Move over, data scientists. Columnist Paul Ford explains why engagement science is set to redefine the way marketers do business.
There’s a new kind of job that’s emerging, blending the skillsets of a marketing expert with that of a data scientist.
The “engagement scientist” is seen as an evolution of the data scientist’s role. We can — and should — see engagement science pick up steam within the next 12 months.
So what exactly is engagement science?
Engagement science enables seamless integration between data and marketing strategies. It provides a platform for marketers and data scientists to interact with customers in a meaningful way. Engagement scientists create a better, stronger understanding of how customers interact with their brands, then implement strategic decisions based on analysis.
It converts a reactive approach into a proactive one.
Sound familiar?
A growth hacker may practice engagement science, but by no means should growth hacking be equated with engagement science. Yes, growth hackers and engagement scientists both bridge marketing, product and engineering teams.
However, the primary differentiator of engagement science is that it connects its measurements (like email opens and clicks) directly with marketing decisions through statistical models, as opposed to intuition or accepted wisdom.
An example: A growth hacker might measure email opens and clicks and then choose to send a specialized email to those who clicked on, say, the last two emails because it is a “best practice.”
An engagement scientist, on the other hand, would build a prediction model for what success means, then send each email to the specific target that maximized its predicted value.
Engagement scientists use a statistical model, not just intuition or a “best practice.”
How Do I Create This Role?
Engagement science doesn’t have to be a role. It can be, but more importantly, it’s a bridge connecting data science, engineering and marketing.
A data scientist, for instance, excels at drawing insights from data sets, but an engagement scientist takes it a step further and ensures that the analysis turns into value for customers and, as a result, your brand.
Think of an engagement scientist as a multiple-hat-wearing mind. This person (or team) understands what constitutes value and success to marketers and knows how to measure it. An engagement scientist also grasps statistics and can identify the difference between cause and correlation.
For instance, just because something correlates doesn’t mean it was the cause. A common mistake is not recognizing this difference, then misusing the data.
Bridging the gap between these skill sets could come in the form of a brand-new individual, but it could also be an investment in already existing teams and people. What if your existing data scientists don’t understand what makes a good marketing campaign?
Invest in training and initiate more interaction between the marketing and data science teams.
What if your marketers don’t understand statistics 101? For starters, suggest they pick up a copy of “The Signal and the Noise” or “Freakonomics.” If that doesn’t suffice, find time for a crash course in statistics — Yes, this could even be taught by your data scientists.
Ready, Set… Not So Fast
Marketers and data scientists speak different languages. Discussions can get lost in translation when you start talking data sets to marketing teams and marketing concepts to engineers.
From my experience, the most fruitful discussions happen at the product level. More often than not, each team member has already worked closely with product language and understands the ins and outs of specific projects.
Like all new spaces, engagement science isn’t yet well understood. This can result in slow or seemingly no progress at first. Everyone involved should approach this new space by being 100 percent honest about what he or she does and doesn’t understand.
Why Should I Care?
Companies from the startup phase to the enterprise world should evaluate the value that engagement science could add.
Why? Because engagement science seeks to seamlessly support the execution of marketing strategies with data through measurement and prediction.
Companies should already be measuring engagement from customers. But the next step is predicting that engagement ahead of time and then integrating those predictions directly into the execution of the overall marketing strategy.
Final Thoughts
Engagement science requires a paradigm shift. Viewing a data scientist or a marketer as another tool isn’t the correct approach. Instead, we need to see this person or team as representing a whole new perspective that can redefine the way brands do business.
Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.
