IndiGo aircraft representing the booking ecosystem
AIRLINE COMMERCE SYSTEM

IndiGo Booking
Ecosystem

Redesigning a 50M-user booking flow for conversion, clarity, and scale.

Role Sr. Manager UI/UX
Company IndiGo Airlines
Duration 18 months
Year 2022 – 2024

The booking flow was costing IndiGo money every day

IndiGo is India's largest airline, carrying 50M+ passengers annually. Their booking flow — the core revenue engine — had grown organically over a decade. It worked, but barely. Conversion rates were below industry benchmarks, ancillary attachment was low, and the support team was overwhelmed with payment and refund queries.

I was brought in as Sr. Manager UI/UX to lead the end-to-end redesign: from discovery through delivery, across the booking funnel, ancillary products, and payment experience.

22%
Ancillary revenue growth post-redesign
18%
Drop-off reduction across the funnel
30%
Reduction in payment support queries
2023
IndiGo Innovation Award — highest conversion growth

47 friction points. One broken funnel.

Before designing a single pixel, I audited the existing booking flow with a structured heuristic analysis. I mapped every screen, interaction, and decision point across the funnel — and identified 47 distinct friction points.

The problems clustered into five categories:

1

Information overload at seat selection

Users were shown too many options simultaneously — 180 seats, 6 price tiers, 3 meal options — causing decision paralysis and abandonment.

2

Ancillary upsell felt transactional, not helpful

Add-ons (meals, baggage, insurance) were presented as a wall of options with no personalisation — users dismissed them entirely.

3

Payment flow lacked trust signals

At checkout — the highest-anxiety moment — the UI had no security indicators, confusing error messages, and poor saved-card UX.

4

Mobile experience was an afterthought

64% of traffic was mobile. The booking flow had been designed desktop-first and adapted badly — tap targets too small, forms too long.

5

Refund and change flows had zero clarity

Users couldn't understand their options, so they called support. 30% of support volume was refund-related queries that should have been self-served.

Listen to 80 travellers. Then look at 50 million.

I ran a mixed-methods research sprint combining qualitative interviews with quantitative funnel analysis — because neither alone tells the full story.

Qualitative: 80 in-depth interviews with IndiGo passengers across leisure, business, and family travel segments. Key finding: users didn't trust the ancillary offers because they felt irrelevant — "I'm flying alone, why am I being shown family meal combos?"

Quantitative: Mixpanel and Hotjar data across 3 months. Largest drop-off: 34% of users abandoned at the ancillary selection screen. Second largest: 22% abandoned at payment OTP entry.

"I just want to book a seat and pay. Why does it feel like I'm doing my taxes?"
— Interview participant, frequent business traveller

This quote became the north star for the redesign. Every decision was filtered through it: does this make booking feel easier or harder?

The behavioural triggers at play

Understanding why users behave the way they do unlocked the design solutions. Three principles drove every major decision:

Choice overload → progressive disclosure

Barry Schwartz's paradox of choice: more options = more paralysis. Solution: show the most popular seat tier first, reveal alternatives on demand.

Loss aversion → reframe ancillary offers

Users respond more to avoiding loss than gaining value. Changed "Add meal for ₹250" to "Your flight includes no meal — add one from ₹250." Conversion increased 14%.

Trust at peak anxiety → payment trust signals

The payment moment is when anxiety peaks. Added bank logos, SSL badge, and a "Secured by JusPay" indicator above the fold. Payment abandonment dropped 14%.

🧠 Hick's Law 🧠 Loss Aversion 🧠 Trust Signals 🧠 Progressive Disclosure 🧠 Peak-End Rule

From 47 friction points to a shipped product

I structured the work in three phases over 18 months, working alongside product managers, engineers, and the revenue team.

1

Phase 1 — Foundation (Months 1–4)

Heuristic audit, user interviews, funnel analysis. Prioritised 47 issues by impact × effort. Built alignment with product and engineering on the top 12.

2

Phase 2 — Core Flow Redesign (Months 5–12)

Redesigned seat selection, ancillary upsell, and payment. 14 rounds of prototype testing with 120 users. A/B tested each major change before full rollout.

3

Phase 3 — Ancillary & Holidays (Months 13–18)

Extended to IndiGo Holidays marketplace and loyalty product. Introduced personalised hotel bundles and gamified loyalty interactions.

Three decisions that moved the needle

Out of 12 major design changes, three had disproportionate impact:

1. Mobile-first seat map. Rebuilt the seat selection component from scratch for touch — larger tap targets, swipe-to-zoom, simplified tier logic. Mobile conversion improved 22%.

2. Contextual ancillary upsell. Replaced the "wall of options" with a personalised recommendation — one primary offer based on route, passenger count, and booking history. Ancillary attachment rate increased from 18% to 29%.

3. Trust-first payment UI. Collaborated with JusPay to redesign the checkout: saved cards above the fold, bank logos prominent, OTP input with auto-advance, clear error recovery. Payment failures dropped 14%.

The Super 6E Sale redesign — built on these foundations — became the highest-converting sale event in IndiGo history, winning the 2023 Innovation Award.

Measured, shipped, and recognised

All results measured against a 12-week baseline period pre-redesign, using Mixpanel with statistical significance ≥95%.

22%
Ancillary revenue growth — meals, baggage, insurance
18%
Overall funnel drop-off reduced
14%
Payment failure rate reduced
30%
Support queries for refunds reduced

What I'd do differently

Start with the worst moment, not the first moment. We spent the first month redesigning the search and results screens — but the data showed the real pain was at payment. I'd front-load effort to the highest drop-off point first.

Involve engineering in the research phase. Some of our best solutions were impossible in the first sprint because of technical constraints we discovered too late. Earlier engineering involvement would have saved 6 weeks.

Personalisation is infrastructure. The contextual upsell only worked because we built a basic recommendation layer. UX improvements that depend on data need data infrastructure investment first — budget for it.

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