant product context and constraints, and review what they produce. They help me build across the front end, back end, data pipeline, email, SEO, and internal tools. I still make the product decisions, test the work, and decide what ships.
This lets me move faster without handing over judgment. Anything that can affect trust, including verdicts, data exceptions, editorial claims, and releases, stays reviewable and reversible.
Turning product data into editorial authority
FaceValue has grown without paid acquisition. I developed repeatable formats for price comparisons, deals, verdict reporting, newsletters, and practical guidance. Each one turns original data into a story fans can understand, verify, and share, then gives them a reason to use the tracker.
My long-form guide, How to Win the Ticketmaster War, translates repeated real-world experience into practical advice for buyers. FaceValue's reporting has also been cited by Exclaim! in its coverage of Stella Lefty's ticket prices.
An artist's ticket prices compared with the venue average
Original analysis becomes useful consumer guidance, recognizable editorial work, and evidence of real savings.





