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What is digital analytics?

Digital analytics are quantitative measurements of the performance of online content, including advertising campaigns, social media, and websites. That means digital analytics provide information from digital sources on how customers respond to or interact with your content and marketing campaigns. Digital analytics is important to help you assess both campaign success and audience responses, providing you with details that inform your marketing strategy.

To get you started, here’s our complete guide to digital analytics. You can explore even more of our research insights for advertisers and marketers.

What are digital analytics and marketing?

Digital marketing is the creation of advertising campaigns for online audiences, and digital analytics measure the performance of that online content. Digital analytics include a wide variety of online marketing and advertising metrics, which help determine the performance of ads and provide insights to inform future campaigns.

Types of digital advertising include display advertising, audio advertising, and streaming media advertising. Digital analytics is the quantifiable measurement of these campaigns. They are broken down into marketing metrics, such as click-through rate (CTR), email open rate, bounce rate, impressions, search traffic, and more. Advertising metrics quantify the performance of your campaigns, and examples include reach, conversions, and returning customer rate.


digital analytic seoindek
Digital Analytic from Google GA4

Why are digital analytics important?

Digital analytics are important because they give insights into what content is working for your brand and what content is not. Digital analytics allow you to directly measure your success and address pain points, helping you create better content and ads.

Considering analytics in digital marketing is important to further optimize your campaigns, reaching audiences with relevant content and driving sales or conversions, for example. It’s instrumental to use analytics to ensure you have a holistic view of campaigns and content, which can help your customer experiences stand out.

Digital analytics can also help measure the cost of your digital ads, often determined by cost-per-click (CPC) or cost-per-mille (CPM) pricing models. These metrics can help understand the return on ad spend (ROAS)of your ads, an integral part of your digital marketing strategy.

Benefits of digital analytics

The benefit of digital analytics is the ability to receive information on your digital content and campaigns, which leads to the visibility on their performance. Using digital analytics reduces the guesswork in your marketing strategy.

Nearly 1 in 5 marketers struggle to measure the effectiveness of their marketing efforts, but utilizing digital analytics could help your brand find success.1 The more information you have on your brand and campaigns, the more effective your digital presence and messaging.

Examples of digital analytics in marketing

There are many ways of looking at your digital analytics, and the most effective approach is highly dependent on your unique brand’s key performance indicators (KPIs) and objectives, and key results (OKRs). In general, a good place to start with web analytics is breaking down your main content and ads by website traffic, product information, search engine optimization (SEO), and social media engagement. There is also digital analytics that can help provide clarity on the needs of your customers, which can then help you pinpoint your top products and opportunities for increasing conversion rate and sales, for instance.

Here are just a few examples of digital analytics and the metrics to consider for each:

  1. Website traffic: page views, unique visitors, clicks, exit rate, bounce rate,
  2. Sessions: overall site visits,
  3. Product information: product page views, ad engagement, sales,
  4. Search engine optimization (SEO): keyword rankings, keyword search volume,
  5. Social media engagement: comments, likes, shares,
  6. Traffic source: referrals from search, ads, social media, etc.
  7. Customer retention: number of new visitors, returning visitors, repeat shoppers, conversion rate,
  8. Customer feedback: complaints, post-purchase surveys, reviews


How Digital Marketing Analytics Connects Every Business Activity With digital marketing analytics, marketers can understand the effectiveness of their entire marketing strategy, not just the effectiveness of their website. Using digital marketing analytics allows marketers to identify how each of their marketing initiatives (e.g., social media vs. blogging vs. email marketing) stack up against one another, determine the true ROI of their activities, and understand how well they’re achieving their business goals.

The central question is: How can you structure an appropriate business goal to visualize your marketing team’s efforts in the most accurate way possible?

As a result of the information they can gather from full-stack digital marketing analytics, marketers can also diagnose deficiencies in specific channels in their marketing mix, and make adjustments to strategies and tactics to improve their overall marketing activity.”A digital marketing analysis is the first step to developing a strong digital marketing analytics strategy. This process can be used to structure a business goal into outcomes based on three broad categories: The relationship between different marketing channels, People-centric Data on the Buyer’s Journey, Revenue attributed to specific marketing efforts

Business Intelligence:

Business intelligence platforms have become very popular within organizations, and you would be hard-pressed to find an organization that doesn’t have a business intelligence platform. Business intelligence platforms provide a high-level summary of KPIs that are critical to the organization. Often, business intelligence platforms take the form of high-level dashboards shared with executives. Business intelligence dashboards often combine data from digital analytics, CRM, physical stores, internal data warehouses, etc.

Data Sources & Cross-Platform Metrics: Business intelligence platforms often incorporate data from many different sources. I like to think of this as the “greatest hits” of data from multiple data systems. While streaming any type of data into digital analytics platforms is undoubtedly possible, most organizations limit data to websites and mobile applications. But as the world becomes more digital, we are seeing more and more customers send digital analytics platforms like Amplitude data from stores, call centers, and even physical products. One of the key selling points of business intelligence platforms is that they can combine metrics from different platforms in ways that would be challenging in one standalone platform. The organization could use a business intelligence platform to divide these two metrics to create a brand new KPI called MQL/Unique Visitor. While there may not be an easy way to connect those unique visitors to sales MQLs, at a high level, it may be possible to view trends and see if there is a relationship between the two. While this organization could import MQL data into its digital analytics platform, most would choose to do it in a business intelligence platform. The power of business intelligence platforms is that they can easily combine multiple data sources and empower organizations to mix and match all sorts of metrics from different systems. Often, the joining factor will be the date, but in some cases, other primary keys can be used to join data from different sources. While some of this could be done in digital analytics platforms, it would be complicated and time-consuming. Dashboards in digital analytics platforms tend to focus on summaries of data related to websites and digital applications.

Data Exploration: The most significant difference between digital analytics and business intelligence platforms is in the area of data exploration. While data exploration can occur in both types of platforms, they are done in very different ways. In business intelligence platforms, there are typically limits on the types of reports available. For example, if there is a KPI for sales, business intelligence platforms can break it down by sales rep or region. But in digital analytics platforms, data exploration includes metric breakdowns and many other report types that don’t exist in business intelligence platforms.

Here are a few examples:

Path Flows

In digital analytics platforms, there are times when you would want to view how customers navigated pages or events. This can be useful to understand page flow or event flow drop-off and fix any flow leaks. But reporting on path flows requires time-stamped, sequenced data associated with unique visitors versus aggregated data. Creating an accurate path flow report in a business intelligence platform would be challenging.

Conversion Funnels

Digital analytics platforms are often used to build conversion funnels. These funnels plot key checkpoints in conversion flows to see how many customers make it to each step. While they sound similar to path flows, they are different in that they are less focused on all of the paths customers take and more interested in a specific set of steps taken.

Conversion funnels are also built such that customers have to perform the actions in a set order to be included. This order sequence requirement means that the digital analytics platform must understand which customers have completed each step and in what order. While a business intelligence platform could likely report on how many times event 1 and event 2 took place, it would be difficult to understand if it was the same user who performed both events in the correct order.

Cohorts & Segments

One of the most powerful aspects of digital analytics platforms is the ability to build ad-hoc cohorts (or segments) of users. These cohorts can be based on event behavior, attributes, or navigation behavior. Once created, cohorts can be used to compare different groups of customers, and cohorts can be sent to other systems for personalization or marketing efforts. Most business intelligence platforms are not user-centric. They focus on numbers more than users. Therefore, it is not common to use business intelligence platforms to create cohorts of users for analysis or marketing purposes.

Identity Resolution

A core component of digital analytics is the concept of identity. In digital analytics, it is important to know if the current user is the same as a user who used the digital property last week.

To address this, digital analytics platforms have built mechanisms to identify users and determine if they are known or unknown. Some do this via third-party cookies, and others do this via first-party authentication. Business intelligence platforms have not traditionally attempted to perform identity resolution. While they can view and join metrics by a customer ID, they are not built to review anonymous user data and determine if the user is a previously known entity.


Understanding which and how many of your customers return to your digital experiences over time is an integral part of digital analytics. Digital teams use digital analytics data to see what features or marketing campaigns drive retention so they can form habits and generate revenue. Reporting upon retention requires identity resolution to know if the customer who is currently engaging with the digital product has been there before and how often.

Business intelligence platforms can report on usage, but many are not built to understand if the same users are returning again and again. There may be some ways to do this by leveraging customer identifiers, but this has to be coupled with time-series data for each customer and reports that use statistics to show retention buckets and time windows.

These capabilities are rarely present in business intelligence platforms.


Another difference between digital analytics and business intelligence platforms is how often each type of user engages with the platform. Business intelligence platforms are usually built for and used by upper management and executives.

While lower-level staff may use the tools to develop reports and dashboards, the primary recipient of the reports and dashboards is often executives. Business intelligence products often tout how easy it is for executives to learn about their business via business intelligence products.

Digital analytics platforms are also built for executives, but they are also heavily used by digital analysts, marketing analysts, or product teams. Since digital analytics platforms provide both high-level and granular information, digital analytics platforms are accessible to almost anyone in the organization.

Executives can view high-level dashboards in digital analytics platforms, but only the data-savvy ones will dig deeper into the data.

I believe that the complexity of digital analytics platforms was one of the contributing factors to the rise of the business intelligence industry. One of the popular business intelligence platforms was founded by the former CEO of a digital analytics platform. He was frustrated that he couldn’t see the high-level metrics he needed to run his business from his digital analytics platform!

Data Granularity

Digital analytics platforms primarily collect data from websites and mobile applications. However, in recent years this has expanded to include many other data types (e.g., store data, call center, etc.). However, the collected data is often at a very granular level. Common data points might include clicks or swipes on buttons and links, viewing specific pages, and phases entered into website search boxes, etc.

Most organizations collect event data in the billions each month, and this data is aggregated in reports within the digital analytics platform. While not always the case, business intelligence platforms often collect data at a less granular level. For example, if you use a business intelligence platform to show CRM data, you might feed in leads from Salesforce.

This data will often not be as granular as hit-level data on a website. While there are exceptions, many organizations send summary information to their business intelligence platform instead of duplicating the source data and all of its granularity.

Another example might be piping in orders and revenue from a digital analytics platform.

Better Together

For most organizations, it is required to have a digital analytics platform and a business intelligence platform, not one. As described here, these platforms are different but can be complementary. Perhaps one day, there will be industry consolidation, and one vendor will own digital analytics and business intelligence platforms, but that hasn’t happened so far. Even Google, which owns the largest digital analytics platform. Typically those who argue for using one platform have not had experience with both types of platforms or are simply looking to cut budgets. It is easy to make the case that digital analytics and business intelligence platforms are very different, have different objectives, different audiences, and solve different problems.