Food | B2C
Product design concept
2 weeks
Turning indecision to booked tables in Swiggy Dineout
About Dineout
Swiggy Dineout is built for individual discovery, yet 40–50% of bookings are for groups. 81% take longer to choose where to eat, and as decision time increases, table bookings drop.
Overview
Help groups decide where to eat in under 10 minutes and turn indecision into booked tables. This could help Swiggy Dineout capture an estimated ₹200–600 crore in additional value.
I designed a collaborative experience in Swiggy Dineout that helps groups discover, decide, and book a table faster.
Problem
Friend groups struggle to decide where to eat because everyone has different preferences, but not everyone voices them. The planning burden often falls on one person, leading to coordinator burnout and longer decision times.
Let's break it down
People hold back what they want, which can reset the plan later.
The goal was to find what makes deciding difficult, measure how satisfied groups are with the final choice & understand how groups plan & decide currently.
What data revealed
Key insight : Groups can reach a “good enough” choice. The real friction is in the planning process.
I shifted focus to finding why friction happens in planning.
What people said
User interviews n=6
I spoke to 6 people to understand why groups take longer to decide.
Key insight : Planning takes longer because of individual preferences & co-ordinator burnout.
Groups decide quicker when someone has already been there. This is a huge trust factor that aligns everyone towards a quicker decision.
Assumptions that confirmed, broke & new findings
Groups tend to choose restaurants someone already knows.
Familiarity wins. Friend's recommendation is the greatest trust factor.
Deciding becomes hard because there are too many restaurants to choose from.
Too many options wasn't a major issue. The real problem was catering to diverse individual preferences.
Food influencers and trends influence where people want to eat.
Only 9.5% trust them while 64.3% trust a friend's lived experience.
Most discussions happen on WhatsApp
WhatsApp is not the primary intitation venue. % plans start when the group is already out which means they need help in real time, not in planning.
High satisfaction rate
81% are satisfied with their final restaurant choice despite frictions in their planning process. This shows that the pain is in the journey and not in the end result.
Preference suppression is social
People say 'anything is fine' to avoid conflict and not because they do not have any real preferences. The real challenge is extracting honest preferences.
Where existing solutions fall short
WhatsApp - no structure, options get buried, last responder often decides by default.
Random pickers/wheel-spinners - remove effort but ignore individual preference entirely
Designated decider - fast, but concentrates social risk on one person and ignores dissent.
Manual polling (WhatsApp polls, Google Forms) - closer to a real solution but disconnected from restaurant data, availability and booking. The group still has to switch apps to actually reserve a table.
Key insight : Existing solutions solve one part of the problem, but miss the rest.
Solving one friction point wasn’t enough. The solution had to make the entire journey easier, from sharing preferences to deciding and booking.
One person does all the work
Key insight : The organizer does most of the work across multiple apps. I focused on reducing their burden and making it easy for everyone to take part.
Group co-ordinator persona
Group member persona
Business opportunity
By solving this problem, Swiggy could benefit in these areas
Increase Conversion Rates: By streamlining the decision process, more group inquiries will translate into confirmed bookings.
Enhance User Engagement: Users will spend more time within the Swiggy Dineout ecosystem, fostering loyalty.
Capture Valuable Data: Insights into group preferences and decision patterns can inform future product development and personalization.
Strengthen Market Position: Differentiate Swiggy Dineout from competitors by offering a more comprehensive solution for group dining.
Key design decisions
The calls i made before touching a single screen in figma
Ideation
How might we capture individual preferences early, let everyone in the group participate and arrive at a decision quicker?
Ideas explored
Directions I explored before going deep on one
The solution
Discover. Decide. Dine
1.Create a plan
Add plan details to show only restaurants with matching table availability.
SCREEN INTENT
Gather user input on guest count, time , date of plan & occassion.
PRIMARY USER GOAL
Set the plan details.
KEY DESIGN DECISIONS
2.Invite your squad
Invite others to join group planning.
SCREEN INTENT
Help the host invite the group and get everyone ready to start.
PRIMARY USER GOAL
Invite their squad and start the plan.
KEY DESIGN DECISIONS
3.Fill preferences
Collect dietary needs, budget, cuisine, and other preferences.
SCREEN INTENT
Help users share their preferences privately.
PRIMARY USER GOAL
Set their real preferences without the social pressure.
KEY DESIGN DECISIONS
4.Discover, discuss & shortlist
Discover restaurants that fit the squad’s needs and shortlist up to 3.
SCREEN INTENT
Help the group shortlist places they’d be happy with and discuss them together.
PRIMARY USER GOAL
Shortlist up to 3 places they like
KEY DESIGN DECISIONS
5.Get matched
Shortlisted restaurants are ranked with the top match shown first, while the other two stay hidden unless needed.
SCREEN INTENT
Help the group agree on the best-matched restaurant before booking.
PRIMARY USER GOAL
Review the top match, see why it fits, and confirm the restaurant together.
KEY DESIGN DECISIONS
Book table & Save your squad
Final step in the process. Booking confirmed and shared with other squad members.
SCREEN INTENT
Confirm that table has been booked
PRIMARY USER GOAL
Confirm booking and save this squad for next time
KEY DESIGN DECISIONS
Validation
Test the new flow
Next steps
Launch if decision time drops without hurting booking conversion or booking quality.
Iterate if users decide faster but bookings don’t increase.
Introduce time-bound sessions for plans that need quicker decisions
The tradeoffs
Challenges & mitigations
Match-to-booking drop-off
There’s a risk that groups drop off after reaching a match and never book.
Mitigated in two ways:
Make booking the natural next step by showing offers and discounts in the match flow.
Reduce booking effort by pre-filling details already collected during the session.
Invite wait time
Requiring everyone to join before starting can recreate the same bottleneck.
Mitigated in two ways:
Let the host start once a majority has joined.
Let latecomers add preferences until the match is finalized.
Scale
The flow was built as a flexible system of reusable patterns, allowing the same order logic to serve frequent use cases and evolving contexts.
Expected Impact
Success = time from plan creation to table booking. Target: 10-15 min per successful session
Next, I'd track the % of groups that completed a table booking after the final match vs %who abandoned without table booking
Learnings
Designing a collaborative tool especially for high-intent and time sensitive groups made me think about prioritizing decisions that are good enough instead of the perfect match.
