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- Restaurants
- Customer
- Delivery partner
- # of app downloads
- # of signup
- # of restaurant register
- # of delivery partner register
- #of users who complete at least one order
- # of a restaurant that received one order and complete it
- # of driver who delivered one order
- Average delivery time linked with this to area
- # of orders per user (weekly, monthly)
- Average order size per user
- time spent to finalize order per user
- # of time order per user / # of time user open app
- CTR on a different channel like (PN,SMS,Email,Whatsapp)
- # of compliant order / # of total order
- NPS score
- App store rating
Context
First off, let's establish a common understanding of what Uber Eats is and how users use it. Uber Eats is a separate app from Uber that let's users order food for delivery or pick up from restaurants in their local area who have opted into the platform. Delivery is fullfilled by third party uber eats drivers in the area. Uber Eats is used by a wide variety of people from young professionals to families.
In order to understand the bigger picture, it's important to look at how the individual product fits into the company and it's mission. Uber's misison statement is to make transportation as reliable as running water. Uber Eats furthers this mission statement by providing delivery services to assist hungry users and restaurants.
Users
For Uber Eats to be successful, we need to be adding value to three different types of users:
- Consumers - People ordering food who want convenience
- Restaurants - Local businesses selling the food who want the additional customers
- Drivers - Users responsible for delivering the food who want their fee + tip
Metrics
There are an innumerable number of Uber Eats based metrics we could monitor, so in order to provide structure and stay organized I like to group them by stage in the user lifecycle:
Awareness
- # of ad impressions
- # of appstore page visits
- # of new users who added their CC details
- # of users who ordered for the first time
- WAU:
- # of consumers who have made at least 1 order
- # of drivers who have delivered at least 1 order
- # of restaurants who have fullfilled at least 5 orders
- Consumers
- Average number of orders per consumer
- # of consumers who order 3 or more times in a week
- Restaurants
- Average number of orders fullfilled per restaurant
- # of restaurants who fullfill more than 15 orders in a week
- Average % of open hours restaurants are also open on Uber Eats
- Drivers
- Average number of orders delivered per driver
- # of drivers who deliver at least 10 orders in a week
- Average time spent driving with the app open
- Consumers
- Average delivery star rating
- % of consumers who ordered 3 or more times last week that also ordered 3 or more times this week
- Average difference between estimated and actual delivery time
- # of orders that were delivered beyond their maximum delivery estimate
- Average number of customer support requests per user
- Restauraunts
- % of restaurants who fullfilled 15 or more orders last week who also fullfilled 15 or more orders this week
- Average number of order complaints / disputes
- Drivers
- Average Tip %
- Average driver star rating
- Average distance to pick up / drop off
- $ per hour
- Number of orders
- Average order size ($)
- LTV/CAC
- # of Uber gifts sent to new users
- # of sign ups from referral codes (drivers & consumers)
- Our chosen metric is an absolute number and not relative. For a product in the growth stage this isn't necessarily a bad thing, but we should be aware of the fact that the average number of successful orders per user could potentially even be going down while our total number of successfull orders goes up if we are onboarding consumers, drivers, and restaurants aggressively enough.
- This is definitely a top level metric and doesn't offer much in terms of details regarding what needs to be improved. or focused on. For example, we could have a large number of restaurants and consumers on the platform but a bottleneck in terms of drivers and we wouldn't know.
- As we mentioned earlier this is a really competitive space and while our chosen metric helps track our progress it does not do so relative to our peers. For example, we could be growing 20% YoY and feel great but if DoorDash grew 4x that then we would actually be in trouble without even knowing it.
Summary
As Uber Eats is a growth stage product we are going to focus on monitoring engagement amongst all three of our user groups, consumers, drivers, and restaurants, by tracking the total weekly number of successfully completed orders.
- Clarifying questions
- Any specific feature or the whole app? Ans: whole app
- What's the current goal of uber eats? is it gain market share or is it to maximize revenue Ans: Assume what's necessary
- Defining the goal of the product. Since food delivery platforms are not mature yet and the online delivery space is the increase I would like to focus on the goal to be getting more customers to order on the platform. The secondary goal would be to maximize profit or minimize losses
- Metrics: User Journey View
- Acquisition:
- # of downloads per day
- # of signups per day
- # of visits on the website
- # of new customers per day
- Activation
- % of users who made first order after signing up within a week
- Engagement and Monetization
- # orders per day
- Total revenue per day
- Average revenue per user, day
- Profit per order
- Retention
- % of users who are premium users
- % of users who order 2nd time withing 30 days of 1st order
- Referral
- # referrals sent per customer
- Conversion rate of referral
- Acquisition:
- Other metrics (Guardrail)
- Average rating of the app
- Average delivery, merchant rating on the platform (indirectly affects Uber Eats)
- No of complaints raised per day
- Cancellation rate of orders per day
- Prioritizing metrics: Since the goal is to increase the usage of the platform we can categorize the above metrics into different buckets based on priority. Following will be our primary metrics
- # of orders per day: North Star Metric
- Total revenue per day
- Average revenue per user
- Other metrics will be supporting metrics and guardrail metrics will give us overall health of the platform in terms of customer satisfaction
Uber metrics PM interview Question: What is the most important metric for UberEats and why?
Clarifying question
The whole product or specific feature? Assume the whole product.
What problems UberEats is solving
Order food
Solution
Connect Customers and Restaurant
Provide delivery service
Goal
What is the short-term and long-term goals of UberEats
Short-term: Increase revenue (due to the covid-19, everyone is using Food Delivery instead of dining out)
Long-term: Market share (= User base)
Should I focus on one or both of them? => Assume we will focus on Short-term
As I know, the revenue of UberEats comes from Commission fee & Advertisement fee.
Customer Journey
Acquisition
Customer visits website
Customer downloads the app
Sign-up for an account
Sign-in to Uber Eats using existing account of Uber Ride
Activation
Find a restaurant
Find a dish
Order food
Receive food
Give feedback
Retention
Feel very good at UberEats experience and come back to order the 2nd, 3rd, ...
Referral
Recommend UberEats to their friends
Metrics
Acquisition
# of website visits
# of app downloads
App Openings (Eyeballs) – # of customers who launch the Uber app looking for food.
# and % of new accounts
# and % of users coming from other apps
Activation
Find a restaurant
Zeroes – The number of customers who open the app and see no Uber restaurants in the area.
# of restaurants (per km, district, city, state, country)
# of kinds of restaurants (Japan, Vietnam, …)
operation hour
time for finding a suitable restaurant
# of finding before ordering
Find a dish
# of dishes per restaurant
Order food
Avg pricing per dish
Order volume
Receive food
Avg distance (Shipper -> Restaurant -> Users)
Avg delivery time (Shipper -> Restaurant -> Users)
Avg delivery fee
Give feedback
Avg satisfaction
Retention
# of DAU, WAU, MAU
Avg retention time
Referral
Viral coefficient
Evaluation
Metrics | Impact to our Goal | Recommendation |
Acquisition | ||
# of website visits | Medium | Secondary metrics to understand where users come from |
# of App Openings (Eyeballs) | Medium | Secondary metrics to understand where users come from |
# and % of app downloads | Medium | Secondary metrics to understand where users come from |
# and % of users coming from other apps | Medium | Secondary metrics to understand where users come from |
# and % of new users | High | Primary metrics to capture the big picture of new users |
Activation | ||
Zeroes | Medium | |
# of restaurants (per km, district, city, state, country) | Medium | |
# of kinds of restaurants (Japan, Vietnam, …) | Low | |
operation hour | Low | |
time for finding a suitable restaurant | Low | |
# of finding before ordering | High | Revenue from MKT & Advertising fee |
# of dishes per restaurant | Medium | |
Avg pricing per dish | High | Primary metrics |
Order volume | Extremely High | North star metrics |
Avg distance (Shipper -> Restaurant -> Users) | Medium | |
Avg delivery time (Shipper -> Restaurant -> Users) | Low | |
Avg delivery fee | High | Primary metrics |
Avg satisfaction | Low | |
Retention | ||
# of DAU, WAU, MAU | Extremely High | North star metrics |
Avg retention time | High | Primary metrics |
Referral | ||
Viral coefficient | Medium |
Recommendation
North star metrics
# of DAU, WAU, MAU
Avg order volume
Primary Metrics
# and % of new users
Avg pricing per dish
Avg delivery fee
Avg retention time
# of finding before ordering
1. UberEats is division of Uber and it provides restaurant to door delivery service. As far as I know, customers can create an account with Uber Eats, find restaurants that offer Uber Eats based delivery, find items to order, view estimated time of arrival and price. If all looks good, then the food arrives within estimated time. UberEats charges the customer a delivery fee.
2. UberEats is available in cities in USA, targeted at people who want the convenience of having good food at home without driving, waiting for the table but they do not want to cook themselves.
Let us understand what success looks like UberEats as a business and see where it should focus on given its state. My assumption is that UberEats has lots of room to grow - the service is not that old, it is competing with a number of other players, the differentiation between the competitors is not that clear. Customers have no reason to be loyal to one delivery service v/s other except due to loyalty related discounts. In the ideal state, UberEats succeeds by having enough customers, ordering enough times in most urban areas so that economies of scale can be achieved and margin can be earned. Restaurant owners are also unlikely to partner with many different delivery services so it is important for UberEats to provide them a significant share of their business. So number of orders brought through UberEats is a very important part of these success.
Therefore, the progress of the business should be viewed in these terms:
1. Number of users using the service at least once a month - service is growing or not
2. Churn rate month over month - service is retaining viable percentage of users
3. Average Order Value per month / per user - service is gaining significant share of business
4. Average # of Orders / UU / month - users are forming a habit and are ordering more frequently with UberEats
Of all the metrics, we should focus on the ones that are most relevant to the stage of the business. UberEats is still not validated as a business. There are many unknowns in determing whether economies of scale can be achieved at city level to sustain the business viably. So at this stage the most important metric would be to keep customers as the most fundamental part of the business. If they are unable to retain customers at a viable rate, month over month, other KPIs would not matter. They reflect deeping engagement and revenue. However, the focus should be on being of value to as many people as possible regardless of the amount of money they are spending on the service.
Let me start with what are different types of users. As it is a marketplace model so we have:
1. Buyers
2. Sellers
3. Delivery Person
Let me break down the goals into Acquisition, Activation, Retention and Customer Satisfaction, Referral and Revenue
And journey as: Entice --> Login --> Discovery --> Add To Cart --> Buy --> Deliver --> Extend
Metrics:
a. Acquisition: # Buyers/#Sellers, Acquisition Channel performance - LPs CTRs, Search Rank, SEO and SMM
b. Activation: Buyer side - #User Signing Up, #users doing one purchase, #users adding to cart, Seller - #listings, #accepting orders, CTAs of Button, #scroll per buyer on the discovery page
c. Retention: DAU, MAU, WAU, and Retention Cohorts, Promo Code Used, #Promo Code/#Purchases, #Promo Code To Purchase Cohorts
d. Referral: #referral/#buyers, #referral/#sellers
d. Revenue: ARPU, ARPB, Average Profit Per Seller, Average Transactions Per Seller, GMV
e. Customer Satisfaction: Delivery Time and Communication, Food Quality, and Food Condition - Hot or Cold, Reviews, Comments and Stars
Most important metric for me will be GMV
Uber eats is a food ordering app which shows menu from restaurants and delivers once customer places the order.
There are restaurants, customers, platform and delivery partners.
If I have to consider most important metric right now - I will gauge the importance of metric as per its alignment with company goals.
I would like to understand the company goals right now.
Assume - Company is right now trying to retain its users and current market share as there is huge competition with other apps like Zomato and Swiggy.
OK
Most important metric then becomes -
NSM -
- D14, D28 retention of customers
- Stickiness of users (DAU/MAU)
- Number of newly activated users daily
Since company wants to retain its user base for the time being, i would like to keep a track of user stickiness and retention of users on Day 14 and Day 28. Also if I am gaining new users daily to compensate for churned customers.
These metrics are to ensure that the company is reaching its goals.
Main business metric -
- Trend of # of orders being placed on app in a day
- Profit per order
- DAU
- CLTV - Customer lifetime value
Now there are metrics that we should track to maintain business on the application. How number of orders are trending on app will show me if my business is intact and my app is still providing value to customers.
Profit per order - will be to measure cost against revenue to see how much we are earning per order or how much cash we are burning per order.
Daily active users will show me my active user base.
CLTV will show me how much revenue I am getting per user and if my CAC is justified.
Ongoing operations -
- trend of daily customer tickets - To see operational efficiency of the business and see what is irking the customers the most.
- Delivery partners metrics - Number of DPs logging in per day, % of orders services in promised time, Average duration of idle time per DP per day, Average earnings per DP per day - To see DP availability and efficiency
Customer centric -
- Average number of orders per customer per week - to understand how frequently customers are returning on app
- Power users trend weekly - Assuming power users as Number of customers ordering > 5 times in a week on average. This metric is to see what percent of total users is power users and how we can retain them.
Clarifying questions:
important metrics for which stage?
do you mean it for specific feature or the whole app?
As Uber Eats has 3 types of users (consumers, restaurants and drivers), for which target audience do you mean?
Assumption:
We consider important metrics for all stages of the Uber Eats application
We consider consumers and restaurants as our target audience
There is a time duration for all of these metric which can be calculated daily, weekly or monthly
1-Acquisition
For consumers:
Number of sign up
Number of App downloads
For restaurants:
Number of new registered restaurants
2-Activation
For consumers:
New customers ordered first time
For restaurants:
New restaurants with first order
3-Engagement
For consumers:
Daily active user
Number of comments
Number of ratings
Number of orders
Number of App opening
Number of email/whatsapp CTR
Time spent to finalize orders
For restaurants:
Number of menu updates by restaurants
4-Revenue
For consumers:
Total numbers of orders
Total sell of Uber
Total profit of Uber
Total payment of each user
For restaurants:
Total amount received by restaurants
5-Satisfaction
For consumers:
Rate of App
Numbers of complaints
customer wait time
number of rejection by restaurants
For restaurants:
Numbers of complaints
driver wait time
6-Retention
For consumers:
Avg retention time
Number of new customers ordered more than once
For restaurants:
Number of didn't accept orders any more
7-Refferal
For consumers:
Number of using referral codes
Number of sending referral codes
conversion rate of referral customers
Uber eats is a 2 sided food delivery platform which connects restaurants with users. Users can search for a restaurant, browse their menu and order food with a tap. Once the order is placed, a delivery person (working for uber or directly for the restaurant ) delivers the food at your doorstep.
Recently, Uber eats has also enabled delivery from grocery stores and pharmacies. Let's keep the scope of the exercise to their "Eats" division.
Mission for Uber eats: The easy way to get the food you love delivered.
Before we dive into the most important metrics for Uber eats, lets analyze a few things:
Let's start with the Customers - Who are the uber Eats customers and what are their needs and wants.
Restaurants:
- Chains (McDonalds)
- High End Resultants ( 5 star)
- Bodegas
- Small businesses (Healthy foods)
- Specialty Resultants (Asian, Indian, etc.)
Needs of restaurants:
- See to more people: Increase revenue.
- Reduce traffic in restaurants and save operating costs.
- Conveniently, track their business metrics and reports
- Address any customer complaints quickly to improve customer satisfaction.
- Delivery delicious food to the users quickly.
- Get a good rating so that more customers can order food
Users:
- Lunch/Breakfast/Dinner.
- Group order.
- Midnight snacks
We can also think about users in terms of the usage:
- Power Users (at least 1 meal a day)
- Moderate Users ( at least 1 meal every 3 days)
- Casuals (at least 1 meal / week)
Needs of Users:
- Search food from diverse set of resultants to order food based on my mood and specific needs.
- Pick the items and order food conveniently and quickly -- without walking to he restaurant or calling them.
- Get my food quickly so that it is still warm.
- Easily reach out to restaurant if there is any issues with the delivery or food quality.
Delivery Person:
- Full time delivery person
- Part time delivery person
- Mode of delivery (car/bike/walk)
Needs of Delivery Person
- Deliver as many orders as possible to make income.
- Deliver to households in reasonable distance.
- Should be able to earn more if the weather is not great
- Should be able to earn tip from happy customers.
- Should be able to deliver food safely and ask for help if the delivery person is at risk.
Now let's analyze the Internal Factors: Company and Product:
- Cure Business: The core business for Uber is the ride sharing business where the users are connected to the riders at the tap of the button. Uber eats is a new division started approx. 5 years ago that uses the same Uber's core platform capabilities to connect users with the restaurants, and hire delivery folks to deliver the food.
- Product Mix - Uber's strategy is to expand in synergetic businesses where they can replicate the success of the ride sharing product. The current product Mix is: Uber, Uber Eats, Pharmacy, and Grocery Delivery.
- They have separate digital Apps for Uber and Uber Eats, however, user can navigate easily from one app to the other.
- Customers: Generally, the customers who use Uber ride sharing users also use Uber Eats.
- Revenue: The company went public a few years ago but has been struggling with profitability. The company has taken steps in increase the profitability by charging more for the rides and reducing the promotions . We have seen a similar effects on the uber Eats app as there are delivery fee associated with each order.
- Another step Uber has taken is it introduced the Uber One subscription for $9/Month. The customer value for the subscription plan is that there will be less fee on each Uber eats delivery.
- Hypothesis there is - users want to save money on the delivery and rides, therefore they will subscribe to uber one platform. Once they subscribe to Uber One, they will be less likely to order food from the competitors
- We can also look at the 3 years trend for Uber Eats revenue and profit margin, but if I were to guess it is not profitable. So the primary goal for uber eats to be a profitable business, especially in this macro environment where shareholders are less bullish on growth companies and want to see the profitability.
- Brand: Uber ride sharing brand is under constant distress because there are many issues with the drivers and strikes, however, Uber Eats doesn't suffer from the same brand issues in my opinion.
Let's take a look at the External Factors: Market and Competition (Lets focus on the US market for now)
- Size of the food delivery Market: Do some rough estimations to figure out the size of the market. Last I checked it was around $500 Billion in the US. The forecast is the market will be 1.7 trillion by 2027.
- Market Share: I believe uber eats has the lion share in 2022.
- Competitors: Doordash, Grubhub, postmates
- Recent Mergers and Acquisitions: Doormates acquired postmates. I believe given the opportunity bigger players will move in the industry and will try to acquire the small players. Especially Amazon or Lyft.
- Uniqueness of Uber Eats:
- First mover advantage
- Robust delivery network and supply chain management tech (synergies from Uber ride sharing)
- Learnings from Uber ride sharing to expand in international market.
Lets Summarize the keys points from the discussion above:
- Focus on Market Share first and then profitability.
- Provide best customer experience for Restaurant, Users, and Delivery people to retain these players.
- Apply learning and tech from Uber ride sharing app to doble down on the uniqueness.
- Increase Uber One sign ups so that users are less like to order from other services.
Top metrics to measure:
- Total number of food deliveries (daily/Weekly/Monthly).
- Active users who at least complete one order (daily/weekly/monthly)
- Daily Active restaurants on the platform.
- Average number of orders fulfilled by restaurant (Daily/ Weekly/ Monthly)
- Daily Active Delivery people on the platform.
- Average number of deliveries per delivery person(Daily/ Weekly/Monthly)
- Average number of orders per user per week.
- Average size of order ($$) per user per week.
- Weekly Churn:
- Users who orders last week, didn't come back this week.
- Total number of unsuccessful deliveries for example, cancelled (daily/weekly/monthly).
- Total number of orders not delivered on time (Daily/ weekly/monthly)
- Total number of issue tickets such as delivery issues/ food quality issues (daily/weekly/monthly)
- Average number of issues per restaurant
- Average number of issues per Delivery Person
- Net new Uber One subscriber (weekly/monthly)
- Uber One subscriber Churn (weekly/Monthly)
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