Analytics Articles

Cheat Sheet / Updated 12-03-2023

Adobe Analytics is the most powerful tool available for analyzing digital consumer data, but it’s easier to grasp if you know where to start. Master the most basic and widely applicable features in the easiest way. Get details on two standard calculated metrics in the analytics industry that focus on analyzing engagement on your site, app, or other digital property. Discover several tips to improve your productivity in Analysis Workspace.

Article / Updated 08-22-2019

When you are evaluating which data analytics solution is right for you, the question will probably arise: What’s the relationship between Adobe Analytics and Google Analytics? As the two main players in the field, it pays to compare Adobe Analytics up against Google Analytics. Countless customers and professionals in the industry ask experts which analytics solution they like best. Instead of answering that question, let’s consider a more objective one: What are the strengths and limitations of Adobe Analytics and Google Analytics? This focus has helped prospective buyers of data analytics solutions quickly map features and integrations to their requirements. And by comparing and contrasting the two, you will understand why your organization made the call to implement Adobe Analytics. Surveying how Adobe Analytics stacks up against Google Analytics Let’s start this comparison by focusing on Adobe Analytics because that’s the topic of this book. Adobe’s analytics solution is often thought of as the Ferrari in the industry — impressively powerful but costly. That analogy has some truth to it. But let’s break down some of the features unique to Adobe Analytics. Peeking at Analysis Workspace In terms of power, nary an analytics solution can top the Adobe capabilities that you'll be walking through in this book. The first key differentiator for Adobe is Analysis Workspace, the default engine in Adobe Analytics for analysis, visualization, curation, and sharing. Built with both the marketer and the analyst in mind, Analysis Workspace provides unlimited breakdowns, on-the-fly segmentation and calculated metrics, a slew of data visualization capabilities, and four key built-in, data-science-powered features. To take just one example, Adobe Analytics employs Anomaly Detection algorithms to identify anomalies such as hard-to-find drops in average order value, spikes in orders with low revenue, statistically significant increases in trial registrations, and drops in landing page views. Adobe has recently added to Analysis Workspace a much-needed component for attribution that allows almost all metrics to have one of ten attribution models that you can apply to almost any dimension in the platform. In short, marketing attribution helps you understand how your customers and clients are interacting with your online presence and what they want in a way that makes possible highly focused and accurate marketing or service decisions. Attribution IQ in Analysis Workspace, for example, lets you add many new types of attribution models to freeform tables, visualizations, and calculated metrics. Attribution IQ is shown below. As of this writing, algorithmic and data-driven models require an upgrade to Adobe’s Data Workbench solution at significant cost. Visualizing flow and fallout in Adobe Analytics Two huge differentiators for Adobe are tied to their customer journey visualizations: flow and fallout. Other vendors seem to fail to get the flexibility and ease of use right for these types of analyses, In addition, Adobe is primed to release Customer Journey Analytics, a feature focused on stitching visits and devices across devices based on logins or Adobe’s Device Co-op. Adobe also has built-in data connectors with dozens of partners that allow for relatively seamless and often bidirectional integrations of datasets across email, search engine optimization (SEO), commerce, advertising platforms, and more. If these pre-built integrations aren’t good enough, Adobe recommends a custom integration via a number of options including the recently released Adobe Experience Platform. The other big selling point of Adobe’s integrations comes from deep within their own Experience Cloud — integrations with other Adobe solutions. Adobe was the first company to have a bidirectional integration with an Analytics and Data Management Platform (DMP). DMPs are used for merging data from multiple datasets, building audiences from that merged data, and activating those audiences in advertising platforms. Don’t worry. If that topic is too advanced, you should just know that marketers can define segments in Analytics that are then enriched by additional data sources in Audience Manager (Adobe’s DMP), and then share those segments back into Analytics for further analysis. Adobe also has quality integrations with Target (testing and personalization), Campaign (1:1 marketing campaign management), Experience Manager (content and asset management), and Ad Cloud (advertising bid optimization). Identifying the limitations of Adobe’s Analytics solution Adobe’s biggest missing feature may be a big one for you: integration with Google Ads. Adobe does have several ways of integrating with advertising data from their biggest analytics competitor, but none are as seamless or as complete as Google’s. In addition, some people complain that Adobe’s solution is too difficult to use, but this opinion seems to be based on the Omniture interface (the program Adobe acquired that evolved into Adobe Analytics), which was, frankly, daunting. Analysis Workspace has removed these limitations and created unique ways to empower new users. If you feel overwhelmed, check out these resources you can use to help you navigate Adobe Analytics. Understanding how Google Analytics fits into the data analysis picture If you’ve never used Adobe Analytics but have used an analytics solution, odds are that you’ve used Google Analytics. Let’s take a step back and look at how Google Analytics fits into the world of analytics. First, it’s important to note the difference between Google’s free tool, Google Analytics, and the enterprise (and not free) level, Google Analytics 360. Distinguishing between Google Analytics and Google Analytics 360 Google has cornered the free analytics solution market, doing the entire industry a service by helping to drive a huge wave of businesspeople to start asking questions about their data. The free version of Google Analytics is a valuable and accessible tool for generating reports on who is coming to a website and how they are interacting with that site. It is not an enterprise-level tool for data analysis. The focus here is on Google Analytics 360. Google released this for-pay solution several years ago. A significant differentiator and advantage of Google Analytics is its native integration with Google Ads. If advertising is your analytics raison d'être, you’re probably spending more of your budget and time in Google’s ad tools than any other tool and therefore will find Google’s ad integrations valuable. Google imports data from Google Ads (formerly DoubleClick for Advertisers), the Google search console, display and video ads, and paid search ads for Google Analytics 360 customers. In addition, segments created in Google Analytics can be enabled for remarketing campaigns via Google Ads. However, note that these remarketing lists are not retroactively updated, so users in your segment prior to the segment being shared to Google Ads are not included in the remarketing list. Only users who become a part of your segment after it is shared as a remarketing audience are available for remarketing. Calculated metrics in Google Analytics and Google Analytics 360 are limited to the four basic arithmetic operators (add, subtract, multiply, divide) and can be used only in custom reports and created only by administrators. Some calculations are pre-built into reports, but they are often simple divisors of other metrics already in the report. Analysts often need more complex operators and functions, such as distinct/unique counts, means, medians, percentiles, and logical operators (if, then, and, or, greater than, and less than). The interface for creating a calculated metric in Google Analytics is shown below. Integrating with Google Cloud Platform Another distinguishing feature of Google’s tool is integration with Google Cloud Platform (GCP). Advanced analysts and data scientists who are comfortable in SQL (Structured Query Language, a language for accessing and manipulating databases) will be able to run queries thanks to the integration of Google data into BigQuery, Google’s fast-moving SQL-based platform for complex analyses of multiple datasets filled with huge data. The caveat or downside here is that accessing this data requires a high level of fluency with SQL to generate the kinds of reports that you can generate without SQL in Adobe Analytics. Surveying Google’s Advanced Analysis interface Google’s recently released interface for Analytics 360 is called Advanced Analysis. It includes a few key features not previously available in standard Google Analytics. For example, Advanced Analysis increases a user’s ability to break down a report, such as breaking down the marketing channel report by landing page. Google’s Advanced Analysis allows for ten breakdowns in a report, whereas the old interface allows for a maximum of five. Segment Overlap is the second report in Advanced Analysis. This report provides analysts with a Venn diagram of segments that show the percentage of users who share a segment. Finally, Google has expanded custom funnel capabilities in Advanced Analysis. Google Analytics 360 customers love the ability to create custom funnels on the fly, whereas non-360 customers have to create the funnel before data flows into it. In Advanced Analysis, Google has expanded these custom funnels to max out at 10 funnel steps, doubling the maximum in Google Analytics. When compared to Adobe’s Analysis Workspace, Google’s Advanced Analysis tool is far less robust, but we’re excited to see what Google cooks up in future releases. Evaluating plusses and minuses of Adobe Analytics and Google Analytics As noted, Google gets high marks for their integration with other Google platforms. However, Google Analytics has only one significant non-Google integration, with Salesforce, so all other data sources require a custom setup via API. Google Analytics evolved from, and retains significant evolutionary holdovers and limitations based on, its origin as a much simpler tool for reporting, as opposed to a full-fledged analytics tool. The limitations associated with calculated metric capabilities, dimensional breakdowns, and custom funnels may be debilitating to analysts who are unable or uninterested in using SQL. The most significant shortcoming may be that Google Analytics, even the premium Analytics 360 solution, uses data sampling in its reports, so some reports may not show a complete view of visitor behavior. Similar to election polling, Google Analytics reports show data associated with a percentage of the full set of data (20 percent, for example) and then multiply that number by the total number of site visitors (by five, in this example). Of course Google’s real sampling algorithm is more complicated than this but the end result is important: Data may provide you with different answers depending on how it is sliced. In Analytics 360, sampling minimums are increased in many reports. In short, the free version of Google Analytics plays a valuable role in opening the door to data analysis for a wide range of small-scale developers, including individual website designers who create their sites with WordPress, Wix, or other tools. It allows them to generate basic reports and perform a limited array of essentially predefined analyses. The less widely known and implemented Google Analytics 360, with the Advanced Analysis interface, adds a few features that overlap in some ways with those in Adobe Analytics. Limitations include the need for SQL programming to get the most from the collected data and, significantly, data accuracy issues. Google Analytics has the advantage of providing the most direct path to data analysis with a focus on advertising and publishing. Other data analytics options Now, it’s time to check out some other options. These analytics products are often more niche oriented, focusing on event-based tracking, real-time stats for publishers, mobile application frameworks, or data built for product managers. Each of these vendors, including MixPanel, Heap, Amplitude, and Localytics, provides more focused but fewer features than Google Analytics 360 or Adobe Analytics. None have aimed to compete with the more complete cloud offerings in Google Marketing Platform or Adobe Experience Cloud.

Article / Updated 08-22-2019

Key to making yourself at home in and productive with Adobe Analytics is curating a set of customized segments that you can deploy to zero in on essential elements of site activity. Here, you find some great custom segments that will help you find the data you need with Adobe Analytics. The net section lays out the instructions for creating a custom segment in Adobe Analytics. The custom segments listed after that don’t contain in-depth instructions, but they do supply what needs changing. Isolating single-page visitors with Adobe Analytics “One and done” sometimes refers to star basketball players who put in an obligatory year in college before signing with the NBA. Data analysts, on the other hand, sometimes have to bucket visitors who hit one page and are gone. Identifying these “one and done” users useful, for example, when analyzing marketing campaigns. What can you identify as a shortcoming in advertising that brought a visitor to our property but wasn’t effective enough to get the visitor to view more than one page? You can ask questions about the landing page, campaign name, device type, geolocation, time of day, and more to help optimize your ad budget to limit the number of single-page visitors that you have in the future. Let’s create a custom segment in Adobe Analytics to isolate single-page visitors now. Follow these steps to create a custom segment that buckets single-page visitors: In the Title area of Segment Builder, enter a title. Type Single Page Visitors. In the Description area, enter a description of the custom segment. Type Visitors who only went to one page. In the Show drop-down menu, choose Visitor. Click the gear (Options) icon on the right and choose Add Container. Then add a second container. Change the first container type to Visit. Change the second container type to Visitor. Then click the gear icon to the right of the second container and choose Exclude. Drag the visit number dimension and then the single-page visits dimension into the first container. Drag the visit number dimension into the second container. Set the values to each of the three dimensions in your segment definition. Below the segment was defined as Visit Number equal to 1; Single Page Visits equal to Enabled; and Visit Number greater than 1. Click Save to save the custom segment. You can apply the segment in any panel. Identifying single-visit, multi-page visitors with Adobe Analytics Here’s a segment for identifying visitors who access multiple pages on your site but have visited the site only once. You might find this segment handy when you need to further analyze the success of an ad campaign that has a better bounce rate than expected but isn’t creating the type of stickiness that would drive multiple return visits. The definition for this segment is almost identical to the preceding segment, single-page visitors. The only difference is that you set the logical operator for the single-page visits dimension in the first container to Does Not Equal instead of Equals. Bucketing SEO to internal search with Adobe Analytics Analysts have been trying to identify proxies for natural search keywords ever since Google removed access to them from analytics platforms. You can use Adobe Analytics to fill the gap. One of the best ways to solve for the missing data is to analyze internal search term data as a proxy. If a visitor arrives at your site by a natural search and then performs an internal search on your site, chances are good that the keywords are related. This segment is great when you need to analyze internal search terms and entry pages to identify opportunities for improved analysis. Because the internal searches metric is non-standard, your visit-based segment might look slightly different than what you see below The key ingredients remain the same: Marketing Channel Equals Natural Search; and a second container that limits your internal search metric to the second hit in a visit. The hit depth dimension ensures that the internal search occurs immediately after the initial landing page view. Segmenting pre-purchase activity with Adobe Analytics The next custom segment will help you better understand what happens before a purchaser enters the purchase/cart flow. The insights you derive from this Adobe Analytics segment will help you better understand the types of activities that often result in purchases. In this custom segment, you need to know how your website/app and the implementation are set up to define the purchase flow. Find the dimensional value or metric that defines the beginning of the checkout process and set that to the first step in your visit-based container. The second complexity occurs after you’ve dragged in Orders to the Segment drop zone and changed to a sequential segment by adjusting the logic operator to Then. To focus your analysis on the action before your segment’s definition, adjust the sequence type from the default Include Everyone to Only Before Sequence. Finding strictly organic traffic with Adobe Analytics The focus here is not on non-GMO, locally sourced vegetables, but on identifying website activity generated from strictly organic, non-paid sources. It can be useful to understand how your visitors are getting to your site naturally, without using advertising dollars to influence their visit. This segment is a great one to throw into Segment Comparison to see how the behavior is different from others. The details of your segment may be slightly different than what you see below, but the gist is the same. Create a visit-based segment that focuses on marketing channels that are unpaid — and be sure to set an Or logical operator between them when you set it up in Adobe Analytics. Finding strictly paid activity with Adobe Analytics The inverse to the strictly organic segment is a strictly paid segment. Zooming in on just paid activity can also be a useful segment for a Segment Comparison to quickly see how the visitors your company is paying for are different from those that occur naturally. This visit-based segment, with the Or logical operator again, may be different in your report suite if you have other paid marketing channels. You can see an example of defining a segment for strictly paid activity below. Filtering out potential bots with Adobe Analytics If Shakespeare were writing today, instead of “out damn’d spot,” Lady Macbeth might have the said “Out damn’d bot!” Okay, maybe not. But for a data analysts, identifying and removing bots from traffic data is essential to working with valid data. With that in mind, here’s an Adobe Analytics recipe for a custom segment that can isolate potential bots. The definition of this potential bots segment was provided by Adobe based on significant research into bot activity. Weeding out unknown operating systems or browsers and Linux servers allows you remove a significant amount of bot traffic from report suites. The only advanced concept is to make sure you have applied an exclusion to the entire segment by clicking Options, Exclude in the Segment drop zone. Defining all three criteria as exclusions will shade the entire drop zone red. Identifying checkout fallout with Adobe Analytics Here, you find a blueprint for creating a custom segment to assist in identifying checkout fallout, specifically visitors who access the checkout page but don’t convert. Here you are identifying activity where the visitor got all the way to the checkout page but didn’t click the Buy button. This segment is useful for identifying common causes for cart abandonment. Plus, it’s a fantastic segment to share to the rest of Experience Cloud to remarket and try to reignite the purchase process for these visitors.

Article / Updated 08-22-2019

The idea behind using any platform to analyze data is to help drive better decision making. Adobe Analytics offers so many different tools to accomplish this goal. Marketing, in particular, is always trying to find ways to identify and target certain market segments. Adobe Analytics provides a custom segment to do exactly that. Here’s a custom segment almost anyone can use in Adobe Analytics: Who’s buying stuff? It's super-valuable in all kinds of reporting to be able to analyze this market segment of visitors. After all, these are your success stories, and the easier it is to highlight them in tables, the more you can harvest and exploit data that facilitates more sales. Let’s conceptualize the objective before you hit Segment Builder: You want to identify visitors for whom an order exists. With that criteria clearly in focus, you can define a custom segment to look at data for purchasers by following these steps: If the Segments component isn’t displayed in the left rail, click Components in the left rail selector. Launch Adobe Analytics Segment Builder by clicking the Create Segment (+) icon. The Segment Builder panel opens. Enter a title, a description, and tags for this custom segment. The title is required. The description will remind you and clue in colleagues as to what this custom segment does. Tags will make it easier for you and your colleagues to find this segment later. In the Definition section of the Adobe Analytics Segment Builder, choose Visitor from the Show drop-down list. You select this container type because you're focusing on showing data for visitors who make purchases. Search for the orders metric using the Search Components box in the left rail. Drag orders into the Segment drop zone. Choose Exists from the local operator drop-down list. With the custom segment defined and titled in Adobe Analytics, click Save. You can now apply this segment to any panel, any table, and most visualizations. Below, the custom segment has been applied to a table of various metrics measuring search engine sources. To quickly view the properties of the custom segment, click the i icon. The properties appear in the top half of the dialog that opens. If you need to edit the custom segment, click the pencil icon.

Article / Updated 08-21-2019

One key advantage of analyzing data with Adobe Analytics is to help drive your marketing and advertising strategies. Once you dive into Adobe Analytics, you’ll see how the platform can be used to tie the data from search engines into your marketing efforts. A key advertising channel for all brands occurs on search engines such as Google, Bing, and Yahoo! Companies apply two types of tactics to increase the visibility of their brand on search engines: search engine optimization (SEO) and search engine marketing (SEM, or paid search). Analysts need to analyze behavior coming from search engines as a channel as well as distinguish between paid and natural. The data helps them determine how the channel affects behavior and conversion rate. Adobe Analytics collects data in several search-focused dimensions, but they are unfortunately less reliable than the marketing channel and referrer dimensions. Our recommendation is to follow Adobe’s best practice by ignoring data in these dimensions and instead using marketing channel, referrer, referring domain, and the dimensions associated with Ad Analytics for Paid Search. To be thorough, and because your installation of Adobe Analytics may be configured this way (it might not be possible or prudent to attempt to change that, at least not quickly), it is useful to provide details on the original goals of these Adobe dimensions. That said, please consider the recommended best practice instead if you are in a position to do so. Detecting paid search visits with Adobe Analytics Adobe Analytics provides administrators with the ability to define rules to help differentiate paid search from natural search. The rules are set in a report suite’s Admin Console, listed under Report Suites → Edit Settings → General → Paid Search Detection. One automatic rule that Adobe provides is that a visit must have a referrer that is a known search engine. Adobe thankfully keeps this list updated so admins do not have to concern themselves with it. The remaining paid search detection rule definitions are based on a query string parameter, for example: cid=PS. Companies can set up different query string parameters based on the search engine, but we’ve found it preferable to use a single variable across all engines to keep data clean more simply. The image below shows how to configure paid search detection, which mirrors Google Analytics standards. If you’re familiar with Google Analytics, you’re probably used to the concept of utm query parameters to define marketing channels such as paid search. Google Analytics requires that you use utm_medium=cpc as the query parameter to properly bucket paid search visits. Because Adobe can define paid search based on any query parameter, brands that transition from Google to Adobe tracking can keep the same query parameter. The report suite’s paid search detection rule simply needs to be taught to look for utm_medium=cpc. Differentiating paid search in Adobe Analytics The simplest of the dimensions focused on search engine data is the paid search dimension. The paid search dimension helps analysts break down search engine behavior as either paid or natural. This high-level breakdown can be used to easily differentiate behavior at a very high granularity. Analyzing paid and natural search engines in Adobe Analytics All behavioral data from all search engines, regardless of paid search detection, is tied to the search engine dimension. The dimensional values are thankfully friendlier than just domains. Adobe returns the data as text, such as Yahoo! or Google — Denmark. The friendlier view of your search engine data can be useful when filtering or segmenting data to find exactly the engines you're trying to analyze. The image above shows the search engine dimension with a paid search segment. Do you see anything strange in the image above? Because the data is sorted by visits, which doesn’t have a segment applied to it, the first line item is listed as Unspecified. Unspecified is listed at the top because it is the result of all the visits that didn’t come from a search engine. If an analyst were to sum all the visits to each of the individual search engines, there would be a significant difference between that sum and the total count of visits to the site; Unspecified acts as the remainder. Adobe adds an Unspecified row by default for almost all dimensions to make it easier to focus on behavior where the dimension was not set (or unspecified) when that metric was captured. Adobe makes it easy for analysts to remove that dimensional item from view through the table filter feature. The image below illustrates the details to remove Unspecified now. Paid search detection rules help analysts by creating two dimensions at the search engine granularity: search engine — natural and search engine — paid. The only difference between these aligns directly with whether the visits met the detection rules. Analysts can use search engine data to help marketers better attribute their marketing dollars. If one paid search engine is driving a significantly higher amount of traffic but a lower conversion rate, it may make sense to adjust the budget for this search engine. Search engine alone isn’t usually enough to make this recommendation. As you would expect, Adobe also provides similar dimensions focused on the search keyword rather than the engine. Initiating search keyword analysis in Adobe Analytics Search keyword allows analysts to dig deeper into their search advertising data to identify what keywords are driving prospects and consumers to visit their site. These keywords can often become some of the most useful dimensional values to an analyst; when else do consumers tell you exactly what they’re looking for? Unfortunately, there’s a catch. Years ago, in the name of privacy, Google blocked natural keywords from view by all analytics platforms. Other search engines soon followed suit, and now our beloved natural search keywords have been removed from Adobe Analytics (and Google Analytics, Webtrends, Coremetrics, and so on). The search engines did, however, continue to provide advertisers with access to capture the search keyword if a user clicked through on a paid search ad, but only if that keyword as sent via query parameter on the landing page. So what does this all mean? All three of these dimensions are mostly useless because they generally list just Keyword Unavailable. You may see some minimal data in them from search engines that have not yet blocked paid search, but you should instead collaborate with your Adobe admin team and advertising team to ensure that paid search keywords are captured in a custom Adobe dimension.

Article / Updated 08-21-2019

Adobe Analytics is a powerful tool for uncovering you audience. As a data analyst, you will inevitably need a considerably detailed view into the channels driving visitors to your site. The marketing channel dimension in Adobe Analytics provides a complete view into each of the categorizes of referrers and ads that drive traffic to your site or app. Adobe Analytics marketing channel dimension is built on a custom set of rules defined in your report suite’s Admin Console. These rules are called the marketing channel processing rules. You would be smart to sync with your Adobe Analytics admins to understand how these rules have been defined and, if possible, even help decide how they are set up and prioritized. Identifying marketing channel dimensions in Adobe Analytics By default, Analysis Workspace has six marketing channel dimensions. To keep things simple, they are grouped into two sets: channel and channel detail. Marketing channel, last touch channel, and first touch channel are the dimensions associated with a higher granularity bucketing of visits, often containing values of paid search, natural search, email, display, social networks, and referring domains. These dimensions are defined by the first drop-down in the channel settings within a rule set. The key difference between the three channel dimensions is tied to attribution: Which value, over time, should apply to the corresponding metrics? The following presents a simple example to help you grasp the concept: Visit Number Channel Action 1 Display Banner Click Research 2 Paid Search Purchase Note how the table describes two separate visits, the first driven from display and the second driven from paid search. The head of advertising will want to know whether to put the budget for next quarter in display or paid search. As an analyst, you will want to attribute the revenue to one of those two channels, but which one? For years, analysts have been using a last touch attribution model to associate the revenue to paid search. In this model, whichever channel is the most recent deserves 100 percent of the credit for the revenue. In this instance, the advertisers responsible for display would argue that the visitor wouldn’t have even known about the brand without their display-driven awareness campaign, so they should deserve 100 percent of the credit. This approach is known as first touch attribution. As you may have guessed, the last touch channel attributes metrics to the channel value by using a last touch attribution model. The first touch channel attributes metrics by using a first touch attribution model. When Adobe released Attribution IQ, a powerful way of changing the attribution model of any metric tied to any dimension, they were concerned that the dimensions of last touch channel and first touch channel could be misleading, because technically the last touch channel could be tied to metrics where the attribution model has been adjusted to first touch! To resolve this potential conflict, Adobe created a more generic marketing channel dimension. Marketing channel has a default attribution of last touch, but doesn’t carry the confusing last touch name distinction because metrics are more customizable now. The best practice is to always use (or migrate your old projects to use) marketing channel and ignore the first touch and last touch dimensions. The second set of dimensions created by marketing channel processing rules are set by the value in each rule set, which is the second drop-down in the channel settings. Marketing channel detail, last touch channel detail, and first touch channel detail provide a more detailed view into the channel. These dimensions are set to capture the keyword for paid search, the campaign name for display, or the search engine for natural search. Because these values are customizable, be sure to work with your Adobe admin team to see how each channel’s value is set in the marketing channel processing rules for each of your report suites. Adobe Analytics provides three separate channel detail dimensions just as they provide three channel dimensions: first touch, last touch, and marketing channel detail. Marketing channel detail is a duplicate of last touch channel detail and is similarly less confusing when new Attribution IQ models are applied to it. Therefore, the best practice is the same as with marketing channel: Use (or start migrating to) marketing channel detail. The image below shows the marketing channel dimension, further broken down by marketing channel detail. Defining your marketing channels in Adobe Analytics Marketing channel processing rules are defined using a combination of dimensions based on referrer, search engine, query parameter, page, any eVar, and more. Be careful! Processing rules are permanent, so be sure to avoid accidents when adjusting them in Adobe Analytics. If your report suite isn’t yet capturing data in the marketing channel dimension, Adobe will suggest a default set of rules the first time an admin accesses their settings (available in Admin Console → Report Suites → Edit Settings → Marketing Channels). A report suite’s marketing channel processing rules are comprised of three key elements. Only an administrator can edit them, but you should understand their capabilities: Rule sets contain one or more rules to set a value for a marketing channel and channel detail dimension. Each rule set defines a single value to the channel dimension and a single value to the channel detail dimension. Rules define how visits should be bucketed into the channel and channel detail dimensions based on conditions that you define. For example, a rule’s condition could be configured to identify whether a visit’s referrer is from a search engine. Processing order is a well-named component of marketing channel processing rules because it defines the priority of each rule set. As soon as a visit matches a rule set, the visit’s channel and channel detail are set based on that rule set. For example, you may have one rule set that defines paid search (based on a search engine referrer and the existence of a CID query parameter) and a second rule set that defines natural search (based only on the existence of a search engine referrer). If the rule set for natural search is prioritized above the rule set for paid search, the paid search channel will never be set because all search engine visits, regardless of the existence of the query parameter, will be bucketed as natural search.

Article / Updated 08-20-2019

You may not know this, but Adobe Analytics users perform data analytics on things beyond 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 Adobe Analytics 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 and analyzing data have emerged. (Check out this article for more on data trends.) 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 data analysis coin, vastly more sources of user data exist than just a few years ago. Today, Adobe has a number of mechanisms to import information for data analysis 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, it’s important to understand 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. Using Adobe Analytics to capture 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 web analytics first came onto the scene, one of the toughest jobs 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. Using Adobe Analytics to capture 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 with Adobe Analytics 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). These technical programming languages are mentioned 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. Using Adobe Analytics to capture data from IoT and beyond Now that you understand 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.

Article / Updated 08-15-2019

Where do you go for resources to expand your command of Adobe Analytics? Here, you will find some great resources. Some are official Adobe sites, with real-time updated documentation. Others are more generic resources for data analytics. And at least one of them is in here mainly for those who, to quote Sheryl Crow, “want to have a little fun” with Adobe Analytics. Adobe's Analytics Implementation Guide You might be too young to remember, but people used to buy software apps from stores, and the apps came with a book documenting how to use the app. Adobe's Analytics Implementation Guide plays that role. This soup-to-nuts set of resources from Adobe provides a macro guide and a micro guide to the tasks you need to complete to implement Analytics. Much of the material in the Analytics Implementation Guide is presented as downloadable PDFs. Those PDFs are supplemented with a wide range of video tutorials. To provide a menu of sorts as to what you ‘ll find in the Implementation Guide, we’ve curated a set of key topics. It’s a god idea to visit the site, bookmark it, and note available white papers, documentation, and videos. You’ll want to keep this site handy as you engage in deeper levels of Adobe Analytics. Topics include the following: Discovery and requirements: How to define your analytics goals and gather requirements for the implementation, starting with developing and documenting an objective understanding of the website and its business goals. During this phase, your consultant or partner gathers measurement requirements. What Adobe calls “gather[ing] measurement requirements” is synonymous with what are often called creating a business requirements document (BRD). This document maps the goals of a website or app to the overall business goal of the enterprise, and suggests industry best practices. Installation and provisioning: How to set up Adobe Analytics and get an email with login credentials. Configuration and implementation: What you need to have in place before launching Analytics, including documenting a solution design reference document and a tech spec. The solution design reference document contains an overview of the website data layer, launch elements/rules, and Adobe Analytics variables. The tech spec is detailed documentation on how to implement each component of the solutions and how to validate them. Post implementation: In this phase of unrolling Analytics, you work with a consultant or partner to identify data accessible through Adobe Analytics, and brainstorm how to use that data to optimize your digital business. This phase also includes enabling various time-saving features of Adobe Analytics, such as Report Scheduler, Workspace, and Microsoft Report Builder. (Report Builder is a Windows-only plug-in.) Implementation resources: Here you find links to three comprehensive additional resources and documentation for Adobe Analytics. Those resources follow: The Analytics Implementation Guide (a downloadable PDF) Analytics Implementation Training (training resources for your team) Analytics Video Learning (a library of helpful videos) A data analytics measurement plan for your Adobe Analytics strategy Measurement plans are emphasized in this top-ten list because they are the foundation on which successful analytics frameworks are built. The article “How to Create a Measurement Plan and Why You Really Need One” is a useful discussion of measurement plans. And, as the title implies, it also provides specific tools for building a measurement plan. Those tools include a nicely formatted and thoughtfully designed Excel spreadsheet that serves as a template (and model) for a measurement plan, including creating an integrated strategy with a website measurement plan based on identified goals. The image below shows the template spreadsheet that comes with the article, as hosted at the UK-based freshegg site (thus the British spelling of Organisation). Data governance and Adobe Analytics The article “Data Governance: The Key to Building Consistent, Outstanding Digital Experiences,” by Eric, identifies the conundrum that “more often than not, marketers have more data than they know what to do with — and that just might be their biggest problem.” The article draws on real-world experiences at Southwest Airlines and Zebra Technologies Corporation (which acquired Motorola). The article and case study provide a concise argument for the following themes that run throughout this book: Keep analytics at the center of your data governance Invest in products, definition, and processes Train your team for success Pay the price for better digital experiences Web analytics solution design A solution design or solution design reference (SDR) connects the business requirements and goals defined in a measurement plan with the technical requirements necessary to successfully deploy analytics technology. The article “7 Steps to Set Up Your Web Analytics Solution Design” identifies and walks through seven strategic steps to developing an effective solution design to protect the integrity of your web analytics implementation. Also at this link is access to a half-hour webinar featuring Jason Call, senior data analytic expert at ObservePoint. Digital Analytics Power Hour One of the most thorough, honest, and irreverent mediums for staying on top of the industry is via podcast. The three hosts of the Digital Analytics Power Hour — Michael Helbling, Tim Wilson, and Moe Kiss — provide their explicit feelings on a wide variety of analytics topics. The hosts often invite other people in the industry to ensure that multiple opinions are represented and new technologies and ways of thought are discussed. Figure 18-5 provides the podcast’s raison d'etre. Analytics agencies and Adobe Analytics The analytics agency world is chock full of smart and successful consultants. It would be impossible to link to all of their content, but here’s a few resources that are especially valuable to the growing Adobe Analytics analyst. The team at 33 Sticks shares a unique set of insights and experiences working with customers to implement digital analytics. Check out the blog articles and 33 Tangents podcasts episodes. The content addresses a wide range of topics from digital analytics to business and technology to remote work. The masters at Analytics Demystified have been writing content about Adobe Analytics for more than 10 years. we highly recommend spending some time on their blog (https://analyticsdemystified.com/blog/) to learn about real-world applications of Adobe technology and how-to’s. Adam Greco’s content is especially valuable to both new and seasoned analysts. Conferences, conferences, conferences…for the data analyst Analytics enthusiasts are a tight-knit group of people who love to share and learn from each other. There is no better way to learn more about analytics, Adobe, and data industry trends than by attending and networking at analytics conferences. Some of our favorite industry events include the following: Adobe SUMMIT: Adobe’s annual multiday conferences in Las Vegas, Nevada and London are worth every penny. These can’t-miss events are the preferred way for thousands of digital marketers and analysts to learn about Adobe’s vision, new features, and best practices. The thousands of attendees include successful business leaders, celebrities, and a who’s-who of the analytics industry — new friends and selfies encouraged! Adobe Insider Tour: In addition to SUMMIT, Adobe hit the road for the first time in 2017 and the feedback has been impressively positive. These fun, free, half-day events bring members of the Adobe Analytics product team to cities around the world (from Chicago and Dallas to London and Sydney) to spread tips and tricks with Adobe solutions, provide a glimpse into the Adobe roadmap, and give Adobe partners and customers the chance to present. If the tour is coming to your city, you’ll be glad you took the time to enjoy the festivities. Sign up as an Adobe Insider to be informed. DA Hub and Measure Camp: Two of our favorite vendor-agnostic events are known as unconferences. An unconference aims to avoid the large keynotes, huge breakout sessions, and generic conversations that some larger conferences are known for. Instead, the unconference focuses on small huddles — group conversations — and a more tight-knit group of attendees. Attendees of these unconferences are a highly loyal group that you will want to meet and discuss analytics with. The Adobe Experience League Adobe’s Experience League is a repository of valuable information about Adobe Experience Cloud products. On this site, Adobe provides videos, tutorials, and a community forum. If you log in with your Adobe ID, you’ll receive a tailored experience based on content you’ve previously viewed and the features you use in Adobe products. The Adobe Analytics YouTube channel The Adobe Analytics YouTube channel is one of the best ways to stay on top of new features and the latest best practices. The Adobe product team manages the content here, and you may even recognize the name of one of the common presenters — one of your two favorite analytics authors, Eric Matisoff! Every time Adobe releases new features or adds new functionality to old features, Adobe creates a playlist of three-to-five-minute videos explaining the changes. Over 10,000 subscribers regularly watch the 180+ videos that are to-the-point and easily accessible thanks to the well-organized YouTube playlists that Adobe has created. The following image shows a comparison of segments in Adobe Analytics. Subscribe today! Hacking the Bracket with Adobe Analytics You can easily draw on sports to understand and apply analytics. The fun, interactive site Hack the Bracket draws on data processed by Adobe Analytics to predict the outcome of NCAA basketball championship matchups. Sound like fun? Try it! Of course, Adobe does not make any warranties about the completeness, reliability, and accuracy of the predictions, and any action you take on the predictions provided is strictly at your own risk.

Article / Updated 08-12-2019

Adobe Analytics provides a wide assortment of built-in dimensions for you to use. These built-in Adobe dimensions will help you analyze products, device technologies, advertising, content, and more. Unfortunately, it’s never enough. That’s where custom Adobe dimensions comes into play. Analysts are a demanding bunch, so Adobe has created two types of custom dimensions to help you capture even more useful data about the behavior occurring online. You can use these custom Adobe dimensions to capture data that is necessary for you to complete your analysis but not available in the preconfigured dimensions you’ve read about. For example, a custom dimension could capture internal search terms that visitors have used on your website. The number of custom dimensions that your company has access to is tied to your Adobe Analytics SKU, but most companies have close to 300 custom dimensions per report suite. Defining expiration and allocation dimensions in Adobe Analytics Adobe provides administrators with several options for customizing the definition of these custom dimensions. Before you dig into the different variable types and how to find them in your report suite, let’s discuss two pieces of terminology: expiration and allocation. Because these options involve complex concepts, you walk through them in some depth. Expiration is the simpler concept, so you start there. Understanding expiration in Adobe dimensions Expiration focuses on how long the values in a dimension should persist. Should the value expire at the end of the page view, like the page dimension, or should it expire at the end of the visit, like a marketing channel? Adobe gives admins the ability to define expiration from as short as the hit that the tag fires to the length of the visitor’s cookie (in other words, never). This consideration is important for administrators because the expiration of custom dimensions needs to extend long enough for the metrics that matter to correlate with the persisted dimensional value. The table below illustrates an example visit where a custom dimension is capturing the name of the video being watched with several metrics firing along the way. Sample Visit with Custom Dimensions and Metrics Hit Number Consumer Action Video Name Dimension Metric Name 1 Landing page Page view 2 Video start Intro to analytics Video start 3 50% of video reached 50% milestone 4 Video end Video complete 5 Video start Advanced analytics Video start 6 50% of video reached 50% milestone 7 Product added to cart Cart add 8 Browser closed The visit example above describes a simple visit: a consumer comes to a website, watches a video to completion, starts a second video, but doesn’t complete it. In this implementation, a custom dimension called video name is set at the start of each video. The company’s Adobe administrator needs to decide the expiration for the custom dimension so that the metrics data correlate to the Adobe dimension properly. If the expiration is set to expire on page view, hits 3, 4, 6, 7, and 8 will not be tied back to any video names that were set. Those hits would align with Unspecified in your reporting. If the expiration is set to expire when the video complete metric fires, all hits will accurately tie back to their assigned video name. In addition, if the expiration is set to expire never, the metrics in this example will also tie back to the video name correctly. However, any additional metrics from the next visit (perhaps downloads or registrations) will also align back to the Advanced Analytics video name because the dimensional value persisted and never expires. Unspecified is Adobe’s way of showing metrics captured when a value was not set for the dimension being analyzed. This includes custom dimensions that have expired. Therefore, any metrics aligned with an Unspecified video name did not have a value set for that dimension when the metric was captured. If the video name dimension was set to expire at the page view, no value is assigned to the custom dimension. Defining allocation in Adobe dimensions The other half of the admin settings for custom Adobe dimensions is called allocation. Allocation decides what value of a custom dimension should get credit when multiple values are sent to the dimension before expiration. Another common name for allocation is attribution, which you may be familiar with already. In the visit example in the provided table, the consumer is sending two values to the custom dimension: intro to analytics, and advanced analytics. If the Adobe administrator for this company sets a visit expiration for this Adobe dimension, how will each of the metrics align with these two dimensional values? By default, custom dimensions apply a most recent/last touch model, meaning that the metrics are attributed back to the most recent value captured in the dimension. This allocation method would be ideal in the stated scenario: 1 video start, 1 50% milestone, and 1 video complete would be attributed to intro to analytics; 1 video start and 1 50% milestone would be attributed to the advanced analytics video. A vast majority of custom dimensions are set to a most recent/last touch allocation, so it’s probably safe to assume that’s how yours are set. Of course, it’s always worth checking with your admin team to confirm. Another option for custom dimensions is to set the allocation to original/first touch model. This approach would not bode well for the scenario in the table because it would attribute both video starts, both 50% milestones, and the video complete to the original value that the custom dimension received: intro to analytics. This result is clearly inaccurate and last touch allocation for this video name Adobe dimension seems like a no-brainer. But as an analyst or data science enthusiast, you’re always thinking about the product purchase cycle, so you probably got excited to see that a product was added to the cart after the second video was partially watched. In this example, which video would you assign credit to — the first one watched or the last one watched? Perhaps partial credit for both? Partial credit is where the last custom dimension allocation comes into play. In addition to first and last touch, admins can set custom dimensions to have linear allocation. In a linear allocation, every value sent to the Adobe dimension gets a percentage of the metric being analyzed. If video name in the table were set to a linear allocation, each video would get 50% credit for the add to cart that occurs at the end of the visit. The best news regarding this situation is that Adobe’s Attribution IQ set of features enables more flexible models than these three and gives you the ability to change the attribution model on the fly for any Adobe dimension. So between the two settings, expiration and allocation, the more important one these days is expiration. Distinguishing between props and eVars Now that you have a good feel for the settings that you can apply to custom dimensions, let’s dig into the two types of variables that exist in your implementation. Adobe Analytics has several names for both of them. The first are often referred to as “props” but are synonymous with sProps and traffic variables. Props are custom dimensions that always expire on the hit. Your company has access to 75 props. They can be quite useful because of their simplicity. Because they expire on the hit, you don't have to worry about allocation because only a single value can be passed into a dimension per hit. Because props expire instantly, they are often considered to be a little less useful than eVars, their custom dimension counterpart. However, analysts who know when props are set can find plenty of uses for props. eVars are custom dimensions that have settings aligned with allocation, expiration, and more. They are more flexible and powerful than props but also more complex. Most companies have between 100 and 250 eVars, depending on their contract with Adobe. If you jump back to the sample visit in the table, you would almost definitely want to use an eVar to capture video name so that the dimensional values persist and the video metrics properly attribute back to them. Your trusty debugger can be used to easily identify all custom dimensions that are firing in any hit. Note how the image below shows the URL being passed into prop4 and just the domain passed into eVar5. You’ll be glad to know that administrators have an interface, known as Admin Console, to create friendly names for all props and eVars. You don’t have to memorize that prop4 means URL and eVar5 means domain; Analysis Workspace would simply show the friendly names automatically. Because you will often find yourself going back and forth between the debugger and Workspace, it makes sense that you will occasionally forget the friendly for an eVar or a prop. Fear not, for Adobe has already made that process easier: eVars and props can be searched in the left rail search box. Try searching for eVar1 or prop2 in your report suite to see how the process works. Applying date ranges in Adobe Analytics The final custom dimension to discuss is a unique one. Did you know that you can also add date ranges to your freeform tables? You can drag preset date ranges (which are listed at the bottom of the left rail) into a table. Any of these can be used as dimensions in freeform tables. In addition, you can easily create new date ranges based on your needs. Let’s create a new date range that focuses on the last full 5 days: Click the plus button in the left rail next to the word Time, or click Components in the main menu and choose New Date Range. Or use the keyboard shortcut Shift+⌘D on a Mac, or Shift+Alt+D on a PC. A dialog appears requesting you to define your date range. The interface is intuitive and mirrors the calendar in panels. Use the calendar to select a date range, such as the last five days. Select the Use Rolling Dates check box, and then click Apply. Add a friendly name in the Title text box of the date range (such as Last 5 Days), and then click Save. Rolling dates allow you to decide whether the dates should update as time passes. If you have a date range of the last 5 full days and choose the Use Rolling Dates option, when you log into Adobe tomorrow, the date range will be updated to include that day’s data. After you’ve defined a custom date range, you can drag that dimension into a table. Dimensions such as eVars, props, and custom date ranges will be useful tools for analysis in Adobe Analytics. Stay involved with your Adobe administrator to ensure that the custom dimensions you need are tracked accurately and consistently.

Article / Updated 08-08-2019

One of the most interesting data-science-influenced features in the Analysis Workspace of Adobe Analytics is called Segment Comparison. It’s one of Adobe Analytics most powerful features because it helps you easily differentiate two groups of visitors based on their behavior. Have you reviewed a Venn diagram recently and wondered about the following? What are the metrics that differentiate my segments? For example, you might discover a correlation between your logged in visitor segment and views of blog articles. What are the dimensions that differentiate my segments? For example, you might discover that iOS device types make up a significant portion of visitors who have converted. What other segments can be applied to my segments to differentiate them? For example, you might see that visitors referred from paid search have a higher likelihood of being in the custom segment you created of first-time visitors who land on a product page. If so, you've come to the right place! Those questions are the ones that Segment Comparison aims to answer, using machine learning every step of the way! First, you learn the steps to compare two segments. Then you dig into a few other examples of useful segment comparisons to try out. Invoking Segment Comparison in Adobe Analytics To compare segments in Adobe Analytics, follow these steps: Use the left rail selector to change the left rail’s view to panels. Drag the Segment Comparison panel into Workspace. Drag a segment into the Add a Segment area in the Segment Comparison panel. In addition to segments, you can also drag dimensions, dimensional items, metrics, and time ranges into the box. In the example, a segment that was already created was used that focuses on iOS devices. If you’d like to create a segment in this workflow, hover your cursor over the Add a Segment box and click the plus sign that appears. Drag a second segment (or any other component) into the Compare Against box. By default, Adobe includes a segment focused on Everyone Else, in case you merely want to compare this segment against its inverse. However, this results in a Venn diagram without any overlap. This can be useful, but for the example, you are comparing against a second segment. In the image below, a Purchasers segment was added to the Compare Against box. Based on the assumption that some visitors on iOS devices have made purchases, you can expect to see some overlap between the two segments. Click the Show Advanced link. The box that appears should look familiar if you remember the Excluded Dimensions box for Contribution Analysis. As shown above, you have the option to exclude dimensions, metrics, and segments from the analysis and results of your segment comparison. Just as in a Contribution Analysis, the reason to exclude dimensions is to avoid annoying results that may be accurate but aren’t actionable. Because Segment Comparison also analyzes metrics and segments, they are added as options for exclusion. Click the blue Build button and examine the results that appear, often in less than a minute. As you can see below, a wealth of visualizations are returned from Segment Comparison. Let’s review them from top to bottom, left to right: The Size and Overlap visualization is a Venn diagram that you could have easily built based on your two segments and a metric of unique visitors. The next three summary visualizations show the count of unique visitors for each of your segments as well as the overlap between them. These summary visualizations can be a good reference as you perform your analysis. The two visualizations in the second row of the results are live-linked, which means that when you click a value in the table on the left, the graph on the right is updated. The table on the left shows the top metrics that differentiate your two selected segments. A column for difference score sorts each of the metrics by their level of statistical significant, just like Contribution Analysis’s contribution score. You’ll be glad to see that Adobe runs Anomaly Detection on all metrics that are trended in the line chart on the right too! As you can see in the third row in the table above, purchasers end up watching a significantly higher amount of media (an average of 285.81 seconds) than iOS visitors (an average of only 66.03 seconds). The third row shows two more live-linked visualizations: a table on the left of the dimensional items that differentiates your two segments and a bar chart on the right that shows the stark difference when each of your two segments are applied to the dimensional items. A difference score is again applied to these visualizations. The fourth and final row of your Segment Comparison results uses your own data to help you differentiate the segments you're comparing. Adobe analyzes all segments you have access to or have created so that it can provide a final list of differentiating segments. The freeform table live-links the differentiating segments to a Venn diagram on the right, which allows you to quickly find overlap between three different segments. Adobe’s Segment Comparison tool is a fantastic and fast way to learn more about your visitors. You’ll be glad to hear that there is no limit to the number of times you or your company can use this tool, regardless of contract or SKU. If you don’t have access to it, be sure to work with your administrator to understand why. Using Adobe Analytics to brainstorm Segment Comparison use cases If your brain isn’t already working in high gear thinking creatively about how to use the Segment Comparison feature in Adobe Analytics, let’s give it a kickstart. Our first recommendation is to start with a segment of converters to your site. As a reminder, someone who converts doesn’t necessarily mean he or she has purchased. For websites and apps that don’t sell anything, a conversion could mean a registration, a video view, or a threshold of unique views of content. Whatever your conversion, create a segment based on visitors who have accomplished it. The most basic comparison to run is converters versus non-converters. At first, Segment Comparison will most likely tell you things you already know — your best marketing channels, micro-conversions that are leading indicators to success, or regions of the country that are more successful than others. Segment clustering in Segment Comparison’s results may come in handy in use cases like these. Are there any unusual combinations of those dimensions that Adobe Analytics suggests reviewing? If Adobe provided you with any segments that are comprised of atypical dimension combinations, start your analysis there. For those of you who do have a purchase funnel, consider creating segments in Adobe Analytics for each of the key steps of the funnel — visitors who get to a product page but don’t add to cart; visitors who add to cart but don’t get to the checkout page; visitors who get to the checkout page but don’t purchase; and visitors who purchase. Play around with each segment in Segment Comparison to learn more about what differentiates one segment from the other. Because you have unlimited access to the Segment Comparison tool, you might as well try it out! Last, and certainly not least, try a mixture of marketing channel and account purchase status. For example, segment visitors who access your site via the most successful marketing channel and compare it to visitors who purchase. Then compare it to visitors who don’t purchase. Segment Comparison will often help you discover metrics, dimensions, and segments that differentiate segments that you hadn’t thought to consider comparing before. Ideally, that newfound information will help drive your data curiosity and improve how you use Adobe Analytics.