IRCTC Redesign
Rebuilding trust in India's largest railway booking platform.
The surface-level problem was a cluttered, outdated interface. The deeper issue: Is the booking confirmed? Did the payment go through? Is this the right train?
This redesign focused on rebuilding that trust through clarity, feedback, and a streamlined booking flow, making the experience faster, more transparent, and accessible across devices.
IRCTC users don't fail because the UI is ugly. They fail because the system doesn't tell them what's happening. The redesign starts there, not at aesthetics, but at feedback and confidence.
Severity 4, Catastrophic
Users couldn't tell if their booking had succeeded or failed. No clear error. No retry. Just ambiguity.
Severity 3, Major
Seat availability, fare class, train timing all required clicking into sub-menus to verify.
No progress indicator
Users dropped off mid-flow, unsure how far they were from completing.
The existing platform was evaluated across four critical flows: search, booking, payment, and account management. Violations were rated by severity (1-4) and tagged by heuristic.
Severity 4, Catastrophic
After submitting payment, the system showed a loading state with no indication of success or failure. Payment is the highest-stakes moment.
Severity 3, Major
Terms like "WL," "GNWL," "PQWL," and fare classes appeared without definitions. Affects every non-frequent traveler.
Severity 3, Major
Changing passenger details or seat class required starting the entire booking over. Every correction restarts a 6-step process.
Severity 4, Catastrophic
Failed payments showed a generic error. No retry option, no saved details, no guidance. Primary dropout trigger.
Conducted contextual interviews and observational tasks with users across experience levels: first-time IRCTC users, regular travelers, and users who had abandoned bookings mid-flow.
Three failure zones emerged: the booking flow (too long, no progress signal), the payment moment (no feedback, no recovery), and the search experience (no persistence, no intelligence).
"How might we rebuild user confidence at every critical decision point in the IRCTC booking flow, so that completing a booking feels certain, not anxious?"
Each failure zone was addressed separately. For booking: how to reduce steps without losing information. For payment: what does real-time feedback look like. For search: what does persistence and predictive behavior require.
Removed redundant confirmation screens
Progress indicator added throughout. Step progress eliminated ambiguity. Users always know where they are in the process.
Processing, success, and failure states
Explicit messaging at every stage. Retry without restarting. The highest-stakes moment gets the most information.
Filters persist across result pages
Predictive station search reduces input errors. Date, class, quota preferences remembered between searches.
The visual system prioritizes information hierarchy: what users need to confirm a decision is always visible without clicking into sub-menus.
Usability testing with first-time IRCTC users, regular travelers, and previous booking abandoners. Tasks: complete a search, complete a booking through payment, recover from a simulated payment failure.
What we measured: Task completion rate, time to completion, number of errors, and confidence rating at the payment stage, specifically because it was the primary trust failure identified in the evaluation.
The redesigned flow reduced cognitive load at every critical decision point. Step progress eliminated ambiguity. Real-time payment feedback gave users the confidence to complete transactions.
6 screens to 3 steps
Removed redundant confirmation screens. Progress indicator added throughout.
Silent failure to real-time states
Processing, success, and failure states with explicit messaging. Retry without restarting.
Reset to persistent
Filters persist across result pages. Predictive station search reduces input errors.
Jargon to plain language
WL, GNWL, fare codes given plain-language explanations inline.
Note: All outcomes are qualitative observations from usability testing. Quantitative metrics would require longitudinal measurement on the live platform.