Rahul Darekar

Introduction

As a product designer and strategist with experience across multiple industries, I've found that user research serves as the foundation for every successful product. My approach to user research has evolved significantly over time, shifting from a validation-focused model to a discovery-driven approach that informs every stage of the product development process. User research isn't just about confirming assumptions—it's about uncovering the underlying needs, behaviors, and pain points that drive user interactions. This case study documents my journey through various projects, highlighting the methodologies employed, challenges faced, and insights gained along the way.

My User Research Methodology Framework

My research methodology is built on following three core principles. This allows me to adapt my research strategies to various project contexts while maintaining methodological rigor.

Context-driven approach

Selecting methodologies that align with the specific product context, user segments, and business objectives.

Mixed-method integration

Combining qualitative and quantitative methods to develop a comprehensive understanding of user needs.

Iterative learning

Continuously refining research approaches based on incoming data and evolving product requirements.

My Research Approach and Execution

My execution of user research follows a systematic yet flexible process that adapts to each project's unique requirements

For each project, I begin with a thorough preparation phase:

Research brief development

Drafting a document with research objectives, key questions, and expected outcomes

Stakeholder alignment sessions

Holding stakeholder workshops to align research with key business questions

Methodology selection

Selecting research methods based on questions, timeline, budget, and participants

Research protocol development

Developing interview guides, test scripts, or surveys for consistency

Participant recruitment strategy

Developing detailed screeners and recruitment plans to engage the right participants

This preparation phase ensures that research is focused on addressing the most critical questions and that the approach is rigorous yet practical.

My approach to participant selection is nuanced and deliberate:

User segmentation

Defining user segments by behaviors, needs, and usage patterns, not just demographics

Diverse representation

Ensuring inclusion of various user types, including edge cases and power users

Recruitment channels

Utilizing multiple channels including internal databases, social media, professional networks

Screening process

Using multi-stage screening to ensure authentic target segment representation

Incentive structure

Creating fair incentives that value time without causing selection bias

For example, in the Tata CLiQ Seller Portal project, I developed a detailed matrix of participant criteria that ensured representation across different seller categories, experience levels, and business sizes.

The execution phase involves multiple techniques adapted to each project's needs:

Qualitative Methods

Semi-structured interviews

Holding in-depth, flexible conversations that allow exploration

Contextual inquiry

Observing users in their environment to see product interactions in real workflows

Think-aloud sessions

Asking participants to verbalize thoughts while using prototypes or products

Cognitive walkthroughs

Stepping through tasks systematically to identify cognitive barriers and decision points

Journey mapping workshops

Facilitating collaborative sessions to document user experiences across touchpoints

Quantitative Methods

Behavioral analytics

Analyzing existing product data to identify patterns, bottlenecks, and opportunity areas

A/B testing frameworks

Designing controlled experiments to measure the impact of design variations

Survey design and analysis

Creating targeted surveys with structured questions that minimize bias and maximize insight

Usability metrics collection

Gathering standardized metrics like task success rate, time-on-task, and error rates

Heatmap and clickstream analysis

Examining how users navigate interfaces and where attention is focused

Hybrid Approaches

Sequential mixed methods

Starting with quantitative data to identify areas for qualitative exploration

Concurrent triangulation

Using multiple methods simultaneously to cross-validate findings

Iterative testing cycles

Implementing rapid research-design-test cycles to continuously refine solutions

My analysis framework emphasizes rigorous interpretation of research data:

Thematic analysis

Identifying patterns and themes across qualitative data through systematic coding

Affinity mapping

Organizing observations into related groups to reveal underlying structures

Statistical analysis

Using statistical methods to extract meaningful insights from quantitative data

Prioritization frameworks

Evaluating findings based on frequency, impact, and alignment with business objectives

Cross-method synthesis

Combining insights from multiple methods for a comprehensive understanding

For example, in the CoinDCX Alerts project, I developed a specialized analytical framework that connected qualitative user feedback with quantitative interaction data, revealing how different alert types influenced user behavior across segments.

Translating research into actionable insights requires thoughtful communication:

Insight repositories

Creating searchable databases of research findings that teams can reference

Visual storytelling

Developing compelling visual representations of data that communicate key findings

Stakeholder workshops

Facilitating collaborative sessions to translate insights into action plans

Design principle development

Crafting project-specific principles based on research findings

Decision support frameworks

Creating tools that help teams apply research insights to daily decisions

Project Applications: User Research in Action

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Seller Portal Onboarding

One of my most illuminating research experiences occurred during the redesign of the seller onboarding process for Tata CLiQ's Seller Portal. What began as a validation exercise for new designs quickly revealed a deeper need for foundational research.

The Pivot Point

After conducting the first five usability tests, I discovered that our redesigned onboarding process wasn't capturing essential information that account managers needed to evaluate potential sellers. The initial research that had informed our designs was too broad and missed critical category-specific requirements.

Research Approach and Execution

I pivoted from validation to foundational research, implementing a multi-phase approach:

Initial usability testing

Created a high-fidelity interactive prototype of the redesigned onboarding flow
Recruited 15 participants representing both sellers and Seller Account Managers (SAMs)
Developed test scenarios that covered different onboarding pathways
Conducted moderated sessions with think-aloud protocol
Documented observations through detailed notes and session recordings

Expanded stakeholder interviews

Identified key stakeholders across different product categories
Developed a semi-structured interview guide focused on category-specific requirements
Conducted in-depth interviews with SAMs from diverse categories
Mapped out the current offline processes in detail
Documented category-specific variations in seller evaluation criteria

Process mapping and validation

Created detailed flow diagrams of existing onboarding processes
Validated these maps with stakeholders to ensure accuracy
Identified decision points and information requirements
Conducted gap analysis between current and proposed processes

Information architecture testing

Developed card sorting exercises to understand how SAMs categorized seller information
Created tree testing protocols to validate the proposed information structure
Identified critical information requirements for each category

This comprehensive approach revealed

Category-specific screening processes for new sellers
Complex commission structures that varied by product type, seller reputation, and logistics model
Detailed documentation requirements that differed across categories
The need for specialized calculation tools to evaluate seller profitability

Methodological Insight

This experience highlighted the importance of category-specific research rather than platform-wide generalizations. It also demonstrated how usability testing can sometimes uncover the need for more fundamental research.

CoinDCX Alerts

For the CoinDCX alerts system, I needed to understand how users processed notifications across different contexts and urgency levels.

Research Approach and Execution

I implemented a multi-phase research strategy:

Competitive analysis

Conducted thorough analysis across both crypto and traditional stock trading platforms
Mapped feature matrices across competitors to identify opportunity spaces
Discovered that some platforms offered dedicated products solely for alerts functionality
Identified innovative approaches like note attachment functionality that could enhance our offering

Internal expert interviews

Leveraged our in-house trading experts as initial research participants
Conducted structured interviews with traders representing different trading strategies
Documented how they translated technical signals into actionable trading decisions
Uncovered nuanced usage patterns of moving averages

Technical indicator analysis

Created a comprehensive framework categorizing different indicator types
Analyzed how traders customized standard indicators for personal strategies
Documented which indicators were most trusted for decision-making
Identified that some traders avoided indicators due to analysis paralysis or knowledge gaps

Quantitative usage analysis

Analyzed platform data through CleverTap to identify most-used indicators
Measured indicator popularity across different user segments and time periods
Determined computational requirements for supporting various indicators
Created prioritization matrix based on usage data and technical feasibility

Prototype development & testing

Designed specialized interfaces for each indicator type with appropriate parameters
Created educational components to support users unfamiliar with technical analysis
Developed contextual market data displays to aid decision-making
Tested information hierarchy with internal trading experts

Key Insight

The research revealed that traders interact with technical indicators differently based on experience and strategy, with novices requiring educational support and experts demanding customizable parameters. This understanding led to a contextually adaptive alert system designed to function both as notification tool and decision framework, prioritizing different information elements for various trading segments.

Paytm Daily SIP

For Paytm's Daily SIP feature, I needed to understand barriers to regular investment among younger users.

Research Approach and Execution

I utilized a combination of methods:

Participant recruitment & segmentation

Strategically recruited 11 participants across diverse demographic and financial backgrounds
Included previously overlooked segments: blue-collar workers, daily wage earners, and students
Deliberately selected participants with varying investment experience levels
Focused on individuals who had never participated in capital markets
Ensured geographic diversity with participants from Mumbai (5) and Bangalore (6)

Semi-structured field interviews

Conducted 3-day intensive interview program combining field visits and telephonic sessions
Developed a progressive interview guide that began with current financial behaviors
Implemented contextual inquiry to understand existing saving patterns in daily life
Used contrast questioning to identify threshold points for different commitment levels
Documented emotional responses to investment concepts and terminology

Financial behavior mapping

Created comprehensive profiles of participants' current investment choices
Mapped common barriers preventing capital market participation
Identified psychological comfort levels with different investment amounts
Documented triggers that might motivate micro-investment behavior
Analyzed language and terminology barriers affecting comprehension

Persona development

Synthesized findings into two distinct user personas:
- Persona 1: Smartphone-dependent users with limited education in blue-collar positions
- Persona 2: Educated users with disposable income and some investment awareness
Mapped each persona's specific needs, barriers, and opportunities
Tested product messaging across both persona groups
Documented language and comprehension differences between personas

Concept testing

Presented the Daily SIP product concept to all participants
Captured immediate reactions to different entry point amounts
Tested comprehension of key product features and investment terminology
Documented questions and concerns specific to each persona
Collected spontaneous feature suggestions from participants

Key Insight

The research revealed significant differences in digital literacy and investment comprehension between user segments, with both groups appreciating the low entry point but diverging in their understanding of investment mechanics. This led to a product approach balancing simplicity for beginners with sufficient depth for experienced investors, using educational components as adoption bridges for less financially literate users.

Vested Finance: Root Cause Analysis

For Vested Finance, I needed to understand why users were abandoning the platform during specific investment processes.

Research Approach and Execution

Quantitative analysis

Analyzed Mixpanel event data to identify significant drop-offs in the authentication flow
Measured conversion rate gaps between signup/login page visits and successful completions
Tracked support ticket volumes from Zendesk and Intercom related to authentication issues
Quantified authentication success rates across different user segments (direct vs. partner users)

Technical investigation

Audited AWS Cognito implementation to identify missing edge case handling
Mapped database architecture conflicts affecting partner user authentication
Documented error paths and message inconsistencies across the authentication flow
Analyzed user session patterns to identify abandonment triggers

User feedback collection

Obtained direct user feedback from those who had raised authentication-related tickets
Gathered insights during partner sync meetings about their users' access challenges
Collaborated with customer support teams to understand common resolution approaches
Identified patterns in user confusion points that led to abandonment

Cross-functional analysis

Worked with development team to assess technical feasibility of proposed solutions
Collaborated with design team to create improved error messaging and UI flows
Coordinated with partner integration teams to address cross-platform authentication issues
Established metrics baseline for measuring post-implementation impact

Key Insight

The research uncovered that authentication failures were primarily caused by poor error communication rather than just technical issues. Users struggled with vague messages ("unknown error", "account does not exist") that lacked actionable guidance, leading to confusion and abandonment. Key problem areas included deactivated accounts, partner user access issues, and authentication method mismatches, revealing a gap between user expectations and system feedback.

Cross-Project Learnings

Working across e-commerce and financial services has revealed several patterns in user research:

Domain context matters

Research methodologies need to be adapted to domain-specific user behaviors and mental models.

Triangulation is essential

No single research method provides complete insights. Combining methods creates a more comprehensive understanding.

Sequential deepening

Starting with broad research questions and progressively focusing on specific areas yields more actionable insights than attempting to cover everything at once.

Research timing

Early involvement of research in the product development process yields better results than validation-only approaches.

Adapted rigor

The level of methodological rigor should match the decision's importance and available resources.

Insights, not data

Focusing on translating findings into actionable insights rather than just collecting data increases research impact.

Research-Driven Design Framework

Translating research insights into actionable design improvements requires a systematic approach. My framework focuses on:

Categorizing findings based on

User
Needs

Business
Requirements

Technical
Feasibility

Evaluating potential design changes by

Potential
Benefit

Implementation Complexity

Strategic
Objectives

Creating a decision matrix that balances

User
Impact

Implementation
Effort

Strategic
Alignment

The goal is not just to collect data, but to generate actionable, impactful insights that drive meaningful design decisions.

Challenges and Solutions in User Research

Every research project presents unique challenges. Herea re the top 3 of common obstacles I've encountered:

Participant Recruitment

Challenge

Finding truly representative participants

Solution

Create multi-channel recruitment with advanced screening processes

Bias
Mitigation

Challenge

Minimizing researcher and participant bias

Solution

Use mixed methods, multiple data collection techniques, structured protocols

Resource Constraints

Challenge

Conducting comprehensive research with limited time and budget

Solution

Create scalable research, prioritize key questions, use cost-effective methods

Impact of Research on Design Outcomes

The true value of user research lies in its ability to fundamentally transform design approaches. Metrics for measuring research effectiveness include:

Reduction in post-launch iterations
Increased user engagement
Improved conversion rates
Enhanced user satisfaction scores

Best Practices and Key Learnings

Recommendations for effective user research:

Start Early and Iterate Often

Integrate research from the earliest stages of product development
Treat research as a continuous process, not a one-time activity

Embrace Methodological Flexibility

Combine qualitative and quantitative approaches
Be prepared to adapt methodologies based on emerging findings

Focus on Actionable Insights

Move beyond data collection
Translate research findings into clear, implementable design recommendations

Maintain Stakeholder Alignment

Continuously communicate research findings
Develop collaborative workshops to translate research into action

Conclusion

The future of user research lies in its ability to become more adaptive, technology-enabled, and strategically integrated. As product designers, our role is not just to create interfaces, but to deeply understand and solve user challenges.

User research is more than a methodology—it's a mindset. It's about maintaining curiosity, embracing complexity, and always seeking to understand the human experiences behind every interaction.

By continually evolving our research approaches, we can create products that don't just meet user needs, but anticipate and exceed them.

Decoding Users

Lessons, Strategies, and Insights from My Experience in Uncovering User Needs and Crafting Research-Driven Designs for Meaningful Impact.

Introduction

My User Research Methodology Framework

My Research Approach and Execution

Project Applications: User Research in Action

Cross-Project Learnings

Research-Driven Design Framework

Challenges and Solutions in User Research

Impact of Research on Design Outcomes

Best Practices and Key Learnings

Conclusion