From 27 Hours to 2 Minutes: How a Prototype Saved an OTT Platform’s Checkout
My role: Product Designer leading discovery and flow optimization
Team: Myself (UX/Strategy), 1 dedicated PM, 3 Back-End Devs, 2 Front-End Devs, and 1 CS specialist
Timeline: 1 intense month of immersion
Key Deliverables: complete journey map, optimized checkout prototype, usability analysis recorded on Loom, recommendation report, and adapted legacy checkout code
When I joined the OTT team, the checkout was a white elephant: outdated UI, poor performance, and bugs that froze transactions for up to 27 hours.
Our clients (on-demand content creators) watched their sales evaporate due to tech issues and long waiting times.
Slack piled up with frustration with messages like: "Payment step froze again" or "We lost R$2,000 on a single sale."
During a CS call we heard the team say: “Client X lost R$3,000 in sales, canceled their subscription, and requested contract termination.”
That’s when our PM Emília said, “Luis, we need a plan. Now!”
We had the pressure of knowing that each bug was costing real money and trust. We froze all feature development and shifted the team's full focus to fixing the buying experience.
I mapped every click and delay in the checkout using session recordings and tests with two real clients. They agreed cause it was urgent.
In a Lean Discovery workshop, we dissected key drop-off points: unclear fields, poor feedback, and no trust signals. Also, the technology was out dated.
Since Checkout is all about human bias, we used some of them.
We crafted HMWs like: "How might we guide users step-by-step without overwhelming them?"
Our core hypothesis: if we eliminate unnecessary decisions and add trust badges throughout the flow, speed and confidence will rise.
To be quick and not affect production I suggested using a deprecated checkout the team had built. We needed something fast and with a bit of tinkering we could make it live while we worked on the real version aligned with our Design System.
In tests with 5 new users, the checkout click-through rate jumped from 52% to 80%.
CS confirmed the changes addressed key frustrations and should go live.
What began as a tense sprint turned into a high-efficiency case study: communication flowed, deliveries sped up, and production went live, saving us from bigger losses.
Within 48 hours, we hit a breakthrough and removed two friction points. The outdated technology (causing bugs) and a horrible checkout UX.
We added trust signals and simplified confirmations. Using human bias based on science to guide the user and make him feel safe while making the transaction.
They could make a purchase in seconds.
I learned that in moments of crisis, old prototypes can be launchpads for real solutions.
For the business, it became a competitive edge: creator churn dropped by 30%.
It was a reminder that in revenue-driven products, every second counts and whoever masters the buying journey, owns the market.
For end-users, the new flow meant less confusion (“I can see my progress”, “My payment is secure”) and less waiting (2-minute checkout vs. 27 hours).
Cognitive biases did more than cleaner UI (people wanted reassurance, not just to spend money).
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