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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Use Adobe Analytics as a marketer --not a programmer! If you're a marketer in need of a non-technical, beginner's reference to using Adobe Analytics, this book is the perfect place to start. Adobe Analytics For Dummies arms you with a basic knowledge of the key features so that you can start using it quickly and effectively. Even if you're a digital marketer who doesn't have their hands in data day in and day out, this easy-to-follow reference makes it simple to utilize Adobe Analytics. With the help of this book, you'll better understand how your marketing efforts are performing, converting, being engaged with, and being shared in the digital space. * Evaluate your marketing strategies and campaigns * Explore implementation fundamentals and report architecture * Apply Adobe Analytics to multiple sources * Succeed in the workplace and expand your marketing skillset The marketing world is continually growing and evolving, and Adobe Analytics For Dummies will help you stay ahead of the curve.
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Adobe® Analytics For Dummies®
Published by: John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774, www.wiley.com
Copyright © 2019 by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Control Number: 2019932875
ISBN 978-1-119-44608-8 (pbk); ISBN 978-1-119-44600-2 (ebk); ISBN 978-1-119-44601-9 (ebk)
Adobe® Analytics For Dummies®
To view this book's Cheat Sheet, simply go to www.dummies.com and search for “Adobe Analytics For Dummies Cheat Sheet” in the Search box.
Table of Contents
Cover
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part 1: Getting Started with Adobe Analytics
Chapter 1: Why Adobe Analytics?
Understanding Why You're Using Adobe Analytics
Identifying Where Adobe Analytics Data Comes From
Configuring and Analyzing Data
Situating Adobe Analytics in the Universe of Data Analysis
Building a Positive Relationship with Your Data Team
Chapter 2: Basic Building Blocks of Reporting and Analysis
Standard Categories of Measurement
Defining Dimensions
Measuring with Metrics
Measuring with Segments
Using Report Suites
Chapter 3: Conquering the Analysis Workspace Interface
Surveying the Analytics Environment
Zooming In on the Workspace
Creating Your First Project
Understanding the Calendar
Using Analysis Workspace Panels
Adding Dimensions, Metrics, Segments, and Time Components
Navigating the Menu Structure
Part 2: Analyzing Data
Chapter 4: Building Analytic Reports with Freeform Tables
Working with Dimensions and Metrics
Adding Dimensions to a Table
Zooming in with Multiple Metrics
Sorting and Filtering Data
Dropping into the Segment Drop Zone
Exploiting the Value of Templates
Chapter 5: Using Metrics to Analyze Data
Analyzing Time Spent
Using Metrics for Bounces, Bounce Rate, and Single Page Visits
Understanding Metrics Unique to Adobe
Exploiting Product and Cart Metrics
Working with Custom Metrics in Adobe
Chapter 6: Using Dimensions to Analyze Data
Wielding Content Dimensions
Connecting Behavior to Advertising
Chapter 7: Using Device, Product, and Custom Dimensions to Analyze Data
Defining Key Technology Dimensions
Dissecting Product Dimensions
Sifting through Time Dimensions
Working with Custom Dimensions
Chapter 8: Productivity Tips and Techniques
Exploiting Essential Keyboard and Mouse Shortcuts
Taking Advantage of One-Click Visualize
Invoking Time Comparisons
Applying Conditional Formatting
Part 3: Massaging Data for Complex Analysis
Chapter 9: Designing Precise Segments
Understanding and Defining Segments
Defining a Segment and Setting the Container
Using Virtual Report Suites Based on Segments
Chapter 10: Creating Calculated Metrics to Accelerate Analyses
Understanding and Defining Calculated Metrics
Creating Basic Calculated Metrics in a Freeform Table
Building Calculated Metrics from Scratch
Getting the Most from Calculated Metrics
Chapter 11: Classified! Using Classifications to Make Data More Accessible
Making Data Coherent and Accessible
Working with Classified Data
Defining Classifications
Sending Data to a Classification
Chapter 12: Applying Attribution Models for Sophisticated Analysis
Applying Attribution to Your Data
Differentiating Attribution Models
Operating Attribution IQ in Workspace
Part 4: Visualizing Data to Reveal Golden Nuggets
Chapter 13: Creating Chart Visualizations for Data Storytelling
Getting the Most from Charts in Adobe Analytics
Creating Charts from Table Data
Building Histograms and Venn Diagrams
Defining Chart Attributes in Detail
Visualization Beyond Data Charts
Chapter 14: Advanced Visualization
Visualizing Flow Paths
Analyzing Fallout Paths
Building Cohort Tables
Customizing and Sharing Curated Projects
Changing Color Palettes
Chapter 15: Leveraging Data Science to Identify Unknown Unknowns
Detecting Anomalies
Discovering Contribution Analysis
Using Data Science to Compare Segments
Chapter 16: Arming Yourself with Data from the Beyond
Drawing Analysis outside Workspace
Visual Analysis Heat Maps with Activity Map
Integrating within Adobe Products
Integrating beyond Individual Products
Part 5: The Part of Tens
Chapter 17: Top Ten Custom Segments
Identifying Purchasers
Defining a Non-Purchasers Segment
Isolating Single-Page Visitors
Identifying Single-Visit, Multi-Page Visitors
Bucketing SEO to Internal Search
Segmenting Pre-Purchase Activity
Going Strictly Organic
Finding Strictly Paid Activity
Filtering Out Potential Bots
Identifying Checkout Fallout
Chapter 18: Top Ten Analytics Resources
Checking Out Adobe's Analytics Implementation Guide
Understanding Why You Need a Measurement Plan
Using Data Governance
Setting Up a Web Analytics Solution Design
Listening In on the Digital Analytics Power Hour
Getting Insights from Analytics Agencies
Attending Conferences, Conferences, Conferences
Joining the Adobe Experience League
Learning the Latest from the Adobe Analytics YouTube channel
Hacking the Bracket with Adobe Analytics
Index
About the Authors
Connect with Dummies
End User License Agreement
List of Tables
Chapter 1
TABLE 1-1 Business Decisions Not Based on Data
TABLE 1-2 Business Decisions Based on Data
Chapter 2
TABLE 2-1 Weather Forecast for New York City
TABLE 2-2 Weather Forecast with an Hour of Day Breakdown on Tuesday
Chapter 6
TABLE 6-1 Sample Visit with Three Hits across Three Pages
Chapter 7
TABLE 7-1 Sample Visit with Custom Dimensions and Metrics
Chapter 9
TABLE 9-1 A Visitor Makes a Purchase Spanning Several Days and Locations
Chapter 12
TABLE 12-1 A Multi-Visit Example with Several Marketing Channels and Actions
TABLE 12-2 Data Using Linear and Participation Models
TABLE 12-3 Data Using U-Shaped and J-Shaped Models
List of Illustrations
Chapter 1
FIGURE 1-1: Using Ghostery to identify tracking technologies associated with the...
FIGURE 1-2: Using Analysis Workspace in Adobe Analytics to identify anomalies.
FIGURE 1-3: Using the Attribution IQ panel in Analysis Workspace.
FIGURE 1-4: Visualizing flow in Adobe Analytics.
FIGURE 1-5: Connecting Google Analytics with a Google Ads account.
FIGURE 1-6: Creating a calculated metric in Google Analytics.
Chapter 2
FIGURE 2-1: A page dimension in a freeform table in Analysis Workspace.
FIGURE 2-2: A page views metric in a freeform table in Analysis Workspace.
FIGURE 2-3: The visits and page views metrics in a freeform table in Analysis Wo...
FIGURE 2-4: Viewing visits, page views and unique visitors in a freeform table i...
FIGURE 2-5: A freeform table without a segment applied.
FIGURE 2-6: A freeform table with a mobile device segment applied.
FIGURE 2-7: The Adobe Analytics module in Adobe Experience Cloud Debugger.
Chapter 3
FIGURE 3-1: A sample Analysis Workspace project with key user interface annotati...
FIGURE 3-2: Choosing Adobe Analytics in the Solution Selector.
FIGURE 3-3: Verifying that you are logged into Workspace in Adobe Analytics.
FIGURE 3-4: Analysis Workspace, with a single project created.
FIGURE 3-5: New project templates, including the option to create a blank projec...
FIGURE 3-6: A new blank project in Analysis Workspace.
FIGURE 3-7: Selecting a range of dates for a timeline.
FIGURE 3-8: Viewing details for rolling dates.
FIGURE 3-9: When the left rail selector is set to Panels, you see the four types...
FIGURE 3-10: The blank panel in Analysis Workspace.
FIGURE 3-11: An empty attribution panel in Workspace.
FIGURE 3-12: A freeform table ready to be configured.
FIGURE 3-13: A Segment Comparison panel is ready for segments.
FIGURE 3-14: A freeform panel's right-click context menu.
FIGURE 3-15: Displaying components.
FIGURE 3-16: Dragging the page dimension into an empty freeform table.
FIGURE 3-17: Adding a second metric to a freeform table.
FIGURE 3-18: Dragging the marketing channel dimension into a freeform table to c...
FIGURE 3-19: Identifying top marketing channels driving users to the site home p...
FIGURE 3-20: Dragging a segment into the drop zone to filter for iOS users.
FIGURE 3-21: Changing project information and settings.
Chapter 4
FIGURE 4-1: A report with a single metric identifies products with the most visi...
FIGURE 4-2: An additional metric identifies more essential information about pro...
FIGURE 4-3: Initiating a freeform table with a page dimension.
FIGURE 4-4: A page dimension with an added metric: occurrences.
FIGURE 4-5: Increased conversion rates for repeat site visitors.
FIGURE 4-6: Replacing a metric.
FIGURE 4-7: Comparing conversion rates and revenue per order for repeat visitors...
FIGURE 4-8: Adding a third metric sheds new light on how many orders are placed ...
FIGURE 4-9: Toggling between a descending and an ascending column sort.
FIGURE 4-10: Choosing a new sort column.
FIGURE 4-11: Sorting a dimension column with numeric values.
FIGURE 4-12: Opening the filter dialog.
FIGURE 4-13: Filtering data results for anything with the characters
category.
FIGURE 4-14: Filtering to show a select set of categories.
FIGURE 4-15: Dragging a segment into a panel’s drop zone.
FIGURE 4-16: Dragging multiple segments into a panel drop zone to create a drop-...
FIGURE 4-17: Toggling between data for iOS and Android users.
FIGURE 4-18: Toggling between time ranges in a drop zone drop-down menu.
FIGURE 4-19: Previewing reports associated with standard templates.
FIGURE 4-20: Top search engine and referring domains generated by the acquisitio...
FIGURE 4-21: Custom templates.
FIGURE 4-22: Saving a workspace as a template.
Chapter 5
FIGURE 5-1: A freeform table showing total seconds spent and a calculated metric...
FIGURE 5-2: A freeform table with data from both the web and a mobile app showin...
FIGURE 5-3: A freeform table showing bounces, bounce rate, and single page visit...
FIGURE 5-4: A freeform table showing video starts and video ID instances.
FIGURE 5-5: A freeform table showing a occurrences, page views, and instances of...
FIGURE 5-6: A freeform table showing visits, average page views per visit, and a...
FIGURE 5-7: Entries, exits, and reloads broken out by the page dimension.
FIGURE 5-8: A freeform table showing how page events are the summation of instan...
FIGURE 5-9: Media initiates, single access, and single page visits by video name...
FIGURE 5-10: Adobe Experience Cloud Debugger shows a tag firing with multiple ev...
FIGURE 5-11: The five cart metrics for the top 10 products sorted by carts.
FIGURE 5-12: Orders, units, and revenue per product in a freeform table.
FIGURE 5-13: Several success events are aligned with marketing channel in a free...
Chapter 6
FIGURE 6-1: A global report suite breaks out each domain and app in the server d...
FIGURE 6-2: Site section mirrors a site’s navigational menu.
FIGURE 6-3: A hierarchy’s level 2 with a value broken down by level 4 in a freef...
FIGURE 6-4: The top ten downloads from a site with partial automatic and partial...
FIGURE 6-5: Identifying where users go when they exit a site.
FIGURE 6-6: Custom links capture several unconventional interactions.
FIGURE 6-7: The data from Table 6-1 is shown using Activity Map and page dimensi...
FIGURE 6-8: The debugger with the Activity Map dimensions populated.
FIGURE 6-9: Referrer shows some complete URLs while others such as google.com ar...
FIGURE 6-10: Referring domain populated in a freeform table.
FIGURE 6-11: Referrer type categories are shown in a freeform table.
FIGURE 6-12: Breaking down marketing channel by channel detail.
FIGURE 6-13: The automatic setup screen to define marketing channel rules.
FIGURE 6-14: A helpful visualization on the marketing channel processing rule se...
FIGURE 6-15: A report suite’s paid search detection mirrors Google Analytics sta...
FIGURE 6-16: A freeform table shows the simplicity of the paid search dimension.
FIGURE 6-17: Search engine is shown with a paid search segment applied.
FIGURE 6-18: An advanced filter is applied to exclude Unspecified.
FIGURE 6-19: A left rail filtered to dimensions associated with tracking code.
Chapter 7
FIGURE 7-1: The browser dimension provides more details than the browser type di...
FIGURE 7-2: The many levels of device data.
FIGURE 7-3: Exploring mobile device dimensions.
FIGURE 7-4: Visits distributed across many levels of geographic dimensions.
FIGURE 7-5: Time spent buckets sorted and with breakdowns.
FIGURE 7-6: A segment definition focused on visits from new visitors.
FIGURE 7-7: A segment definition focused on visits from repeat visitors.
FIGURE 7-8: A segment definition focused on visits from loyal visitors.
FIGURE 7-9: Revenue based on the number of days between first visit and purchase...
FIGURE 7-10: Key activity based on the days since customers’ most recent purchas...
FIGURE 7-11: The length of time since a visitor’s most recent visit.
FIGURE 7-12: Single page visits is broken down by the page dimension.
FIGURE 7-13: Experience Cloud Debugger captures values in both props and eVars.
FIGURE 7-14: Defining a date range with rolling dates.
FIGURE 7-15: Applying a custom date range.
Chapter 8
FIGURE 8-1: Keyboard shortcuts for the Project menu in Analysis Workspace.
FIGURE 8-2: Selecting a noncontiguous set of rows in a table.
FIGURE 8-3: Applying a breakdown to a set of noncontiguous rows in a table.
FIGURE 8-4: Pasting a table into spreadsheets (left-to-right: Microsoft Excel, N...
FIGURE 8-5: Generating a line chart with one-click visualization.
FIGURE 8-6: A line graph generated with one-click visualization.
FIGURE 8-7: Accessing visualization settings.
FIGURE 8-8: Changing to an area chart.
FIGURE 8-9: A selected row is visualized as a line graph with data from June.
FIGURE 8-10: Visualizations locked to position automatically change when the dat...
FIGURE 8-11: Accessing data source settings.
FIGURE 8-12: Locking selected items.
FIGURE 8-13: Without date comparison, you can see the relationship between marke...
FIGURE 8-14: Accessing the time comparison options for a metric column.
FIGURE 8-15: Choosing a custom date range for a custom time period column.
FIGURE 8-16: Displaying marketing channel revenue for two time periods.
FIGURE 8-17: Viewing a percentage change from one time period to another.
FIGURE 8-18: Examining conditional formatting for percentage change values.
FIGURE 8-19: Applying standard, auto-generated formatting to a metric column.
Chapter 9
FIGURE 9-1: Sorting revenue by the city that is the source of a hit to the site.
FIGURE 9-2: Two ways to create a new, blank segment.
FIGURE 9-3: A hit-based segment where Page Equals Home.
FIGURE 9-4: Dragging a second page dimension into a segment.
FIGURE 9-5: Identifying an impossible combination of segments.
FIGURE 9-6: Clicking a segment’s information icon in the left rail to display it...
FIGURE 9-7: The left rail search box can filter based on tag, approvals, and fav...
FIGURE 9-8: The dialog box that appears when sharing from Segment Manager.
FIGURE 9-9: A segment is shared to Experience Cloud.
FIGURE 9-10: The report suite selector drop-down menu with five virtual report s...
FIGURE 9-11: The Report Builder report suite selector drop-down lists displays v...
Chapter 10
FIGURE 10-1: A freeform table without a calculated metric for context.
FIGURE 10-2: The right-click menu when two metrics are highlighted.
FIGURE 10-3: The information circle is clicked on an on-the-fly calculated metri...
FIGURE 10-4: A column sum function is applied to a metric.
FIGURE 10-5: Adobe provides several options for creating a calculated metric.
FIGURE 10-6: Calculated Metric Builder.
FIGURE 10-7: Calculated Metric Builder with page views added to the definition.
FIGURE 10-8: A visual cue assists you when dropping a second metric into Calcula...
FIGURE 10-9: A completed calculated metric: page views per visit.
FIGURE 10-10: A completed sticky rate metric.
FIGURE 10-11: An inaccurate calculated metric set without containers.
FIGURE 10-12: An accurate calculated metric set using containers.
FIGURE 10-13: The text box where you can search for cities.
FIGURE 10-14: The page views metric has been applied to the Philadelphia segment...
FIGURE 10-15: Page views is added as a denominator, outside of the segment conta...
FIGURE 10-16: Dropping the page dimension into the approximate count distinct fu...
FIGURE 10-17: A segment is added so it applies to the function container.
FIGURE 10-18: A calculated metric’s description is shown via the information cir...
Chapter 11
FIGURE 11-1: Renamed SKUs with classification.
FIGURE 11-2: Consolidated values with classification.
FIGURE 11-3: We are searching in the left rail for eVar1.
FIGURE 11-4: A classified dimension is shown with its classifications in the lef...
FIGURE 11-5: An admin prepares to click report suites in Admin Console.
FIGURE 11-6: Conversion classifications are selected after highlighting a report...
FIGURE 11-7: The product variable is collecting some very unfriendly SKUs.
FIGURE 11-8: The first classification to product is about to be added.
FIGURE 11-9: A name and description are added to a new classification.
FIGURE 11-10: A classification was successfully added to the product variable.
FIGURE 11-11: Preparing a classification browser export.
FIGURE 11-12: The classification file is ready to be imported.
FIGURE 11-13: Classification data is shown and broken down.
FIGURE 11-14: Some values of the page dimension that are ready for classificatio...
FIGURE 11-15: A new rule set named page grouping.
FIGURE 11-16: A variable is included in the Variable Selection dialog box.
FIGURE 11-17: A rule classifies pages that start with
purchase:
.
FIGURE 11-18: A more complete rule set with six rules.
FIGURE 11-19: Some values aren’t handled in the classification rules.
FIGURE 11-20: All variables are matched in the rules in the rule set.
FIGURE 11-21: Setting the first rule of every rule to use a regular expression.
Chapter 12
FIGURE 12-1: A company has access to all features of Attribution IQ.
FIGURE 12-2: A freeform table is shown with revenue attributed to just three pag...
FIGURE 12-3: The Model menu with Inverse J selected.
FIGURE 12-4: The same metrics are applied to the marketing channel dimension.
FIGURE 12-5: The gear icon for revenue is clicked.
FIGURE 12-6: The attribution model is changed to inverse J.
FIGURE 12-7: The Inverse J Revenue (Visitor) calculated metric is ready to save.
FIGURE 12-8: A blank attribution panel is shown in a new project.
FIGURE 12-9: The attribution model selection process begins.
FIGURE 12-10: The settings for the custom attribution model are defined.
FIGURE 12-11: Resulting visualizations at the top of the attribution panel.
FIGURE 12-12: The second set of visualization results from an attribution panel.
FIGURE 12-13: The last visualization returned by the attribution panel.
Chapter 13
FIGURE 13-1: Opening the Acquisition Web template.
FIGURE 13-2: Examining a donut chart.
FIGURE 13-3: Identifying the data source for a chart.
FIGURE 13-4: Changing settings to display a more limited set of values.
FIGURE 13-5: Breaking down a bar chart.
FIGURE 13-6: Converting a bar chart to a donut chart.
FIGURE 13-7: Displaying data as a horizontal bar chart.
FIGURE 13-8: Defining display properties for a line chart.
FIGURE 13-9: A bar chart with three labeled values.
FIGURE 13-10: Stacking a bar chart.
FIGURE 13-11: A scatterplot.
FIGURE 13-12: Identifying and examining source data for a scatterplot.
FIGURE 13-13: A table with marketing channel as the dimension, and orders as the...
FIGURE 13-14: Selecting a row of data to chart.
FIGURE 13-15: A single row displayed as a line chart.
FIGURE 13-16: Multiple values displayed as a line chart
FIGURE 13-17: Displaying data as an area stacked chart.
FIGURE 13-18: Locking data for charting instead of rows.
FIGURE 13-19: Generating a histogram from the left rail.
FIGURE 13-20: Generating a histogram from a blank panel in Workspace.
FIGURE 13-21: A histogram displaying revenue spent by different numbers of uniqu...
FIGURE 13-22: Changing visualization settings for a histogram.
FIGURE 13-23: Generating a Venn diagram.
FIGURE 13-24: A set of segments and a metric for generating a Venn diagram.
FIGURE 13-25: Zooming in on an intersection to reveal detail data in a Venn diag...
FIGURE 13-26: A stacked bar chart without and with 100% Stacked enabled.
FIGURE 13-27: Editing a chart label.
Chapter 14
FIGURE 14-1: Creating a flow visualization in a blank panel.
FIGURE 14-2: Defining a flow visualization by dragging a dimension into the Entr...
FIGURE 14-3: Examining a flow visualization with a defined entry dimension.
FIGURE 14-4: Displaying data for a path in a flow visualization.
FIGURE 14-5: Viewing a summary of entry data in a flow visualization.
FIGURE 14-6: Defining settings for a flow container.
FIGURE 14-7: A path is right-clicked in the flow visualization.
FIGURE 14-8: The page dimension is added to the right as the next dimension in t...
FIGURE 14-9: A new fallout visualization, minus useful data (yet), ready to be c...
FIGURE 14-10: Four touchpoints in a fallout visualization.
FIGURE 14-11: The Android Devices segment is added to a fallout’s All Visits and...
FIGURE 14-12: A touchpoint is enhanced with a second dimensional item.
FIGURE 14-13: A touchpoint is changed to be the next hit.
FIGURE 14-14: Creating a cohort table in a blank visualization.
FIGURE 14-15: A cohort table’s definition is ready.
FIGURE 14-16: Examining the results of a cohort table.
FIGURE 14-17: The results of a cohort analysis in Google Analytics.
FIGURE 14-18: The Curated Components bar is shown with two dimensions included.
FIGURE 14-19: Adding two dimensions to a curated components set.
FIGURE 14-20: Available color palettes for Workspace.
Chapter 15
FIGURE 15-1: A freeform table on the hunt for anomalies.
FIGURE 15-2: The details of an anomaly in a freeform table full of anomalies.
FIGURE 15-3: The Email channel is trended and anomaly detection runs on the line...
FIGURE 15-4: Preparing to click Analyze from an anomaly in a line chart.
FIGURE 15-5: Adding dimensions to exclude from Contribution Analysis.
FIGURE 15-6: Contribution Analysis results.
FIGURE 15-7: Dragging the Segment Comparison panel into workspace.
FIGURE 15-8: Defining the first segment for Segment Comparison.
FIGURE 15-9: Defining a second segment for Segment Comparison.
FIGURE 15-10: The top portion of results from Segment Comparison.
Chapter 16
FIGURE 16-1: Copying a freeform table to the clipboard.
FIGURE 16-2: The Projects menu provides access to download as CSV and PDF.
FIGURE 16-3: Getting ready to send a CSV export.
FIGURE 16-4: The types of conditions for a metric trigger.
FIGURE 16-5: The Experience Cloud login screen in Report Builder.
FIGURE 16-6: The first step in the Request Wizard is complete.
FIGURE 16-7: The second step of the Request Wizard.
FIGURE 16-8: A simple Report Builder request is returned to Excel.
FIGURE 16-9: Sample data on adobe.com shown in Activity Map.
FIGURE 16-10: A few visualizations based on Audience Analytics data.
FIGURE 16-11: The search engines template is shown for use with ad analytics.
FIGURE 16-12: The landing page for creating and editing Data Connectors.
Chapter 17
FIGURE 17-1: Displaying components.
FIGURE 17-2: Launching Segment Builder.
FIGURE 17-3: Titling, describing, and tagging a custom segment.
FIGURE 17-4: Selecting Visitor from the Show options in Segment Builder.
FIGURE 17-5: Locating the orders metric.
FIGURE 17-6: Using the orders metric to define a custom segment.
FIGURE 17-7: Defining Exists as the criteria for segmenting purchasing visitors.
FIGURE 17-8: Applying the custom segment.
FIGURE 17-9: Viewing the properties of the custom Purchasers segment.
FIGURE 17-10: Defining a custom segment for visitors who do not make a purchase.
FIGURE 17-11: Segmenting in a table visitors who do not make a purchase.
FIGURE 17-12: Adding a container in Segment Builder.
FIGURE 17-13: Changing a container setting to Visit.
FIGURE 17-14: Setting the second container to an Exclude.
FIGURE 17-15: The complete definition for a custom single-page visitors segment.
FIGURE 17-16: The complete definition for a single-visit, multi-page visitors se...
FIGURE 17-17: The complete definition for this SEO to internal search segment.
FIGURE 17-18: The complete definition for this pre-purchase activity segment.
FIGURE 17-19: An example definition of a strictly organic segment.
FIGURE 17-20: An example definition of a strictly paid segment.
FIGURE 17-21: An example definition of a potential bots segment.
FIGURE 17-22: Defining a segment for visitors who begin to checkout but don’t pu...
Chapter 18
FIGURE 18-1: The first in a series of video tutorials from Adobe introduces key ...
FIGURE 18-2: A model and a template spreadsheet for a measurement plan and an ev...
FIGURE 18-3: A summary of implementing Adobe Analytics to manage integrating acq...
FIGURE 18-4: A high-level exploration of SDR concepts.
FIGURE 18-5: About the DAPH podcast.
FIGURE 18-6: The blog at 33 Sticks.
FIGURE 18-7: The 2019 SUMMIT conference home page.
FIGURE 18-8: Comparing more than one segment in a report.
FIGURE 18-9: Playing Hack the Bracket with Adobe Analytics.
Guide
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Introduction
Adobe Analytics For Dummies is a comprehensive survey of creating and managing analysis projects with Adobe Analytics. We’ve endeavored to make the bulk of the content accessible to those of you new to Adobe Analytics, while providing plenty of depth and substance to carry data analysts through advanced and complex challenges. We had a blast writing this book, and appreciate your coming along with us on a journey of discovery to wield the state-of-the-art application for data analytics.
About This Book
Adobe Analytics For Dummies provides an in-depth exploration of how to use Adobe Analytics. As an analyst, you face unique challenges analyzing and leveraging data from users who engage your company’s or institution’s web presence. With that in mind, throughout this book we show you how to accomplish essential and complex tasks, and we bring these processes to life by using a range of real-life examples. We’ve also drawn on experiences in the trenches to share globally applicable tips and techniques that you can use throughout Adobe Analytics — especially in Analysis Workspace, Adobe’s most-used analytics product.
In organizing and presenting the material, we embrace and adhere to the easy-to-access structure of Dummies books: Although you can read the chapters sequentially, they also stand alone as explorations of specific functionalities in Adobe Analytics.
Here are some conventions we use throughout the book:
Text that you’re meant to type just as it appears in the book is
bold
.
There is little coding in this book, but where appropriate, web addresses and programming code appear in
monofont
. If you're reading a digital version of this book on a device connected to the Internet, note that you can click web addresses to visit websites, like this:
www.dummies.com
.
As you’re familiar with in other
Dummies
books, we use the command arrow to identify sequential steps. For example, to share a file, choose Share ⇒ Send File Now.
Foolish Assumptions
This book aims to fill the needs of two audiences (and those of you who fall in between). One audience consists of folks who are new to Adobe Analytics and have only some acquaintance working with data in general. The second audience consists of people who have substantial expertise in data analysis but are adopting or transitioning to Adobe Analytics (including from Google Analytics). This book combines specific techniques to take advantage of the full power of Adobe Analytics, with frequent excursions into why you would want to use the rich toolset you get with this industry-leading application.
Other than that, we have no assumptions. Come as you are. Welcome. And get ready to discover how to wield Adobe Analytics to enhance the success rate of your enterprise, whatever it is.
Icons Used in This Book
If you’ve read other For Dummies books, you might have noted that they use icons in the margin to call attention to particularly important or useful ideas in the text. In this book, we use four such icons.
The Tip icon marks tips (duh!) and shortcuts that you can use to make working with Adobe Analytics easier.
Remember icons mark information that’s especially important to know. To siphon off the most important information in each chapter, just skim through these icons.
The Technical Stuff icon marks information of a highly technical nature that you can normally skip over.
The Warning icon tells you to watch out! It marks important information that may save you headaches and avoid potentially costly mistakes.
Beyond the Book
Throughout this book, we provide links to detailed reference material from Adobe (and in some cases other sources) that will support your Adobe Analytics journey.
In addition, we have two handy cheat sheet articles that will help you instantly get more value from Adobe Analytics. The topics focus on getting around Analysis Workspace and building two useful calculated metrics. To get to the cheat sheet, go to www.dummies.com/cheatsheet/adobeanalyticsfd. Or go to www.dummies.com and type Adobe Analytics For Dummies cheat sheet in the search bar.
Where to Go from Here
This book isn’t linear. That is to say, you can flip to the material you need, get help with any particular aspect of Adobe Analytics, and come back for more when you’re ready. That said, if you’re new to Adobe Analytics, we suggest starting with Part 1 for a basic foundational introduction.
Part 1
Getting Started with Adobe Analytics
IN THIS PART …
Get an overview of the role Adobe Analytics plays in the world of data analysis and how data is fed to Adobe Analytics.
Understand the basic terms of analysis used by every analyst daily.
Learn to navigate Analysis Workspace.
Create a project in Analysis Workspace.
Chapter 1
Why Adobe Analytics?
IN THIS CHAPTER
Understanding why you're analyzing data
Identifying where your data comes from
Configuring and analyzing data in Adobe
In this chapter, you begin your journey into analytics powered by Adobe. In the remainder of this book, we dive deeply into specific features of Adobe Analytics, enabling you to perform in minutes analyses that would take days with other tools. But here at the beginning, it’s important to be able to identify why you're analyzing data as well as how the data is populated and configured.
Adobe Analytics has been a premier web, mobile, and customer-focused analysis tool for well over a decade. If you’re new to Adobe Analytics or reading this book to beef up your ability to wield this powerful set of tools, experience with similar tools, such as Google Analytics, Webtrends, or Microsoft Excel, is valuable. But whether you're reading this with substantial background in data analytics or the concept is new to you — or anywhere in between — we first pull the lens back to understand the history of web data so you can better understand the role it plays today.
In this chapter, we give you a chance to expand your horizons in terms of how you think about why you're analyzing data using Adobe in the first place. Next, we answer that age-old question: “Where does my data come from?” That is, we dig into how data gets pushed onto the Adobe platform. Finally, we present an overview of what's involved in sifting and squeezing valuable insights out of all the data you have access to in Adobe Analytics. So, buckle up your seat belts and let’s begin!
Understanding Why You're Using Adobe Analytics
People have been attempting to analyze data generated by interactions with the World Wide Web since Tim Berners-Lee invented it. Yes, that process has become exponentially more developed and complex, but we’re pretty sure one of the first questions asked after the first website went live was: “So, is anyone going to it?”
If we fast-forward a few decades, you’ll be hard-pressed to walk through an international airport today without seeing ads for cloud technology, data security, and digital transformation. The business of data analysis has exploded, and there is no sign of it slowing down. According to a 2018 Forbes study, “Over the last two years alone, 90 percent of the data in the world was generated” (www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/ - 7ceaab1460ba). That equates to 2.5 quintillion (18 zeros) bytes of data captured every day. And most of it is coming from the web, mobile phones, and the Internet of Things (IoT), meaning the universe of devices that connect to the Internet, each other, or both, ranging from wearable devices to refrigerators.
Now that you have a feel for this ever-expanding amount of data, it’s time to think about what to do with it. You might remember a time when you couldn’t go into a meeting without hearing the words “big data.” The thinking was, “Let’s collect all the data we can and figure out what to do with it later.” However, as made clear by the consistent decline in searches of that term on Google Trends (https://trends.google.com/trends/explore?date=all&geo=US&q=big%20data), a new sheriff is in town. And that new sheriff is driven by analysis. Collecting data is a start, but analysis is required to derive meaningful insights, form hypotheses, and take action.
Avoiding HiPPO!
Data analysis is what helps people avoid HiPPO. Zoo animals? Have Eric and David gone crazy in the first chapter? No, we’re talking about highest paid person’s opinion. The HiPPO acronym has come to describe the phenomenon of people in any grouping in an organization deferring to the opinion of the person highest in that group (generally the highest paid), resulting in unscientific and often harmful analysis and conclusions not supported by data. Table 1-1 provides a scenario of how this plays out.
TABLE 1-1 Business Decisions Not Based on Data
Employee
Comment
HiPPO
We need to sell more widgets!
Marketer
A lot of our new customers tell us they like our new TV ad.
HiPPO
I don’t like the jingle; it gets stuck in my head.
Marketer
I really think we should try expanding it to more markets.
HiPPO
Here’s the budget for additional ads in the local newspaper.
Marketer
[Sheds a tear while heading back to desk to look for a new gig]
Don’t let your organization’s decision-making process be driven by HiPPO. Imagine how much better that conversation could have gone if it was based on actual data, as shown in Table 1-2.
TABLE 1-2 Business Decisions Based on Data
Employee
Comment
CEO
We need to sell more widgets!
Marketer
A lot of our new customers tell us they like our new TV ad.
CEO
That jingle really gets stuck in my head; how can we learn whether it’s positively affecting sales?
Marketer
Let’s run some online preroll video tests (short audio or video ads that run before a user’s selected audio or video) and judge the ad's effectiveness.
CEO
Sounds great. Can you use segmentation to make sure the results are not skewed by the fact that I’ve viewed the video a thousand times? (Segmentation is a marketer’s ability to filter analysis or action to a specific set of users based on behavior, demographics, or other factors. Chapter 2 dives into segments.)
Marketer
Of course. We’ll test, learn, and even save some money!
See how much better that went? It's all due to the decision-making process based on analyzing data that has been segmented (filtered) to avoid distorting the results. HiPPO shouldn’t drive decisions when data can provide context and insight.
Now that we’ve used a hyperbolic (but revealing) example to illustrate why data needs to be the basis for decision-making in marketing, it’s time to think about how else we can use data. We’ve seen data used to help make decisions on brand logos, campaign headlines, button colors, navigational menu hierarchy, internal and external search optimization, article titles, product bundle options, checkout steps, page layout, and more! And we've seen data used to measure not just sales but the effectiveness of customer support tools, educational resources, and branding campaigns. In essence, data analysis can inform the quality of any part of a website, mobile app, digital screen, desktop application, or even voice skill.
Knowing when you need Adobe Analytics
We hope you agree that data analysis needs to be ingrained in your everyday work life, but you may be asking yourself, “How do I know when it’s time to use Adobe Analytics?” The Adobe Analytics sales team has been trying to answer this question since the product was first sold as SuperStats by the Omniture team in 1996. Before that, most web masters (remember that term?) like us were using basic server-log analysis tools just to figure out if anyone was even visiting the site!
Analyzing the effectiveness of websites has become even more complicated as the analytics industry has matured. In 2005, Google purchased Urchin — an early pioneer in the business of analyzing web traffic — and quickly made it available for free. Today, that product is known as Google Analytics, and it paves the way for tens of millions of people to take their first steps into the world of web analytics. Adobe purchased Omniture in 2009 to kick-start a slew of acquisitions that became the Adobe vision for an integrated enterprise marketing cloud, now called Adobe Experience Cloud.
Adobe has succeeded with this vision of an enterprise marketing cloud vision in large part because of the success of Adobe Analytics. It is the foundation that sits as the data hub in Adobe Experience Cloud. Adobe Analytics has been successful for plenty more reasons than this. Forrester, a market research firm that tests and compares developments in technology, reported that Adobe was the clear leader in its current offering. Forrester writes that Adobe “has concentrated on making the UI more intuitive and building on capabilities that allow the exploration of data breakdowns, relationships, and comparisons.”
Knowing the difference between reporting and analysis
When it comes to data, we believe it’s important to distinguish reporting from analysis. These terms are often used interchangeably outside the analytics industry but certainly not within it.
Reporting is a process used to organize data into static summaries. When you think of a report, do the words interactive and flexible come to mind? Or does the word report take you back to school, where you were asked to provide a summary of a book you just read? Analysis is the process of exploring data to derive meaningful insights and optimization opportunities. A report will often force its end users to ask questions; an analysis answers questions. A report will tell you that something is happening, such as the following:
“Page views are increasing month over month by 3.5%. We have added 500 new keywords to our paid search campaign.”
An analysis provides the context that explains why something is happening and what can be done, for example:
“Page views have increased significantly this month due to new paid search keywords added to our campaign, but bounce rate has skyrocketed and conversion rate has dropped across the nation. Attached is a list of keywords that are driving the majority of this unqualified traffic and that should be removed from the campaign.”
See the difference? The analysis does more than simply describe what happened. The analyst performing this analysis dug further into the data by answering questions about the who, where, when, and why. That, my fellow analyst, is where you come in.
Adobe Analytics may have some of the most advanced data science features powered by one of the most innovative web analytics engines available for the enterprise, but it takes an inquisitive analyst to apply these features to their dataset to derive insights. Good analysts know so much more than just the data at their fingertips and the tool providing it to them. Good analysts are curious and creative, and they sweat the details. To become one of the best analysts, you must have conversations with teams you’ve never spoken to before and join meetings you didn’t know existed. And we hope you’ll prove, once and for all, that HiPPO is useless without data to back it up.
Identifying Where Adobe Analytics Data Comes From
You may not know this, but Adobe Analytics users analyze much more than data from their websites. Adobe also captures data on behalf of their customers in mobile apps, tablet apps, and more. Plus, Adobe has built significant flexibility into their product to handle a more digitally connected consumer world that seamlessly switches from voice assistant to phone to laptop.
Perceptions of the nature of data analysis were defined in the realm of popular culture by the Jonah Hill character in the movie adaptation of the book Moneyball. In that true story, a small-market baseball team (the Oakland A’s) managed to dramatically outperform teams with much larger payrolls by innovatively identifying and acting to acquire underpriced players based on statistical measures of a player’s effectiveness beyond and in many ways going against traditional metrics, such as batting averages, home runs per season, and RBIs (runs batted in).
Since that movie came out, new and ever more complex challenges in collecting data have emerged. For example, users of online devices have been conditioned to quickly navigate from one place to another, requiring more nuanced and detailed metrics to accurately track user activity. And users are increasingly conscious of privacy considerations and making more informed decisions about how they want to manage the relationship between the convenience provided by having their activity tracked versus maintaining confidentiality in their online activity.
On the other side of the coin, vastly more sources of user data exist than just a few years ago. Today, Adobe has a number of mechanisms to import data from digitally disconnected sources such as call centers, customer relationship management (CRM) systems, and in-store commerce engines.
Before diving into the details of how data is collected, we want to emphasize that capturing data and pumping it into Adobe Analytics is not normally the domain of data analysts. Your job as an analyst is to, well, analyze the data captured from user activity. But the following basic overview of how data is collected is important to analysts for two reasons. One, it’s good to know where data comes from when you want to assess its validity; and two, having a basic grasp of the process of mining and sending data into Adobe Analytics allows you to have more productive interactions with the folks who set up the tools that extract data. At the end of this chapter, we discuss how to forge this relationship.
Capturing data from websites
Let’s start with the most common Adobe Analytics data source: websites. Web data was originally analyzed based on server logs. Server-log data is automatically generated by servers that host websites and provide a count and timestamp of every request and download of every file on the site. Unfortunately, the data is highly unreliable because server logs don’t have the capability to distinguish bots from humans.
Bots are automated computers that scan websites. These bots are often friendly and used to rank websites for search engines or product aggregator websites. Some bots, however, are unfriendly and used for competitive intel or worse.
Because server logs can't tell a human from a bot, the industry quickly migrated to tags, which are now the industry standard. Generally, tags are JavaScript-based lines of code that append an invisible image to every page and action on your website. These images act as a beacon to analytics tools, where several things happen in just a few milliseconds:
JavaScript code runs to identify browser and device information as well as the timestamp of the page view.
More JavaScript code runs to look for the existence of a
cookie,
which is a piece of text saved on a browser. Cookies can be accessed only by the domains that set them and often have an expiration date.
If it exists, a visitor ID is extracted from the cookie to identify the user across visits and pages. If a visitor ID doesn’t exist, a unique ID is created and set in a new cookie. These IDs are unique for each visitor but are not connected to a user’s personal data, thus providing a measure of privacy for users.
More JavaScript is used to capture information about the page: the URL, the referrer, and a slew of custom dimensions that identify the action and behavior of the visitor.
After all that JavaScript logic runs, the image beacon is generated to send data into the collection and processing engine in Adobe’s analytics.
Intimidating isn’t it? Well, that’s how web developers felt. When we first started working in web analytics, our toughest job was teaching developers how to write and test all this JavaScript to ensure that our tags fired accurately. Teaching developers to develop — not a fun job.
Lucky for us, an even smarter developer came up with an idea to move all that JavaScript into a single UI (user interface). Web developers only had to add one or two lines of code to every page of the site, and the marketer could then manage their tags in this new platform named a tag management system, or TMS. It wasn’t long before the tag management industry exploded, leading to dozens of vendors, and then acquisitions, mergers, and technology pivots.
The good news is that the tag management system industry has become commoditized and is available for free from Adobe in the form of Dynamic Tag Manager (DTM) and Adobe Launch. You may already be familiar with Google’s TMS, Google Tag Manager, or one of the independent TMS players such as Tealium, Ensighten, or Signal. Chances are your company is already using one of these technologies to deploy marketing tags on your website. All of them can deploy Adobe Analytics, although Adobe’s recommendation for best practice is to use Adobe Launch.
Capturing data from mobile devices
If standard websites delivered to a laptop are the natural place to start with our data collection discussion, moving to a smaller mobile screen is the logical next step.
You may already know that at this stage of the evolution of web design, mobile websites are fully functioning web pages, not afterthought appendages to laptop, desktop, or large monitor sites. These smaller-scale websites are created by using an approach to web development called responsive design, in which the code used to create website content is the same regardless of the size of the web visitor’s screen and browser. Your company is most likely already leveraging responsive design.
When responsive design is applied, the same tags that fire on the desktop site should work on mobile- and tablet-optimized websites because they're essentially the same thing, which is good news in the tag management world. However, the world of responsive-design-based mobile apps is completely different than that of native apps.
Mining data from native apps
Native apps present particular challenges for data collection. These mobile and tablet applications are programmed in a different way than responsive websites. In general, native apps don’t run in browsers, don’t use HTML, and can’t run JavaScript. In fact, applications built for iOS are built in a different programming language (Objective C) than Android apps (Java). We mention these technical programming languages for one important reason: A tag management system is not going to work on your mobile and tablet applications.
Some tag management system vendors have hacked the capability to incorporate JavaScript into apps, but the result has limited capabilities and is far from a best practice. The most complete, accurate, and scalable way to deploy Adobe tools is to use the Adobe mobile software development kit (SDK). The Adobe mobile SDK is built to work as a data collection system, like a tag management system, but uses the app’s native programming language (Objective C for iOS or Java for Android).
The Adobe SDK is important because it has deeper access into the code that runs the app and therefore can be used for more than just data collection. In addition to sending data to Adobe Analytics, the Adobe SDK is required to do the following:
Capture geographic location data based on GPS.
Utilize geofences based on that GPS data for analysis or action.
Send push notifications to users.
Update content in the app via in-app messaging, personalization, and testing.
Access to these capabilities may be limited to the SKU, or version, that your company has purchased from Adobe. Work with your Adobe Account Manager to understand which of these capabilities is included with your contract.
Data from IoT and beyond
Now that we’ve discussed data collection standards for the two biggest use cases (web and mobile), it’s time to branch out to a more generic set of the Internet of Things (IoT). Everyone who asks questions about data needs to be thinking about digital kiosks, smart watches, connected cars, interactive screens, and whatever other new devices our tech overlords have announced since this sentence was written.
Vendors such as Adobe find it difficult to stay on top of every new device because building SDKs takes time, money, research, engineers, code, quality assurance, and more. But don’t worry: Devices that don’t have native-built SDKs can still send data to Adobe Analytics.
The best practice for sending data from one of these devices is through an application programming interface (API). In short, this means the developers of the IoT application can write their own code to create a connection to your Adobe Analytics account and then send data to it. APIs have become the default way in which data is sent from any device connected to the Internet either full time or part time. Adobe has some recommendations to share too, especially for some of their big bets when it comes to these new devices, such as voice and connected car. At the time of this writing, SDKs are not available for voice-activated devices or connected car applications. However, Adobe does have best practices for data customizations, variable settings, and code options for both of these technologies.
Enterprise software — software licensed to institutions — is updated regularly, and Adobe releases best practices for tracking data associated with new digital mediums such as voice and the connected car.
You’ve now explored all types of data generated by devices that have part-time or full-time access to the web: computers, phones, tablets, and IoT.
People’s digital experiences and interactions on those devices are captured by some combination of TMS, SDK, and API. According to marketers and analysts, that list is missing something: data that isn’t based on behavior. Perhaps the best example of nonbehavioral data comes from your customer relationship management (CRM) tool. CRM tools are used to organize, categorize, and manage your prospects and customers. Other examples of nonbehavioral data that marketers and analysts would be interested in include the following:
Call center
Offline or in-store purchases
Returns or cancellations
Product cost of goods sold
Ad campaign
Customer satisfaction
Adobe Analytics can import any of these data types along with plenty of others. In general, this data is imported into Adobe Analytics via either File Transfer Protocol (FTP) or API. In Chapter 16, we describe some of the options for connecting data into Adobe Analytics.
Configuring and Analyzing Data
Can you imagine a chef who didn’t know the source of the food she cooked? The chances of getting that coveted Michelin star would be significantly worse. The same concept applies to becoming a rock star analyst.
That’s why we dug as deeply as we did into where data comes from. As an analyst, you'll be working with that data, and you need to know its source. And you need to be able to communicate in a meaningful and productive way with the team that harvests that data.
Preparing to slice and dice data
With so many options for collecting and customizing Adobe Analytics data, an analyst needs to understand the details of how data is collected in his or her organization. The more you know about the intricacies of your implementation, the faster you’ll be able to slice and dice your data and think creatively to solve problems. In fact, that creative thinking is some of the most fun you’ll have as an analyst. One particularly creative portion of the Adobe Analytics process is tied to the decisions associated with your data configuration and implementation.
