In the most recent February 2020 edition of the CMO Survey, when asked “How often does your organization use analytics to make decisions?”, chief marketing officers responded with a bewildering 37.7% in the affirmative. Only 37.7% of CMOs say their organization uses analytics to make decisions.

What are they using the rest of the time? Guesswork? Instinct? Making things up and hoping it works out for the best? We don’t know.

It’s even more confusing when we consider the logic behind it. From the August 2019 version of the CMO Survey, when asked, CMOs indicated that marketing analytics drives company performance more than social media marketing or mobile marketing. So on the one hand, we have poor use of analytics, and on the other hand, when used, marketing analytics drives company performance more than channels in which we invest significantly more.


The reason is straightforward: we don’t know how to use our data and analytics. When we examine most marketing reporting, analytics, and dashboards, we’re presented with what can charitably be described as information overload. We see dashboards that are more complex than an airplane cockpit or NASA Mission Control. We see reports that are dozens of pages long.

And what we don’t see is the most critical component of all:

We don’t see decisions.

The single most important thing we could do with our analytics is to make decisions from it.

What should we do more of?

What should we do less of?

What strategies are working?

What strategies are failing?

When should we pivot?

Most reports, most dashboards, most analytics don’t answer these questions or even provide supporting data to help answer these questions. Instead, they just pour data all over our desks and expect us to put together the pieces, like a jigsaw puzzle without a picture guide of our goal.

It’s no wonder that CMOs see analytics as a cost center, rather than as a driver of marketing impact.

To solve the challenges facing marketers and their analytics, we need to examine two key areas – determining what’s important in our data, and communicating essential information well.

Fixing Marketing Analytics: Determining Importance

A quick look at any marketing analytics tool reveals the surprising amount of information such tools present. In Google Analytics, for example, how many data points are available to a marketer? A dozen? Fifty?

Five hundred ten. There are 510 different dimensions and metrics available to marketers in Google Analytics. How many of these are important? As with most software, different pieces of information matter to different kinds of companies. Now, combine that with other data sources, such as SEO, social media, content marketing, public relations, advertising, and the amount of data available to us can be overwhelming.

How much of this data matters?

One of the most impactful exercises we can conduct is to do multiple regression analysis – a statistical technique for understanding how different measures relate to each other and to a key outcome – on our marketing data. Beginning with the key performance indicators, or KPIs, that we are held accountable for, conduct a multiple regression analysis against all the marketing data available.

What metrics or combination of metrics have a mathematical relationship to the key outcome we are measured on? Once we understand that, we test it for causality.

For example, if retweets and email open rate correlate highly with lead generation, what happens if we increase the number of retweets we earn? What happens if we stimulate open rates of emails with paid advertising? Do we see a commensurate, causal increase in leads generated? If so, then we’ve proven the metrics are related, and we can now make decisions about how to increase those leading indicators to increase our key outcome.

Most marketers don’t perform this kind of analysis, but for those who do, the impact is profound. Instead of worrying about every number available, we focus our efforts on only those metrics that deliver true impact. We allocate budget, energy, time, and people to the things that matter most, significantly improving our marketing ROI.

Fixing Marketing Analytics: Communicating Importance

Once we know what’s working and we’re measuring the things that work best, our next challenge is to communicate that information to our stakeholders in a purposeful way. Simply pouring data all over someone’s desk is inefficient at best and damaging at worst.

Data, without decisions, is distraction.

Our primary goal with marketing reporting is to tell a story with our data that leads to a decision. What should we do more of? What should we do less of? How do we amplify the impact of marketing? The answers are embedded in our data.

The word analytics comes from the Greek word analein, to unlock, to loosen up, to shake free. What often happens with our data and analysis is that we don’t unlock its value. How do people learn best? How do we compel people to make decisions?

For millennia, the way we’ve conveyed information is through storytelling – presenting a logical sequence of information in a memorable way. Whether it’s how badly the barista messed up our name on the coffee cup or how likely we are to hit this quarter’s targets, we communicate through stories – and our data should be no different.

Great data-driven storytelling should have three parts, something I call the three whats: what happened? So what? Now what?

When we communicate data, we often tell only one part of a story, the “what happened” component. We invest incredible amounts of time and energy into focusing on what happened, and we neglect the rest of the story.

What happened is important. Website traffic went down. Leads went up. Email open rates declined. That’s vital information to judge our past performance, but we must not focus solely on the past. The second part of the story is, “So what?” So what? Why does this information matter? Why did that happen? Our ability to explain “so what” helps create memorable context around our data.

The third part is the most important part of the story: now what? What decision will we make? If website traffic was up, what are we going to do about it? Will we put up a popup to capture more audience? Will we invest more heavily in remarketing?

If we invest a quarter of our time into telling what happened, a quarter of our time into telling why something happened, and half our time into asking for decisions to be made from our analysis, we’ll drastically increase the effectiveness of our analytics and reporting. Instead of collecting dust as shelfware, we’ll drive business impact by using our data to make decisions – and as we know from the CMO Survey, that puts us in uncommon company.

Turn your data into action. Turn your analysis into action. When you focus on understanding what’s important and communicating it effectively to your stakeholders, you’ll see marketing impact far beyond what you’ve seen in the past, and beyond what your competitors can do.


Christopher Penn

Co-Founder and Chief Data Scientist

Trust Insights