Swiggy Group Order

Razorpay Design Exercise

Design Brief

Design a new feature for Swiggy/ Zomato which allows multiple people to add food to a single order using their own devices. Cover the whole journey from finding restaurants & placing the order to delivery.

Deliverables
Documentation of Design Process
User stories / Use-cases
Interactions and interface

Duration
7-8 Hours

My Role
UX Design
UI Design

The Problem

Limited time was there to complete the challenge, so I took an autoethnographic approach towards research. I analyzed my personal experience in order to understand this problem.

I have had a bad experience with ordering food for a group of friends using food delivery apps. So I used that as a starting point to make sense of this problem. I listed down the reasons why it was such a frustrating experience.

Deciding on a single restaurant to order food feels like a compromise that no one wants to make.
A lot of time goes into deciding what food items to order. Some are too picky about their food, some are indecisive and some just agree to whatever is being decided to avoid unnecessary discussion. It almost feels like that cart is never going to get checked out
Then someone else pulls their phone out and the almost built cart gets abandoned because now there is a chance of ordering something you actually want.
In the end, 2-3 orders get placed from different phones and now half of the people are busy with tracking the order and talking to delivery partner etc.
Multiple orders get delivered at a different time and the whole experience of eating together comes crumbling down.
Then comes the part of splitting the bill. It is difficult to have this conversation. Sometimes the bill gets split equally amongst the group members which can be extremely unfair to some.

There have been situations where I have actively avoided being part of such group orders, but because of this challenge, I looked at it as an opportunity to solve a problem.

Analysis

I listed down the problems and delved deeper asking why are these things happening. Do people generally behave like this when it comes to eating food together? I compared the experience of dining out with online ordering. Here, I found out there is a stark difference in the way people behave at restaurants and the way they behave when they are ordering from home or office. After doing some reading and thinking I got this insight that the experience of dining out is full of human Interactions. There’s a lot of attention given to customers’ comfort and the experience has been perfected over the years. On the other hand, the food delivery experience, a fairly new service, is majorly an interaction of humans and machines. A world of machines is a world of one and zero. So it requires a thoughtful intervention and a great understanding of user needs if one wants to design a humane experience.

I decided to get some inspiration from the interactions that take place when people eat out and bring them to the online food ordering experience.

Common behaviors that are seen when people eat out in groups:

People get agitated if everyone at the table hasn’t gotten a menu card. The restaurant staff is very prompt in providing menu card.
A person’s order is influenced by what others are ordering. Many times people order the same thing as the other group members.
When someone wants to try something new, they tend to look for a companion who will share it with them.
If everyone has decided what they want to order but someone is taking longer to decide. People give the first round of orders. No one feels bad about it either.
Mostly when friends/families go out for casual meals, they tend to order dishes in a peculiar way, say ‘2/3 soup’. It is cost-effective and easy to serve.

By this point, I could see how some of these observations could become helpful features in the experience of the group order.

Solution

To define the journey of a user in the app I made a flow diagram. It follows the journey from creating a group to receiving the order. I also found some loopholes in the path that needed some work.

*User flow from creating a group to placing the order and paying money to the host*

It was difficult to envision a solution as an abstract concept, so I came up with a story that helped me flesh out the features in an effective way. It is a story of six friends: Renuka, Kalyani, Priyanka, Uttara, Tejal and Aastha. For now, you can just remember Renuka. She is the host of the party and she has called her friends at her place. Few of Renuka’s friends have reached her place. A couple of them are on their way. Now is the time when Renuka decides to use the ‘Group Order’ feature from Swiggy to order food for all of them.

Get everyone on the same page

Renuka opens the Swiggy and taps on group order. Now she sees a map on the next screen. So generally Swiggy delivers food from restaurants that are within a radius of 10 to 12 Km. However, when it comes to group orders, users can place orders from multiple restaurants. In that case, it is impossible for a single delivery partner to commute to different restaurants in such a large area. Therefore Renuka has to narrow down the serviceable area to start with. She can edit it later if other group members feel differently.

Now it’s time to get her friends on the board. They scan the QR code on Renuka’s screen using their phones. After scanning they are immediately taken to Swiggy app where they all are part of a big, shared cart. There are two of her friends who has not reached her house yet. For them, she shares the link of QR code with them using WhatsApp, so that they can also start ordering the food.

“I’ll have what you are having.”
”Let’s make it 1/2.”

As everyone gets access to the cart, they get busy browsing through the menus. Renuka adds Dosa to her cart and wants to see what others are ordering. She sees Kalyani has ordered Mushroom Soup. Renuka loves mushroom soup so she wants to have it as well. She sends a share request to Kalyani and Kalyani accepts it. Now their bill is split and the restaurant will send two separate containers for soup. That just makes sharing food easy.

Renuka has added all that she wanted to a plate. So now she taps on the grey tick mark to indicate that her plate is ready.

The cart is almost ready.

Right now there are five plates in the cart. Other users have also put green tickmarks in front of their name that means their plates are also ready. Every user can see what is in their plate and how much they have to pay individually. They can also see what others have ordered. It’s time to place the order but…wait…everyone has completed their plate except for Tejal.

Gotta place the order, complete your plate. Tick tick one….

Everybody has completed their plate except for Tejal. It is understandable she isn’t with them and in transit right now. To nudge her Renuka sends a reminder to her. It has a timer in it but no need to get pressurized, Tejal can extend the timer. If she is running extremely late she can cancel her plate. This way cart building gets streamlined and quick.

No hairsplitting over splitting the bill.

Though it’s a unified cart, the price of every person’s plate is calculated differently, which is upfront for everyone to see. Prices of shared items are also split equally amongst the sharers.

It is difficult to ask each group member to pay his/her own share of the bill because then the order will get placed only after all the payments are successful. A lot of uncertainties could arise if some of the payments get processed and some don’t.

Therefore for a frictionless payment experience, the host pays the bill. In case the host cannot pay the bill, he/she can make someone else the host. Then the new host can pay the bill from her account.

After the payment goes through, all other group members get a G-pay like request on their Swiggy account. It mentions how much money they have to pay to the host. They can choose to do a regular payment or they can do it using Swiggy money. If they choose the latter option they can avail some discount on their next order.

Experience of delivery partner who is assigned for group order

When someone orders food, the app must dispatch the delivery partner to get there right when the food is ready to be delivered. If they get there too early they’re waiting around and it’s not the best experience for them or the best way to maximize their earnings. If they get there too late then the food gets cold.

The historic data can help in this. The system at the backend will take input from restaurant partners on how long it takes for dishes to be prepared. It will also consider traffic conditions in a particular area at a particular time. Based on these data points optimized route will be suggested.

Final Screens