Diploma in Usage Analytics and Experience Improvement

2.2 Data Collection and Cleaning for UX: Tools and Techniques
2.2 Key UX Metrics: Identification and Definition
2.3 Quantitative UX Analysis: Methods and Tools
2.4 Qualitative UX Analysis: Research Techniques
2.5 Segmentation and Personas: Creating User Profiles
2.6 Heatmaps and Click Analysis: Interpretation and Action
2.7 A/B and Multivariate Testing: Design and Evaluation
2.8 Designing Experiments for UX: Methodology and Application
2.9 Competitive Analysis and UX Benchmarking
2.20 UX Reporting and Communicating Findings

3.3 Fundamentals of UX Data Analysis: Collection and Visualization
3.2 Key Metrics and KPIs in User Experience
3.3 UX Analysis Tools: Google Analytics, Hotjar, etc.

3.4 Quantitative UX Analysis: Data and Numbers
3.5 Qualitative UX Analysis: Interviews and Testing
3.6 Segmentation and Personas in UX Analysis
3.7 Heatmaps, Conversion Funnels, and User Flows
3.8 A/B Testing and Experience Optimization
3.9 Reporting and Communicating Findings
3.30 Implementing Data-Driven Improvements

4.4 Introduction to UX Analytics: Fundamentals and Key Concepts
4.2 Data Collection and Analysis: Essential Methods and Tools
4.3 Key UX Metrics: Identification and Practical Application
4.4 User Behavior Analysis: Navigation, Interaction, and Conversions
4.5 User Journey Mapping: Visualizing the User Experience
4.6 User Feedback Analysis: Surveys, Interviews, and Usability Testing
4.7 User Segmentation: Personalizing Analysis and Improvement
4.8 Identifying Weaknesses and Opportunities for Improvement
4.9 Implementing Data-Driven Improvements and A/B Testing
4.40 Measuring Impact and Continuously Optimizing the User Experience

5.5 Introduction to UX Analytics: Definitions and Key Concepts

5.5 Fundamental UX Metrics: KPIs and Their Importance

5.3 UX Data Collection and Management: Tools and Methods

5.4 Qualitative and Quantitative Data Analysis

5.5 Interpreting Results and Creating Basic Reports

5.6 The Role of Analytics in User-Centered Design

5.5 Data-Driven User Research: Surveys, Interviews, and A/B Testing

5.5 User Segmentation and Profiling

5.3 User Behavior Analysis: Flows, Heatmaps, and Conversion Funnels

5.4 Identifying Weaknesses and Opportunities for Improvement

5.5 Designing Experiments and Usability Testing

5.6 Data-Driven Personalization Strategies

3.5 Advanced KPIs for Measuring User Experience
3.5 Cohort Analysis and Behavioral Trends

3.3 UX Data Modeling: Predicting and Anticipating Needs

3.4 Conversion Optimization: Data-Driven Strategies and Tactics

3.5 Implementing Feedback Systems and NPS Surveys

3.6 Integrating UX Analytics with Other Business Areas

4.5 Advanced UX Data Analysis Methods: Data Mining and Machine Learning

4.5 Sentiment Analysis and Voice of the Customer

4.3 Identifying Patterns and Trends in User Behavior

4.4 Creating High-Level UX Data Analysis Reports

4.5 Presenting Findings and Recommendations to Stakeholders

4.6 Competitive Analysis and UX Benchmarking

5.5 Selecting and Implementing UX Analytics Tools

5.5 Integrating UX Analytics into the Development Workflow 5.3 Defining objectives and key performance indicators (KPIs) for measuring the user experience.

5.4 Establishing a data-driven continuous improvement process.

5.5 Aligning UX analytics with business strategy.

5.6 Managing change and adopting a data-driven culture.

6.5 Continuously monitoring user experience performance.

6.5 Identifying areas for improvement and prioritizing actions.

6.3 A/B and multivariate testing for UX optimization.

6.4 Analyzing results and validating hypotheses.

6.5 Implementing changes and monitoring their impact.

6.6 Iterating and continuously improving the user experience.

7.5 Transforming data into actionable insights.

7.5 Designing and executing usability tests.

7.3 Optimizing the information architecture. 7.4 Improving content readability and accessibility.

7.5 Personalizing the user experience.

7.6 Measuring the impact of changes.

8.5 Analyzing the user lifecycle.

8.5 Optimizing navigation and interaction.

8.3 Designing intuitive and engaging interfaces.

8.4 Improving website speed and performance.

8.5 Analyzing customer satisfaction and loyalty.

8.6 Creating a user-centric culture.

6.6 UX Data Collection and Preparation
6.2 Key User Experience Metrics
6.3 Quantitative Data Analysis: Tools and Techniques
6.4 Qualitative Data Analysis: Methods and Applications
6.5 Identifying UX Patterns and Trends
6.6 Data Visualization for Decision Making
6.7 Interpreting Results and Generating Insights
6.8 Designing Experiments and A/B Testing
6.9 Reporting and Communicating UX Findings
6.60 Implementing Data-Driven Improvements

7.7 Introduction to UX Analytics and its Importance
7.2 Key Metrics for Measuring User Experience
7.3 UX Analytics Tools and Platforms
7.4 Collecting and Organizing UX Data
7.7 Data Interpretation: From Behavior to Understanding

2.7 Defining SMART Objectives for Data-Driven UX Research
2.2 Quantitative Research Techniques: Surveys, A/B Testing
2.3 Qualitative Research Techniques: Interviews, Focus Groups
2.4 Integrating Quantitative and Qualitative Data
2.7 Evidence-Based Experience Design

3.7 Advanced KPIs for Measuring UX Excellence
3.2 Heatmap and Clickmap Analysis
3.3 User Segmentation and Personalization
3.4 User Journey Analysis
3.7 Optimizing Conversion and Engagement

4.7 Analyzing User Behavior Patterns
4.2 Identifying Pain Points and Opportunities for Improvement
4.3 Feedback Analysis User feedback (surveys, comments)
4.4 Designing experiments and usability testing
4.7 Iteration and continuous improvement of the experience

7.7 Selecting analytics tools and platforms
7.2 Integrating analytics into the UX design process
7.3 Establishing a measurement and monitoring system
7.4 Automating reports and analyses
7.7 Data-driven culture in the UX team

6.7 Defining key performance indicators (KPIs) for improvement
6.2 Data analysis for problem identification
6.3 Designing experiments and A/B testing
6.4 Implementing changes and tracking results
6.7 Data-driven continuous improvement cycle

7.7 Transforming data into actionable insights
7.2 Data-driven decision-making
7.3 Prioritizing user experience improvements
7.4 Communicating results and findings
7.7 Creating prototypes and rapid tests

8.7 Analyzing quantitative and qualitative data for the Improvement
8.2 Evaluation of usability, accessibility, and functionality
8.3 Identification of optimization opportunities
8.4 Implementation of improvements and monitoring of results
8.7 Measuring the impact of improvements on user experience

8.2 Evaluation of usability, accessibility, and functionality
8.3 Identification of optimization opportunities
8.4 Implementation of improvements and monitoring of results
8.7 Measurement of the impact of improvements on user experience

8.8 Introduction to Data Analysis in UX: Definitions and Key Concepts
8.8 Fundamental UX Metrics: Usability, Accessibility, Satisfaction
8.3 UX Data Sources: Surveys, A/B Testing, Heatmaps
8.4 UX Data Analysis Tools: Google Analytics, Hotjar, etc.

8.5 Principles of Data Visualization for UX
8.6 The UX Data Analysis Lifecycle
8.7 UX Data Collection and Cleaning
8.8 Basic Quantitative Analysis: Metrics and KPIs
8.9 Basic Qualitative Analysis: Interviews and User Feedback
8.80 Ethics in UX Data Analysis

8.8 Design of Experiments: A/B and Multivariate Testing
8.8 User Segmentation: Identifying Profiles and Groups
8.3 Experience Personalization: Data-Driven Adaptation
8.4 Conversion Analysis: Sales Funnel Optimization
8.5 Predictive Modeling in UX: Anticipating User Behavior
8.6 User Behavior Analysis: Flows, Funnels, and Events
8.7 Research Techniques: Usability Testing, User Testing
8.8 Sentiment Analysis and User Feedback
8.9 Competitive Analysis
8.80 Continuous Improvement Strategies in data

3.8 Defining and establishing UX KPIs
3.8 Implementing UX dashboards
3.3 Cohort analysis: Behavior over time
3.4 RFM analysis: Frequency, monetary value, and recency
3.5 Customer journey analysis
3.6 Journey map optimization
3.7 Creating UX reports
3.8 The importance of accessibility
3.8 Omnichannel experience analysis
3.80 Communicating results and data-driven decision-making

4.8 Collecting and preparing UX data
4.8 Analyzing survey and feedback data
4.3 Analyzing heatmaps and session recordings
4.4 Analyzing conversion funnels and funnels
4.5 Using Google Analytics and similar tools
4.6 Identifying critical points in the user journey
4.7 Root cause analysis of UX problems
4.8 Designing data-driven solutions
4.8 Testing proposed solutions
4.80 Measuring the impact of Improvements

5.8 Strategic Planning of UX Data Analysis
5.8 Selection of Analytical Tools and Platforms
5.3 Implementation of Data Collection
5.4 Integration of Data from Multiple Sources
5.5 Development of Dashboards and Reports
5.6 Automation of Data Analysis
5.7 Alignment of Data Analysis with Business Objectives
5.8 Training the Team in UX Data Analysis
5.8 Change Management and Data Adoption
5.80 Measuring the ROI of UX Initiatives

6.8 Establishing a Continuous Improvement Process
6.8 Ongoing Monitoring of UX Metrics
6.3 Identification of Areas of Opportunity
6.4 Conducting A/B and Multivariate Testing
6.5 Analysis of Test Results
6.6 Implementation of Data-Driven Improvements
6.7 Communication of Results to Stakeholders
6.8 Analysis of Performance and Usability Data
6.8 User Feedback and Sentiment Analysis
6.80 Adjustment of Strategies UX

7.8 Transforming Data into Actionable Insights
7.8 Using Data for Decision Making
7.3 Prioritizing UX Improvements
7.4 Analyzing Data for Personalization
7.5 Measuring the Impact of Improvements on Conversion
7.6 Optimizing Design and Usability
7.7 Improving User Satisfaction
7.8 Analyzing User Behavior Data
7.8 Creating Reports and Dashboards
7.80 Communicating Results

8.8 Defining Key UX Metrics
8.8 Collecting and Analyzing Quantitative Data
8.3 Collecting and Analyzing Qualitative Data
8.4 Integrating Quantitative and Qualitative Data
8.5 Competitive Analysis
8.6 Identifying Usability Issues
8.7 Designing Data-Driven Solutions
8.8 Usability Testing
8.8 Measuring the Impact of Improvements
8.80 Iteration and Optimization

9.9 Introduction to web analytics and user behavior.

9.9 Key metrics in UX: Definitions and uses.

9.3 Collection and analysis of quantitative data.

9.4 Introduction to qualitative data: surveys and interviews.

9.5 Data analysis tools: Google Analytics, Hotjar, etc.

9.6 Principles of data visualization for UX.

9.7 Interpreting reports and dashboards.

9.8 Ethics in the collection and use of user data.

9.9 Defining SMART goals for UX analytics.

9.90 Case study: Basic data analysis and initial decision-making.

9.9 User segmentation and profiling.

9.9 Designing A/B and multivariate tests.

9.3 Heatmap and clickmap analysis.

9.4 Conversion funnel analysis. 9.5 Form Analysis and Optimization

9.6 Data-Driven Personalization Strategies

9.7 Customer Journey Analysis

9.8 Data-Driven UX Writing Techniques

9.9 SEO Optimization to Improve User Experience

9.90 Case Study: Designing a Data-Driven UX Strategy for a Specific Product

3.9 Cohort Analysis and User Retention

3.9 Advanced Navigation Path Analysis

3.3 Using Machine Learning in UX: Predictions and Personalization

3.4 Sentiment Analysis and User Feedback

3.5 Designing Recommendation Systems

3.6 Optimizing Website Speed ​​and Performance

3.7 Accessibility and Usability Analysis

3.8 Mobile User Experience Analysis

3.9 Advanced UX Metrics: NPS, CES, etc. 3.90 Case Study: Implementing Advanced Analytics to Improve User Loyalty

4.9 User Research: Methods and Techniques

4.9 Designing Interviews and Focus Groups

4.3 Analyzing User Feedback

4.4 Qualitative Data Analysis: Coding and Themes

4.5 Designing User Personas and Customer Journeys

4.6 Designing Prototypes and Usability Testing

4.7 Competitive Analysis and Benchmarking

4.8 Designing Remote Usability Tests

4.9 Data Storytelling Techniques

4.90 Case Study: Developing a User Research Report and Improvement Recommendations

5.9 Defining Business Objectives and Key Metrics

5.9 Selecting Analytics Tools

5.3 Designing an Analytics Implementation Plan

5.4 Integrating Analytics with Other Tools 5.5 Data Access and Permission Management

5.6 Team Training in Data Analysis

5.7 Measuring Return on Investment (ROI) in UX

5.8 Designing Custom Reports and Dashboards

5.9 Data Culture and Evidence-Based Decision Making

5.90 Case Study: Implementing an Analytics Solution for a Digital Product

6.9 Designing a Continuous Improvement Plan

6.9 Monitoring Key Metrics

6.3 Real-Time Data Analysis

6.4 Identifying Areas for Improvement

6.5 Designing and Running Experiments

6.6 Iteration and Optimization Based on Results

6.7 Testing and Learning Culture

6.8 Managing Feedback and Surveys

6.9 Communicating Results and Recommendations

6.90 Case Study: Implementing a Continuous Improvement Cycle for a Product or Service

7.9 Identifying data-driven opportunities for improvement.

7.9 Prioritizing optimization actions.

7.3 Designing prototypes and testing.

7.4 Implementing changes and monitoring progress.

7.5 Measuring results and analyzing data.

7.6 Optimizing conversion rates.

7.7 Improving design and usability.

7.8 Optimizing content and messaging.

7.9 Personalizing the user experience.

7.90 Case study: Data-driven website or application optimization.

8.9 Holistic evaluation of the user experience.

8.9 Analyzing the complete customer journey.

8.3 Integrating qualitative and quantitative data.

8.4 Analyzing customer satisfaction.

8.5 Analyzing customer loyalty and retention.

8.6 Analyzing the return on investment (ROI) of the user experience. 8.7 Designing a comprehensive improvement strategy.

8.8 Measuring the impact of implemented improvements.

8.9 Customer-centric culture and data-driven decision-making.

8.90 Case study: Comprehensive user experience analysis in a specific business.

1.1 Defining Objectives and KPIs for UX Analytics

1.2 Data Collection and Cleaning: Sources and Methods

1.3 Quantitative Data Analysis: Key Metrics and Trends

1.4 Qualitative Data Analysis: User Feedback and Studies

1.5 Identifying Weaknesses and Opportunities for Improvement

1.6 Designing A/B and Multivariate Tests

1.7 Implementing and Monitoring Changes

1.8 Impact Evaluation: Metrics and ROI

1.9 Presenting Findings and Strategic Recommendations

1.10 Integrating Analytics into the UX Culture