The 10 Best Features of Adobe Analytics

Wondering if the many features of Adobe Analytics is worth the investment?

As an Adobe Solutions Partner with 21 certified employees and 34 certifications – including a specialization in Adobe Analytics – we definitely love everything the platform has to offer.

But what about when it comes to your specific business?

While every company is different, if we cover the 10 best features Adobe Analytics brings to table, we think you’ll have a much easier time deciding for yourself.

We’ll continue to update this post every year as Adobe Analytics continues to introduce new features.

And it’s worth pointing out that Adobe is really good about doing this on a regular basis. They’re constantly researching, developing, and updating the ways their platform can help business owners better understand data about how customers are interacting with their websites.

But as of 2023, these are the best features of Adobe Analytics.

While Google Analytics has a real-time report, it only shows that information for the last 30 minutes. After that, the data isn’t really available for any kind of practical use until the following day.

On the other hand, with Adobe Analytics, real-time reporting makes data available almost instantaneously. How fast this “real-time data” is available varies depending on several factors, like:

• Data Volume
• Network Latency
• The Complexity of the Data Processing

But still, you’ll have access to this data much quicker than you would using Google Analytics – no need to wait an entire day.

And with this processed data available, Adobe Analytics will let you access prebuilt dashboards and reports or even create custom visualizations to monitor key metrics and trends as they occur.

Although this probably won’t be a feature you take advantage of every single day, but it can definitely come in handy when you’ve made an update to your website and want to see how it’s impacting your traffic and conversions right away.

If those changes are hurting one or both, you’ll be glad Adobe Analytics was able to alert you sooner rather than later.

Unless you’re running a one-person company, one of the most important features any Analytics platform can offer is support for sharing reports with ease.

If you can’t create reports and easily share them with your teammates, it’s going to be hard to use the corresponding data to improve your numbers.

Adobe Analytics allows users to create customizable dashboards and reports tailored to specific business needs.

These custom dashboards can be populated with a wide range of metrics, visualizations, and dimensions.

Then, you can easily share these dashboards with team members by granting them access or by exporting and distributing the reports in various formats, like PDF or Excel.

Adobe Analytics provides tons of collaboration features to streamline teamwork.

You and your colleagues – or even employees from other companies or clients – can collaborate on reports and dashboards by assigning different roles and permissions to team members.

For example, administrators can grant view-only access, editing capabilities, or restrict access to specific sections of a report, so you can protect sensitive information from unapproved eyes.

When work gets busy, it’s easy to forget mundane tasks like distributing reports throughout your team and sending them to clients – even though it’s still a very important duty.

Well, you won’t have to worry about that going forward if you use Adobe Analytics, which will let you schedule the delivery of these important reports via email or other channels. Set up automatic distribution of reports to team members or stakeholders at predefined intervals and this issue becomes a thing of the past.

Workspace is a powerful tool within Adobe Analytics that lets users create and manage projects for collaborative analysis.

This feature provides a shared workspace where team members can work together on data analysis, create visualizations, and share their findings.

It also allows for simultaneous collaboration, which makes it that much easier for all of your colleagues (and/or clients) to learn from the data you’re harvesting and provide feedback about what to do based on the numbers.

Adobe Analytics provides APIs and data feeds that allow data to be extracted and integrated with other systems or tools used by different teams within an organization.

These APIs enable seamless data sharing between Adobe Analytics and other platforms, such as:

• Data Warehouses
• Business Intelligence Tools
• Customer Relationship Management Systems

So, it’s not just great for collaborating with other people. Adobe is great for collaborating with other platforms to get the results you need from analyzing your data.

Adobe Analytics allows users to set up custom alerts based on predefined thresholds or specific data conditions.

These alerts can be sent to relevant team members whenever defined criteria is met, notifying them of important changes or anomalies in the data. This ensures that teams stay informed about critical metrics and can take immediate action when necessary.

Without segmentation, analytics platforms would offer little more than just huge block of data, which isn’t very useful if you have no more than customer type visiting your website.

Good segmentation should let you group and analyze data based on specific criteria, like:

• User Demographics
• Behaviors
• Actions

Adobe Analytics offers a range of tools and features that enable powerful and flexible customization of segmentation.

Let’s look at some of the ways Adobe Analytics do this.

Adobe Analytics provides a wide range of built-in dimensions and metrics to segment and analyze data.

These include standard dimensions like page views, visits, and geolocation, as well as custom dimensions that can be defined based on your specific business needs.

You can create all kinds of custom metrics that derive insights from various data points and perform calculations specific to your organization.

Adobe Analytics offers a comprehensive set of tools for advanced segmentation.

You can create segments using simple conditions, such as combining dimensions and metrics with operators like equals, contains, or greater than.

But Adobe also lets you leverage Boolean logic to create complex segmentations based on multiple criteria. This allows for fine-grained segmentation that aligns with your specific analysis requirements.

Adobe Analytics enables sequential and fallout segmentation, which is particularly useful for analyzing user paths and conversion funnels.

Sequential segmentation allows you to define a series of steps or events that users must go through in a specific order.

Fallout segmentation helps identify where users drop off in a multi-step process, enabling you to optimize user experiences and conversion rates. We’ll talk about the powerful Fallout Reports in more detail a bit later.

Adobe Analytics supports cohort analysis, which involves segmenting users based on shared characteristics or behaviors over a specific time period. Cohorts allow you to track and compare the performance of different groups of users, providing insights into how user behavior evolves over time.

You can also apply a latency view to them, but again, we’ll talk about that in more detail in just a moment.

Adobe Analytics allows you to define custom variables and events to capture and track specific user interactions or attributes. This flexibility enables you to create highly-tailored segments based on custom data points specific to your unique business.

And as we touched on earlier, real-time data processing means that marketers can also apply segments to this information, allowing them to make immediate decisions based on up-to-the-minute insights.

This makes it easy to create real-time personalization, targeted marketing campaigns, and immediate responses to user behavior.

Adobe Analytics is highly regarded for its capabilities in working with calculated metrics, which allow you to create new metrics by performing calculations on existing dimensions and metrics.

This lets you create custom metrics that align with your specific business objectives and analysis requirements.

And Adobe Analytics is extremely adept at doing this compared to other analytics platforms. Let’s look at why.

Adobe Analytics provides a user-friendly interface that lets you define calculated metrics using a wide range of mathematical and logical operators.

You can:

• Perform simple arithmetic operations (addition, subtraction, multiplication, division)
• Apply statistical functions
• Create ratios or percentages
• Use conditional statements to define complex calculation logic

This incredible level of flexibility makes it incredibly simple to create custom metrics that accurately reflect the unique aspects of your business and analysis needs.

Calculated metrics in Adobe Analytics can leverage both built-in and custom dimensions and metrics, so you can combine existing data points to create new, meaningful metrics.

For example, you can calculate:

• Average Revenue Per User
• Conversion Rates
• Engagement Scores

Those are common examples, but you can create just about any other metric that helps you understand user behavior and business performance through Adobe Analytics.

And, once again, all of this can be done in real-time, as well, so you never need to wait around to apply calculated metrics to your data.

Once created, calculated metrics in Adobe Analytics can be reused across various reports, dashboards, and segments.

This reusability saves time and effort as you can leverage the same custom metrics in different analysis scenarios, ensuring consistency and comparability across your analytics efforts.

Calculated metrics in Adobe Analytics can be used in conjunction with other analysis features, such as segmentation, trend analysis, or cohort analysis to create even deeper insights and more advanced visualization of your data.

Among other things, you’ll be able to see how calculated metrics interact with other dimensions and metrics to uncover patterns, trends, and correlations.

Adobe Analytics provides tools to test and validate the accuracy of your calculated metrics, as well, which is arguably one of the most important capabilities if you’re going to be creating your own metrics and sharing them with other members of your team.

So, you can easily preview and verify the results of your calculations before deploying them to ensure they align with your intended logic and expectations.

Freeform Tables in Adobe Analytics are a powerful reporting feature that allows users to create highly customizable and dynamic tables for data analysis.

These user-friendly tables provide a flexible framework to organize and display data based on specific dimensions and metrics, so you can create ad-hoc reports and explore data in a more granular and personalized way.

Some of the many key characteristics and capabilities of Freeform Tables include the following.

Freeform Tables may be one of the most user-friendly features of this analytics platform. As we mention in the video, it’s also representative of why people with practically no analytics experience can get up and running using Adobe within a couple of hours.

If you can drag and drop, you can use Free Form Tables to build and customize all kinds of table structures.

Just select dimensions and metrics from a list, drag them into rows or columns, and rearrange them as necessary. This intuitive interface makes it easy to create and modify table layouts without requiring advanced coding or technical expertise.

Freeform Tables also support multiple dimensions and metrics within a single table, which allows for more comprehensive analysis by examining data from different perspectives simultaneously.

Combine dimensions and metrics to create custom views and comparisons, providing deeper insights into your data.

So, while Free Form Tables may seem too user-friendly to truly be effective, nothing could be further from the truth.

You can also create custom calculations within the table itself.

Apply mathematical operations, perform calculations using existing metrics, or use functions to derive new metrics.

This feature is great because you can create derived metrics and perform calculations on-the-fly without having to define them as separate calculated metrics.

Freeform Tables offer sorting and filtering capabilities that allow you to organize and manipulate data within the table. You can sort data based on specific columns or apply filters to focus on subsets of data. This helps you quickly identify trends, outliers, or specific segments of interest.

Adobe Analytics provides various formatting options for Freeform Tables.

Customize the appearance of the table by adjusting font styles, colors, and background settings.

You can also apply conditional formatting to highlight specific data points based on thresholds or rules, making it easier to spot patterns or anomalies.

Flow Reports and Visualizations in Adobe Analytics are a powerful feature that will help you better understand user journeys, navigation paths, and conversion funnels on your website or application.

They provide a visual representation of how users move through different pages, sections, or events, enabling you to identify bottlenecks, drop-off points, and opportunities for optimization.

Let’s break this down further.

Flow Reports in Adobe Analytics provide a graphical representation of user paths, showing the sequential progression from one page or event to another. These reports help you understand the most common paths users take and identify the popular entry and exit points on your site. They’re similar to Behavior Flow in Google Analytics.

In Adobe, you can visualize the flow at different levels, such as the entire website, specific sections of your site, or through entire conversion funnels.

But that’s not all.

Adobe Analytics will also let you perform detailed pathing analysis within Flow Reports.

You can explore how users navigate through different pages or events, view common entry points, and observe the subsequent paths they take. This helps you understand user behavior, identify preferred pathways, and common routes that may not be leading to the result you want.

Flow Reports can also identify drop-off points in user journeys or conversion funnels, so you can see the percentage of users who exit or abandon certain steps, you can pinpoint the areas of your site where UX improvements would have the biggest impact to your bottom line.

And it should probably go without saying at this point, but all of these features can be executed with custom visualizations, in real-time, and with as many segments and filters applied as you need to better understand your data.

Alright, we mentioned these earlier, but let’s now delve into Fallout Reports in a bit more detail, because they are a fantastic feature for optimizing your conversion funnels.

In short, Fallout Reports provide insights into user drop-off points and the reasons behind those drop-offs.

Fallout Reports focus on analyzing conversion funnels, which represent a series of steps or events that users must complete to achieve a specific goal (e.g., making a purchase, completing a form).

These reports illustrate the user flow through the funnel, starting from the initial step and showing the percentage of users who drop off at each subsequent step.

Fallout Reports also show the drop-off percentages at each step of the conversion funnel, so you can identify the specific points where users are most likely to abandon the process. The visual representation helps you visualize the drop-off patterns, bottlenecks, and potential areas for improvement.

But Fallout Reports also let you to dive even deeper into the performance of individual steps within the conversion funnel. You can analyze the drop-off rates, conversion rates, and other relevant metrics for each step. This level of analysis helps you pinpoint specific areas that require attention and optimization.

Use Fallout Reports to dig into the root causes of drop-offs from your site. You can drill down into specific steps to understand the factors contributing to drop-offs, such as user experience issues, confusing forms, or lengthy checkout processes. This information guides optimization efforts to address the identified pain points and improve conversion rates.

As a web agency that does A LOT of site development work, we love the Latency Table feature that can be applied to Adobe Analytics’ Cohort Tables.

This feature will show you the behavior of a specific cohort prior to a specific date and what it looks like after. So, for example, if we update your site to PWA, you’ll probably want to see what that means for traffic and conversions. Latency tables make this as easy as possible.

But you could also do this for a new marketing campaign, new product launches, new product categories applied, and so much more.

Latency Tables are structured with the event/date you care about positioned in the center, displaying time periods before and after the inclusion event on both sides.

In this YouTube video by Adobe on Latency Tables, Travis Sabin, a product manager for Adobe Analytics, explains the significance of the Latency Table feature.

He presents a scenario where a large apparel retailer wants to track the effectiveness of a product launch.

By building a Cohort Table with new users as the inclusion event and online orders for the product "Amazing," the retailer can examine the data with a granularity set to daily. In this hypothetical, his report shows that while the site is attracting plenty of new visitors, only a small percentage of them actually order the product in question.

We’ve become big fans of all things AI in recent years and, not surprisingly, Adobe loves Artificial Intelligence, too, using it to empower countless features throughout their Adobe Experience Cloud.

One prime example of this is how Adobe Analytics utilizes artificial intelligence (AI) for anomaly detection to help business owners identify unusual patterns, outliers, and deviations in their data.

Anomaly detection plays a crucial role in detecting unexpected changes in metrics, behaviors, or trends, allowing organizations to uncover valuable insights and take timely actions. This could mean finding a potential problem before it grows or identifying a huge opportunity before it’s too late.

Here are five reasons this kind of anomaly detection is such an advantage for companies that use Adobe Analytics.

Either way, by leveraging the power of AI, Adobe Analytics will allow you to do this without investing any more of your valuable time into scouring through your data.

It leverages AI algorithms to automatically analyze large volumes of data and identify anomalies without the need for manual analysis. The AI-powered anomaly detection system continuously monitors metrics and dimensions, detecting any significant deviations from expected patterns or baseline performance.

Adobe Analytics also uses advanced statistical models, such as time-series analysis and machine learning algorithms to detect these kinds of anomalies. These models learn from historical patterns and behaviors, so they can identify outliers and anomalies that may indicate abnormal events or activities.

The AI-powered system in Adobe Analytics establishes baselines by analyzing historical data and understanding typical patterns and variations. By establishing these baselines, the system can compare new data points against expected behavior and identify deviations that require attention.

Adobe Analytics allows users to customize anomaly detection settings based on their specific business needs.

Users can define the sensitivity of anomaly detection and set thresholds to control the detection of these anomalies.

Furthermore, the system can generate alerts or notifications when anomalies are detected, ensuring that stakeholders are promptly informed about significant deviations in the data.

Anomaly detection results can be seamlessly integrated with data visualization capabilities in Adobe Analytics. Anomalies can be highlighted in reports, dashboards, or visualizations, so it’s easy to share these discoveries in the more effective ways with your colleagues or clients.

Attribution models are another central feature of analytics platforms that Adobe absolutely nails. This feature is critical to success because these models are what you use to assign credit or value to different marketing touchpoints along the customer journey. They attribute conversions or goals to specific marketing channels, campaigns, or interactions, providing insights into the contribution of each touchpoint in driving conversions.

With Adobe Analytics, you can choose from nine different attribution models:

This model gives all the credit for a conversion to the most recent touchpoint before it happened. A basic and widely used model for conversions with a short consideration cycle, it is often employed by search marketing or internal search keyword analysis teams.

The First Interaction model assigns full credit (100%) to the first touchpoint seen within the attribution lookback window.

It's good for analyzing marketing channels aimed at brand awareness or customer acquisition and is commonly used by display or social marketing teams. The model is also very effective for evaluating the impact of onsite product recommendations.

The Same Hit model allocates full credit (100%) to the specific hit where the conversion occurred.

If a touchpoint doesn't occur in the same hit as a conversion, it is categorized as "None."

This model is useful for evaluating the content or user experience presented at the moment of conversion and is regularly employed by product or design teams to assess the effectiveness of the conversion page.

The Linear attribution model distributes equal credit across all touchpoints leading to a conversion.

It's recommended for conversions with longer consideration cycles or user experiences that require frequent customer engagement.

Teams that measure mobile app notification effectiveness or for subscription-based products usually choose the Linear model.

The U-Shaped model assigns 40% credit to both the first and last interactions, and distributes the remaining 20% among the middle touchpoints.

It gives 100% credit for conversions with a single touchpoint, and 50% credit for conversions with two touchpoints.

This particular model is great for those who value interactions that initiate or close a conversion, while still acknowledging supporting interactions. It's usually used by teams seeking a balanced approach but that still want to give more credit to channels that drive or conclude a conversion.

This attribution model allocates 60% credit to the last interaction, 20% credit to the first interaction, and divides the remaining 20% among the middle touchpoints.

Just like the U-Shaped version, it provides 100% credit for conversions with a single touchpoint, but 75% credit to the last interaction and 25% credit to the first interaction for conversions with two touchpoints.

It is ideal for prioritizing finders and closers while ultimately focusing on closing interactions. This model is usually picked by teams seeking a balanced approach and assigning more credit to channels that conclude a conversion.

The Inverse J-Shaped gives 60% credit to the first interaction, 20% credit to the last interaction, and distributes the remaining 20% among the middle touchpoints. Similar to the last two, it provides 100% credit for conversions with a single touchpoint, and 75% credit to the first interaction and 25% credit to the last interaction for conversions with two touchpoints.

This model is unrivaled for prioritizing finders and closers while emphasizing finding interactions. Employed by teams seeking a balanced approach and giving more credit to channels that initiate a conversion.

Custom allows customization of weights for first touchpoints, last touchpoints, and intermediate touchpoints.

Values are normalized to 100% even if they don't add up to 100. This model provides 100% credit for conversions with a single touchpoint, and for interactions with two touchpoints, the middle parameter is ignored, while the first and last touchpoints are normalized to 100% and credit is assigned accordingly.

It's the perfect model for those who desire full control over the attribution model and have specific requirements not met by other models.

This model follows an exponential decay with a customizable half-life parameter, typically set to 7 days. The weight of each touchpoint depends on the time elapsed between the touchpoint and the conversion.

The credit calculation formula for Adobe's Time Decay attribution model is 2^(-t/halflife), where t represents the time between a touchpoint and a conversion. All touchpoints are then normalized to 100%.

This model is a good choice for companies that do video marketing or use other channels where they want an immediate conversion, as the longer the conversion takes, the less credit a channel is given.

Adobe Analytics integrates seamlessly with other platforms within the Adobe Experience Cloud, allowing organizations to leverage a comprehensive suite of tools for digital marketing and customer experience management. Here are a few examples of how Adobe Analytics integrates with other Adobe Experience Cloud platforms:

Adobe Analytics and Adobe Experience Manager work together to provide a holistic view of content performance and customer engagement. Integration with AEM enables marketers to track and analyze user interactions with content, such as page views, click-through rates, and engagement metrics, helping optimize content and improve the overall user experience.

Integration between Adobe Analytics and Adobe Target enables data-driven personalization and optimization of digital experiences. The combination of these platforms allows marketers to create targeted experiences based on analytics insights, perform A/B testing, and measure the impact of personalized content on conversion rates and customer engagement.

Integration with Adobe Campaign allows marketers to leverage analytics data for advanced segmentation and targeting in their email marketing campaigns. By combining analytics insights with customer data in Adobe Campaign, organizations can create personalized and relevant email campaigns, track campaign performance, and gain a deeper understanding of the customer journey.

Adobe Analytics can obviously benefit a wide range of companies across various industries, but here’s a list of the types of companies that tend to use Adobe Analytics the most:

Companies operating in the eCommerce and retail sectors can leverage Adobe Analytics to gain insights into customer behavior, optimize product offerings, improve conversion rates, and enhance the overall customer experience.

It helps analyze purchase patterns, track cart abandonment, measure campaign effectiveness, and personalize product recommendations.

We recently profiled Wilson Sporting Good's Adobe Commerce site if you'd like to see an in-depth exploration of a company that relies on Adobe Analytics.

Companies in the media and entertainment industry utilize Adobe Analytics to understand audience engagement, measure content consumption, and optimize content strategies.

The platform enables tracking of user interactions with videos, articles, and digital assets, helping organizations make data-driven decisions for content creation, distribution, and monetization.

Businesses in the travel and hospitality sector benefit from Adobe Analytics to analyze website traffic, booking patterns, and customer journeys.

These companies use the platform to optimize marketing campaigns, the complexities of personalize travel recommendations, and to improve customer satisfaction by understanding preferences, booking behaviors, and engagement across multiple touchpoints.

Companies in the financial services industry use Adobe Analytics to gain insights into customer behavior, track online transactions, measure marketing campaign effectiveness, and improve digital experiences.

Adobe helps users analyze customer interactions with banking or investment platforms, identify areas for process improvement, and personalize financial offerings based on customer segments.

Organizations in the healthcare and pharmaceutical sectors can leverage Adobe Analytics to understand patient behavior, measure the effectiveness of digital health initiatives, and optimize patient engagement. Users can also track patient journeys, which can make it easier to personalize healthcare content and services to deliver a better overall patient experience.

Business-to-business (B2B) companies can use Adobe Analytics to track website usage, analyze lead generation, and measure the effectiveness of marketing campaigns targeting other businesses.

Adobe Analytics is very popular with B2B eCommerce companies, too, as these organizations often need lots of options for understanding how their markets behave when it comes to making product purchases on their websites.

We’ve actually done a quick video going over the price of Adobe Analytics, which you can watch here:

But, simply put, the price depends on how much you’re going to be using Adobe Analytics based on traffic.

Adobe typically offers different tiers of pricing, including entry-level packages for smaller businesses and enterprise-level solutions for larger organizations with more extensive analytics needs. Pricing may also vary based on factors such as the number of users, data volume, and any additional services or integrations required.

This is another popular topic we’ve done a YouTube video on, which you can watch right here:

We do a feature-by-feature comparison between the two for a pretty thorough breakdown.

But remember that it’s not one-or-the-other.

If you’re working with a pretty lean budget, then sure, it probably doesn’t make sense to pay for Adobe Analytics right now – even though it comes with so many great features.

However, even if you can afford Adobe Analytics, that doesn’t mean you have to forego using Google Analytics.

In fact, we recommend you do just in case you ever decide to ditch Adobe Analytics at some point in the future. That way, you’ll still have an archive of analytics data that you can immediately use to continue analyzing your efforts.

As we mentioned at the beginning, we’re big fans of Adobe Analytics because of all it can do for a wide range of businesses. It doesn’t matter how big your company is, what industry it’s in, or to whom you’re trying to sell, if you rely on data to power your profits, it’s worth considering what Adobe Analytics could do for your business.

And if you’d like any help with this decision, feel free to contact us with any questions you have.