Jennifer Azubuko

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Jennifer Azubuko

Let’s build products that feel as good as they perform.

I’m currently open to opportunities to collaborate on customer-centric digital products.

Let’s Collaborate

Role: Product Research & Strategy

Duration: 2 weeks

My Contributions:

I handled everything from complaint sourcing to survey design, data analysis, and actionable recommendations.

My Contributions:

I handled everything from complaint sourcing to survey design, data analysis, and actionable recommendations.

Fixing Trust in Ride-Hailing:

A Comparative Research Study

Understanding where reliability breaks, and how to rebuild user trust in Nigeria’s ride-hailing ecosystem.

Read Full Research Doc

Context

Ride-hailing apps are essential for mobility in Nigeria, yet trust remains the weakest link.

Users consistently complain about cancellations, fare manipulation, and poor driver behavior — issues that technology alone hasn’t fixed.

I conducted a comparative research study to understand these pain points and propose evidence-based strategies to improve user trust and reliability.

Objective

To validate common user complaints across Nigerian ride-hailing platforms, identify the weakest product area, and recommend both product and operational strategies for improvement.

Process

1. Complaint Sourcing & Assumption Building

Sourced user complaints from App Store reviews and Twitter (X) discussions

Clustered platforms by business model:

Class A: Uber, Bolt (fixed fares)

Class B: InDrive, Rida (negotiated fares)

Class C: LagRide (government-owned)

1. Complaint Sourcing & Assumption Building

Sourced user complaints from App Store reviews and Twitter (X) discussions

Clustered platforms by business model:

Class A: Uber, Bolt (fixed fares)

Class B: InDrive, Rida (negotiated fares)

Class C: LagRide (government-owned)

1. Complaint Sourcing & Assumption Building

Sourced user complaints from App Store reviews and Twitter (X) discussions

Clustered platforms by business model:

Class A: Uber, Bolt (fixed fares)

Class B: InDrive, Rida (negotiated fares)

Class C: LagRide (government-owned)

2. Survey Design

Targeted respondents who had used ≥5 ride-hailing apps in 2025.

Mixed question types to combine quantitative and qualitative validation.

Designed to uncover frequency, impact, and emotional intensity of pain points.

2. Survey Design

Targeted respondents who had used ≥5 ride-hailing apps in 2025.

Mixed question types to combine quantitative and qualitative validation.

Designed to uncover frequency, impact, and emotional intensity of pain points.

2. Survey Design

Targeted respondents who had used ≥5 ride-hailing apps in 2025.

Mixed question types to combine quantitative and qualitative validation.

Designed to uncover frequency, impact, and emotional intensity of pain points.

3. Data Collection & Findings

Pain Point

Rude drivers

Dirty vehicles

Distant drivers / long ETAs

Cancellations

Extra charges

Poor customer support

App update or map issues

0–14

✅

❌

% Experienced

Validation

Key Insights

Driver reliability — not app performance — is the weakest product class.

Customer support issues were overstated; most users never reached out.

Operational execution, not UX design, determines user satisfaction.

Recommendations

Product Changes

Real-Time ETA Validation: Assign drivers within a fixed radius (≤6 minutes).

Driver Commitment Check: On-acceptance reconfirmation to reduce cancellations.

Fare Lock System: Prevent renegotiation and ensure transparent pricing.

Smart Cancellation Policy: Protect riders from penalties when drivers are late.

Product Changes

Real-Time ETA Validation: Assign drivers within a fixed radius (≤6 minutes).

Driver Commitment Check: On-acceptance reconfirmation to reduce cancellations.

Fare Lock System: Prevent renegotiation and ensure transparent pricing.

Smart Cancellation Policy: Protect riders from penalties when drivers are late.

Product Changes

Real-Time ETA Validation: Assign drivers within a fixed radius (≤6 minutes).

Driver Commitment Check: On-acceptance reconfirmation to reduce cancellations.

Fare Lock System: Prevent renegotiation and ensure transparent pricing.

Smart Cancellation Policy: Protect riders from penalties when drivers are late.

Operational Changes

Quarterly driver audits and retraining.

Reward punctuality and low cancellation metrics.

Zero-tolerance for fare manipulation or misconduct.

Operational Changes

Quarterly driver audits and retraining.

Reward punctuality and low cancellation metrics.

Zero-tolerance for fare manipulation or misconduct.

Operational Changes

Quarterly driver audits and retraining.

Reward punctuality and low cancellation metrics.

Zero-tolerance for fare manipulation or misconduct.

Expected Impact

Shorter wait times & fewer cancellations.

Improved fare transparency.

Strengthened rider trust and retention.

Better alignment between tech, ops, and customer experience.

Reflection

This project helped me sharpen my research-to-strategy translation — turning raw complaints into actionable frameworks.

It reinforced a core PM lesson:

“Technology can deliver rides — but trust delivers loyalty.”

Read Full Research Doc

Other Projects

Skype Lifecycle Analysis - Strategic Product Audit

Product Strategy

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Ride-Hailing Apps Research - Service Experience Insights

Product Strategy

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Opportunity Mapping

View Case Study →