User Personas for Indian commuters. Personas for 1.4B?!

Caren Felicia
Jul 1, 2023
5 min read

Updated: Sep 9, 2025

Disclaimer: Due to NDA's, I will not be able to share any final insights and areas of application and

will be focusing on process & high-level findings + recommendations.

What is Rapido?

Rapido is India’s first and fastest-growing Bike taxi app with a whopping 25 Million+ app downloads. We are now running operations in more than 100 cities. Rapido is an Indian ride-hailing app, but it offers more than just cabs

https://www.rapido.bike/. It functions primarily as a bike taxi aggregator, meaning you can book a motorbike ride with a driver for an affordable price. Rapido also allows you to book autos (rickshaws) and cabs through their app.

This app lets you zoom around town on a trusty motorbike taxi, dodging traffic like a Bollywood chase scene (minus the explosions). It's the speedy, budget-friendly way to get your chai fix, conquer commutes. So next time you're in India and need a ride that's both thrilling and chillin', Rapido is your quick and quirky sidekick.

Why User Personas?

Research on understanding Rapido customers is crucial to navigate the different streams of business targeting the customer front. But before knowing about Rapido users, it is paramount o understand where Rapido is fitting in the natural commute ecosystem

To understand the general commuter's behavioral patterns

Decision making styles
Preference formation triggers
Subjective well-being of commuters
Moods and Emotions
Motivation to Action Nudges
Personality Traits and its effects
Habit formation + Reward cycles

To understand the user’s needs, preferences, motivations and frustrations.

Who is our customer?
Who is our potential customer?
What are their goals?
What are the barriers to achieving these goals

Phase 1

Approach and Methodology

I Extensive Secondary research -

i) Understanding Mobility & Commuters in India: /Trends/News/Stats/

ii) Stakeholder Workshops

iii) Play store Reviews + In-app feedbacks

iv) Meta-Data analysis

Deliverables:

i) Archetypes of Indian Commuters

The infamous question: "So, Now What...?

Utilised seasoned stakeholders legacy knowledge on users
Mapped Archetypes to Brand Personas to see overlaps/differences
User Data Analytic Segments started getting answers on the "Why's" of data signals

Learnings and Next Steps: Reality Check!!!!

Nudging Stakeholders for knowledge transfer was harder than expected
Had to learn how to speak the language of data scientists
Storytelling for Secondary Research was an unique yet a fulfilling experience

Phase 2 & Phase 3

II Primary Research Methods

i) Participatory behavioural design workshops

++ Picture-based storytelling ++ Memes (Expectation vs Reality) ++ Puppet based storytelling ++ Day journaling ...etc

ii) Personality Tests + Subjective Well-Being Test

iii) User Interviews

iv) Naturalistic Observation (Ethnography- Time + Event sampling)

v) Street Intercepts (Ethnography)

Sampling Processes:

Step 1: Sampling criteria definition:

Ride hailing app users vs Public transport users vs Personal Vehicle users

Frequency of mode usage

Gender and Age ratio

City of Interest (as per business)

No.of people per method

Step 2: Screener Survey (Demographic details)

Step 3: Participant Recruitment + Incentive Model

Vendor management Recruitment tool management

Method dependant Incentive structuring

Data Analyses Used:

Affinity mapping
- Keywords mapping
- Thematic clustering [Miro]

Projective technique analysis (Workshop)
- Thematic appreciation test
- Open Storytelling (picture)
- Open Storytelling (puppet)
Intra-test validation of Personality tests and SWB

Co-Analysis session
- Research team co-analysis session for avoiding researcher bias
- Product managers co-analysis sessions to bring business relevance

Digital ethnography data mapping
- Pictures and Videos thematic tagging and clustering

Deliverables Phase 2

i) General Commuter’s Proto-Personas for product and design development

ii) Qualitative city-level business insights on city-specific problems

Deliverables Phase 3

i) Complete User personas for Rapido contextualised from Commuter personas

ii) Dynamic User personas

iii) Qualitative city-level business insights on city-specific problems

iv) Ethnographic principle frameworking for Brand Guidelines

The infamous question: "So, Now what,...?

Rapido Customer App redesign to make the app more Persona-relevant (user centred design)
City level insights from the study led to the creation of new product called as "Bike Pink" in Chennai. Bike taxi services for women only
City level insights from the study led to 21% growth in Kolkata over hyperlocal campaigns
Product development started focussing more on habit building through the app (Features: Favourites, Smart drop suggestions, Smart Service suggestions, One tap booking (WIP)
Push Notifications were following persona dependant to create more effective nudges which improved CTRs by 11%
Customer lifecycle management as Dynamic User Personas gives a behavioural prediction on where and how the user will migrate next. (Created service level hooks to manage user lifecycle). Increased Retention rate by ~35%

Learnings and Next Steps: Reality Check!!!!

Translating Rapido User personas to the business context for PMs and Leadership was a separate research activity by itself.
I had to maintain a rich documentation on how each insight was utilised, who was using it and what's the pipeline of work to get it converted into action.
Evangelised Rapido user personas amongst the organisation as a regular language to use to develop products- Treasure hunt, Stickers, Posters, Workshops. Traditional methods didn't work!!!

Phase 4

III Qual meets Quant (Validation)

i) Converting qualitative personas into potential data signals

ii) Layering existing Data segments with Qual user personas for validation

iii) Building on Market Segmentation data for Persona validation

The infamous question: "So, Now what,...?

Used to map user behaviour real-time to the data signals from the app data
Data signals gives the What and User Personas gave the Why's of the signalling
Created better data signals to capture more relevant user information for better experience.

Learnings and Next Steps: Reality Check!!!!

Market Research team did not have the budget to layer User personas with Market segments
I had to revise my ML skills to help data engineers create effective user-centred data signals
Similarly, had to use some basic ML framework to structure insights for faster data mapping of qual user personas

Top-level Insights/Findings

I User Personas

We have 5 Personas to represent our users base:

Pocket Planner (conservative on money)
Battery Saver (comfort-seeker)
Roller Skater (conserve time)
Sloth’s friend (creature of habit)
Social Butterfly (social commute experience)

Are they just 5 discrete categories?

NO! They are not. They can flow from one persona to another based on differing triggers across a user's lifecycle.

II Expectations of a commuter

Commuters want commute to satisfy 3 of their primal needs

Gain needs (Functional)
- E.g. Affordable commute, Safety, On time

Normative needs(Social)
- Eg: Social status, Culturally acceptable

Hedonic needs(Emotional)
- Eg: Relaxed driving, Less anxious over unpredictable ETAs

III The Secret Formula for Optimization

Gain goals + Normative goals = Hedonic goals

For a service to truly become a “lifestyle”, it needs to be intrinsically motivated by the consumers.

When our service helps in achieving the Functional and Social goals, Emotional goals are naturally fulfilled resulting in optimised results

Thus, Product Development + Relevant Marketing strategies = Intrinsic motivation to use Rapido

IV Habit models of commuters

-- Planning a commute depending on the context (Time, Distance of commute) = Schedule

-- Small steps taken to fulfill the schedule (Eg: Checking prices across modes) = Routine

-- Repeated and effortless following of a routine (Eg: Choose bus no matter what) = Habit

Commuters experience mental discomfort when moving away from their habits whereas diverting from routines need not create that discomfort.

So, when routines keep on changing, schedules become flexible. But schedules become rigid when habits are formed. So, users who use Rapido services are within the purview of Routine followers

V Mental Model of Time

Commuters have a distorted sense of time when they subjectively interpret “commute time”.

Perception of time is dilated/expanded when they say “Waiting period is too long”, “Travel time is short”, “XXX mode travels faster than the other” etc.

Eg: A commuter will think & say, a metro travels faster than a Bike taxi. Another commuter from same city, same context might say the exact opposite ie BT is faster than metro.

What creates the subjective interpretation of time?