Here is my take on the problem statements which were of particular interest to me. For ease of scanning, these have been structured as follows:
Metrics
Approach
Recommendations
Artefacts
How would I improve Zomato
There are certainly various to 'improve' an app or service, in this scenario I have focused on improvement of app by suggesting new features.
Metrics
10% increase in active users per month
30% adoption of features discussed within 2 months
Read More
Approach
For the problem, I decided to proceed by first identifying some user segments/applications and then identify some pain points for each.
The segments I will be focusing on are : ' Working people': people who use the app during office hours, 'Party people': people who use app while in a party/function, and 'Lazy people': People who use app because they do/can not cook.
Approach
Pain Points/ Insights
Some insights for 'Working' segment are as follows:
They want to eat food during specific interval(lunch)
may order regularly
need the delivery to be fast and on time.
can not afford luxurious meals
Some insights for 'Party people' are as follows:
Order in bulk
Usually couple their order with beverages
Some insights for 'Lazy people':
Order regularly
Splurge on occasions
Recommendations
Features for Working segment:
A 'tiffin' service which highlights and suggests restaurants near the area which provide such meals.
Collaborating with existing tiffin service providers via a monthly subscription system
Pre-order which will deliver at specified time
Special offers for people who pre-order for couple days(3 days or entire week).
Recommend popular restaurants locations within 5 min of their workplace.
Recommendations
For Party segment
Pre-set Discount on orders whose total pass certain setpoint
Create 'packages' of popular bulk orders based on data analysis(eg. 5 medium pizzas+7 cokes,etc.)
Allow Restaurant reservations for group/event through app
For 'Lazy' segment :
Discount coupons or points as reward for regular ordering
These points can be redeemed in next order
Provide top suggestions based on user preferences
Recommendations
Use of ML and AI
Zomato can provide a highly personalized home feed to the user leveraging ML and AI as follows:
Top suggestions of the day based on most common order on the app.
Top suggestions(restaurants) for the user based on analysis of previous orders.
Customised Packages/Orders for users consisting of different food items based on analysis of previous orders.
The observation focuses on the app's interesting payment dynamics as follows:
People answering the questions can charge for their knowledge.
People who ask questions are charged ONLY if their questions are answered within 48 hours.
People asking questions are paid eeverytime some other user unlocks the paid answer to their question.
This motivates both sides to keep the quality of content high.
Microsoft Bing Birthday display
here are a lot of apps and services out there that use user birthdays for such personalization, but this is how Bing differentiates itself:
Bing also sends you an email saying they have a birthday homepage for you, prompting you to visit it.
This way people who don't use Bing as their default search engine are also made aware of this.
Provides some interesting animations like 'Make a wish' and candles blowing off in background to complete the effect.
It tells the user that they got their birthday from the account information that the user filled out (for those concerned with their privacy)
It also gives a link to change the information, implying that the user is in control here.
m-indicator: A must have for mumbaikars!
For mumbaiakars, trains are an integral part of life...and also one of the biggest headaches! This is where m-indicator comes to rescue! Mobond, the company that made this started off trying to solve the problem of finding routes and times for public transport, but the app has now developed into a must have because of the following reasons:
The app is tuned down for new travellers in Mumbai alongside the veterans.
For veterans who already know the routes, there is a quick and easy up down routes.
For new users, they can search from point A to B, it will give them a time and how to get there.
Moreover there is also a community section which lets you know automatically when trains are down (monsoons) which is a huge life saver.
SonyLIV and Gamification!
Despite their laggard UX on streaming, nice to see SonyLIV build an entire new vertical on their app related to gaming. They have done this as follows:
They’re building these games on the back of their hugely popular content IPs like KBC, Comedy Nights etc.
This gives an instant nudge for users to try it out.
SonyLIV worked on this engaging and monetizable feature into the main product.Therefore making it more than just an OTT platform.
Apparently users spent 60 million hours playing just the KBC game on the app in 2019.
How Linkedin helps improve user network and connections
Linkedin shows various categories in 'My Network' section. With different categories and roles there is more chances that you'll end up connecting with a lot of folks as opposed to only accepting all the requests. Categories include :
People you may know with similar roles
Industry leaders in [Your country] you may know
Online events/groups for you
People you may know in [Your Area]
People you may know from [Same Group you're part of]
