Nykaa Fashion Cart Revamp

Nykaa Fashion is one of India’s leading fashion and lifestyle marketplaces, with 3,000+ brands across apparel, footwear, accessories, and home. At this scale, the cart is not just a checkout step. It is a high-intent decision surface where small improvements to confidence, clarity, and flexibility can meaningfully affect conversion and revenue.

I owned the cart experience across app and mobile web, working with Product, Engineering, Research, and Analytics to diagnose friction, prioritize interventions, design solutions, and track impact.

MY ROLE

Design Owner (Cart)

Scale

High traffic, sale-led spikes

Collaboration

PM, Eng, Analytics

Platform

App + Mobile Web

What I owned

Audit

Research

Data Deep Dives

Prioritisation

Stakeholder Management

UI Design

Prototyping

Interaction Design

Quality Control and Execution

Tracking

When I took ownership of the cart experience, I began with a comprehensive audit rather than a redesign. Approaching it without legacy bias, I conducted a heuristic and intuitive evaluation to understand how the cart functioned as a decision-making surface, and supplemented this with secondary research, competitive analysis, past user research, and internal learnings. While the cart appeared functional on paper, user behavior told a different story. In collaboration with Manogna (PM) and Analytics, I deep-dived into funnel metrics, navigation flows, cart interactions, and back-navigation triggers and that’s when the underlying issues became impossible to ignore.

Old Cart Designs

Key Problems Identified

Problem 1

Cart didn’t support safe decision-making

SKU cards were hard to scan, destructive actions were too easy, and size or quantity edits forced users away from the cart. For a high-intent surface, basic corrections felt risky instead of reversible.

Problem 2

Purchase intent was leaking

~72% of users moved from Cart to PDP. That behavior looked like drop-off at first, but the path analysis suggested something more specific: users were seeking reassurance.

Problem 3

Users had to leave the cart to make a decision

Users left to zoom into images, check product details, validate ratings and reviews, and confirm return policies. Every context switch added friction at the exact moment we needed confidence.

Problem 4:

Out-of-stock handling created anxiety

Out-of-stock items were mixed with active SKUs, availability status was not immediately scannable, and users had to manually resolve blockers before they could move forward.

Problem 5

Cart amplified commitment anxiety

Users treated the cart like a temporary wishlist, Adding multiple items, then removing or abandoning entirely. Cart forced an all-or-nothing checkout decision

Design Strategy

The goal was not to visually refresh the cart. The goal was to reduce uncertainty at the point of purchase.I prioritized the work across three product bets:

  1. Make the cart easier and safer to act on.

  2. Bring reassurance into the cart instead of forcing users back to PDP.

  3. Reduce checkout commitment by allowing users to buy only what they were ready for.

This prioritization mattered because the cart was a critical revenue touchpoint. Alignment took time, risk aversion was high, and the experience was constrained by legacy architecture. We had to sequence changes in a way that reduced user friction without creating checkout instability.

The goal was not to visually refresh the cart. The goal was to reduce uncertainty at the point of purchase. I prioritized the work across three product bets:

  1. Make the cart easier and safer to act on.

  2. Bring reassurance into the cart instead of forcing users back to PDP.

  3. Reduce checkout commitment by allowing users to buy only what they were ready for.

This prioritization mattered because the cart was a critical revenue touchpoint. Alignment took time, risk aversion was high, and the experience was constrained by legacy architecture. We had to sequence changes in a way that reduced user friction without creating checkout instability.

Fixing the foundation: Making Cart Items Easier and Safer to Act On

The first release focused on fixing the foundation: SKU card clarity, removal safety, and out-of-stock handling. I redesigned the SKU card hierarchy so users could quickly understand product image, title, price, size, quantity, offer signals, and availability. Primary and secondary actions were separated more clearly, reducing accidental taps and making the next step easier to identify.

We also introduced confirmation before removal. This was important because removing an item from cart is a destructive action, especially in fashion where users may have spent time comparing sizes, variants, and prices.

Out-of-stock items were grouped separately instead of being mixed with active products. This made blockers easier to resolve and kept purchasable items visually cleaner.

What changed:

  • Redesigned SKU card hierarchy

  • Clearer separation of product information and actions

  • Confirmation before item removal

  • Easier size and quantity changes

  • Grouped out-of-stock items

  • Clearer availability and intent-building signals

Result from A/B test:

  • +2.6% Cart View → Order

  • 3% decrease in product removals

  • +3.85% increase in wishlist additions

This release improved the cart’s baseline decision quality. Users could understand what was in their cart faster, recover from blockers more easily, and act with less risk.

Solves for problem #1 & #4

The first step wasn’t adding new features it was plugging intent leaks caused by friction and uncertainty. I restructured the SKU cards to improve scanability, clearly separate primary and secondary actions, add confirmation before removals, simplify size and quantity edits, surface intent-building nudges like Price Drop, and group out-of-stock items together for faster resolution and cleaner decision-making.

Solves for problem #1 & #4

Cart SKU Card

CONFIRMATION BEFORE REMOVAL

Impact

+3.85%

Increase in wishlist additions

+2.6%

Increase in Cart View → Order

~1.15%

Conversion Uplift

Reducing Cart → PDP Loops With Quick View

After the first release, we continued reviewing funnel and journey data. Users were still moving from Cart to PDP, but Mixpanel helped clarify why.

They were not simply abandoning checkout. They were looking for reassurance: larger product imagery, image carousel interaction, size validation, and policy checks.

Instead of treating this as a navigation problem, we treated it as a confidence problem.

Quick View brought key PDP-level reassurance into the cart, allowing users to validate their choices without losing checkout momentum.

Quick View Phase 1 focused on:

  • Larger product imagery

  • Product detail visibility

  • Basic decision-support content

Quick View Phase 2 expanded into:

  • Richer product validation

  • Ratings and review signals

  • Policy and return reassurance

  • More complete product context inside cart

The design principle was simple: if users need reassurance before buying, the cart should provide it instead of pushing them backward in the funnel.

Solves for problem #2 & #3

QUICK VIEW PHASE 1

QUICK VIEW PHASE 2

Impact

70% → 22%

Cart-PDP Drop off reduction

~8%

Reduction in time to checkout

Improving Coupons and Offers

Coupons and offers were another source of uncertainty. Users had to figure out which discounts applied, compare conditions, and understand whether they were getting the best possible deal.

That created unnecessary cognitive load at checkout.

The redesign shifted the experience from manual coupon selection to guided savings. Instead of making users decode offers, the platform surfaced applicable savings more clearly and guided users toward the best available option.

What changed:

  • Clearer offer eligibility

  • More transparent discount application

  • Reduced manual comparison

  • Step-by-step savings guidance

This made the cart feel more trustworthy because users could move forward without worrying that they were missing a better deal.

Impact

₹21.6 Cr

Revenue Uplift (Android)

+468 bps

Coupon Adoption Rate

~7 bps

Conversion Uplift

Increasing Flexibility With Partial Checkout

Cart behavior showed that users often added multiple products but were not always ready to buy everything together. The old model forced an all-or-nothing decision: either checkout with the full cart or remove items.

Partial Checkout allowed users to proceed with selected items while keeping the rest in cart.

This better matched real shopping behavior. Users could buy what they were confident about without discarding products they were still considering.

Impact:

  • Increased AOV

  • ₹23 Cr annualized revenue impact

Partial Checkout reframed the cart from a rigid checkout list into a more flexible purchase decision surface.

Impact

₹23 Cr

Revenue Uplift

~1.9%

AOV uplift

What I Learned

The biggest learning was that cart hesitation does not always mean weak purchase intent.

In this case, hesitation was often a signal that users needed more confidence: better imagery, clearer size controls, policy reassurance, safer actions, and more flexibility around what to buy now.

The strongest design opportunity was not to push users harder toward checkout. It was to remove the uncertainty that made checkout feel risky.

Home

WORK

Writing

About

Home

WORK

Writing

About