What is Digital Analytics? The Complete Guide

Intro to digital analytics

Raise your hand if you’ve ever heard a company describe themselves as “data-driven.” It’s one of those buzzwords that can mean anything and nothing—like throwing on a sweater based on a blustery weather report. In practice, however, data-driven means leveraging behavioral data—information on how users interact with your brand and product—to make strategic business decisions. This requires a combination of dedicated tools and processes, otherwise known as digital analytics.

In this handy guide, you’ll learn about how digital analytics works, the most important metrics to track and best practices for implementing a digital analytics strategy, so you can:

• Gain a deeper understanding of your customers’ behavior, motivation and pain points

• Stand out from competitors by delivering a better digital experience at every stage

• Identify sources of friction undermining your conversion rates, product adoption, customer satisfaction and retention

• …and a lot more

Data analytics is a big topic, so let’s dive right in!

What is digital analytics?

Digital analytics is an umbrella term for the various tools and processes used to collect, measure, analyze and interpret behavioral data—how prospects and customers interact with your product and brand. It can encompass data across a wide range of digital sources, including:

• Websites

• Landing pages

• Mobile apps

• Software products

• Digital marketing campaigns

• Social media channels

• Customer interactions

Although primarily used to inform marketing and product decisions, these insights can also be invaluable for customer success, product support and other use cases—but more on those in a minute.

Benefits of digital analytics: Why is it important?

Users expect a seamless and personalized customer experience every time they interact with your brand—and the stakes are high if you don’t deliver. Seventy-six percent of consumers would stop doing business with a company after just one bad customer experience. Personalization is no longer a bonus, but a necessity. This is impossible to deliver without a steady supply of data—94% of businesses say data and analytics is important to their growth.

The problem is, while many teams find themselves buried in data, a lot of it may be worthless. Over 80% of data collected by enterprise organizations is either siloed or unstructured—meaning it can’t be processed, organized or analyzed by machine learning algorithms. This is dark data—or as Gartner puts it, “information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.”

On the flip side, companies might guide their decision-making with static personas and top-level metrics, like pageviews, downloads or purchases. This data can be equally problematic on its own. If you’re not also drilling down into user interactions, personalization can feel disconnected and hollow.

Digital analytics is your best defense against data deprivation and overload. It provides a solid framework for continually gathering, measuring, analyzing and interpreting data you can actually use to deliver—and scale—real personalization for your prospects and customers.

Top 3 benefits of digital analytics

Digital analytics tools and processes can vary wildly by organization. Some teams manually stitch together multiple data outputs into a single spreadsheet, while others rely on a fully loaded digital analytics ecosystem with automated integrations. Generally speaking, however, there are some serious advantages to systematically tracking and assessing behavioral data from your digital sources. These include:

1. Understanding your users—in the right way

Users may demand personalized experiences whenever they interact with your brand, but they’re also fiercely protective of their data.Nearly half (48%) of consumers have stopped buying from a company over privacy concerns.

So how do you strike the right balance between personalization and privacy? The good news is, you don’t need to understand users in every aspect of their lives to deliver an outstanding customer experience—just the pain points that are relevant to your business. In fact, 83% of consumers are willing to share their data to enable a personalized experience.

Digital analytics can capture a lot of nuance in user behavior, but only through direct interactions with your brand and product. This affords deep insight using the right data, in the right way.

2. Accurately measuring performance

Users don’t just expect a personalized experience. It also has to be seamless and consistent from one interaction to the next. This can be a challenge when you factor in the sheer volume of touchpoints. More than 50% of customers engage with three to five channels, while B2B buyers average 27 interactions before they make a purchase decision.

Buyer’s journeys are often complex and nonlinear, and one channel can easily cause friction in another—like a customer having to repeat information to a live agent that they already shared with a chatbot, or a digital ad misrepresenting the content on a landing page. That’s why it’s not enough to evaluate channels or campaigns in isolation. You need to see how all the pieces fit together to accurately measure performance.

Digital analytics gives you the ability to capture disparate interactions across multiple channels and combine them into a unified, 360-degree view of the buyer’s journey. It’s equally important to track, measure and analyze interactions after prospects convert into customers. Providing a seamless experience on the path to purchase only to throw users a curveball with friction in the actual product is a recipe for churn.

3. Effectively optimizing campaigns and conversion rates

One great thing about digital analytics is the flexibility. You can use the same behavioral data to form a holistic view of the buyer’s journey and drill down into specific channels, campaigns—even app or product features—to identify patterns, trends and sources of friction.

This can mean a serious boost to conversion rates, since optimization efforts are informed by real user behavior, rather than more general KPIs or guesswork. According to McKinsey, organizations that leverage customer behavioral insights outperform peers by 85% in sales growth and more than 25% in gross margin.

You can even supplement digital analytics with transactional data to connect different touchpoints to revenue and growth.

Who is digital analytics useful for?

Since digital analytics is specifically focused on gathering, analyzing, and interpreting user interactions with your brand and product, it can support an enormous range of roles, from solopreneurs with their own online store to marketers at an enterprise software organization. Functions that benefit the most from digital analytics include:

• Demand generation: Digital analytics enables marketers to delve beyond top-level KPIs and evaluate channels, campaigns and nurtures based on real user behavior. This not only provides more accurate insights into performance, but helps uncover critical gaps and sources of friction. Digital analytics also makes it possible to replace a more generic, one-size-fits-all marketing funnel with highly specific customer journey maps created just for your users in mind—a powerful framework for driving campaigns and nurtures that feel more relevant and personalized.

• Content marketing: Digital analytics can help marketers align content strategy with meaningful trends, interests and preferences among their audience. This can also be useful when it comes to content planning and distribution—you can select new topics based on previous interactions rather than just keyword research, or determine if a particular asset actually resonates with users before dedicating more time and resources to repurpose it.

• Customer marketing: Digital analytics can go far beyond digital channels. With the right tools, you can also track behavioral data within your actual product. Understanding these user interactions can help consumer marketers identify new opportunities for cross sells, upsells and customer advocacy, as well as sources of friction that may threaten retention or renewals.

• Product marketing: Product marketers can use digital analytics to ensure alignment between features and messaging. No one wants to promote a new feature prematurely, while customers are still struggling with adoption.

• Product management: Digital analytics is essential for effective product roadmapping and development—you need behavioral data to effectively evaluate features, identify sources of friction and conduct A/B testing.

• Customer support: The two biggest customer complaints when they contact support are having to wait and having to repeat themselves. However, with some products—especially software—troubleshooting isn’t always straightforward, and may involve looping in multiple people who require further explanation. If you lead a customer support team, digital analytics can help track, anticipate and address recurring challenges, so you can equip agents to resolve faster. Depending on your tools, agents themselves may also be able to leverage digital analytics to view interaction history for individual users, making it easier to diagnose a problem.

• User experience: Usability, function and design have a strong influence on customer experience and whether users actually enjoy interacting with a brand or product. To improve those interactions, however, you need to know what they are. With digital analytics, UX and UI teams can tap behavioral data to identify sources of friction and continually monitor website and app performance.

• Digital application performance: Technical teams can also benefit from keeping a close eye on interactions. Digital analytics can support maintenance and troubleshooting by making it easier to spot glitches, bugs, usability and performance issues in web or mobile-based apps—before the users notice it themselves.

• E-commerce: On average, only 2% of e-commerce users make an actual purchase. That means if you run an online store, there’s probably room for improvement. Are there too many steps in the checkout process? Do users learn too late in the process that shipping will take longer than they expected? These questions may be difficult to answer with more general KPIs. Whether you’re a solo shop or major e-commerce brand, digital analytics can help you dig into the “why” behind the “what”—and optimize accordingly. Learn more in the blog, 5 ways to boost e-commerce revenue with digital analytics.

Sources of digital analytics data

We know digital analytics can serve an exhausting variety of use cases, but how does it actually track and measure user interactions? Digital analytics combines qualitative and quantitative behavioral data from numerous digital sources:

Website data

• Number of visitors

• Traffic sources by channel

• Traffic sources by device

• Traffic sources by keyword

• Time on page

• Bounce rate

• Page views

• Sessions

Digital marketing data

• Form submissions

• Click-through rates

• Newsletter sign ups

• Free trial sign ups

• Leads to close ratio

• Conversion rates

Email marketing data

• Open rates

• Open rates by device

• Click-through rates

• Unsubscribe rates

• Bounce rates

Social media

• Engagement rate by channel—including likes, comments and shares

• Channel growth rate (followers and subscribers)

• User mentions by product, feature or theme

Product/e-commerce data

• Sales conversion rate

• Shopping cart abandonment rate

• Average order value

• Average customer lifetime value

• Revenue by source

• Transaction histories

Interactions with prospects

• Product inquiries

• Chatbot engagement

• Live chats

Interactions with customers / Voice of the customer (VoC)

• Onboarding sessions

• Support tickets

• Chatbot engagement

• Live chat and agent support

• Ratings and reviews

• Interviews

• Surveys

• Satisfaction KPIs like Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT)

How to implement a digital analytics strategy

So, how do you actually pull this all together and create your own digital analytics strategy? Although digital analytics tools and processes can vary significantly by organization, there are some general best practices you can follow to ensure you’re setting yourself up for success from the start:

1. Define your mission and goals: The first step in building an effective digital analytics strategy is determining what you want it to actually achieve. Define your overarching mission, and then break it down into smaller goals and milestones.

2. Establish your KPIs: Work backwards from your goals and milestones to establish KPIs. What does success look like at each step and how will you measure it? What benchmarks will you be comparing it to?

3. Identify limitations and knowledge gaps: Once you specify what you’re working towards—and how you’ll know when you get there—it’s time to evaluate your existing tools and processes to make sure they’re up for the job. Are there any limitations or knowledge gaps? What information do you need to reach your goals or milestones that you don’t currently have?

4. Select your tools accordingly: There are seemingly infinite combinations of tools and processes out there for running your digital analytics machine, and it’s easy to get overwhelmed. Taking the time to identify existing limitations will help clarify what functionality you need to prioritize, and select your tools from there. Then, you’ll have a solid base to continually improve and expand your digital analytics capabilities.

Whether you wind up pulling together multiple data sources yourself or opt for a fully loaded digital experience intelligence platform to do the heavy lifting, it’s important to remember that digital analytics isn’t “set it and forget it.” User behaviors consistently evolve—and so do their expectations. Digital analytics will enable you to collect and manage behavioral data like a well-oiled machine, so you can keep on top of these changes and consistently generate usable, actionable insights to deliver personalized customer experiences at scale.