Average order value
plus 5.65 percent
+₹117per order, on average
E-commerce · Product design
A cart can contain multiple deliveries, prescription checks, competing offers and incomplete checkout details. My focus was to make the next decision clear.
Average order value
plus 5.65 percent
+₹117per order, on average
Coupon application
plus 8.36 percent
Rings show the rate out of 100%.


01The problem
Too much complexity was competing with the decisions shoppers needed to make before checkout.
One order can arrive in several deliveries.
Applied and available savings compete for attention.
Prescriptions introduce conditional delivery.
Login, location and payment states vary.
Business goals
Goals set for the work, not proven outcomes.
03The solution
The revamp reorders the cart around what a shopper has to decide: delivery first, then items, then money, then the next valid step.
Before: "Order will be delivered in 2 deliveries", yet both items read "Delivery by Today, 2pm". Shoppers had to work out what arrives when.
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04How we got there
Every element in the revamped cart maps to one of six questions a pet parent asks before paying.
05Key design decisions
Five decisions carry most of the weight. Crops are from the revamped design file.

One header per delivery shows what arrives together, with the shipment count always visible.
After
Smaller rows make room for delivery context. Quantity controls stay.

Speed tier and arrival time sit in each group header. An info icon explains why items split.


Offers and coupons show what each one saves. The price summary lists every line.
Saved address, missing coordinates
The footer button follows the shopper's checkout state, not one generic button that fails later.
06State design
This is not a visual refresh. It's a system of states, each designed up front rather than patched in later as an error.







07Reported outcome
A/B test, Jun 25 – Jul 7, 2026 · 36,331 units.
Average order value
+5.65%
Control₹2,076Revamp₹2,193
Coupon application
+8.36%
Control49.9%Revamp54.1%

Relative change, revamp vs control. Raw data wasn't available, so no causal revenue claim is made.
08Learnings + next steps
What this work taught me, and what still has to happen before the story is complete.
Split fulfillment and verification decided what the cart had to explain. Delivery groups make that model visible.
Location and prescription states change what checkout can promise, so they were designed as states, not patched in as errors.
The next action has to be understandable and safe, not just one tap closer.
AOV and coupon use rose together, so discount cost has to be read alongside the gain.
Capture matching old and new states with app version, date and location.
Test delivery count, savings attribution and recovery tasks.
Review metric definitions, exposure and subgroup effects.
Prioritize observed blockers and accessibility issues.
Portfolio takeaway
A state-aware cart that explains delivery, money and readiness, supported by transparent evidence.
What I owned
Sources: Cart Revamp project page and use-case framework, previous and revamped design files, and the embedded A/B report. Prices and savings in the screens are mock design content; they don't all reconcile and are not transaction data. Lifestyle photos are AI-generated.
Process and research detail