How would reduce cancellations for Uber?

Clarification for the root cause analysis

1. Does these cancellation happens on Uber cabs or uber eats

Assuming cabs

2. Is it happening on driver side or customer side?

Assuming Driver side

3. Is it happening gradually week on week basis or month on month basis or suddenly?

Assuming Gradually

4. Is it happening with new drivers or the old regular drivers as well

 

Assuming No

6. Is there any impact/fall on the specifc metric like customer engagement or revenue or impact on driver partners

Uber is helps the people to travel from point A to be B conveieneintly by booking the cabs at thier door steps.

Internal Factors from the app side

1. High loading time in the application

2. App get stuck in between some to some glitiche and then the user needs to refresh it again which takes time

3. A low frequency bug where the app is rejecting the requests abnormally on random basis/ocassionally

4. User account has been locked ocassionally abrumptly and they have to reset the password to unlock the same during that time the uber request get time out

Internal factors

1. Drivers does not like going to long trips during odd time like night or after lunch or early morning

2. Reducing in the driver perks and cuts

3. increase in uber commission

4. Poor maintiance of the old cabs causing trouble to perform the duties and driver rejects the request

External factors

1. New competitor arrived providing better perks to the driver partners

2. Hike in fuel prices

3. Too much congested traffic and poor road infra on the routes where rejections are happening

4. Private travel agencies hiring the cabs at better cuts

5. Customers calling the drivers offline for the quick availability of cabs at addtional price

6. Any specific change in the rules or regulations by the transport depratment

7. Sudden rise in the sale of cars for the inidiivduals, familes due to festive season

Solutions

If at all it is a teachnical problem from the system side then I would like to connect with devops team investigate the reason for it. I would like to set up a war room for the same to understand the analysis why it has happened and what could the solution with timeline and comlexity.

If it happens due to internnal factors then I would like to provide bundle offers and coupon with uber eats, maintinaince subscriton at a discounted price. Partship with the banks and NBFC to provide loans to the drivers at lower rate of interrest to buy new cars

If it happens due to external factors then undertand the policy or rule chnage and impact any that change has been brought find the alternatives. We can also provide attractive cuts to motivate the drivers to partner with ubers and premium charge for the odd time driving.

Questions:

- Do we have any specific research attesting why users cancel?

- What is the goal of this initiative? --> I am going to assume it is to increase revenues for Uber drivers and hence increase their loyalty

 

To structure my answer, I would like to:

1. Talk about Cancellation and see if we are at the same page

2. Go through the effects of cancellation on both the demand and supply side. I especially want to identify the risks and trade offs involved with both and quantify them

3. Come up with some hypothesis and see how we can test them 

4. Come up with some solutions based on the hypothesis

 

 

1. Cancellation

Cancelling a ride in this case would be intended as both when the customer is cancelling the ride from the moment in which a rider has been identified and is coming to meet the customer, as well as cancelling the ride before that, when the system is looking for a matching rider. 

Both parties can cancel the ride. 

 

On the customer side:

- Good to have such flexibility when cancelling--> removes the frustration of being eventually charged for a ride that is not needed and allows a more efficient system

- Very negative and frustrating when riders cancel after waiting

 

On the rider side:

- Frustrating to go in a direction and then not have the ride

- Might need to cancel in some occasions and needs the flexibility to do so

 

Why a buyer might want to cancel?

Hyp 1: Mismatch between the time declared to wait and actual wait

Test: Look in the data - time declared vs actual time waited (and waiting time at cancel time) or cancelling group vs noncancelling  -- Limit: not enough, might be for other reasons --> in depth interviews are needed first 

Hyp 2: Prefer switching to other services (cheaper Taxi ride, cheaper competitor - Lyft, Bolt etc ride, Bike sharing available etc)

Test: monitor competitor prices in the area - Limit: not enough info on the user preferences --> in depth interviews are needed first 

Hyp 3: Last minute chages

 

 

 

Why a driver might want to cancel?

Hyp 1: Might have last minute changes 

Hyp 2: Want to finish at a certain hour / want to avoid an area 

Test: First in depth interviews

 

Action items:

as a first step, we would want to collect user research (qualitative and quantitative) for both groups and the reasons why the cancel. 

If the major reasons are the one as listed above:

Hyp 1: Mismatch between the time declared to wait and actual wait

Solutions:

- Improve time montioring system

- If the customer is price sensitive (using "Wait and Save") --> offer a coupon for next time regular ride for the "Wait and Save" price 

 

Hyp 2: Prefer switching to other services (cheaper Taxi ride, cheaper competitor - Lyft, Bolt etc ride, Bike sharing available etc)

Solutions:

- Leverage price sensitivity to make customers more loyal: New pricing system for example, where customers can buy miles or rides at a fixed price 

  

 

 

Approach:

1. Understand the prompt and establish a business goal

2. Factors that drive cancellations

3. Recommendations to lower cancellations

4. Prioritize

Understand the prompt:

Candidate: Is related to on-demand ride service or pre-scheduled ride service?

Interviewer: Both

Candidate: Are we focusing on only shared rider or both shared and solo riders

Interviewer: Both

Candidate: Should we analyze rider initiated cancel ro both rider/driver intiated cancels

Interviewer: Both

Business goal:

Trip cancellations craete negative experience for riders as well as drivers. I assume that the goal is offer a reliable ride service at all times, key factor to earn customer trust and maintain customer retention. We can increase the total number of riders offered through uber and revenue.

User journey:

Rider goes to the app

Select the pick-up and drop-off locations

Selects the ride options (shared or individual ride) based on the cost and estiamted wait time

Waits for the vehicle to arrive

Spots the vehicle

Takes the ride

Reaches the destination

Factors that drive cancellations:

Rider-related

A) Incorrect selection of the pick-up location

B) Actual wait time is longer than the estimated wait time

C) Unable to be ready for a ride when the driver arrives

D) Unable to identify the vehicle

E) Rider forgets about the pre-scheduled booking

Driver-related

A) Rider is not ready when the vehicle arrives. That driver had to wait for a long time

B) Unable to spot the rider

C) Driver makes the deal with the rider outside the uber app to avade the commission to uber and hence cancels the trip on the app

Recommendations to lower cancellations:

Rider-related

A) Incorrect selection of the pick-up location: When the pick-up location selection is far from the rider location as indicated by the GPS signal, ask the rider to confirm the trip again

B) Actual wait time is longer than the estimated wait time: Adjust the prediction model to increase the accuracy of the wait time estimates for the arrival of vehicle. In case driver takes longer than estimated wait time that was communicated to the rider, inform the rider about the unexpected delay and offer uber credits as a concession to remediate the negative customer experience. It will help to lower the ride cancellations as well as improve the customer satisfaction and retention

C) Unable to be ready for a ride when the driver arrives: Provide a wait-time menu in terms of increments of five minutes such as 5 minutes, 5-10 minutes, 10-15 minutes, instead of giving just a fixed wait time. This will provide a flexibility and convience for the rider to select a appropiate wait time.

D) Unable to identify the vehicle: Sometime rider simply cancel the trip because they cannot spot the vehicle. I would install the display on top of the vehicle that would read uber driver "X" is looking a passenger "X". It will make it easy for the passenger to identify the vehicle especially in the high-traffic zone

E) Rider forgets about the pre-scheduled booking: Send a timely reminder to the rider about the upcoming trips

Driver-related:

A) Driver had to wait for a long time after arrival: I know uber charges for the wait time to compensate for the driver's time. But there are other constraints as to parking- it is not always possible for the driver to find a parking space where the driver can safely wait for a long time. 

We can detect the live traffic and asses the parking situation using the GPS and share that information with rider so that rider can get to the car faster. 

For riders that consistently takes long time to get to the car, uber will personalize the wait time, pushing these riders down the queue. It will help to provide sufficient time for rider to get ready

B) Unable to spot the rider: AI assitant will track the rider movements and inform the driver about exact whereabout of the rider. It will help the driver to easily track the rider and thereby lower the cancellations

C) Driver makes the deal with the rider outside the uber app: Use the gps signal/location signal information of the driver and riders to spot the bad actor activities and charge a penatly or fine for the bad actors. 

Prioritization:

A) If I were to prioritize one initiative, I would choose the intiative where we guarantee the time to pick-up the rider. If we cannot meet the wait time promise, we inform the rider about the same and issue the uber credits as a concessions to remediate the negative customer experience.