A virtual party experience with Treatsure
PROBLEM STATEMENT
When COVID-19 hit India, life shifted indoors. People were hesitant, even terrified, to step outside and meet relatives or friends. Celebrations that once meant laughter, food, and shared experiences suddenly became limited to video calls.
How might we bring people together again, not just on screen, but also through food that arrived at everyone’s homes at the same time?
OVERVIEW
Treatsure was designed to bring back the joy of shared celebrations during COVID-19 by synchronizing food delivery and virtual gatherings. The goal was simple yet ambitious: make online parties feel as warm and effortless as being together in person.
RESULTS
50,000+ sparks ignited, because genuine connections start with personality.
10,000+ people chose personality over profile pictures.
2 million+ conversations that prove real chemistry can’t be swiped.
An experience users loved, rated 82 on the System Usability Scale
User research results & insights
We began by validating the PRD with stakeholders, surfacing intent, constraints, and aligning on goals. This was followed by analyzing competitors to identify gaps and opportunities.
01. Stakeholder alignment
Interviews clarified that Treatsure had to design connection, not just food delivery. The focus was on creating shared experiences that felt effortless and memorable.
02. Design strategy
a. Group orders from multiple restaurants
b. Same-meal or customizable options
c. Synchronized delivery for shared celebrations
03. Target audience & constraints
The platform needed to serve students, professionals, families, and large groups (50+), while factoring in real constraints like stable internet and device access.
04. Competitive Analysis
a. Houseparty / Zoom / Rave / Evibe.in → enabled conversation, but not dining
b. Swiggy / Zomato → delivered food, but treated each order as isolated
c. Gap Identified → no one recreated the shared dining table.
Research methods
9 interviews showed the issue was emotional, not just logistical.
The research strengthened the brief and became the north star for every decision that followed.
Quantitative Research
A survey of 60+ confirmed the patterns:
Missed synchronous eating
Wanted variety without hassle
Found online celebrations fragmented
Introducing our persona
Scenario: User wants order food for a virtual party (multiple people and customize it too)
Journey map
Scenario: User wants order food for a virtual party (multiple people and customize it too)
User flow
Key flow mechanics that mattered:
Real-time Order Summary: persistent, right-hand panel that updated as users added items and quantities. (Applied visibility of system status and reduced mental model load.)
Per-guest assignment UI: users could create “guests” and either assign items to a guest or mark items as shared. This solved the core failing in existing flows where users lost track of who ordered what.
Synchronous Scheduling CTA: After items were finalized, the interface asked for a party start time; the UI then showed a “synchronized delivery window” that combined partner ETA estimates into a single suggested slot.
Why this mattered: the stepwise flow reduced confusion and prevented the single-page overwhelm that we observed in formative tests.
Information architecture
We built the IA to match how people plan events in reality:
decide occasion → choose headcount → choose food → confirm logistics.
That sequence followed users’ mental model and reduced friction. Specific IA choices:
Initial Sketches
We did rapid pencil explorations of 12 variants (grid vs list, single page vs stepper, inline summary vs floating panel).
Early moderated tests (with 8 participants) surfaced two hards findings:
Visual Design
When moving to hi-fi, we layered in visual systems that built trust and reduced anxiety:
User Testing
A product that coordinated multiple restaurants and deliveries required strong error-handling UX:
01. Checkout complexity → guided step-wise flow and summary
a. Problem: Users lost track of guest orders and abandoned checkout.
b. Design decision: Step-wise customisation with a persistent order summary.
c. Outcome: Checkout completion rose 62% → 87%, errors dropped by half.
02. Support discoverability → Visible fallback CTAs
a. Problem: Users couldn’t find help when stuck.
b. Design decision: Added always-visible ‘Get a Quick Call’ and WhatsApp fallback.
c. Outcome: Faster support access; confidence scores rose in usability tests.
03. Predefined packages & upsells → Curated cards
a. Problem: Too many meal options caused decision fatigue.
b. Design decision: Introduced curated packages with visible upsells in each card.
c. Outcome: Projected +20% order value; 6/10 users shared celebration pages, showing strong organic growth.
Business thinking meets user delight
I was explicit about trade-offs we accepted:
