Venkata SudheerBhuvangiri

The Philips GroomTribe team needed to decide between two competing feedback systems for a new AI-guided shave feature. The choice had significant UX and emotional implications — one design felt gamified, the other more clinical. Research was needed to inform a high-stakes product decision.

Ran comparative moderated usability sessions with 19 consumer participants. Each user tried both variants with task scenarios. Collected preference data, think-aloud observations, and post-session ratings. Conducted thematic analysis to identify why preferences differed beyond surface-level choices.

Numeric scores were preferred because they felt precise, comparable, and progress-oriented. Users described stars as "vague" and "game-like." Key issues: motion feedback terminology was confusing, the summary page was hidden, and there was no actionable guidance tied to scores. All 5 recommendations were adopted into the OneBlade product roadmap.

Philips needed a detailed understanding of shaving behaviours and attitudes across three key markets before localising the GroomTribe app. Research covered: state of mind when shaving, wet/dry preference, charging habits, brand loyalty triggers, AI feature trust, and app usage patterns.

Designed and deployed a multi-section survey on Alchemer. Analysed data for cross-market patterns and divergences across 10+ behavioural and attitudinal dimensions. Delivered findings as executive reports to Product, Design, and Engineering stakeholders across geo-teams.

Findings directly shaped app localisation decisions across 3 markets. Identified key market-specific feature priorities and UX considerations that diverged significantly from assumptions. Research championed within the team as a model for future global survey programmes.

MMD served MU Leads, Client Group Leads, and Biz Ops teams globally — but adoption was alarming low. Users were abandoning it for myWizard AI Fusion and CHC. A commissioned usability study was needed to identify root causes and provide a clear redesign brief.

Remote moderated task-based usability testing across 12 countries. 8+ individual sessions, 1 focus group discussion. Passive Voice-of-User feedback channels active during MVP. Full thematic analysis across sessions producing 9 clustered insight themes with supporting quotes, ratings, and design principles.

Top themes: Cognitive Overload (90% overwhelmed), missing Personalisation, broken IA, unreliable RAG status, Data Reliability doubts, Siloed experience, Non-communicative system. Research became the backbone of an MMD redesign sprint and was cited in executive programme reviews.

iAspire was visually impressive but had critically low learnability. Novice users abandoned tasks; key features were invisible; terminology confused people across career levels. A rigorous multi-method study was needed before the platform scaled to 500,000 employees.

Phase 1: 9 remote moderated usability sessions with 8 career-level archetypes. Phase 2: 2-day Design Thinking Workshop with 42 participants using Mentimeter for real-time synthesis. Segmented analysis across Executive (L5–7) and Non-Executive (L8–11), lateral hires, and freshers.

3 critical themes: Learnability failure for novices, Missing Guided Narrative (users needed contextual hand-holding), Navigation & CTA confusion (premature action forcing). Workshop surfaced 20+ features and validated 7 design principles. Findings shaped the full platform redesign roadmap.

Comparing 1–7 Likert satisfaction ratings across Netherlands, USA, and India is misleading without accounting for cultural response tendencies. Indian respondents culturally rate higher on average (acquiescence bias); Dutch respondents tend toward conservative ratings. Raw data comparison produces false conclusions.

Applied Z-Score Standardisation — the established method in cross-cultural survey research (Kantar, 2024). Calculated per-market mean and standard deviation, standardised each respondent score. Produced a normalised comparison table enabling genuine market-to-market analysis.

Raw data: India rated highest (mean 4.89). Post-normalisation: USA rated highest (Z=+0.018), India adjusted down (Z=+0.012). India's high raw scores reflected cultural response patterns — not genuine satisfaction. Without standardisation, product teams would have deprioritised the US market incorrectly.

Established the UX Research function across GBS teams — defined ReOps processes, templates, and governance frameworks that were adopted organization-wide. Created the research intake, prioritisation, and reporting infrastructure from zero.

Led tree testing and thematic analysis sessions to redesign the navigation architecture on the ServiceNow portal. Findings surfaced key findability failures across 3 major user journeys, directly informing the restructured IA that reduced task completion friction.

Developed and monitored an NPS dashboard strategy for mobile portal adoption. Identified the top 3 friction drivers through thematic synthesis of qualitative feedback — contributing to measurable improvement in portal satisfaction scores reported to senior stakeholders.

Enphase's solar monitoring app surfaced raw technical metrics (kWh, voltage, inverter data) with no personalisation or contextual translation. Homeowners felt disconnected from their investment — unsure whether the system was performing well, when to dispatch battery power, or how much they were actually saving. Over time this eroded confidence and app engagement. Installers faced a parallel problem: ambiguous pairing feedback and absent real-time diagnostics pushed them into troubleshooting instead of installing.

Conducted secondary research across solar homeowner surveys (US and India) covering motivations, pain points, financial expectations, and demand trends. Developed two evidence-based personas (Anand — homeowner; Ravi — installer) with full needs, pain points, and empathy maps. Created separate journey maps for both user types across all key touchpoints from installation to ongoing engagement. Defined two problem statements and HMW questions. Designed a complete user flow and 8-screen wireframe set in Figma — including AI insights, battery management, sustainability goal tracking, and monthly summary screens.

Core insight: homeowners need emotional connection and plain-language translation of technical data — not more metrics. Key opportunity areas: AI-driven comparative insights ("today vs last year"), gamified sustainability milestones, smart battery scheduling, and proactive service alerts. For installers: auto-scan pairing, guided commissioning flow with progress feedback, and one-tap remote diagnostics. Delivered full Figma prototype (8 screens) + Mural journey maps for both user types + complete research deck.

Kotak Mahindra Bank needed a clear-eyed view of where its home loan digital experience stood relative to the market. Research covered product-level comparison (interest rates, processing fees, borrower categories), digital feature parity (calculators, online applications, repayment automation), and AI/conversational capability maturity across all major private and public sector competitors.

Systematic benchmarking across three analytical dimensions. Mapped each bank's AI and chatbot strategy — from Kotak's Keya voice bot and HDFC's EVA to Axis's Aha!, SBI's SIA, and ICICI's iPAL. Assessed digital channel coverage (desktop and mobile app parity), unique feature advantages, and social media presence as proxy for brand reach. Segmented users across demographic, psychographic, and lifecycle dimensions to identify underserved audience opportunities.

Kotak's 'Everything Me' personalisation and Keya voice bot represented differentiated strengths no competitor had matched. Key gap: no standalone home loan microsite or virtual tour equivalent to SBI's offering. AI chatbot sophistication varied dramatically — HDFC's EVA led on NLP maturity. Research informed Kotak's digital roadmap priorities and shaped the experience strategy for the home loan acquisition journey.

Oberoi Realty, one of Mumbai's leading real estate developers with 42+ projects, was losing prospective buyers at the inquiry form stage. The core business question: why are users landing on the site but not converting? Research was commissioned to understand both the user reality and the competitive landscape before redesigning the acquisition experience.

Developed two evidence-grounded personas — Ravi (Mumbai-based IT professional, first-time buyer navigating mortgage complexity) and Amrita Gupta (NRI investor, site-visiting remotely and dependent on digital trust signals). Conducted heuristic evaluations across 5 competitor sites using a structured 10-criterion assessment scorecard (based on Forrester's framework). Created As-Is journey map documenting 2–3 hours of daily portal browsing, failed phone enquiries, and form abandonment patterns.

Four high-severity violations identified across competitors: inconsistent navigation patterns (hamburger menu absent on Lodha), broken search-to-project-page routing, absent error feedback on form submission, and missing home loan service information on homepages. Journey mapping revealed the decisive insight: users didn't abandon because of distrust — they abandoned because of cognitive overload from options, poor filtering, and no guided need-discovery flow. Recommendations centred on progressive disclosure, guided questioning, and reduced upfront form friction.