Adobe Analytics Mastery: Workspace, Segmentation & Metrics

This lecture walks students through the process of creating a new project in Analysis Workspace. Students will be able to start a new project from scratch and choose the right components to begin their analysis.

This lesson focuses on familiarizing students with the user interface of Analysis Workspace. After this lecture, students will be able to efficiently navigate the platform, locate key tools, and understand the layout of the workspace.

This lecture teaches students how to add and use metrics in their projects. Students will learn to drag and drop metrics into their analysis and understand how they quantify user behavior.

This lesson covers the use of dimensions, which provide context to metrics. Upon completion, students will be able to apply dimensions to break down their data and gain deeper insights into user actions.

This lecture explains how to use segments to filter and analyze specific groups of visitors. Students will be able to create and apply segments to focus their analysis on relevant user cohorts.

This lesson teaches students how to specify custom date ranges for their reports. Students will be able to set and apply custom date ranges to analyze data over specific time periods relevant to their business questions.

This lecture dives into the Freeform Table, a powerful and flexible analysis tool. Students will be able to build, customize, and analyze data using Freeform Tables to answer complex questions.

This lesson focuses on data visualization within Analysis Workspace. Students will learn how to create and customize line, bar, and stacked bar charts to visually represent their data and tell a clear story.

This lecture teaches students about the fallout visualization, a tool for understanding conversion funnels. Students will be able to build and interpret a fallout report to identify where users are dropping off in a conversion process.

This lesson explores the Flow visualization for understanding user navigation paths. Students will be able to use the Flow visualization to see how visitors move through their site and identify common paths.

This lecture covers the use of Cohort Tables to analyze user retention over time. Upon completion, students will be able to create and analyze a cohort table to understand how user behavior changes after their first visit.

This lesson introduces Quick Insights, a feature that uses natural language processing to analyze data. Students will be able to ask natural language questions and receive quick, data-driven answers without manually building reports.

This lecture focuses on the Page Summary panel for quick insights into specific pages. Students will be able to use the Page Summary panel to instantly see key metrics for any given page.

This lesson teaches students how to use the Segment Comparison tool, which leverages machine learning to find differences between segments. Students will be able to automatically identify key differences between two user segments.

This lecture covers the Attribution panel for understanding how different channels contribute to conversions. Students will be able to apply various attribution models to accurately credit different marketing efforts.

This lesson focuses on the Media panels for reporting on video and audio content. Students will be able to track and analyze media consumption metrics, such as views and completion rates.

This lecture explores the integration between Adobe Analytics and Adobe Target. Students will be able to access and analyze A/B testing data directly within the Analytics interface.

This lecture focuses on advanced analytical techniques within Adobe Analytics. Students will be able to perform more complex and nuanced analyses to uncover deeper insights from their data.

This lesson provides a review of foundational Adobe Analytics concepts. Students will be able to revisit and reinforce their understanding of key concepts before moving on to more advanced topics.

This lesson helps students access their demo data for advanced exercises. Students will be able to locate and use demo data to practice advanced analysis techniques.

This lecture provides a deeper dive into core traffic metrics. Upon completion, students will be able to understand and interpret key metrics like visits, unique visitors, and page views.

This lesson clarifies the difference between Occurrences and Instances. Students will be able to differentiate between Occurrences and Instances and use them correctly to count events and unique actions.

This lecture defines and differentiates these important metrics. Students will be able to understand the nuances of bounces, single-page visits, and single access to accurately measure site engagement.

This lesson focuses on metrics for e-commerce sites. Students will be able to interpret and analyze standard commerce metrics like revenue, orders, and products viewed.

This lecture covers metrics related to time spent on site. Students will be able to find and understand time spent metrics to measure user engagement and content performance.

This lesson explains how to interpret the value and format of time spent metrics. Students will be able to evaluate the significance of time spent metrics and understand their different display formats.

This lecture shows students how to find and use custom metrics. Students will be able to identify and use custom metrics created for their specific business needs.

This lesson covers the use of core traffic dimensions. Students will be able to apply core dimensions to segment their traffic data by variables like country, browser, and device.

This lecture focuses on marketing channel dimensions. Students will be able to use marketing channel dimensions to analyze the effectiveness of their marketing campaigns.

This lesson explains the process of capturing marketing channel data. Students will be able to understand the data collection process for marketing channels to ensure data accuracy.

This lecture covers advanced methods for analyzing visitor retention. Students will be able to apply techniques to measure and improve visitor retention.

This lesson explores how to use geography and technology dimensions. Students will be able to segment visitors by their geographic location and technology to provide tailored content and experiences.

This lecture focuses on the Activity Map data. Students will be able to analyze user clicks and engagement on a page using the Activity Map overlay.

This lesson teaches students how to use custom dimensions. Students will be able to leverage custom dimensions to analyze data specific to their company's unique needs.

This lecture clarifies the meaning of 'None' and 'Unspecified' in reports. Students will be able to interpret and use the 'None' and 'Unspecified' values to understand incomplete or unclassified data.

This lesson covers the creation of simple segments. Students will be able to create quick, basic segments to perform simple filtering of their data.

This lecture teaches students how to create complex segments from the ground up. Students will be able to build detailed segments from scratch to analyze specific user behaviors.

This lesson explains how to add containers to segments. Students will be able to use multiple containers to create complex, multi-layered segments.

This lecture covers the different options for segment containers. Students will be able to use various container options like "Hit," "Visit," and "Visitor" to define segments precisely.

This lesson focuses on advanced segment logic. Students will be able to use exclusion and sequencing to build highly specific segments that follow or exclude certain behaviors.

This lecture teaches students how to create their own metrics. Students will be able to build calculated metrics from scratch to create custom KPIs tailored to their business.

This lesson covers the integration of segments and containers into calculated metrics. Students will be able to incorporate segments and containers to create highly specific and conditional calculated metrics.

This lecture introduces advanced functions for calculated metrics. Students will be able to use key functions to perform complex calculations and create sophisticated metrics.

This lesson focuses on the table builder tool. Students will be able to use the table builder to quickly create complex tables with multiple metrics and dimensions.

This lecture covers the use of histograms for data distribution. Students will be able to create histograms to visualize data distribution and understand where most values fall.

This lesson focuses on summarizing data visually. Students will be able to use combo charts and key metric summaries to provide high-level, at-a-glance overviews of their data.

This lecture teaches students how to use Venn diagrams to compare segments. Students will be able to visualize and analyze the overlap between different segments using a Venn diagram.