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Leveraging Product Analytics On Ridesharing Apps

  • Writer: Priank Ravichandar
    Priank Ravichandar
  • Mar 24
  • 4 min read

Exploring how rideshare apps can leverage product analytics data.

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Ridesharing apps offer a convenient way for us to travel between places. Ridesharing is rapidly growing in popularity and companies like Uber, Lyft, and Bolt are dominating the global market. There could be over 2 Billion rideshare users by 2028, based on research estimates. All these users mean that these apps will have access to a massive amount of data.


You can learn so much about people’s behaviors and motivations through their interactions with your product. Product Analytics is the analysis of the data captured by your product. As a Product Manager, I use analytics insights to solve complex problems and validate solutions through data-driven decision-making.



App: How do Ridesharing Apps work?

While each ridesharing app has different features and ride options (solo rides, shared rides, luxury vehicles, etc.), they all offer the same basic functionality: a user requests a ride, a driver picks them up and drops them off at their destination.


To explore how we can use product analytics, imagine a Simplified Ridesharing App that allows users to request rides from point A to point B. Let’s include the following constraints:

  • Vehicle Type: The user rides in whatever vehicle is available.

  • Ride Options: The user does not share the ride with other users.

  • Price: The user is offered a single price option for the ride.


How do users interact with the rideshare app?

To visualize how users interact with our ridesharing app. Ridesharing apps cater to 2 distinct users:

  • Riders: The people who request rides from the app.

  • Drivers: The people who pick up and drop off the riders.


Typically rideshares apps have separate apps to cater to Riders and Drivers. But since the rideshare app matches Riders with Drivers, we need to look at the user flow from both perspectives. For additional context, we can look at what’s happening within the application during each step of the user flow and the user behaviors associated with in-app decisions.


User Flow for a Simplified Rideshare App

Below we have a user flow diagram for the rideshare process. We look at four elements in this user flow:

  • Rider App Experience: What does the Rider see and do in the app?

  • Driver App Experience: What does the Rider see and do in the app?

  • Application: What is going on within the app during this process?

  • User Behavior: What specific actions does the user take? What factors might motivate their actions?

Let’s focus on Riders specifically to understand how we can use product analytics to get some actionable insights on the rider user experience.


Rider User Experience

The Rider’s experience can be summarized in five steps

  1. The Rider opens the app and enters the Ride Pick Up and Drop Off Location

  2. The Rider reviews the Price and Duration of the Ride and books the ride.

  3. The Ride is confirmed and the Driver's details shared with the Rider

  4. The Rider is picked up

  5. The Rider is dropped off (Optional: The Rider leaves a rating)


UI Mockups for a Simplified Rideshare App

Here are screens that the Rider might see.

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Data: What data do we have?

Our Rideshare app captures user data and event data. We have two types of users (Riders and Drivers). Therefore, we would have two sets of user and event data - one for Riders and one for users. Let’s focus on just the Rider’s data.


User Data

Let’s imagine what a potential user’s user data might look like. Each piece of user data allows us to extract some insights about the users.


User Data Visualized

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Event Data

Event Data is information on each unique user’s actions within the app.

  • Each ride has a unique Ride ID that identifies all the associated ride information.

  • Each event has a unique Event ID that identifies all the associated event information.

  • Each event allows us to extract insights about the user experience.


Example Event Data

For example, let’s say our user (John) wants to go from the Louvre Museum to the Eiffel Tower. When they use the app, the following event data might be generated during their ride

  • Event: Rider Opens App

    • User ID: uid-0001

    • Event ID: oa-123-001

    • Timestamp: 01-02-2024 9:00:00 AM

    • App Version: 10.0

    • Platform: iOS

  • Event: Ride Estimate

    • User ID: uid-0001

    • Ride ID: rd-123

    • Event ID: rd-123-001

    • Timestamp: 01-02-2024 9:00:00 AM

    • Ride Price: €20.00

    • Ride Pick Up Location: Louvre Museum, Paris, France

    • Ride Drop Off Location: Eiffel Tower, Paris, France

    • Estimated Pick Up Time: 01-02-2024 9:05:00 AM

    • Estimated Drop Off Time: 01-02-2024 9:20:00 AM

    • Estimated Ride Duration: 00:15:00 (HH:MM: SS)

  • Event: Ride Requested

    • User ID: uid-0001

    • Ride ID: rd-123

    • Event ID: rd-123-002

    • Timestamp: 01-02-2024 9:01:00 AM

    • Ride Price: €20.00

    • Ride Pick Up Location: Louvre Museum, Paris, France

    • Ride Drop Off Location: Eiffel Tower, Paris, France

    • Estimated Pick Up Time: 01-02-2024 9:05:00 AM

    • Estimated Drop Off Time: 01-02-2024 9:20:00 AM

    • Estimated Trip Duration: 00:15:00 (HH:MM: SS)

  • Event: Ride Confirmed

    • User ID: uid-0001

    • Ride ID: rd-123

    • Event ID: rd-123-003

    • Timestamp: 01-02-2024 9:01:00 AM

    • Driver ID: drid-001

    • Driver Name: Charles

    • Driver Car: Renault Clio

    • Driver License Plate: AA-123-AA

    • Driver Rating: 5.0

    • Estimated Pick Up Time: 01-02-2024 9:05:00 AM

    • Estimated Drop Off Time: 01-02-2024 9:20:00 AM

    • Estimated Wait Time: 00:04:00 (HH:MM: SS)

  • Event: Rider Picked Up

    • User ID: uid-0001

    • Ride ID: rd-123

    • Event ID: rd-123-004

    • Timestamp: 01-02-2024 9:05:00 AM

    • Ride Pick Up Location: Louvre Museum, Paris, France

    • Actual Pick Up Time: 01-02-2024 9:05:00 AM

    • Pick Up Wait Time: 00:04:00 (HH:MM: SS)

  • Event: Ride Completed

    • User ID: uid-0001

    • Ride ID: rd-123

    • Event ID: rd-123-005

    • Timestamp: 01-02-2024 9:20:00 AM

    • Ride Price: €20.00

    • Ride Drop Off Location: Eiffel Tower, Paris, France

    • Actual Drop Off Time: 01-02-2024 9:20:00 AM

    • Actual Ride Duration: 00:15:00 (HH:MM: SS)

    • Payment ID: pid-001


Event Data Visualized

Product Analytics: What can we do with this data?

Now that we know what data we have available, we can analyze this data based on our goals. Imagine we’re on a product team that’s responsible for revenue growth on the app.

  • Our goal might be to "increase Monthly Recurring Revenue (MRR) by 25% over the next 3 months."

  • To accomplish this goal we need to

    • Identify the relevant data/product metrics connected to our goal.

    • Evaluate the current state of revenue (problems, opportunities, etc.).

    • Determine potential metrics we can influence (through features, initiatives, etc.).

    • Develop ideas to move these product metrics as desired.

    • Test these ideas by measuring the impact on target metrics.

    • Refine ideas and iterate until the desired results are achieved.


Breakdown How Metrics Connect To Revenue Goals

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