Jessica Porges

A Complete Guide to Digital Analytics: From Strategy to Tools

At first glance, digital analytics may seem like any other data your marketing team collects to improve your work. You hypothesize, test, and measure, then implement feedback and learnings. 

What can set you apart from any other marketing team, however, is the why behind these actions. 

Ultimately, you shouldn’t be measuring and analyzing data analytics and business intelligence just for the sake of it. (And if that sounds obvious, you’re ahead of many marketing teams!) 

The why behind your data analytics should always point back to one thing: the customer experience (CX). 

By now, most companies know the customer’s perception of your brand is not to be underestimated. 81% of marketing leaders surveyed by Gartner say they expect to be competing mostly or completely on the basis of CX within the next two years.

In this article, we’ll walk you through why data analytics is much more than the tools you use—and why it’s so critical to the success of your digital customer experience.

Table of contents 

What is digital analytics and how does it tie into the digital experience?

Let’s start with the basics. Digital analytics is the process of analyzing both quantitative and qualitative data from anywhere you have digital customer touchpoints (your website, email, mobile application, etc.). 

Using that working definition, you can see how data collection without a customer experience angle is only half the story. 

The difference between digital analytics and web analytics

From here on in this article, when we talk about digital analytics, our focus is on digital experience analytics. This means using our digital analytics to measure the quality of the user experience. 

Web analytics tend to be surface-level data collected for the purpose of optimizing website performance. But in order to improve the customer experience—which is the true purpose of any of this data collection—you need to focus on your why

Why you need to lead with a data-driven customer experience mindset 

When you collect data in silos, you’re only scratching the surface of your brand’s CX potential.

Laying the foundation for a great customer experience means ensuring you’re collecting all the data you need to keep learning and improving. Once your team or organization is collecting data at every touchpoint, you’ll want to first understand it, then use your findings to optimize the customer experience, and in turn your marketing performance. 

While it may be easier to collect quantitative data—for example, X% of customers dropped off a certain conversion-focused landing page—it’s just as, if not more, important to make sure you’re also focused on qualitative data. Understanding the customer’s motivations and their journey will enable you to adopt a customer experience mindset. 

The good news is, there are modern qualitative analytics tools that can help deliver subjective, conditional insights, like user testing and session replay. Gone are the days where you need to organize and sit through endless focus group meetings to better understand your customer’s thought process. 

Seeing the numbers, like a 1% landing page conversion rate, is only part of the picture. Saying that, quantitative data is important because it alerts you to dig into the reasons behind the metrics—which is where qualitative data comes in. 

Together, this data can paint a picture that allows you to get into the customer’s head and experience their journey. You can then optimize that journey with your findings. 

What you can learn from digital experience analytics

In short, digital experience analytics help you understand a customer’s experience of interacting with your business. This data can help you learn where people came from (attribution), their intent, motivation, what they clicked on, why they bounced, etc. 

For example, you may want to dig into a high bounce rate on a key conversion page. The numbers alone can’t help you see why customers are dropping off at this step in your funnel. So you employ user testing to gather feedback on the page and discover that customers feel like they’ve navigated away from the site because the brand colors of the website have changed. 

Optimizing the page to make checkout more trustworthy improves the customer experience as well as your sales. 

Why digital experience analytics is about much more than the tools

Understanding your customers above all is going to help you maximize the value the tools bring. This is why a digital experience roadmap is so important. You want to ensure you’re matching tools to the journey, not the other way around.

One Smart Panda Labs (SPL) client example is Viceroy Hotels, where we really saw how the data you retrieve from any tool—no matter how sophisticated—is only worthwhile if it’s accurate. 

Viceroy had thousands of campaigns that were mostly untraceable and manual reporting that was inefficient and prone to errors. 

With this lack of visibility making digital marketing analysis nearly impossible, a personalized and efficient customer experience was suffering. Understanding the breakdown led us to take a look at their setup of Adobe Analytics so we could create an analytics strategy that works. 

SPL’s technology experts implemented a tracking code convention to classify all the campaign data that was being collected. They enabled new automation features to find patterns, logically name tracking codes and generate legible reports, which resulted in increased reporting visibility for each marketing channel and product type. 

We also reduced 404 error page views by 88% by surfacing the data on the source of the errors.

While these changes may not sound overly exciting, the improvements not only made the Viceroy team’s jobs easier, but improved the end users’ experience. The hotel group is now better able to match customers to the properties, rates, and rooms most relevant to them. 

The Results

130 %

Increase in Key Data Points

 

95 %

Reduction in Man Hours

Gathering accurate data is also useful for running optimization tests and experiments. Positioning your strategy around the data you collect stops you from wasting resources heading in the wrong direction. 

For example, we were able to use data to create a conversion strategy for privately-owned real estate firm Related Companies

Recognizing a need to quickly make and test changes to ensure recommended optimizations were leading to measurable improvements in the customer experience, we implemented a tool called Optimizely

The result of our early efforts with the tool was a 26% increase in lead conversions. Additionally, changing the call to action on the website increased web form initiation rate by 112%, and changing the form submit button copy on campaign landing pages increased the lead conversion rate by 25.6%. 

From both examples, knowing which improvements you want to see in your customer journey before selecting a tool is critical to efficiency and success. 

Digital experience analytics tools that can help you optimize the user journey

Once you know what you want to leverage data analytics for, you can consider the tools you’ll use to obtain the data. Continuing with the example above, Viceroy had an issue with centralizing data and effectively analyzing it to improve the customer experience. 

Starting with the customer experience, ask and answer questions to help identify where your customer journey needs improvement. 

These might look like:

  • Where are all of the places customer data is kept? Is it centralized in a way that gives visibility to all relevant team members and stakeholders?
  • What is your team’s current methodology for pulling and analyzing marketing analytics?
  • What tools do you currently use to gather and analyze customer data? Are any of them irreplaceable?
  • Where do you know there are breaks or bottlenecks in your customer journey? Where do you have questions or unknowns?

Once you have a clear idea of what you need, you can begin to look at specific tools. 

There are many tools on the market for digital experience analytics. The best tool for you will depend on your business’s unique problems. 

Today’s top tools harness the powers of machine learning and artificial intelligence to provide deeper, actionable insights than previously possible. We’ve already mentioned Adobe Analytics. Here are a few more digital analytics tools we recommend.

  • FullStory: Automatically collect data on factors where you may be losing conversions, such as buggy forms and slow-loading pages.
  • Contentsquare: Easy to use dashboards help you visualise important metrics and automatically captured sessions show you the why behind the numbers.
  • SessionCam: Get heatmaps, session replays and conversion funnels to see the most popular routes to conversion on your website.

Key digital analytics metrics and how to leverage these insights to improve the experience

In thinking through the sample questions we shared above, you may start to zero in on which digital analytics metrics you need to focus on to get started. 

Let’s take a look at what some of these basic metrics are and how leveraging the insights they provide may improve your customer experience. 

Website

Session

A session refers to the period of time a user is active on your website or within your mobile application. While time on page refers to the time they spend on one page, a session refers to the collective time spent across multiple pages. 

Traffic by channel and device

Where are your users finding you? How are they accessing your pages? Traffic by channel and device helps you understand this question. 

If the vast majority of your customers are finding you via social media while on their phones, you’ll approach marketing very differently than a business whose users are mostly on web browsers discovering them through search. 

Time on page

This refers to how much time a user is spending on any given page within your website. For example, if your time on page metrics are low for a conversion landing page but customers are still converting, that could mean they’re very quickly understanding what they need to do, and then doing it.

But if they’re spending very little time on the page and then not completing the sale, something is getting lost in translation. To solve this, you may want to A/B test different copy and/or CTAs (like we did with Related Companies) to see where the miscommunication is occurring. 

Clickthrough rate (CTR)

CTR is the number of clicks on a link (like an ad, CTA, or email) divided by the number of impressions it receives. This metric helps you understand where and how the language you’re using resonates with your customers. 

In the Related Companies example above, we shared how using a tool like Optimizely helped dramatically increase conversions. We tested different languages to see what landed with customers and led them to convert. 

Email 

Open rate

Email open rate refers to the percentage of your list who open your email. It’s a good gauge for understanding both how your subject lines are performing (which is easy to A/B test) as well as how engaged people are with a given email campaign.

Clickthrough rate (CTR)

In email, CTR refers to the percentage of people who clicked on at least one link in a given email message. 

If your open rates are high but CTR is low, that may suggest people want to hear from your business and/or are intrigued enough by the subject line, but they aren’t getting what they need from the email content.

Bounce rate

Refers to the percentage of emails that have not successfully reached recipients. This can mean an email address is no longer active, or, more problematically, that your email is being classified as spam. 

The action items here would be to clean up your email list and make sure your emails don’t look spammy.

Social

Engagement

Social media engagement is a tricky metric in that it’s important to look at both quantitatively and qualitatively to fully understand user behavior. Quantitatively, it refers to the total number of interactions your content receives divided by your total number of followers. 

But—and this is a big “but”—it’s important to analyze the data qualitatively because higher numbers don’t necessarily mean better for this metric. For example, if you posted something that received a shockingly high number of comments, you’d want to go in and read them to find common themes and insights. 

People can comment on things they love, but we all know by now that people tend to be very vocal on social media when they see something they dislike. 

Shares

Similar to engagement, when someone shares something you’ve posted on social media, you will want to analyze it qualitatively, as well. Are they sharing something because it resonated with them, or because they found it offensive? 

CTR

Here, we’re referring to the clickthrough rate on paid ads. This one is a bit more straightforward than other social metrics—typically people click on ads that resonate with them. 

In SPL’s work with MIT Sloan Executive Education (MIT), we focused on understanding the participants and using that data to improve all stages of the funnel, starting with the awareness stage. The improved database analysis led to more targeted social ads. In partnership with personalized marketing efforts, we were able to drive qualified traffic to the decision stage and increase unique link clicks by 107%.

Key takeaways

Most marketers will tell you that it’s important to track digital analytics, but the most successful understand the connection between that data and their customer experience. 

The ability to interpret how digital experience analytics illustrate the experience a customer has while interacting with your website, emails, social media platforms, app, or ads separates top marketers from the rest. 

Armed with this knowledge, it becomes easier to choose the right tools and optimize your business’ digital customer experience.

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