Christopher Penn on Data Science Basics for Content Marketing

by | Mar 5, 2020 | Content Strategy

As an official media partner of ContentTech Summit 2020, we spoke with keynote speaker Christopher Penn about the importance of data science and what healthcare marketers can learn from Facebook and other data-driven organizations.

Christopher Penn


Christopher Penn, co-founder and chief data scientist of Trust Insights, has helped global brands such as McDonald’s, Toyota and others leverage the power of data to level up their marketing efforts.

In this guest post, find out why Christopher believes data science is an essential skill and the implications of data-driven marketing for healthcare organizations.

 

 

The Value of Data Science

Simply put, data science is the extraction of meaning. Using the scientific method, data science helps marketers prove or disprove a hypothesis—and if you’re not using the scientific method, then you’re not doing data science.

To derive meaningful insights from information, data science combines four disciplines—business acumen, domain expertise, technical skills, and mathematical and statistical skills—into one. At the very least, marketers need a solid foundation in technical and statistical skills while partnering with experts in the other domains to ensure better results, lower costs and fewer mistakes.

A fundamental understanding of data science is critical for marketers because it allows them to repeat and scale their successful initiatives. This, however, can be a challenge as marketers typically don’t have a strong quantitative background. We’re often winging it as marketers and while we might get lucky and have a campaign take off, we don’t know why it worked and therefore we can’t repeat or scale the successful initiative—much less make it better. There are many brilliant healthcare marketers out there whose work could be accelerated if they were able to leverage data science, machine learning and artificial intelligence.

On the other hand, there are healthcare organizations doing excellent work through data science, including The Johns Hopkins Center for Health Security. Their researchers help prevent the spread of infectious diseases like coronavirus by looking at code, doing the math and using the latest technology to inform policy decisions and unlock the value of domain experts.

The Intersection of Data and Content Marketing

One of the easiest ways to explain how data science applies to marketing is in the area of publishing. For example, a content strategy typically includes blog posts and white papers that offer information to the end customer in a way that delivers value.

We recently created a white paper titled Social Media 2020 that involved analyzing search and social data to determine whether marketers need a presence on Tik Tok. We crunched the numbers to figure out how many people search for “How to join Tik Tok” as well as “How to quit Tik Tok account” and found that the platform is not growing as fast as it has been. In fact, more people want to quit than are signing up. The implication for marketers: Go ahead and set up an account but don’t invest a lot of time. The data doesn't support diving headfirst into it.

When you think about all the time and resources that go into publishing, the scenario above is a good example of what data-driven marketing looks like. Data science helps you to make decisions and create value for your community using data and research, instead of laboring over onerous peer-reviewed papers to inform your marketing plans.

Healthcare Data Sources

Along with your own research, there are myriad public data resources available to marketers. Almost every country has a government organization that shares a tremendous amount of data. We often use HealthData.gov to draw insights when developing content.

Another one of my favorites is the Agency for Healthcare Research and Quality, which offers a robust data set of hospital quality outcomes. Marketers can see how organizations rank for specific conditions and build their own benchmarks.

The Medicare data set is useful; however, some hospitals do not report certain metrics so about 20–25% of the data is missing. I recommend blending Medicare data with U.S. census data for a more complete picture of hospital ratings and population health.

As a marketer, these and other resources help you understand where to focus your content. You could, for example, translate outcomes data into a travel guide that helps consumers know where to go for specific conditions.

The Ethics of Data Science

Big tech companies such as Google, Amazon and Facebook are leading the way in using data for marketing, but they can also be the most unethical and dangerous. Facebook is a perfect example of what happens when data science is decoupled from ethics. Look at how the Facebook News Feed functions. The goal is to keep users engaged and ultimately to create compulsive behavior. By collecting a tremendous amount of data, Facebook learned that making people angry and afraid all the time is the best way to keep people engaged.

As you apply data science, both you and the institution must have the highest ethical standards as to how you use data and be proactively looking for bias and adverse outcomes. When you see skews in data sets, these can have substantial outcomes down the line.

In health care, our primary imperative is the Hippocratic Oath: First, do no harm. If your marketing is discriminating or causing a bias, you are not following that principle.

Data Science Resources for Marketers

There are relatively few marketing data science resources as the disciplines grew up separately. My personal blog at Trust Insights is one  resource that tries to bring both of these functions together.

There are also organizations like Women in Analytics and other blogs, conferences and Twitter lists where marketers can access data science information.

A few of my favorite resources include:

Along with these resources, one of the most important things you can do is to start following individuals who share a lot of information on data science and can function as information mentors.

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