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Understanding Digital Analytics – The Hottest Industry Right Now

Digital marketing can be incredibly useful to educate prospects about your products and services. But virtually all of your digital marketing efforts are rendered useless without proper analytics and measurement. It’s vital to see how your marketing activities and spending impact your company’s bottom line if you plan to reap the full benefits of digital marketing. Because of its impact, digital analytics is one of the hottest growing needs for business owners growing their businesses worldwide.

What is Digital Analytics? 

As the world becomes more connected with smartphones, tablets, and laptops, more and more information is available to marketers, enabling visibility of every aspect of their sales funnels. The advancement of cloud technology makes it easier for businesses to collect data that can assist marketers in making better, informed decisions. Digital analytics is the lifeblood of the modern organization.

Digital analytics aims to collect data, analyze it, and use it to improve the outcomes of your online advertising efforts. Ultimately, you want to leverage digital analytics to get a higher return on your investment.

The biggest mistake most marketers make is being married to an idea of who they believe their customers are. To get started, you need to understand your customers, but limiting yourself to preconceived notions is a cardinal sin. People on their cellphones behave differently online than they do in a physical store. They have entirely different motivations, goals, and paradigms than someone physically present.

Your customers may refer to your product using terms you don’t recognize. Every traditional marketing persona can’t account for the plethora of online audiences, segments, demographics, and behavioral patterns.

Rarely will you know upfront what content will resonate with what audience, how often they need to see that content, what content will drive sales, who will buy, or when. There is more than enough data to make informed marketing decisions instead of attempting to predict the future.

When planning any marketing activity, it’s essential to determine your desired outcomes. Your outcomes are synonymous with your ultimate business objectives.

  • What are you hoping to achieve?
  • Do you want more prospects to call you?
  • Do you want more people to talk about you on social media?

It is essential to measure your efforts and know how they’re helping you to achieve your goals. After all, you can’t effectively gauge your marketing progress and growth without a clear destination in mind.

Types of Analytics

Many people like to think of analytics as some intelligent guy’s thing. Analytics is a part of your daily life without you not giving it a second thought. 

When you check your Apple watch to check your steps, you are using analytics. When you’ve decided who to start for your fantasy football team, if you’re good, most likely you are using analytics. When you check your sales numbers or gauge the miles per gallon of your next vehicle, you’re using data to make decisions. These aren’t examples of “big data usage.” However, we are becoming increasingly aware of the importance of data when it comes to making good decisions.

Data aggregation and analysis are the core components of digital marketing. Digital analytics sets digital marketing apart from traditional marketing by allowing us to make real-time data-driven decisions.

Data is like the soil plants need to grow. It’s everywhere around us, looking to be fashioned and utilized to spark growth and sustainability. Just as soil can be used to both grow plants, serve as a habitat, stabilize structures and hold water, data has many uses. Most of those uses can be summarized into four types of analytics, descriptive, predictive, prescriptive, and diagnostic. 

Descriptive Analytics

The vast majority of organizations use descriptive analytics. Descriptive analytics is best defined as answering the question: “What happened?” or “What is happening?”. It’s a way to look at data from the past. The ‘past’ refers to any particular period in which an event or events has occurred. Regarding digital marketing, we usually look at the past in increments of 7 days, 14 days, and 30 days. 

A commonplace way to represent data is a WoW (week over week) or MoM (month over month) analysis. My experience is that clients always want a story. Narratives are key. Descriptive analytics gives you a story to tell. Hopefully, that story is a good one that details success, improvement, and growth.

You should be testing and learning in a perfect world, and a descriptive analysis of campaign performance should reflect your efforts. Marketers managers need to understand the company’s overall performance at an aggregate level and describe the various aspects of their digital marketing strategy. Descriptive analytics gives us this capability.

Predictive Analytics 

Predictive analytics is my favorite form of analytics. Predicting the future is risky but also lucrative. It could be quite the ego boost if you can develop even a marginally competent predictive analysis system. Thinking about, analyzing, and predicting the future is a core component of intelligence. Think about it, what characteristics genuinely separate us from other primates?

Analysts place their reputation and credibility on the line whenever they aspire to make a prediction using analytics. Predictive analytics is a double-edged sword because no one can predict the future, but few can create a reputable model that at least gets it right half the time. Yes, that’s right, half the time. A successful, sharp NFL bettor’s bar is 55%. Therefore, you must be content with getting it wrong regularly and only slightly above average to be considered competent.

Predictive analytics is used less with digital marketing and more in business development, sports, and economics. However, it can be astonishingly predictive when digital marketers use predictive analytics. Some examples of predictive analytics in digital marketing are projecting clicks, impressions, click-through rates, spend or conversions.

Analyzing past data patterns and trends can inform you of future probabilities. This analysis helps set realistic goals for your marketing campaigns, plan for specific outcomes, and manage expectations. Predictive analytics answers the question: “What could happen in the future based on previous trends and patterns?”

Still, the accuracy of predictions is not 100%, as these predictions are based on probabilities. Machine learning algorithms take data and fill in the missing data with the best possible predictions. One of my favorite forms of predictive analytics is point projections for my fantasy football teams. These projections are rarely spot-on accurate, but they are relatively reliable when you use them in a cumulative and relative form of analysis. 

For example, if Patrick Mahomes is projected to score 21 points, Mac Jones is projected to score 12 points. Russel Wilson is projected to score 18 points, you can deduce that Patrick Mahomes and Russell Wilson will have more similar games, eliminating Mac Jones as a starting option and moving into an analysis of Mahomes vs. Wilson.

The exact process works with paid media ads. Google Ads projects clicks, impressions, and spend for various keywords based on past trends. Although you can’t say that Google will be accurate regarding how many clicks and impressions you’ll receive. Still, comparatively speaking, you look at individual keyword projections and deduce which keywords will drive the most traffic for your future campaigns.

Prescriptive Analytics

Prescriptive analytics highlights problems, helps you understand why those problems occurred, and helps you to identify possible solutions. Prescriptive analytics is a mix of predictive and diagnostic analytics that advises you on the possible outcomes and results in actions that maximize vital KPIs. 

Algorithms do all the work here. In our fantasy football example, these algorithms take it further than projecting points. When algorithms projects points, the expectation is that you use those projections to make educated decisions. Prescriptive analytics tell you what decision to make. It uses simulation and optimization to ask, “What is our next course of action..What should we do?” 

Prescriptive analytics is my least favorite since algorithms never know all the real-world circumstances that go into making a decision. In our fantasy football example with the quarterbacks, the algorithms may suggest Patrick Mahomes is the best start based on their projections. Those projections prioritize past performance as a barometer to predict future results. 

However, those algorithms rarely factor in random variables associated with being human such as the fact that Mahomes was sick with the flu last night, half of his linemen are out, he has trouble throwing in the rain or the defense he’s playing against is getting back their star defensive back early from injured reserve. Maybe better, more robust algorithms could do this effectively, but the costs severely outway the benefits of such a robust system. Would you pay thousands of dollars a month for an incrementally better system for a recreational hobby?

I dont want to throw prescriptive analytics under the bus. Facebook Ads and Google Ads have prescriptive analytics systems that are reasonably competent when treating them as predictive analytical systems. 

Google Ads Recommendations gives you budget, bidding, keyword, ads, and other recommendations based on prescriptive analytics models. Facebook Ads are a little more advanced with their campaign recommendations. However, in many of these circumstances, the advertiser has little choice but to follow these recommendations since specific attributes of your campaign operate within a black box.

Diagnostic Analytics

Diagnostic analytics is analytics performed on the internal data to understand the “why” something is happening. Diagnostic Analytics helps you get an in-depth insight into any given problem, provided that you have enough data at your disposal. Diagnostic analytics helps identify anomalies and determine casual relationships in data.

As a marketer, you may not place diagnostic analytics into practice as it is more of an “analytics for analytics sake” problem. In my experience, diagnostic analytics only comes to play when tracking pixels, javascript, links, parameters, HTML code, or other website code used to collect data and statistics aren’t working correctly. 

Usually, a deep dive into the data can help uncover such issues. Diagnostic Analytics helps determine what elements and events led to particular outcomes or anomalies in your data. The only catch is that you’ll need enough data to properly analyze and diagnose tracking issues.

Diagnostic analytics can also be applied in a broader sense to diagnose run-of-the-mill performance issues such as low traffic problems, high cost per acquisition, low click-through rates, and sudden drops in conversions. It will come into play more when you connect various platforms and tools to your marketing technology stack.

How Analytics Can Help You Reach Your Goals

When planning any marketing activity, it’s essential to determine your desired outcomes. Your outcomes are synonymous with your ultimate business objectives. As a company, what are you hoping to achieve? Do you want more prospects to call you? Do you want more people to talk about you on social media? 

Next, measuring your efforts and how they’re helping you achieve those business objectives is essential.

After all, you can’t analyze your marketing efforts if you don’t know what you’re trying to achieve. Most online business objectives will fall into one of four categories: ecommerce, lead generation, awareness, or SaaS websites. I discuss these sites in an earlier blog if you need clarification.

  1. Lead generation: Lead generation campaigns measure the number of leads captured and the ability to book a meeting or make a sale. Your cost per lead or cost per conversion is a critical factor in measuring your marketing effectiveness and efficiencies.
  2. E-commerce: Eccomerce campaigns measure the number of online sales, which isn’t typically applied to those selling a service. Other key metrics include return on ad spend, conversion rates, and cost per conversion.
  3. Brand Awareness: Brand Awareness must be measured in terms of the end user’s ability to find information, such as bounce rates, exit rates, onsite time, etc. As for measuring the proliferation of your website’s content, you can use social follow, shares, comments on content, and mentions on the internet as critical measurements.
  4. SaaS: Software as service websites usually use a combination of lead gen, e-commerce, and brand awareness metrics. The awareness KPI’s are usually directed inwardly. Each SaaS product has unique metrics that let you know if you are fulfilling your customers’ needs or if there is something that is missing in their experience.

Differences between Quantitative and Qualitative Data 

When we talk about analyzing quantitative and qualitative data in digital analytics, it’s essential to understand what those two terms mean. After all, you can’t analyze information if you’re unsure what information to look at. Quantitative data, for starters, is all about information you can count, such as event tracking in terms of website engagement. You can track page load time, clicks, impressions, average time on page, pageviews, etc. 

Next, we have qualitative data. This refers to the information behind changes or trends. Qualitative data consists of consumer surveys, heatmaps, user reviews, ratings, and other data about your audience and why they make the decisions that make. It’s incredibly valuable, but it’s harder to analyze. Below are some examples.


  • Web Analytics
  • Business Analytics
  • Campaign Reporting


  • Customer Surveys
  • Heatmaps
  • User Reviews
  • Ratings

The Digital Analytics Cycle

Throughout running various marketing activities, you should be looking at digital analytics to provide real-time insight into how those marketing activities are working. This tends to happen in four simple steps that can be referred to as the analytics cycle:

  • Collect: Data collection is a crucial part of the process. If you get this part wrong, everything else you do is null and void. Make sure your tracking pixels, UTM parameters, javascript, and applications collect and organize data in a valuable and beneficial way to all stakeholders.
  • Organize, Curate, Prepare: Your data has been collected. Now it’s time to organize it and categorize it in a digestible way to you and all other stakeholders. Usually, this process is done in an analytics suite such as Google Analytics. However, it could be another platform since your data may be qualitative instead of quantitative. It is crucial to understand project or campaign scope, milestones, and timelines. Know your KPIs, goals, objectives, and benchmarks as well.
  • Report: Many models have the analysis coming before reporting. However, I find it hard to analyze without a report. The report I create for my analysis always ends up being the report I use to share insights. Keeping the same reports throughout the process makes for a smoother process when it comes to communicating performance.
  • Analysis and Insights: After collecting your data, organizing your data, and creating a visual report of your data, it’s time to analyze your data. Look for actionable information, patterns, trends, and anomalies. Look to tell a story. You can review industry benchmarks to understand better where you’re at.
  • Share: This where a lot of digital analytics people drop the ball. Sharing your analysis and insights is an art form. Sharing too much can overwhelm and confuse those who aren’t close to the campaigns. Sharing too little will not get your message across. Make sure any report or visual aid you create has just the right detail to effectively communicate your findings.
  • Create Course of Action: Most models tell you to “test” at this point. Testing is only one of many actions you can take in the cycle. You may have wasted thousands of dollars targeting a specific population segment. In that scenario, there is no need to test. Sure, you’ll have very little pushback by wanting to eliminate that segment. Given the opportunity, testing is always the best approach in situations that aren’t so obvious.

Universal Online Metrics 

There are some universal online metrics all businesses should pay attention to. This means you’ll need a tool that gathers, at a minimum, the following: 

  • Audience: This refers to who visits their site and content and any demographics found on those individuals.
  • Behavior: This refers to the trends of what users do when they’re browsing your website. What links do they click? What do they read?
  • Acquisition: This refers to how individuals find your websites, such as referral links, direct searches, advertisements, and other avenues.
  • Conversions: This refers to whether individuals have completed the desired action on your website, such as filling out a form or using the chatbox.

digital analytics work

Website Tracking Methods 

As mentioned above, tracking is incredibly crucial for any marketing activity. Although many people dread the task of monitoring, it’s vital to avoid wasting money on something that’s not working as well as it should. Here are five of the best tracking methods to keep in mind: 

  • Website analytics: Web Analytics is the most straightforward form of tracking. You want to see who is coming to your website and where they’re coming from. Google Analytics is a fantastic choice as it’s relatively easy to set up and accurate.

    You can paste a tracking code into the back end of your website and get started. All of your data will be compiled into an easy-to-digest format for you to go through.

    Additionally, using UTM parameters, we give you a holistic view of how your marketing efforts are going.

  • Phone tracking: Many business owners simply enjoy it when their phone rings without considering where those calls come from. If you’re not using call tracking technology, you’re missing out on precious information to make or break your marketing budget. Call tracking uses dynamic number insertion (DNI) to assign phone numbers to visitors can find you in various areas. Each time someone calls, it’s tracked back to that lead source.
  • Ad network conversion tracking: All ad networks offer the ability to set up some form of conversion tracking. This includes Facebook Ads, Google AdWords, Bing Ads, and virtually all other online advertising types. You want to ensure you have ad network conversion tracking set up at all times as you’re likely testing various graphics, ad copy, and other elements. Ad network conversion tracking lets you see how your advertisements perform in newsletter sign-ups, phone calls, website purchases, and more.
  • CRM tracking: A CRM (customer relationship management) is a type of database that enables you to organize information on your prospects. The most specific information collected is your prospect’s name, phone number, lead source, company, and other relevant information throughout the sales process. Many CRM’s offer the ability to organize leads by lead source, which is incredibly valuable as you can see where most of your customers are coming from and in turn, spend more money in that area.
  • KPI tracking: Lastly, we have KPI tracking. KPI tracking involves taking the results in the previous methods and putting them into one helpful list. A KPI is a key performance indicator, which means anything insightful regarding data included in KPI tracking. Ensure you’re adding every KPI to one list to quickly and easily review that list. 

A Look at Google Analytics

We’ve mentioned this fantastic solution quite a few times. Google Analytics is one of the best tools for any marketer regarding digital analytics. It allows you to analyze relevant data and KPIs in one easy-to-access place. 

Here are a few of the most common questions we hear about the solution: 

  • What traffic sources send traffic to my website? 
    While it’s great to be aware of the number of visitors to your website, it’s even better to know who is sending them your way. This can be found under “acquisition > channels,” wherein you’ll see a high-level view of where traffic is coming from based on the channel type.
  • How do visitors get to my website, and why do they leave? 
    You can go to “behavior > site content > landing pages” to determine which landing pages and site sections perform the best. Typically, visitors end up on your website by clicking an ad, social media posts, or something else directing them to a landing page. Pay attention to how they’re leaving your website too. Go to “behavior > site content > pages” to find this information.
  • What do visitors do once they’re on my website? 
    Knowing what visitors do on your website is incredibly essential. You can see how they’re navigating through and what content they’re interested in when you review the analytics behind their behavior. Go to “behavior > site content > content drill down” to find this information quickly for each page on your website.
  • Does my website perform well regarding my call to action? If you’re strategically setting up your website, chances are, every page drives to a specific call to action. This tells the visitor exactly what they should do next, whether signing up for your newsletter or downloading a whitepaper. Google Analytics lets you measure each call to action. Simply go to “admin > account > property > view > goals” to create a goal.
  • Is my website working correctly? 
    Google Analytics gives you great insight into your website’s effectiveness and overall health. You can go to “behavior > in-page analytics” to see if your website’s design/layout leads visitors where you want them to go. You can also check the website’s speed under “behavior > site speed” to see if it performs well in load times.

A Guide to Attribution Modeling

Life would be a lot easier if the first time a prospect visited your website, they converted into customers. But that’s simply not how it works. Nowadays, most prospects visit websites they’re interested in a few times before they convert. In fact, they might find you on social media and click the link to read a blog post. Next week, they’ll see a retargeting ad and click to end up on a landing page. And eventually, they’ll convert. So, where do you attribute that conversion to? 

What Are Marketing Attribution Models?
In regards to marketing, attribution modeling is a framework wherein you can figure out exactly what marketing channel or activity resulted in a specific conversion. There are six common attribution models — each of which distributes the value of any given conversion. These include: 

  • First interaction 
    This refers to the first touchpoint for any given prospect. Essentially, the first interaction gets all of the credit for the conversion. If that individual found your company via a specific ad or social media channel, that ad or social media channel is what resulted in the conversion. If you’re in an industry where customers convert almost immediately, it would make sense to pay attention to this model. However, in many cases, this model doesn’t work well as it ignores the often lengthy process involved with conversion.
  • Last interaction
    This refers to the last touchpoint for any given prospect. Essentially, the last interaction gets 100% of the credit for the conversion. This means if they’ve seen your retargeting ads but didn’t sign up on the landing page, then eventually went directly to your website, direct traffic is the last interaction. This model is an excellent choice for businesses with a short buying cycle as there are likely very few touchpoints before conversion.
  • Time Decay
    This refers to the process of spreading out the value of the conversion over various events. Essentially, this is similar to the linear model, except for the time the touchpoint is considered. Interactions that occurred close to when the prospect converted have more value given to them. This means the last touchpoint gets the most credit. This works well for long sales cycles, such as B2B service companies.
  • Last non-direct click 
    This refers to the last non-direct click in that any direct interactions happening before the conversion are ignored. Suppose a prospect ends up on your website because they’ve manually entered the URL. In this model, we ignore that direct traffic and focus on what led to the click.
  • Linear
    This refers to the process of splitting credit for any given conversion. Essentially, there are multiple touchpoints monitored and tracked to give each touchpoint a measurement. For instance, if a prospect goes to your social media post, clicks to read a blog, then calls you afterward, there are two touchpoints. Each touchpoint would get 50% of the credit, or 50% of the conversion value is attributed to that touchpoint. This model demonstrates the value of all marketing channels.
  • Position-based
    This refers to splitting the credit between the prospect’s first touchpoint and the moment they convert. This means 40% of the credit is given to the first touchpoint and the last. The leftover 20% is given to each interaction happening between these two points in time. This is the perfect model for any business with a long sales cycle and multiple touch-points throughout the process.

    Although these are the six most common attribution models, the custom attribution model is one last one to consider. Google Analytics allows you to create a custom attribution model to consider any particular touch-point you value the most.

Reporting and Analytics

Reporting is the process of organizing data, while analytics is the process of analyzing and organizes data into actional insights.

Raw data isn’t intelligible and needs to be organized after it’s collected so that it is easier to visualize. In other words, it is much easier for analysts to use the information when viewing it from within the reports. It is during this process that a transformation takes place. It is no longer simply data – through reporting, it becomes usable information. Not all reports are created equal, but when reporting is done well, it should help enterprises monitor all aspects of their business operations.

Turning Information into Insights

While reporting turns data into information, analytics helps enterprises turn the information into insights. The goal of analytics is to take the information and interpret it. Analysts start by asking questions that may arise when looking at the data organized in the reports. Once the analysis is complete, a good analyst can also provide recommendations as to a course of action that will ultimately improve business performance.

Therefore, the end goal is to use the process of analytics to uncover the problems themselves and the solutions. Analytics uses the information provided in the reports to derive insights as to why something within the organization is happening and what can be done to fix it.

When it comes to reporting and analytics, you can’t have one without the other. The whole process begins with the raw data itself. The data needs to be organized to present it as information. Reports can be detailed and use a variety of tools. Still, the end goal is the same – to make it easier for those performing the analysis to see what is happening within the organization. This means that the integrity of the reports makes all the difference in the analytics phase, which is focused on uncovering any problems within the organization and the solutions.