Boosting Swiggy’s Cart Conversion Rate
Introducing a New Feature that declines the Swiggy’s Cart Abandonment Rate by conducting research, generating creative solutions, and implementing them effectively.
Ever added items to your cart on a food delivery app but left without completing the payment?
In this case study, we delve into the world of online food delivery and uncover a common challenge faced by users — cart abandonment. Have you ever wondered why some users add items to their cart but fail to complete the payment? We explore this phenomenon and present a solution that aims to tackle cart abandonment head-on.
Problem Space
User Segmentation
First, let’s delve into the various types of users that can be identified within the Swiggy platform. These users can be categorized based on their demographics, including factors such as city, age, and income level. Or by their behavior, such as frequency of shopping and spending habits, or else any other factors. By understanding and segmenting the user base, we can gain valuable insights to inform our strategies for improving the Swiggy experience and addressing specific user needs.
After conducting initial research, we can segment the users into two categories:
- Power Users: These are the users who frequently shop on Swiggy, making up to 10 purchases per month. However, they exhibit a high cart abandonment rate, abandoning their carts multiple times, up to 20 instances.
- Rare Users: These users shop infrequently on Swiggy, typically making only one purchase per month. Similar to power users, they also experience cart abandonment, abandoning their carts multiple times, up to 10 instances.
After considering factors such as market expansion, competitive advantage, and long-term growth, I have decided to focus on addressing the cart abandonment issue specifically for the Rare User group.
User Persona
So, From the analysis of the survey results and User Interviews conducted , A persona for this User Segment(Rare Users) would look like:
User Problems
Based on the research, analysis, and persona development, it has become evident that there are multiple user problems that need to be addressed.
- Hesitation to order from new or unfamiliar restaurants, resulting in
lack of variety and frequent cart abandonment - Forgetting or losing interest in items added to cart for later, resulting
in frequent cart abandonment - Not able to find promo codes , forgetting to use promo codes or
discounts, leading to missed savings and frustration - Overwhelmed by the number of food items, leading to confusion and
cart abandonment - Difficulty in finding preferred dish , example : vegetarian
Now, I would like to prioritize these user problems and select the ones that are most important to solve at this stage that will help the users most. It is crucial to understand that not all problems can be addressed simultaneously with a single solution.
The prioritization is done as follows:
I have identified the user problem that I will focus on solving:
Hesitation to order from new or unfamiliar restaurants, relying on preferred options and resulting in a lack of variety and frequent cart abandonment.
Understanding Root Cause of the Problems
Prior to brainstorming solutions for the problem, it is essential to understand the underlying reasons behind their existence. This approach provides us with a better understanding of the problem’s context and allows us to generate solutions that are tailored to address the specific causes.
A mind map illustrating the various factors contributing to the identified problem would appear as follows:
Ideating Solutions
Based on the previously identified factors several solutions have been identified .
Brief Note on each of the solution identified:
- Recommendations from family , friends
- Allow users to get recommendations from their group of friends
or family members when they try any new food. - Preferred food among similar user types
- Getting recommendations from the system , by analyzing on
how other similar type of users are having( Age , Food type ,
Region) - “ Discover New” Feed
- A new feature where users would see the recommendations in
the form of posts from other users
- Other users will be rating , reviewing it and posting it after they
tried any food . - Surprise feature
- Where the system places a random order. - Rewards / Badges
- Users would be getting rewards in the form of promo codes if
they ordered from any new restaurant , for the next order they
can redeem them. - Leader Board
- Ranking the users based on their region for the number of new
dishes they have tried.
- Later giving incentives to the top % of users.
I have generated a significant number of solutions, but it is important to note that not all of them can be implemented simultaneously. We need to devise a method to select the most suitable solution for implementation.
To determine the best solution among the six ideas, I have opted to utilize the popular RICE Framework. This framework will help evaluate and prioritize the ideas based on their Reach, Impact, Confidence, and Effort factors. By applying the RICE Framework, we can make an informed decision on selecting the most viable solution for implementation.
After careful consideration, I have chosen to implement the “Discover New” feature in the Swiggy application to address the user problem of hesitancy in ordering from new restaurants and exploring a variety of dishes. This strategic decision aims to effectively reduce the cart abandonment rate
Implementation
The initial phase of implementing the Discover New feature involves creating a user flow that seamlessly integrates into the existing Swiggy application. The focus here is to develop a well-designed user flow that effectively showcases the Discover New functionality and ensures a smooth user experience.
The implementation of the Discover New feature will involve engaging users from both sides of the platform
- The users who recommend the food they have tried and enjoyed. These users will have the opportunity to share their positive experiences and provide recommendations for others to discover.
2. These users will access to the recommended food items and will have the opportunity to provide feedback on the food recommended by others.
Wireframes
During this phase, the focus is on creating the first draft of visual elements. The objective is to sketch the envisioned design and have a reality check on how the UI should be developed when handed over to the designer.
Into the Functional Specifications
How a user orders through discover new Feature:
- Taps on the feature from the home screen
- Lands on the feature screen , views a variety of recommendations
from other users. - Can use the search function for finding any specific thing( like dessert
, chicken..) - Can use filters and sort the list eg: No.of orders placed through that
recommendation , rating , price , Restaurant type… - Afterwards, they can choose the quantity and order the food.
How a user recommends food in discover new section:
- After receiving the order , system will remind them to post this in
discover feed or After rating a food item system will ask whether they
want to put this in discover feed. - Opting yes will take them to a screen- where the user needs to put
information like , food category , any tags like(minimal price..) ,
thoughts on that food. - Submitting it will push that into the feed section.
- The user will be getting feedback in the form of reactions , and no
orders placed for that food.
How would swiggy know which posts/food to show in a user “discover new”
section:
- This is dependent on the user location/city
- Showing the restaurants that are available for delivery for a user
location. - Ranking the posts based on : no of orders placed , positive
feedback received.
Edge Cases
There are couple scenarios to consider and before this feature could go live
- The price of a food item has been updated after a user recommended
it in the discover feed. Will that decrease the value of the food
suggested? - There are no posts to display for a user at a particular location
- Does the new user who has seen the post be charged the same
amount in other words is the user able to apply the same promo
codes/offers when compared to the user who recommended it. - Can the user who posted it can buy it again from the CTA shown at
his/her post. - What if the restaurant/food is not available or closed after
recommending it.
Metrics
Couple of metrics to keep track of to know how this feature is performing
Go To Market Strategy
In order to address the issue of high cart abandonment rates among Swiggy
users, we will be introducing the launch “Discover New” Feature in selected metro cities : Hyderabad, Bengaluru, and Chennai In the upcoming quarter .
These features will be rolled out to a targeted group of users who have a
current cart abandonment rate of over 60%.
The goal is to reduce the cart abandonment rate by 10% with the
implementation of these new features.
Throughout the upcoming quarter, we will closely monitor several metrics in order to assess the effectiveness of these features and identify areas for improvement.
These metrics will include :
- order completion rate — The percentage of transactions done after adding food items to carts when compared to old ways of ordering.
- Time saved — The average amount of time spent per user in the app
for ordering food when compared with old ways of buying. - User engagement — The % of users who are engaging with these
features , frequency of users using these for their ordering
experience.
Thanks for Viewing💗, This is my first Story in Medium.
I’m currently open to exciting opportunities in the field of product design. Find my work here on my Behance Portfolio. Additionally I invite you to connect with me on LinkedIn. There, you can learn more about my professional background, experiences, and skill set.
