- Getting started with the course.
- Organizational matters.
- Clarify the course schedule and expectations.

- Introduction to the course topic.

- Getting started with the course.
- Organizational matters.
- Clarify the course schedule and expectations.

- Introduction to the course topic.

- Getting started with the course.
- Organizational matters.
- Clarify the course schedule and expectations.

- Introduction to the course topic.

• Identify and evaluate data sources in the area of marketing & sales along the customer journey (ACCRA model).
• The importance of different channels and touchpoints for data acquisition.
• Data specifics of online and offline channels.
• Approaches to overcoming cross-channel data collection challenges.
• Practical exercise for application.

• Identify and evaluate data sources in the area of marketing & sales along the customer journey (ACCRA model).
• The importance of different channels and touchpoints for data acquisition.
• Data specifics of online and offline channels.
• Approaches to overcoming cross-channel data collection challenges.
• Practical exercise for application.

• Identify and evaluate data sources in the area of marketing & sales along the customer journey (ACCRA model).
• The importance of different channels and touchpoints for data acquisition.
• Data specifics of online and offline channels.
• Approaches to overcoming cross-channel data collection challenges.
• Practical exercise for application.

• Data sources and data collection in practice.
• Discussion of the practical exercises.
• Open questions and case studies.

• Data sources and data collection in practice.
• Discussion of the practical exercises.
• Open questions and case studies.

• Data sources and data collection in practice.
• Discussion of the practical exercises.
• Open questions and case studies.

• Overview of data collection methods: Automated data collection (web tracking and data sources from other funnel stages), manual data collection (surveys, feedback).
• Development of a targeted tracking concept including selection of suitable tracking tools and technologies.
• Implementation and validation of tracking, including best practices and methods for testing correct data collection.
• Extract - Data extraction from various sources (databases, APIs, flat files).
• Transform - data cleansing, enrichment and integration.
• Load - loading the prepared data into target systems: understanding of staging areas, data marts and direct connection to analysis tools.
• Visualize (preparation) - Optimally prepare data for analysis and visualization.
• Continuous data quality assurance (data quality management).
• Ensure up-to-date data and manage it strategically.
• Tool landscape and practical application in the ETLV context
• Insight into relevant tool categories for the ETLV process.
• Overview of tools for data extraction, integration, cleansing and transformation.
• The role of tag management systems and customer data platforms (CDPs) in the data process.
• Practical exercise for application.

• Overview of data collection methods: Automated data collection (web tracking and data sources from other funnel stages), manual data collection (surveys, feedback).
• Development of a targeted tracking concept including selection of suitable tracking tools and technologies.
• Implementation and validation of tracking, including best practices and methods for testing correct data collection.
• Extract - Data extraction from various sources (databases, APIs, flat files).
• Transform - data cleansing, enrichment and integration.
• Load - loading the prepared data into target systems: understanding of staging areas, data marts and direct connection to analysis tools.
• Visualize (preparation) - Optimally prepare data for analysis and visualization.
• Continuous data quality assurance (data quality management).
• Ensure up-to-date data and manage it strategically.
• Tool landscape and practical application in the ETLV context
• Insight into relevant tool categories for the ETLV process.
• Overview of tools for data extraction, integration, cleansing and transformation.
• The role of tag management systems and customer data platforms (CDPs) in the data process.
• Practical exercise for application.

• Overview of data collection methods: Automated data collection (web tracking and data sources from other funnel stages), manual data collection (surveys, feedback).
• Development of a targeted tracking concept including selection of suitable tracking tools and technologies.
• Implementation and validation of tracking, including best practices and methods for testing correct data collection.
• Extract - Data extraction from various sources (databases, APIs, flat files).
• Transform - data cleansing, enrichment and integration.
• Load - loading the prepared data into target systems: understanding of staging areas, data marts and direct connection to analysis tools.
• Visualize (preparation) - Optimally prepare data for analysis and visualization.
• Continuous data quality assurance (data quality management).
• Ensure up-to-date data and manage it strategically.
• Tool landscape and practical application in the ETLV context
• Insight into relevant tool categories for the ETLV process.
• Overview of tools for data extraction, integration, cleansing and transformation.
• The role of tag management systems and customer data platforms (CDPs) in the data process.
• Practical exercise for application.

• Consolidate ETLV knowledge.
• Transfer to everyday working life.
• Discuss practical applications.

• Consolidate ETLV knowledge.
• Transfer to everyday working life.
• Discuss practical applications.

• Consolidate ETLV knowledge.
• Transfer to everyday working life.
• Discuss practical applications.