Revolutionising Ride-Hailing Brand Analytics in Singapore: A Deep Dive into UX and Sentiment Insights

Revolutionising Ride-Hailing Brand Analytics in Singapore: A Deep Dive into UX and Sentiment Insights

In the fast-paced world of Singapore’s ride-hailing industry, where users demand seamless, intuitive experiences, staying ahead means more than just offering rides; it’s about crafting journeys that feel effortless and human-centred. At Momentro, we recently conducted a comprehensive UI/UX analysis for a client in this space, benchmarking their app against key competitors. By blending visual breakdowns, user flow mapping, and sentiment analysis, we uncovered actionable insights that validate design decisions and highlight improvement opportunities. This approach not only spots bottlenecks but also correlates technical evaluations with real-user emotions, making it a powerhouse for app strategy. Here’s how we did it and why it’s a game-changer.

Step 1: Capturing UI Dynamics Through Visual Analysis

We began by meticulously documenting the visual and functional essence of the client’s app and three major competitors. Instead of relying solely on screenshots, we captured comprehensive UI visuals for core user journeys: onboarding (e.g., sign-up and profile creation), ride booking (pickup/destination selection, vehicle options), payment processing (method selection, confirmation), real-time tracking (map interfaces, ETA updates), and post-ride feedback (ratings, issue resolution).

Using advanced UX tools, we deconstructed these visuals into individual elements—buttons, input fields, navigation menus, and interactive maps. This allowed us to compare apps objectively based on UX principles like intuitiveness, accessibility, and efficiency, alongside HEX factors that prioritize emotional resonance, trust, and inclusivity. For example, we measured interaction steps (e.g., 4-8 taps for booking) and assessed visual clarity, noting how some apps streamlined flows with predictive inputs (cutting taps by 50%) while others suffered from cluttered interfaces, leading to 25% abandonment rates. This visual analysis set the stage for understanding how design impacts human connection.

Step 2: Mapping User Flows with HEX at the Core

Building on the elements, we conducted a detailed user flow analysis framed through the HEX lens, focusing on how interfaces support human needs like trust, efficiency, and safety. We charted each app’s paths in a comparative table format, timing steps (e.g., 10-60 seconds per stage) and highlighting strengths/weaknesses.

For example:

• Onboarding: Flows ranged from zero-friction social logins (2-3 taps) to manual inputs (6 taps), with insights showing how innovative integrations (like messenger-based setups) appeal to Gen Z, boosting completion rates by 50%.
• Ride Booking: Predictive matching cut waits to 2-5 minutes in top performers, versus 5-10 in others, where inaccurate pins caused 30% cancellations, undermining HEX trust.
• Payment: Diverse options (cards, wallets, even crypto) shone in seamless apps, with zero-commission models earning praise for fairness, while hidden fees sparked frustration.
• Tracking: Real-time alerts and visual aids (e.g., proximity maps) enhanced safety perceptions, but lags led to stress in 15-20% of cases.
• Post-Ride Feedback: This was a revelation, handling scenarios like lost items varied wildly. Top apps offered in-app chatbots and direct driver contact (resolving 70-80% of issues in 1-7 days), complete with photo evidence and incentives (e.g., $20-50 tokens). Others relied on external offices or manual reports, resulting in delays and unresolved cases (up to 30%), eroding user loyalty.

By quantifying these (e.g., error rates 2x higher in underperformers), HEX helped us pinpoint where designs fail humans, such as in high-stakes moments like item recovery, where empathetic tools correlate with 15-20% higher NPS.

Step 3: Bucketing Feedback and Layering Sentiment Analysis

To ground our analysis in reality, we aggregated user feedback from diverse sources: Play Store and Apple App Store reviews, Google Maps comments, Reddit threads, and online forums. We bucketed thousands of comments into functional categories matching our flows, e.g., “booking frustrations” or “payment praise.”

Then, using Momentro (a robust sentiment analysis platform), we processed this data to gauge emotional tones: positive (e.g., “blazing fast”), negative (e.g., “fees kill the vibe”), or neutral. This involved natural language processing (NLP) to score sentiments on a scale, identifying trends like Gen Z’s love for futuristic features (crypto payments driving 50% referral uptake) or widespread gripes on reliability (e.g., “stressful tracking” in 40% of negative reviews).

Key buckets showed:

• Positive sentiments dominated affordability and speed (e.g., surge-free pricing as a “game-changer”).
• Negatives clustered around accountability, like lost-item hassles where manual processes felt “unfair” versus integrated chatbots praised as “responsive.”

Step 4: Mapping for Reliability and Correlation

The magic happened here: We overlaid our UI/UX findings with sentiment data to validate reliability. For each flow, we calculated correlations—e.g., apps with 35% faster sessions (via innovations) saw 20-30% fewer drop-off complaints, directly mirroring positive sentiments. Statistical tools confirmed spot-on matches: High HEX scores in tracking aligned with 25% more accurate benchmarks and user acclaim for “no surprises.”

This cross-mapping exposed gaps, like how clunky post-ride flows led to 30% unresolved issues, correlating with low trust sentiments. It proved our analysis wasn’t just theoretical 85% of UX pain points echoed real-user voices, enhancing credibility.

Why This Approach Powers App Strategies

In Singapore’s competitive ride-hailing scene, this methodology, combining visual breakdowns, HEX-driven flows, and NLP-powered sentiments, delivers powerful, data-backed strategies. It reduces guesswork, prioritises fixes (e.g., mini-app integrations for Gen Z appeal), and can lift retention by 15-20%. We’ve seen it transform apps from “unreliable” to “premium” through targeted redesigns.

If you’re in the industry and want to uncover hidden UX gems in your app, reach out—we’d love to run this analysis for you and turn insights into impact.