In The Headlights: July 24, 2019 Issue

In The Headlights

Before we begin, a special announcement. We’re holding a half-day Google Analytics for B2B Marketers Bootcamp in Washington, DC on October 15, 2019. Come get your analytics in shape in this half-day, hands-on event. Click/tap here to learn more and register.

Correlation is not causation.

We’ve heard this axiom repeated so often that even non-statistical, non-analytical people can repeat it. It’s become so common that it’s had an unwanted side effect: analytically-minded marketers have come to believe that correlation is not useful or even bad.

Is it bad?

The textbook example of correlation vs. causation is an examination of the mathematical relationship between ice cream sales and drowning deaths. As sales go up, so do drowning deaths. Intuitively, we understand that there’s a third, hidden variable at work: summertime temperatures. As temperatures go up, people consume more ice cream, and people go swimming more (and a few folks unfortunately drown).

So this correlation is bad, right?

Not necessarily. If you worked in, say, public health, and you wanted to forecast drowning deaths, do you need to know the third hidden variable? As long as ice cream sales and drowning deaths continue to march in lockstep, you could make the valid prediction of drowning deaths based on ice cream sales data.

It sounds funny – why wouldn’t you just use temperature data for the forecast? In this example, we would. But there are clearly times when we don’t have access to the underlying variable. As long as we can test for statistical validity of a correlation, for the purposes of prediction, we don’t necessarily need to establish causation.

For example, as privacy regulations increase, we will have less and less attribution data to work with, especially in tools like web analytics and marketing automation software. We will know what we did – what we posted, where we shared, where we ran ads – but we may not be able to attribute website visitors to a specific source. As long as we have our activities documented and counted, and we have the end result – website traffic – a correlation may be enough to help us understand what’s working.

Think about your own marketing data and what you’d like to predict – lead generation, website visitors, sales. Do you have the necessary ingredients – data – for the prediction, regardless of causation? If so, then I’d encourage you to start testing for statistical validity and building a predictive model.

The Bright Idea

The Bright Idea

This week’s Bright Idea is a look at the media and agency landscape. How many people work in newspapers? Radio? TV? Ad agencies? We look at the last decade of data to show what’s growing and what’s fading.

Click/tap here to see the framework (no form fill required), and if you like it, please share it.

In Case You Missed It

In Case You Missed It

Shiny Objects

Shiny Objects

This week we’re testing article summaries in addition to the links and headlines, as well as a new URL shortener – our own. Hit reply to let us know what you think. Is this better?

Social Media Marketing

Media Landscape

Tools, Machine Learning, and AI

Analytics, Stats, and Data Science

SEO, Google, and Paid Media

Business and Leadership

Join the Club

Are you a member of our free, private Slack group, Analytics for Marketers? Join 300 like-minded marketers who care about data and measuring their success. Membership is free – join today.

Upcoming Events

Where can you find us in person?

  • BACon, August 2019, Chicago
  • Content Marketing World, September 2019, Cleveland
  • INBOUND, September 2019, Boston
  • MarTech East, September 2019, Boston
  • Google Analytics for B2B Marketers Bootcamp, October 29, Washington, DC
  • MarketingProfs B2B Forum, October 2019, Washington DC

Going to a conference we should know about? Reach out!

FTC Disclosure: Events with links have purchased sponsorships in this newsletter and as a result, Trust Insights receives financial compensation for promoting them.

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