Data Science for Content Marketing with Christopher Penn

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 con­tent 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 Science 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 Content Marketing

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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