Digital marketing is forcing creatives to become math nerds.
Put yourself in the shoes of a content marketer just five years ago, working to build out a content hub for an enterprise-scale business. For many such marketers, the day-to-day operation of the business was more akin to a publishing pursuit than marketing: editorial planning with writers, meetings to discuss stylistic direction and tone, and the occasional report on social media performance from your amplification team.
But today, as access to points of data continues to grow at an astounding rate, most every marketer is now asked to participate in some way with the collection, reconciliation, or presentation of data. So imagine the dismay of our veteran content manager on the day that her boss tells her he wants to start seeing more regular data-driven reports about what they’re working on and where they’re hoping to go.
Phrases such as “data driven” and “data science” get thrown around the conversation as easily and as often as if they were conjunctions or pronouns, but the content marketer thinks to herself “Isn’t this above my paygrade? Or just completely outside of my wheelhouse?”
Image attribution: Roman Mager
Data, data driven, data science—these buzzwords of modern digital marketing seem to take on new importance and new meaning every day. But what does this actually mean for marketers in the trenches who are supposed to be pivoting their workflows to incorporate data more into their everyday?
Type “data science” or “data scientist” into any job search engine, and it’ll become clear to see why lay-marketers get concerned about the idea of trying to double as a data hound amongst their other responsibilities. Most places looking to fill roles specific to data are seeking out masters- and PhD-prepared candidates, specifically ones with the skills to build and manage SQL databases or program statistical simulations in R.
Thankfully, it’s safe to expect that your boss isn’t looking for you to turn your content engine into a data center for scientific practice. You don’t have to go out to earn a master’s or doctoral degree to stay relevant in a data-driven world. That’s what we have dedicated data scientists for.
This displays a sort of disconnect or inconsistency that marketers have when they talk about data. It seems to have grown naturally from the parallel between marketers being called in to have more data involvement and the field of data science growing—these are happening at the same time. But good content marketers know that words matter, and that (thankfully) what your leadership expects from data-driven practices is probably closer to straight reporting than it is to scientific practice.
But as a creative marketer in any capacity, what if there were a way to meld some data-driven practices with your creative sensibilities? What would this look like?
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To bring your digital marketing reporting to the next level, try to incorporate some of these less-intimidating data science practices into your workflow.
A common mistake that marketers make when working with data is that they’ll observe a apparent trend, interpret that trend, and make an immediate decision about what course of action that should be taken as result. The problem is, trends are often the result of confounding factors or untested means that we aren’t considering when we make a snap decision. Snap interpretive decisions tend to make us vulnerable to confirmation bias, where we interpret data to support our underlying assumptions. Try to set up a simple workflow to institute A/B tests whenever your team is looking to make a decision based on an observed trend. This will elevate your data reporting to (very basic) data science and improve your ability to demonstrate actual marketing ROI.
If your team is anything like the average marketing team, you’re likely using between five and 20 tools on a daily basis to meet your digital marketing needs. It’s also likely that many (if not all) of these tools spit back data for you to use. Much of the time, however, when marketers have questions about their marketing ROI, they aren’t just looking at one discrete set of data; they’re comparing sets of data from multiple sources. Building and maintaining a simple data lake for questions you regularly examine will save you a lot of time when constructing A/B tests or reports and give you a central place to keep an eye on the integrity of your data.
As the saying goes, “Show, don’t tell.” The tendency once you start working with data is to get excited about observations and hypothesis, and then to subsequently flood all of these ideas over to the person you’re reporting to. This not only fails to strongly convey your more salient points, it’s also frustrating for your reportees to listen to. Try to focus on taking the results and observations of you data and condensing them into the most concise, (preferably visual) format you can. Remember that data lake you built? It can be plugged right into Google Data Studio to give you a full suite of free and easy tools to build visual data reports that run automatically. No fuss, no confusion.
Ultimately, data will continue to be an more prevalent part of digital marketers’ lives as time presses on. But if you’re a professional, or in a role that isn’t inherently data oriented, and are worried about this ongoing shift, don’t be. Good data means better and easier marketing for you and your team. All you have to do to participate is continually ask questions, be careful about making assumptions, and seek help when the numbers go a little over your head. The goal is always to use data to help you do your job—not to replace the work you love with something you never thought would be your responsibility.
Featured image attribution: Pexels