Ride-Hailing Partner App: Introducing a Performance Tracking System for Drivers to Empower them Not to Cancel Accepted Rides

THE GOAL
To reduce cancellations after ride acceptance for drivers with limited literacy
NUMBERS MOVED
Rides cancelled after acceptance dropped from an average of 28% to 12% within 2 months
FOR WHO?

Bike-Taxi Drivers A.K.A. Captains who use Rapido Captain app to make money by getting rides, finding their way, and handling their schedule.

It is particularly challenging to design solutions for this user base as a lot of captains can’t even read or write and are new to technology. Google refers to such users as the Next Billion Users.

PROJECT TYPE
  • Mobility
  • SaaS
  • Native - Android
  • App Navigation
TIMELINE
  • December 2021 - February 2021
THE PROBLEM

28%

of accepted
rides get cancelled by drivers

~ 1,200,000

rides are accepted by drivers daily

~ 336,000

of those accepted rides never start

I conducted interviews with drivers and studied their cancellation patterns to gain insights into their thought processes and learned:

Hypothesis

Captains offered performance-based incentives (e.g., bonuses) will be more likely to finish accepted rides to lift their performance score.

HOW PERFORMANCE IS TRACKED

Rapido internally tracks the performance of captains through DAPR (Drop to Accept Ping Ratio) which is based on the number of rides they are accepting and how many of those they are actually finishing. This is calculated by:

City-wise diversification of HP (High Performing) and LP (Low Performing) drivers throughout India

43%

drivers finish less than 4 rides out of every 20 rides they accept

The lack of performance tracking within the app disconnects drivers from its impact and limits their motivation to optimize their performance

HOW MIGHT WE

Design a transparent and informative performance tracking system for drivers so they can easily understand and optimize their performance

QUICK EXPERIMENT TO VALIDATE OUR HYPOTHESIS

To validate the need of this intervention, I realized that its best to conduct a quick experiment first with captains using CT events which don’t require dev efforts.

Objective:

To communicate about the ride cancellation and risks involved

Goal:

Reduce the number of cancellations

How:

Using full screen and half-interstitial banner pop-ups & giving them warning messages, creating awareness abot high cancellation

We ran a test and control experiment in 2 major cities and found:
In Bangalore:
  • Cancellations of long rides decreased by 19%
  • Rides per customer increased by 11%
In Hyderabad:
  • Cancellation of long rides decreased by 13%
  • Rides per customer increased by 8.5%

The results of this experiment were promising and here on, with high confidence, we began to build a new system to track driver performance

PERFORMANCE TRACKING SYSTEM MAP

Before solutioning, we visualized the whole system to set clear expectations of the change we wanted to see

IDEA GENERATION FOR PERFORMANCE REPRESENTATION

We explored some ideas to find the best ways to that fit our context for the representation of performance of drivers

FINAL CONCEPT

Performance board highlighting number of cancellations in the last 20 rides.

Progress bar to show where they stand among other drivers with a touch of gamification

Personalized tips and nudges to empower them

BENCHMARKING

I started looking at how other players in the industry had setup their performance tracking system

SHAPING WIREFRAMES

Based on the problem statement and benchmark I was able to come up with the important components that would go on the performance page.

Completed Rides
Cancelled Rides
Warning Message
Zone Seggregation
Last Accepted 20 rides
Zone Understanding
Zone Pros & Cons
Visual Representation
Current Position
MID FIDELITY DESIGN USABILITY TEST

I tested mid fidelity designs with drivers to understand what resonates with them the most and learned:

VERSION 1.0

Based on the insights from the usability test and prior research, I was able to come up with final high fidelity visual design.

FINAL DESIGNS

Based on the insights from the usability test and prior research, I was able to come up with final high fidelity visual design.

NUMBERS MOVED

Ride cancellation after acceptance dropped from an average of 28% to 12% within 2 months

Rides cancelled after acceptance dropped from an average of 28% to 12% within 2 months.

Drivers viewing their performance page have a clearer and more accurate understanding of optimizing their performance by not cancelling rides.

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project