Your company may not employ data scientists today. And you might be fine with that. Consider, though, that if you’re not working with a data scientist or at least thinking like one, you’re missing something: the ability to say “I know” instead of merely “I think.”
That distinction matters when you’re talking with executives, says content marketer and consultant Katrina Neal. “If you walk into a meeting in a next-generation data-driven organization and announce, ‘I think this campaign is going to work,’ you could risk being humiliated in front of your colleagues and asked to leave the room.”
On the other hand, if you walk in with what you know, people listen. They might even approve your budget.
Katrina spoke on the importance of data science and why data scientists need to be marketers’ new best friends at Intelligent Content Conference (see that talkhere) and Content Marketing World (see that talkhere). This post summarizes her main points. All images come from her slides.
Why does data science matter in content marketing?
I like this straightforward definition of data science: the practice of “surfacing hidden insight” using data in a way that helps “enable companies to make smarter business decisions.”
Smarter business decisions come from better predictions. As a marketer, when you think like a data scientist, you make predictions that keep shareholders happier, you make customers happier, and you increase respect for your profession. Your content teams make better decisions, you build support for the content initiatives you propose, and your company gets more value from its content.
As NewsCred’s Neil Barlow says, predictability is “how the world’s largest brands continuously delight Wall Street investors and increase stock prices. Within many businesses, CMOs are under particular scrutiny to transform marketing from a cost center to a predictable profit center.”
When you work with (or work like) a data scientist, you build predictive models that enable you to say “I know” instead of “I think” – an important part of transforming marketing into a
What is a data scientist, anyhow?
A data scientist is someone skilled in math, tech, and business, as shown in this diagram:
Data scientists care about three types of analytics:
What are marketers doing with these types of data today?
Most marketers collect descriptive analytics. This data, gleaned from a tool like Google Analytics, gives a sense of what has happened – the historical results such as cost per link, click-through rates, and so on. Looking at this kind of data is a bit like looking in the rear-view mirror of your car.
Few marketers use predictive analytics. This data enables people to predict the most likely outcome based on historical and real-time data. For example, using predictive lead scoring can give your hard-earned marketing-qualified leads (MQLs) the best start with your sales teams. It’s a bit like using a navigation app that predicts your car’s arrival time, updating the prediction on the fly as circumstances change.
Prescriptive analytics kick things up a notch, beyond where most marketers are today. This kind of analytics tells you not only what’s likely to happen but what you should do to capitalize on what’s likely to happen. It’s a bit like an autonomous driving car that not only predicts your arrival time but also drives you to your destination.
How can data scientists help you?
Data scientists (or a data-science mindset) can help you plan your content, refine the content you create, and measure your results by building predictive models using a number of techniques and statistical models.
Planning your content
Data scientists can build predictive models to make content marketing more effective. Here are some common things these models predict:
-Total addressable market (TAM)
-Segmentation and account selection
Refining the content you create
Whether you’re working with a data scientist or cultivating your own inner data scientist, Katrina emphasizes the importance of testing. Scientists in any field start with a hypothesis and then test against it to see what they can learn.
You might do A/B testing, serial testing, or whatever kind of testing gives you the feedback to determine what’s working and how well.
For example, LinkedIn conducted A/B testing on sponsored content – native advertising in the feed – to determine which word performed better, “guide” or “e-book.” The post that used “guide” had a 95% higher click-through rate. In a similar test, “register” outperformed “join” by 165%.
Imagine having quantifiable certainty that one word performs better than another to help optimize your demand-generation engine. Even data that simple could make a difference in your content’s performance.
Test and learn. Test and learn. That’s what a data scientist does, and that’s what you must do, too, to make content-related decisions that you can defend with confidence and credibility. Katrina’s advice is to do your homework: “Don’t rely on generic content best practices. You need original thinking and a test-and-learn culture to find your own unique blueprint that works for you.”
Fellow CMWorld presenter Alicianne Rand essentially summed up Katrina’s main message when she said in her own talk (How the Estée Lauder Companies Drive Sales Through Content Marketing), “All the best modern marketers I know know how to look at data. They don’t need to be data scientists but they know how to ask the right questions.”
As Katrina says, “The best bit is that any of us can think more like a data scientist.”