But marketing is getting more difficult. Marketers are responsible for a growing list of digital channels. They must personalize creative and manage target audiences for each one. They’re being held accountable to optimize every dollar of their media budgets.
The marketing data has always been there — consumers’ browsed websites, abandoned shopping carts and some channels performed better than others — but the difference is that now, thanks to new technology, marketers have access to more data and better visibility into it.
No substitute for analytical human beings
It’s fair to say every brand is now collecting customer data. But having it is not enough. Your information is only as significant as you make it.
Indeed, many marketers collect data and find themselves stuck in “analysis paralysis.” It takes advanced technical skills and business acumen to access, organize, interpret and then take action on customer data. Then, data scientists must take the next step to create machine learning tools and provide analysis of a customer’s lifetime value. As a result, we’ve seen a historic rise in marketing science or advertising science roles within brands. Even a great technology platform is no substitute for great analytical human beings.
The art side of marketing — compelling design, great branding — will always be the heart of a brand. But science is the brain. And more and more when it comes to talent needs, the pendulum is swinging toward science.
According to a recent report from LinkedIn, data scientist roles have grown more than 650 percent since 2012. A lot of marketing scientists are really just data scientists with advertising expertise. Many of the most sophisticated brands are investing in “marketing and advertising science” talent. Facebook, Booking.com and Netflix have director and VP-level marketing science roles. Other big digital brands like Uber, Nike, Expedia and Wayfair are hiring marketing data scientists and analysts in droves.
The question, then, for your marketing team is: Are you more art than science? If your answer is yes, you need to upgrade your data science and analytics talent.
Marketing scientists can turn data into dollars
First and foremost, marketing data scientists can help a company by setting meticulous measurement standards and interpreting the results to understand which investments are paying off and which ones aren’t. When it comes to advertising, a great marketing scientist can prove that every channel or tactic has a positive revenue impact by conducting lift studies.
Lift is simply the difference in revenue between a treatment group (who see ads) and a holdout group (who do not see ads). These tests allow advertisers to measure the additional revenue generated by advertising that otherwise would not have existed without the ad spend. Further, such measurements allow marketers to target “persuadables” — users who are more likely to be influenced by ads — rather than waste ad budget on consumers who likely would have purchased regardless of seeing an ad.
Marketing scientists can also take the reins on analyzing consumer website behavior. Most commerce sites contain enough consumer data — product views, add to carts, site visits — to accurately predict within a certain time period if consumers are going to purchase. Knowing precisely who these consumers are will alert the “art” side of marketing about who to focus on with relevant messaging.
The days of the annual awareness campaign are over. Marketing has gotten noisier, more fragmented and more complex. The art side of marketing — compelling design, great branding and storytelling — isn’t going anywhere. It will always be the heart of a brand. But science is the brain. And more and more when it comes to talent needs, the pendulum is swinging toward science.
CMOs looking to edge out the competition should invest in marketing data scientists who can scrutinize user behavior and ad performance and help turn that data into revenue.
Are you optimizing your advertising toward the only performance metric that truly matters to your bottom line? Here’s everything you need to know about incrementality.
Download the Guide