Marketing Analytics: Tools, Terms & Techniques

How are you using marketing data to improve your business? Perhaps the better question is: How do you wish you could be using data to improve your business?

Data is everywhere these days, and it’s not going away. Increasingly, intelligent use of marketing analytics is the key differentiator between successful companies and unsuccessful ones.

And it’s easy to see why. With thousands of types of data, and billions of individual data points, making sense of it all can be difficult, at best.

At worst, you could be throwing time, money and energy away on analytical tools and insights that aren’t even correct and don’t lead to better business outcomes.

So how to avoid this? Understanding of what marketing analytics can do and a process-driven approach to collecting and utilizing these analytics in your business decisions. Let’s discuss exactly how to do that.

What Is Marketing Analytics?

Marketing analytics is anything relating to the collection, curation and use of data as it pertains to your marketing efforts.

That’s a pretty broad scope, though this is deliberate. There are thousands of tools and types of ways analytics can be

Fortunately, some common uses for them will cover a lot of ground for businesses.

Ways to Use Marketing Analytics

Marketing analytics doesn’t have to be an endless, impenetrable sea of options. There are specific uses for it that many businesses use on a regular basis.

ROI

ROI, or Return on Investment, speaks directly to your bottom line. How much did you spend? How much did it make?

Comparing ROI figures between different types of marketing can be valuable in decision-making.

The trickier problem comes with the fact that exact ROI for a marketing channel sometimes isn’t possible. Other times, a marketing campaign is aimed at brand awareness, not immediate revenue, so an ROI calculation will be an imperfect way to represent success.

That’s a larger issue that we’ll discuss further down, but suffice it to say that in a lot of cases, an ROI calculation is going to give you at least a general sense of a campaign or channel’s marketing success.

Customer Demographics

Who is visiting your website? Who is purchasing your products?

You can answer these questions, at least approximately. What is the typical age range, gender, income level, geographic region, and so on.

These insights can help you to better target your ads to the people who are most interested in your brand. Or perhaps the reverse is true, and you’ll want to target a new demographic that you think you could appeal to, but who currently isn’t engaging with your brand.

Either way, you’ll be making these decisions from a place of confidence.

Purchasing Trends

Why is one product doing better than a similar one on your site? Analytics can help with this.

Here, there isn’t going to be a binary answer. The answer to the question above could be one of several things. A paid search ad on Google might be driving traffic to it. Your site’s navigation might better lead them to one product over the other. Or maybe the pages themselves are laid out differently.

You may need to conduct various tests to uncover what the issue is. But marketing analytics will be used regardless.

Other purchasing trends are more easily identifiable. You can monitor for shifts in consumer interest and adjust your product base accordingly, for example, but only if you’re closely tracking sales figures over time.

Website Usage

Most companies know about high-level website metrics like number of users and sessions. But do you know the average time on site for your web users? Do you know the most common paths they take through your site’s navigation? And could those paths be streamlined to better facilitate sales?

Numerous tools exist to analyze and interpret these metrics, which can be used to make informed decisions about website design and how many resources you devote to it.

Campaign Success

A multi-channel marketing campaign that lasts for months likely has a lot of time and money behind it. How will you know if it’s successful or not?

Clear, realistic goals help. But to actually track and analyze success, you’ll need digital marketing analysis tools and people who can collect, curate and interpret results across multiple channels to coalesce them into reports and insights that can be used by leadership.

Comparative Data

We did a full deep dive into A/B Testing for websites and digital marketing channels, and it’s analytics that underlies a company’s ability to process and compare this data.

Collecting and Utilizing Data

A big mistake (we’ll talk about more mistakes below) managers and company leaders make is to simply request all of the data 

This is pointless. Why? Because not all of the data you have will be useful, so tracking all of it will be a waste of company time and resources.

There are a few large steps you’ll want to take before instituting an analytics plan:

1. Understand what metrics you want to track, and why.
2. Understand what you have the capability of tracking currently.
3. Bridge the gap between those two items above with software tools that will allow you to track and analyze the specific metrics that are most important to your company.
4. Establish benchmarks for the metrics, so that you can track progress over time.

Skip any of those steps and you’ll probably just end up with a lot of wasted time, frustrated employees, and data that’s either useless or nearly so.

Using Marketing Analytics in Decision Making

Ok, so you’re a marketing manager or Chief Marketing Officer, and you need to make decisions that involve thousands or sometimes even millions of dollars.

How can you incorporate marketing analytics into your decision making?

Predictive Analytics

Tracking relevant data, like the metrics we mentioned above such as ROI and conversion rate, can give you expected benchmarks for future campaigns.

So, for a simple example, a proposed marketing campaign is going to cost you $100,000 and is expected to drive 50,000 people to a specific landing page on your site for a new product.

At this point, you can dive into predictive analytics to determine if those 50,000 visitors are going to justify the $100,000 spend, and to what extent. Because, for example, maybe the campaign is predicted to make $105,000. 

That's a profit, right? Sure, but it’s not much of one. Those same analytics might inform you that a handful of other marketing channels perform better on average. So maybe you skip the campaign entirely and realign the budget to

Budgetary Decisions

That last paragraph leads into the next big point: budget.

You don’t have an infinite amount of money to spend on marketing, so how you spend your money matters.

What’s performing better or worse? Why is this? Is a lower profit margin on a specific ad activation still worthwhile because of the exposure it gives your brand?

Some of these questions don’t have exact answers. But you’ll make better, informed decisions when you can compare numbers directly between marketing channels.

Common Mistakes in Marketing Analytics

At this point, a CMO might feel confident in incorporating marketing analytics into their decision making. “Bring me the ROI for every marketing channel, now! It’s time to make some adjustments,” they might say.

This will often be a mistake. Let’s look at why.

1. Lack of Statistical Literacy - Parsing data for relevancy is hard. Leaders either need a deep understanding of how analytics can be misused, or someone on staff capable of distilling complex data into actionable insights.
2. Not Enough Data - Making sure you have the volume of data for results to be statistically significant can be hard, and since you’ll often get mixed signals, clear takeaways from data insights can take months or even years to form.
3. Branding vs. Revenue - Your TV advertising might have low ROI, but it serves a secondary purpose of increasing brand awareness, even if it isn’t always responsible for immediate revenue. This can be just as beneficial long-term, but is harder (though not impossible) to track using typical marketing analytics.
4. Lack of Comparative Data - Similar to the point above, if you have a billboard up in town, you’re getting business from it that you’ll never be able to track directly to the billboard, even if there’s a phone number or QR code or something similar on it. This gap in data can lead to false conclusions.
5. Data is Incomplete or Misinterpreted - A client of ours here at Leadflask has amazing Google Ad revenue, but largely for searches related to their business name. Yes, this is revenue-generating, but a lot of those searches for the company would have resulted in business even without the ad spend. So are the ads performing well, or is it just a case of marketing to people who already want to do business with you? This is one area where being able to interpret data is important.

There are other potential pitfalls, but you can start to see from the above that this isn’t easy! On the contrary, this is why many marketing teams have roles devoted specifically to the curation and analysis of marketing analytics data.

Segmenting & Attributing Data in Analytics

An important concept in marketing analytics is segmenting your audience. We also discussed this briefly in our article about A/B testing.

In brief, segmenting an audience involves grouping larger groups of customers, website visitors, subscribers or similar data into clusters with common attributes. The attributes can be things like age range, gender, geographic region, or household income.

This allows you to analyze trends within these specific segments of your audience. This can be a lot more powerful than analyzing the entirety of your audience, because you can get mixed signals from different demographic audiences if you mix them together.

Another important concept is that of attribution models. The problem with something like an ROI calculation is that very rarely does someone make a purchasing decision based on a single advertisement or communication.

So if a person visited 15 web pages, was served three different ads across your paid advertisement channels, and saw 20 social media posts and 12 email newsletters before they purchased anything from you, which of those things gets the credit for the revenue? The ads? The social posts? The emails? The website itself?

There are sophisticated attribution models that can break up revenue between marketing channels, but it’s still not going to be a precise calculation that perfectly represents what led that person to purchase from you.

Deciding how to attribute revenue in a way that makes the most sense for your business is just as important as having the data available to be able to calculate attribution.

Software Tools for Marketing Analytics

This section is not going to be able to do the topic justice, because there are hundreds of popular tools, and likely thousands of tools total that you can use to manage your marketing analytics.

Below we’re going to list some popular ones and those we’re personally familiar with, but the list is by no means comprehensive.

1. Google Analytics - Also known as GA4 these days, Google Analytics is sort of the baseline analytics tool for a lot of companies, to track things like web traffic, demographic trends in your site’s visitors, and tracking “events” (purchase clicks, email subscriptions, etc.).
2. Google Search Console - If you have a GA4 account set up, you also have access to Google Search Console (GSC). Where Google Analytics tracks traffic to your site, GSC tracks searches that lead to your site being seen and, ideally, visited.
3. Moz - Moz is a packaged SEO tool used in content marketing frequently that tracks numerous analytics related to a website’s general health and its performance in searches.
4. SEMRush - SEMRush is a direct competitor of Moz and offers many of the same services, including backlink audits for your site and assistance with keyword research.
5. Salesforce, Hubspot and other CRMs - Most popular customer relationship management (CRM) tools will have built-in analytical capabilities to help you segment and analyze website visitors and customer information.
6. DashThis - DashThis collects data from a variety of sources, such as social media accounts, Google Analytics and Search Console, ad platforms such as Bing and Google Ads, and then quickly turns them into readable reports that you can custom-design and prepare for review.

Is this just scratching the surface of marketing analytics tools available? Yes. But it’s also a good high-level overview of the types of tools you may want to consider for your business.

Role of Analytics in a Company

There are forces that drive decision-making outside of analytics in most companies. This is normal and expected.

However, it’s the companies that intelligently use analytical tools to make more informed decisions that have a leg up on their competition. But this isn’t easy.

Having a controlled, documented process that you can iterate on is the best way to ensure that you’re able to improve your analytics usage over time. This means having employees dedicated to marketing analytics collection and reporting, and scheduling time to discuss the results and their implications for business-level decisions.

If you’re a smaller company or even a company of one person, this could include training on marketing analysis in order to better understand this field. As thorough as this blog is attempting to be, it’s not a tutorial on how to track and analyze marketing results. This could mean reading books on marketing analytics, taking a course on it, or finding similar training.

Marketing Analytics: Cutting Through the Noise

Data can amount to noise if you aren’t careful. There can be so much of it that it’s impossible to parse it for actionable insights. And it can be messy enough that the wrong conclusions are drawn from it.

This is why a careful approach to marketing analytics is not only recommended, but vital to the long-term survival of any business looking to maintain or expand its digital presence without bloating its budget or workforce.

Considered analysis of data with a trained analytical marketing team and intuitive software tools is the only rational approach to our data-driven world. The companies who master it will be the leaders in their industry for years to come.