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CONATCT
WORK
ABOUT
RESUME
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RESUME
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WORK
ABOUT
RESUME
CONATCT
Digital Twin Customer Modeling
Digital Twin Customer Modeling
Digital Twin Customer Modeling
Designing an AI-powered platform that creates accurate digital twins of target customers to help businesses make data-driven decisions and improve customer understanding.
Designing an AI-powered platform that creates accurate digital twins of target customers to help businesses make data-driven decisions and improve customer understanding.
Designing an AI-powered platform that creates accurate digital twins of target customers to help businesses make data-driven decisions and improve customer understanding.
Years
Years
2024 - Present
2024 - Present
2024 - Present
Role
Role
Lead Product Designer
Lead Product Designer
Scope
Scope
Product Strategy, UX/UI Design, Prototyping
Product Strategy, UX/UI Design, Prototyping
CHALLENGE
CHALLENGE
CHALLENGE
Businesses struggle to understand their customers deeply, often relying on outdated personas and fragmented data sources. This leads to misaligned products, ineffective marketing, and missed opportunities.
Businesses struggle to understand their customers deeply, often relying on outdated personas and fragmented data sources. This leads to misaligned products, ineffective marketing, and missed opportunities.
Businesses struggle to understand their customers deeply, often relying on outdated personas and fragmented data sources. This leads to misaligned products, ineffective marketing, and missed opportunities.
SOLUTION
SOLUTION
SOLUTION
An AI-powered platform that ingests multiple data sources to create dynamic, behavioral digital twins of customer segments, providing real-time insights and predictive analytics..
An AI-powered platform that ingests multiple data sources to create dynamic, behavioral digital twins of customer segments, providing real-time insights and predictive analytics..
An AI-powered platform that ingests multiple data sources to create dynamic, behavioral digital twins of customer segments, providing real-time insights and predictive analytics..
A comprehensive research approach combining qualitative and quantitative methods to understand user needs, validate assumptions, and inform design decisions.
A comprehensive research approach combining qualitative and quantitative methods to understand user needs, validate assumptions, and inform design decisions.
A comprehensive research approach combining qualitative and quantitative methods to understand user needs, validate assumptions, and inform design decisions.
Quantitative Research
Quantitative Research
Quantitative Research
Deep insights into user motivations, pain points, and mental models
Deep insights into user motivations, pain points, and mental models
Deep insights into user motivations, pain points, and mental models
User Interviews
User Interviews
User Interviews
24 semi-structured interviews with marketing managers, data analysts, and product managers
24 semi-structured interviews with marketing managers, data analysts, and product managers
24 semi-structured interviews with marketing managers, data analysts, and product managers
60-90 min sessions
60-90 min sessions
60-90 min sessions
60-90 min sessions
Remote & in-person
Remote & in-person
Remote & in-person
Remote & in-person
Cross-industry
Cross-industry
Cross-industry
Cross-industry
Cross-industry
Cross-industry
Contextual Inquiry
Contextual Inquiry
Contextual Inquiry
Observed users in their natural work environment using current customer analytics tools
Observed users in their natural work environment using current customer analytics tools
Observed users in their natural work environment using current customer analytics tools
2-3 hour sessions
2-3 hour sessions
2-3 hour sessions
2-3 hour sessions
Task observation
Task observation
Task observation
Task observation
Think-aloud protocol
Think-aloud protocol
Think-aloud protocol
Think-aloud protocol
Think-aloud protocol
Think-aloud protocol
Qualitative Research
Qualitative Research
Qualitative Research
Statistical validation of findings and behavioural patterns
Statistical validation of findings and behavioural patterns
Statistical validation of findings and behavioural patterns
Survey Research
Survey Research
Survey Research
50 responses validating pain points, feature priorities, and current tool satisfaction.
50 responses validating pain points, feature priorities, and current tool satisfaction.
50 responses validating pain points, feature priorities, and current tool satisfaction.
85% completion rate
85% completion rate
85% completion rate
85% completion rate
Mixed methods
Mixed methods
Mixed methods
Mixed methods
Statistical significance
Statistical significance
Statistical significance
Statistical significance
Statistical significance
Statistical significance
Analytics Review
Analytics Review
Analytics Review
50 responses validating pain points, feature priorities, and current tool satisfaction.
50 responses validating pain points, feature priorities, and current tool satisfaction.
50 responses validating pain points, feature priorities, and current tool satisfaction.
6 month period
6 month period
6 month period
6 month period
Multi-platforms
Multi-platforms
Multi-platforms
Multi-platforms
Behavioral data
Behavioral data
Behavioral data
Behavioral data
Behavioral data
Key Findings
Key Findings
Key Findings
Research revealed critical gaps in current customer understanding approaches and significant opportunities for AI-powered solutions.
Research revealed critical gaps in current customer understanding approaches and significant opportunities for AI-powered solutions.
Research revealed critical gaps in current customer understanding approaches and significant opportunities for AI-powered solutions.
Data Fragmentation
High Impact
Customer data scattered across 5+ tools on average
Severity
92%
"We have data everywhere but insights nowhere"
Data Fragmentation
High Impact
Customer data scattered across 5+ tools on average
Severity
92%
"We have data everywhere but insights nowhere"
Data Fragmentation
High Impact
Customer data scattered across 5+ tools on average
Severity
92%
"We have data everywhere but insights nowhere"
Data Fragmentation
High Impact
Customer data scattered across 5+ tools on average
Severity
92%
"We have data everywhere but insights nowhere"
Static Personas
High Impact
Personas updated less than once per year
Severity
84%
"Our personas feel like fiction, not customers"
Static Personas
High Impact
Personas updated less than once per year
Severity
84%
"Our personas feel like fiction, not customers"
Static Personas
High Impact
Personas updated less than once per year
Severity
84%
"Our personas feel like fiction, not customers"
Static Personas
High Impact
Personas updated less than once per year
Severity
84%
"Our personas feel like fiction, not customers"
Manual Processes
High Impact
40+ hours per month on manual data compilation
Severity
74%
"I spend more time preparing data than analyzing it"
Manual Processes
High Impact
40+ hours per month on manual data compilation
Severity
74%
"I spend more time preparing data than analyzing it"
Manual Processes
High Impact
40+ hours per month on manual data compilation
Severity
74%
"I spend more time preparing data than analyzing it"
Manual Processes
High Impact
40+ hours per month on manual data compilation
Severity
74%
"I spend more time preparing data than analyzing it"
Lack of Predictive Insights
High Impact
No way to predict customer behavior changes
Severity
87%
"We're always reacting, never proacting"
Lack of Predictive Insights
High Impact
No way to predict customer behavior changes
Severity
87%
"We're always reacting, never proacting"
Lack of Predictive Insights
High Impact
No way to predict customer behavior changes
Severity
87%
"We're always reacting, never proacting"
Lack of Predictive Insights
High Impact
No way to predict customer behavior changes
Severity
87%
"We're always reacting, never proacting"
User types and Needs analysis
User types and Needs analysis
User types and Needs analysis
Different user segments have varying needs and pain points with customer data
Different user segments have varying needs and pain points with customer data
Different user segments have varying needs and pain points with customer data
Marketing Managers
Marketing Managers
Marketing Managers
45% of users
45% of users
45% of users
Primary Needs
Primary Needs
Campaign targeting
Audience segmentation
Performance attribution
Campaign targeting
Audience segmentation
Performance attribution
Campaign targeting
Audience segmentation
Performance attribution
Main Pain Points
Main Pain Points
Outdated personas
Channel fragmentation
ROI measurement
Outdated personas
Channel fragmentation
ROI measurement
Outdated personas
Channel fragmentation
ROI measurement
Product Managers
Product Managers
Product Managers
30% of users
30% of users
30% of users
Primary Needs
Primary Needs
Feature prioritisation
User journey insights
Market validation
Feature prioritisation
User journey insights
Market validation
Feature prioritisation
User journey insights
Market validation
Main Pain Points
Main Pain Points
Assumption validation
User feedback synthesis
Roadmap alignment
Assumption validation
User feedback synthesis
Roadmap alignment
Assumption validation
User feedback synthesis
Roadmap alignment
Data Analysts
Data Analysts
Data Analysts
25% of users
25% of users
25% of users
Primary Needs
Primary Needs
Data visualisation
Trend analysis
Automated reporting
Data visualisation
Trend analysis
Automated reporting
Data visualisation
Trend analysis
Automated reporting
Main Pain Points
Main Pain Points
Data quality
Tool switching
Stakeholder communication
Data quality
Tool switching
Stakeholder communication
Data quality
Tool switching
Stakeholder communication
Design Solution
Design Solution
Design Solution
A comprehensive AI platform that transforms fragmented customer data into actionable, dynamic insights through intelligent digital twin modeling.
A comprehensive AI platform that transforms fragmented customer data into actionable, dynamic insights through intelligent digital twin modeling.
A comprehensive AI platform that transforms fragmented customer data into actionable, dynamic insights through intelligent digital twin modeling.
Core User Flow
Core User Flow
Core User Flow
How users interact with the platform from data connection to actionable insights
How users interact with the platform from data connection to actionable insights
1.
1.
Data Connection
Data Connection
Data Connection
Connect existing data sources with guided setup
Connect existing data sources with guided setup
CRM integration
CRM integration
CRM integration
CRM integration
Analytics platforms
Analytics platforms
Analytics platforms
Analytics platforms
Customer surveys
Customer surveys
Customer surveys
Social media data
Social media data
Social media data
Social media data
Customer surveys
Customer surveys
Social media data
Social media data
2.
2.
Model Training
Model Training
Model Training
AI analyses patterns and builds customer digital twins
AI analyses patterns and builds customer digital twins
Real-time personas
Real-time personas
Real-time personas
Real-time personas
Trend predictions
Trend predictions
Trend predictions
Trend predictions
Opportunity alerts
Opportunity alerts
Opportunity alerts
Performance metrics
Performance metrics
Performance metrics
Performance metrics
Opportunity alerts
Opportunity alerts
Performance metrics
Performance metrics
3.
3.
Insight Generation
Insight Generation
Insight Generation
Dynamic personas and recommendations are created
Dynamic personas and recommendations are created
Behavioural clustering
Behavioural clustering
Behavioural clustering
Behavioural clustering
Preference modeling
Preference modeling
Preference modeling
Preference modeling
Journey mapping
Journey mapping
Journey mapping
Predictive scoring
Predictive scoring
Predictive scoring
Predictive scoring
Journey mapping
Journey mapping
Predictive scoring
Predictive scoring
4.
4.
Action & Optimization
Action & Optimization
Action & Optimization
Teams use insights to make informed decisions
Teams use insights to make informed decisions
Campaign optimisation
Campaign optimisation
Campaign optimisation
Campaign optimisation
Product roadmap
Product roadmap
Product roadmap
Product roadmap
Customer experience
Customer experience
Customer experience
Content strategy
Content strategy
Content strategy
Content strategy
Customer experience
Customer experience
Content strategy
Content strategy
Data Layer
Multi-source data ingestionReal-time data processingData quality monitoringPrivacy-compliant storage
Data Layer
Multi-source data ingestionReal-time data processingData quality monitoringPrivacy-compliant storage
Data Layer
Multi-source data ingestionReal-time data processingData quality monitoringPrivacy-compliant storage
Data Layer
Multi-source data ingestionReal-time data processingData quality monitoringPrivacy-compliant storage
AI/ML Engine
Customer clustering algorithms Behavioural prediction models Continuous learning system Explainable AI features
AI/ML Engine
Customer clustering algorithms Behavioural prediction models Continuous learning system Explainable AI features
AI/ML Engine
Customer clustering algorithms Behavioural prediction models Continuous learning system Explainable AI features
AI/ML Engine
Customer clustering algorithms Behavioural prediction models Continuous learning system Explainable AI features
Interface Layer
Intuitive dashboard design Interactive visualisations Mobile-responsive interface API for third-party integration
Interface Layer
Intuitive dashboard design Interactive visualisations Mobile-responsive interface API for third-party integration
Interface Layer
Intuitive dashboard design Interactive visualisations Mobile-responsive interface API for third-party integration
Interface Layer
Intuitive dashboard design Interactive visualisations Mobile-responsive interface API for third-party integration
Analytics Dashboard
Analytics Dashboard
Analytics Dashboard
Real-time customer insights and behavioral analytics
Real-time customer insights and behavioral analytics
Real-time customer insights and behavioral analytics
The central command center providing executives and customer success teams with real-time visibility into customer health metrics. Features behavioral trend analysis, segment distribution, and live activity monitoring to enable data-driven decision making at scale.
The central command center providing executives and customer success teams with real-time visibility into customer health metrics. Features behavioral trend analysis, segment distribution, and live activity monitoring to enable data-driven decision making at scale.
The central command center providing executives and customer success teams with real-time visibility into customer health metrics. Features behavioral trend analysis, segment distribution, and live activity monitoring to enable data-driven decision making at scale.
Cross-industry
Cross-industry
Real-time Data
Real-time Data
Real-time Data
Real-time Data
Behavioral Analystics
Behavioral Analystics
Behavioral Analystics
Behavioral Analystics
Custom Metrics
Custom Metrics
Custom Metrics
Custom Metrics
Digital Twin
Digital Twin
Digital Twin
Comprehensive customer digital twin with AI insights*
Comprehensive customer digital twin with AI insights*
Comprehensive customer digital twin with AI insights*
A complete 360-degree view of individual customers combining behavioural patterns, preferences, and journey milestones. AI-generated insights predict future actions and recommend personalised engagement strategies to enhance customer experience.
A complete 360-degree view of individual customers combining behavioural patterns, preferences, and journey milestones. AI-generated insights predict future actions and recommend personalised engagement strategies to enhance customer experience.
A complete 360-degree view of individual customers combining behavioural patterns, preferences, and journey milestones. AI-generated insights predict future actions and recommend personalised engagement strategies to enhance customer experience.
Cross-industry
Cross-industry
AI Insights
AI Insights
AI Insights
AI Insights
Behavioral Patterns
Behavioral Patterns
Behavioral Patterns
Behavioral Patterns
Journey Timeline
Journey Timeline
Journey Timeline
Journey Timeline
Predictive Analytics
Predictive Analytics
Predictive Analytics
ML-powered predictions and risk assessment
ML-powered predictions and risk assessment
ML-powered predictions and risk assessment
Advanced machine learning models predict customer churn risk with 94% accuracy. Identifies early warning signals and provides actionable insights to proactively address customer retention challenges before they impact revenue.
Advanced machine learning models predict customer churn risk with 94% accuracy. Identifies early warning signals and provides actionable insights to proactively address customer retention challenges before they impact revenue.
Advanced machine learning models predict customer churn risk with 94% accuracy. Identifies early warning signals and provides actionable insights to proactively address customer retention challenges before they impact revenue.
Cross-industry
Cross-industry
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Risk Prediction
Risk Prediction
Risk Prediction
Risk Prediction
Model Performance
Model Performance
Model Performance
Model Performance
Impact on UX Research Practice
Impact on UX Research Practice
Impact on UX Research Practice
This project fundamentally changed how I approach AI product research, establishing new methodologies for complex B2B platforms that balance user needs with technical constraints.
This project fundamentally changed how I approach AI product research, establishing new methodologies for complex B2B platforms that balance user needs with technical constraints.
This project fundamentally changed how I approach AI product research, establishing new methodologies for complex B2B platforms that balance user needs with technical constraints.
Research Framework
Developed reusable methodology for AI product research
Research Framework
Developed reusable methodology for AI product research
Research Framework
Developed reusable methodology for AI product research
Research Framework
Developed reusable methodology for AI product research
Team Processes
Established cross-functional collaboration patterns
Team Processes
Established cross-functional collaboration patterns
Team Processes
Established cross-functional collaboration patterns
Team Processes
Established cross-functional collaboration patterns
Knowledge Sharing
Created documentation and training for future projects.
Knowledge Sharing
Created documentation and training for future projects
Knowledge Sharing
Created documentation and training for future projects.
Knowledge Sharing
Created documentation and training for future projects.
PERSONAL GROWTH AND INSIGHTS
PERSONAL GROWTH AND INSIGHTS
PERSONAL GROWTH AND INSIGHTS
Skills Developed
• AI/ML product research methodologies. • Cross-functional stakeholder management
• Complex B2B user journey mapping. • Data-driven decision making frameworks
• Technical constraint integration in design
Skills Developed
• AI/ML product research methodologies. • Cross-functional stakeholder management
• Complex B2B user journey mapping. • Data-driven decision making frameworks
• Technical constraint integration in design
Skills Developed
• AI/ML product research methodologies. • Cross-functional stakeholder management
• Complex B2B user journey mapping. • Data-driven decision making frameworks
• Technical constraint integration in design
Key Realisations
• AI products require fundamentally different research approaches
• User trust in AI must be earned through transparency
• Technical limitations often drive the best design innovations.
• Continuous user feedback is critical for AI product success
• Cross-team collaboration is essential for complex products
Key Realisations
• AI products require fundamentally different research approaches
• User trust in AI must be earned through transparency
• Technical limitations often drive the best design innovations.
• Continuous user feedback is critical for AI product success
• Cross-team collaboration is essential for complex products
Key Realisations
• AI products require fundamentally different research approaches
• User trust in AI must be earned through transparency
• Technical limitations often drive the best design innovations.
• Continuous user feedback is critical for AI product success
• Cross-team collaboration is essential for complex products