Total number of rides has dropped for Uber

I’d first establish whether the decline comes from fewer ride requests or a lower ability to fulfil those requests. Then I’d narrow it by city and ride type before deciding how to respond.

1. Clarify the product and metric

For this case, I’d focus on Uber’s cab business, excluding food delivery and other services.

The agreed completion event is when the driver marks the trip complete after reaching the destination and the rider sees the feedback screen. Payment settlement is separate from this definition.

Completed rides = Count of unique trips that reach the agreed completion state during the measurement period

I’d verify the underlying trip records as well as the feedback-screen event. A missing screen event should not lead us to mistake a tracking problem for fewer actual journeys.

The case describes a cumulative 15% decline over six months, with roughly 2–3% monthly decreases. I’d confirm the exact baseline and comparison periods rather than add monthly percentages together.

2. Validate the decline and identify affected segments

I’d check whether the metric definition, reporting dashboard, or event pipeline changed. I’d also compare equivalent periods to account for holidays, seasonality, and the number of days in each month.

The case tells us measurement is unchanged, the decline is unusually large, and it affects all platforms but only certain Indian cities. There is no clear demographic pattern.

I’d segment further by:

• City, neighbourhood, and pickup location.

• Time of day and day of week.

• Scheduled versus on-demand rides.

• Trip distance, including short versus longer journeys.

• Vehicle category and passenger capacity.

• New versus returning riders.

• Airport, commute, and other trip purposes where identifiable.

I’d measure each segment’s contribution to the total loss. A large percentage decline in a small segment may explain very little of the overall 15%.

3. Separate rider demand from fulfilment problems

I’d examine both sides of the marketplace.

The rider journey is:

Open the app → Enter destination → Review fare and vehicle options → Request or schedule → Get matched → Get picked up → Complete the trip

On the driver side, I’d check availability, acceptance, arrival, cancellations, and completion.

I’d also review app releases, experiments, interface changes, marketing, fare changes, and driver compensation. A change in driver incentives could reduce availability even if the rider experience looks unchanged.

The case does not provide these funnel results, so I would keep them as checks rather than assume fulfilment is healthy.

4. Investigate the competitive and external context

Some competing cab platforms are also declining in the same cities. That makes an Uber-only technical issue less likely and raises two possibilities: local demand has weakened, or riders are moving to another alternative.

I’d investigate:

• Competitor discounts, lower fares, or new service offerings.

• New entrants and locally supported mobility services.

• Alternatives such as public transport or private vehicles.

• Regulations, negative publicity, strikes, weather, or other disruptions.

• Changes in office attendance, travel frequency, income, or spending habits.

The case offers no strong evidence of regulatory or operational disruption. It instead points toward a new entrant.

I’d use BluSmart as the competitor named in this interview scenario, with its availability and service proposition treated as case assumptions. The analysis should establish why riders switch, rather than rely on recognising a company name.

5. Narrow the problem to scheduled rides

The next clue is that most of the decline comes from planned or scheduled rides.

For someone booking an airport trip, I’d expect three important needs:

• Reliable pickup: Confidence that a vehicle will arrive at the required time.

• Predictable pricing: A clear understanding of the expected fare.

• Suitable vehicles: Cleanliness, passenger space, and enough luggage capacity.

These are hypotheses I’d validate through rider interviews, cancellation feedback, and booking data. Scheduled rides are not necessarily long-distance trips, so I’d examine distance separately.

In the scenario, the competing service offers an EV-based proposition centred on predictable fares and dependable scheduled pickups. The affected cities overlap with its operating footprint.

That supports a competitive-switching hypothesis, but geographic overlap alone does not establish causation. I’d compare:

• Scheduled-ride trends before and after the entrant’s launch.

• Similar cities or routes without that alternative.

• Uber’s scheduled pickup reliability and price competitiveness.

• Feedback from riders who reduced their Uber usage.

I’d also avoid assuming that an EV automatically has more luggage space or lower operating costs. Both depend on the vehicle and operating model.

My leading diagnosis would be a gap in scheduled-ride reliability and value that an alternative service is addressing.

6. Prioritise the response around the rider’s need

I’d start by improving scheduled-trip fulfilment, then test whether EV supply helps deliver that experience economically.

In the short term, I’d pilot stronger scheduled-trip operations on the routes with the largest losses. Drivers would explicitly accept the commitment, receive appropriate compensation, and have a clear process for legitimate cancellations. Backup allocation would help recover disrupted trips.

I wouldn’t rely on simply banning driver cancellations. That does not create vehicle availability and could encourage drivers to avoid scheduled bookings altogether.

I’d use targeted offers after improving reliability, so returning riders experience a service worth using again.

7. Build a sustainable longer-term model

If the pilot shows that EVs improve service and economics, I’d explore a dedicated scheduled EV category and partnerships with vehicle, financing, fleet, and charging providers.

Charging access near airports or other high-demand locations could help, but I’d evaluate permissions, utilisation, downtime, and costs before committing to infrastructure.

Any vehicle-transition support would depend on verified programme eligibility and driver economics. I wouldn’t assume subsidies are available or that switching vehicles benefits every driver.

A free upgrade at existing prices could be a limited experiment. Scaling it would require evidence that retention and operating benefits justify the cost.

8. Measure recovery and its cost

My primary outcome would be completed rides in the affected cities, with scheduled rides reported separately.

Supporting metrics would include:

• Scheduled request-to-completion rate.

• Pickup punctuality within a defined window.

• Driver and rider cancellation rates.

• Repeat scheduled bookings.

• Fare-check-to-booking conversion.

• Rider satisfaction and inactivity among previously regular users.

I’d track scheduled-ride share and EV-ride share as diagnostic measures. Either share can increase simply because other rides decline, so I’d always examine absolute volumes too.

Market share would be useful where credible market-level data is available. Guardrails would include contribution margin, incentive spending, driver earnings, safety, and any deterioration in on-demand pickup times.

My recommendation is to fix the scheduled-ride experience first and test EV expansion as one way to support it. The evidence points to riders choosing a more dependable alternative, so the response needs to earn back their confidence in the trip.