Editor's note: This article has been updated to reflect best practices as of February 1, 2024
What exactly do we mean by marketing analytics in a post-pandemic, highly digital world? Do we mean the baseline ability to track marketing performance or marketing campaign effectiveness, or is it more than that? Something deeper?
Marketing analytics defined
What is marketing analytics?
Marketing analytics is the process of managing and analyzing your data to improve the performance of your marketing campaigns. It includes collecting and transforming data about your marketing efforts to draw actionable insights that make your marketing dollars go further and grow the business. Marketing analytics software can show you where your marketing strategies are working—and where they could use some work.
Also read: 6 tips to better analyze your marketing data
Why is marketing analytics so important for future marketing efforts?
Marketing analytics tools may be one of the most powerful solutions in a modern marketer's stack.
With a robust marketing analytics process, you can begin to predict future actions your customers will take and align your marketing efforts. But more on this in a bit.
Basically, marketing analytics solutions allow you to measure marketing performance, leverage data from consumer behavior to shape a target audience, or from customer behavior to gain insights into customer lifetime value (CLV), and marketing initiatives across multiple channels to create a more holistic picture of your sales and marketing funnel, improve future campaigns, and, ultimately, improve future return on investment (ROI).
What is the normal marketing analytics process?
We can break down a typical marketing analytics process into four phases:
The primary phase is about collecting data you're currently generating and analyzing past data. This can include historical data from a year or two ago, or even something as recent as yesterday's actions. In this phase, marketing analytics helps marketers simply organize data.
Why did it happen?
In the second phase analyzing data further, marketers measure data and try to extract insights from their analysis. They aim to understand why customers converted or left a cart full before leaving the site. At this stage, it's about drilling into the data to gain insights, learn customer preferences, and determine the "why."
What will happen next?
Remember when we spoke about seeing the future? In the third phase, savvy marketers with a robust data analytics process gain real insights. They can lean on historical data and trends uncovered in their analysis process to begin modeling potential future behaviors. Factors like seasonality or broader consumer trends can be factored into these models to adjust sales forecasts, marketing KPIs, and more.
What can I do with this marketing data?
With reliable customer data now, detailed insights, and a view of what customers will do next, you can begin to take business decisions that anticipate your customers' (and the market's) next moves.
A maturity framework
These phases of a marketing analytics process can also be thought of as tiers in maturity growth.
Most, if not all, modern marketers can say they have some handle on phase one — capturing and analyzing performance data. This is the bedrock upon which more advanced analytics are built and performed.
Some marketers may also be able to draw actionable insights from their data — particularly if they use data aggregation tools like Google Analytics, business intelligence software such as Power BI, or a marketing data hub built for data-driven decisions. The latter, in particular, can more easily achieve an automated holistic view of all their channels, leading to the real "magic" of predicting the future to create campaigns more well-aligned.
This is where we separate the wheat from the chaff. Once you get to the third and fourth tiers of the maturity framework, we are dealing with some hardcore analytics. In these tiers, you will be able to inform your next ever marketing campaign and strategy using the data from the previous ones. In a smart and reliable way, that is.
A superpower for marketing teams
Think about it. The ability to reliably plan and deliver marketing campaigns in direct response to specific shifts in marketing spending can feel almost like a superpower. It is not something all marketing teams have.
An example of predictive and prescriptive analytics
Let's take social media ads as an example. With predictive marketing data analytics (our third tier of maturity), we may want to determine what will happen if we spend $1,000 versus $100,000 a month on TikTok. In this case, we may want to know if we will see the same return on investment at both levels. We also want to know if the high spending affects the customer journey.
With prescriptive analytics, you can answer these questions, which brings us to prescriptive analytics (our fourth tier). We can predict that TikTok will drive more video views, and social media engagement, but Facebook will drive more clicks. That means we can start optimizing TikTok and Facebook ads for views and clicks, respectively.
As you will understand by now, this means we can better predict and plan our marketing efforts.
An example of marketing analytics for agencies and in-house marketers
Now that we've got the basics and advanced phases of analytics maturity down, let's take some time to walk through a typical example agency and in-house marketers are likely to experience: the quarterly budget review.
Yup. It's time to ask for more money from your CMO or your client. How will you know how much to ask for, though? Also, how do you determine and justify how it will be spent?
Time for marketing analytics to save the day!
Think back to the four questions that marketing analytics help us answer. First, determine what happened with your current marketing spending levels. Take a look at how you performed against your KPIs. Identify any gaps over surpassed goals.
Perhaps you identify that you are short of targets, but one particular channel (maybe YouTube Ads) is driving the most conversion despite a smaller share of the budget. That could be a great insight to highlight.
With predictive and prescriptive analytics, you could model the outcome of an increase in spending solely for this platform, or an increase in overall budget to account for the greater focus on YouTube ads. You could also identify that our YouTube Ads conversions carry a higher profit margin, so long as all other channels receive current spend allocations.
Boom! Now, you have a solid business case to ask for a specific increase in budget.
How do you start using marketing analytics?
So, you know you want to become so savvy that you can gain all of the analytical superpowers, but you need to figure out how to get started.
Thinking back to our tiers of maturity, you'll want to ensure that the base levels are solid and can be built upon. That means you'll need to have robust data collection and analysis systems in place. So, first make sure you can get all the data from your social media marketing, campaigns, website analytics and other important platforms into one place.
Marketing analytics tools
If you have coding skills or don't mind outsourcing to your BI team, try exploring data warehouses like Google Bi Query and Snowflake. However, if your coding skills are less than a master developer (or you don't want to wait around for your BI team to answer your service ticket), you may want to explore a marketing data hub like Funnel.
Depending on your capabilities and business model, each option can begin to build the foundations that can take you to the "magical" third and fourth tiers of marketing analytics maturity. Overall, marketing analytics software is not the holy grail to good data analysis yet - but it might well be the foundation you need to get going.
How does data transformation fit into marketing analytics?
We mentioned earlier that a core part of our analytics foundation is collecting and transforming your data. This is a critical element in our foundation.
Data transformation allows us to ensure that our data is uniform and reliable. Through transformation multiple data formats, currencies, and text formatting are normalized and made consistent.
Without this step, your analysis may consider double-counted data or even broken formulas—leading to irregular or broken dashboards.
By taking the time to transform and map our data properly, we ensure that our analysis and insights are accurate. Otherwise, we might draw false insights that lead to incorrect predictions and business decisions.
Much more than just stats and performance tracking
As you can see, the state of marketing analytics in 2024 encompasses much more than the simple capacity to track your digital marketing efforts and performance. Sure, that data collection is still an integral piece of the pie, but there is now so much more that we can consider. Ideally, collecting the data and analyzing it, real-time analytics leads to new choices regarding your marketing channels and investments. Which leads to better overall marketing performance too.
To learn more and see other examples of advanced marketing strategies and tips come to life, check out our latest Funnel video about marketing analysts (and how to become one!).