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Uber Case Study: New users install but don't complete their first ride

Uber Case Study: New users install but don't complete their first ride

1. Understanding the User Problem

The moment a new user downloads an app like Uber, they are buying into a promise. It's a promise of convenience, speed, and reliability. They've seen the ads, they've heard from friends—you tap a button, a car appears.

But what happens when that promise is broken on the very first try?

! The Core Problem: We see a high number of new users who successfully install the Uber app but fail to complete their first ride. This isn't just a small drop-off; it's a critical failure to activate a new user. This problem is especially severe in smaller, emerging cities (Tier 2 cities).

While Uber may be a habit in a major metro area, in these smaller cities, it's a new, unproven service. The first impression isn't just important—it's everything.

2. Scoping the Problem

To understand the challenge, we first need to define its boundaries. This isn't a simple bug. This is a complex marketplace problem.

  1. It's a 24/7 Structural Issue: This failure isn't tied to a specific time. It happens at all times, whether it's day or night, a festival or a normal weekday. This points to a structural, consistent problem.
  2. The Paradox of Growth: The problem actually gets worse as more new users join. This signals a classic mismatch between supply (drivers) and demand (riders). A flood of new riders is hitting a supply of drivers that isn't growing at the same pace.
  3. A Marketplace Failure: Both riders and drivers are part of the equation. The first ride isn't being completed because the two sides of the marketplace are failing to connect successfully.
  4. The Vehicle Focus: For this analysis, we are focusing specifically on 4-wheel vehicles, like UberX and UberXL. We are excluding bikes and autos. This is a key distinction, as the supply dynamics, cost, and user expectations for a car service are entirely different.

3. The User Flow: Where Are They Dropping Off?

Let's walk in the shoes of a new user in a smaller city. What does their journey look like?

  1. Step 1: Open the App
    The user installs and opens the app. They've successfully created an account. They have a destination in mind. They are at their peak moment of intent.
  2. Step 2: Request the Ride
    They enter their destination. The app starts searching. This is the moment of truth.
  3. Step 3: The Moment of Truth (The Two-Headed Monster)
    The user is now looking at a screen that gives them two critical pieces of information. In smaller cities, both of these can be a problem.
  4. Drop-off Point 1: The Long Wait Time (Opportunity 1)
    The app returns an Estimated Time of Arrival (ETA) of 15, 20, or even 25 minutes. Why? Because there is a low number of drivers online and near them. A new user, who was promised instant service, sees this and bails. They will close the app and find a local auto. This is a failure of execution.
  5. Drop-off Point 2: The Price Shock (Opportunity 2)
    The user might get a reasonable ETA, but then they see the price. In a smaller city, new users are highly price-sensitive. They immediately compare this quoted price to their known, cheaper local alternatives. If the price is too high, it confirms their suspicion that Uber is too expensive. They drop off. This is a failure of pricing.
  6. The Goal: Trip Completion (The North Star)
    Our ultimate goal, our North Star, is to get this user from Step 2 to a completed trip. This is the only way we activate them and turn them into a repeat customer. Right now, they are churning before they even get to experience the product.

4. The Root Causes: Why Is This Happening?

We've identified the where. Now we need to understand the why.

Root Cause 1: Low Supply and Long Wait Times

This is the primary driver of the long ETA drop-off. In smaller cities, the driver-partner network is not as dense.

  1. The Vicious Cycle: It's a classic chicken-and-egg problem. Drivers don't go online because they don't think there's enough demand. When a new user does create demand, there are no drivers to serve them. The user then churns, confirming the driver's belief that there's no demand.
  2. The Expectation Gap: New users are coming in with expectations set by big city service. They expect a car to be available within 5-10 minutes. When that expectation is broken, they don't just wait; they cancel the request and leave the app, feeling disappointed.

Root Cause 2: Competition with Public Transportation (Pricing)

This is the root cause of the price shock drop-off.

  1. Local Alternatives: In Tier 2 cities, the competition isn't another ride-sharing app. It's the established, cheap, and reliable local infrastructure. Buses and local autos are a known quantity.
  2. Price Sensitivity: New users are testing the waters. They do a quick mental calculation: "Uber says 150, but the bus is 10 and the local auto is 50." If Uber's value (convenience, comfort) doesn't justify that price gap on the very first try, the user is lost.

5. The Two-Sided Solution Strategy

! We have two core problems: long waits and high prices. Both are primarily caused by a single root issue: low driver supply.

This is a marketplace problem, so we need a marketplace solution. We can't just fix the rider (demand) side without fixing the driver (supply) side. This means we have two main levers to pull.

  1. Lever 1: Stimulate Demand (The Rider Solution). We can make the ride so cheap the user can't refuse it, even if the wait is long.
  2. Lever 2: Fix the Supply (The Driver Solution). We can get more drivers on the road so the wait is short and the price is naturally lower.

Let's explore both.

6. Solution Deep Dive #1: The Rider Incentive (User Discounts)

This is a direct, demand-side solution.

The Solution:
  1. First Ride Discounts for New Users.
How it Works:
  1. This is a direct, powerful attack on the pricing sensitivity problem. The moment a new user signs up in one of these target cities, they get a clear, attractive offer: "Your first ride is 50% off!" or "Get 100 off your first trip."
Why it Works:
  1. It directly addresses Drop-off Point 2 (The Price Shock). It makes that mental calculation an easy win for Uber. The user is more willing to forgive a slightly longer wait time (Drop-off Point 1) if they feel they are getting an amazing deal.
The Pros:
  1. This solution has a high and immediate user impact. It's also a low implementation effort—it can be run as a simple code or coupon flag. It will yield strong business value by boosting activation rates quickly.
The Cons:
  1. This is a short-term fix. It's like putting a band-aid on a broken bone. It doesn't fix the long wait times, which are the root cause. In fact, it might even make the problem worse by increasing demand on a supply that is already broken. It's expensive and doesn't build long-term, sustainable loyalty.

7. Solution Deep Dive #2: The Supply-Side Fix (Driver Incentives)

This is the strategic, long-term solution.

  1. The Solution: The Tier 2 Supply Stabilization Program.
  2. How it Works: This is not a single app feature but a targeted operational program. It works in three steps:
  3. 1. Allocate Funds: First, we must dedicate a budget specifically for targeted driver incentives.
  4. 2. Implement Surge Shield Incentives: This is the core mechanic. Instead of just raising prices for riders, we use our funds to offer guaranteed higher pay for drivers at specific times and in specific locations.
  5. 3. Targeted Execution: This isn't a blanket pay raise. It is a smart, surgical allocation. We will target zones and times that data shows have the lowest driver activity and highest new user drop-offs. This ensures drivers are paid more to be exactly where we need them, when we need them.
  6. Why it Works: This program directly addresses Drop-off Point 1 (The Long Wait Time). By paying more, more drivers will go online or join the platform. This increased supply of drivers will directly maximize driver density, ensuring new users can find a ride quickly. It fixes the root cause of the marketplace failure.

8. Why The Supply-Side Fix Creates a Virtuous Cycle

This Supply Stabilization program is so powerful because it kicks off a positive feedback loop.

  1. Immediate Impact: Drivers respond to incentives. They will be more willing to go online and drive to areas they previously avoided because they are guaranteed to make good money. This immediately reduces ETAs for new users.
  2. Secondary Impact: When other drivers see that Uber drivers are getting good, consistent incentives, they will join the platform. This increases the total supply pool.
  3. The Long-Term Win (The Big One): This is the most important part. As the number of drivers on the platform increases, the marketplace becomes more efficient.
  4. Drivers have less downtime.
  5. Riders have lower ETAs.
  6. And critically, with a high supply of drivers, the system doesn't need to surge prices as often. This leads to naturally reduced prices for riders.
  7. Connecting the Dots: The long-term effect of increasing driver supply is that prices will stabilize and come closer to the cost of public transportation. This solves both of our root causes. The long ETAs are gone, and the price shock is minimized. This entire cycle has a massive positive impact on our North Star Metric: the number of Trip Completions.

9. Prioritization: The Quick Win vs. The Real Fix

We have two clear solutions. Which one comes first?

! Solution 1: First Ride Discounts (The Quick Win)
Priority: #2 (Short-Term Tactic)
Justification: This is a high-impact, low-effort feature. It's fantastic for this quarter. But it is a tactic, not a strategy. It doesn't solve the underlying problem, and it's not sustainable.

* Solution 2: Supply Stabilization (The Strategic Fix)
Priority: #1 (Long-Term Strategy)
Justification: This is the high-effort, high-impact strategic investment. It's harder, it takes longer, but it's the only one that fixes the actual problem. It builds a healthy, stable, and efficient marketplace.

The Final Verdict: A smart strategy does both, but with clear priority.

Priority #1 is the Supply Stabilization Program. This is our primary, long-term strategic focus.

We will simultaneously run a small, targeted Priority #2 First Ride Discount program. This acts as a temporary measure to keep new users from churning while we fix the supply. The discount makes the long wait more bearable, but we must fix the wait.

10. How to Measure Success (How We Know We Won)

We will know this two-pronged approach is working by tracking three key metrics.

  1. Primary Metric: New User First Ride Completion Rate (Activation)
    This is the main metric we are trying to move. What percentage of new users who install the app successfully complete their first trip? This number must go up.
  2. Execution Metric: Decrease in Average ETA for New Users
    This metric confirms our supply solution is working as intended. Are the wait times for new users actually going down? If this number doesn't move, our incentive program isn't working.
  3. Value Metric: Increase in Request Conversion Rate (RCR)
    This metric validates that our price and value proposition is now being accepted. It measures the percentage of users who see a price and ETA, and then actually hit the request button. If this number goes up, it means fewer users are dropping off due in total, from both price shock and long waits.