By Julia Amorim, CEO | This article was originally published on SmartBrief 12.16.19.
Maybe we should stop using the term “data-driven marketing” until we have a better idea of what we’re driving towards.
Think about what happens the moment you get into a car. Almost instantly you are surrounded with information. This includes how much gas you have in the tank, the speed at which you’ll be driving, whether or not you need to change the oil soon and even whether you closed the door properly. There’s even more when you glance across the dashboard to the radio, the air conditioning system and whatever’s being shown in the side or rear-view mirrors.
All of this represents data, or at least raw data. When we drive somewhere, we pull all that information together and cross-reference it with third-party data sources like traffic lights and where we see pedestrians and other cars moving.
This happens almost instantly if you’ve had your license for a while. Many marketing departments, however, are still working with data at a stage that might be considered roughly equivalent to having their learner’s permit.
While 62% of CMOs plan to increase their spending on data analytics products and services, for example, marketers rank the technology only 4.1 on a seven-point scale in terms of its effectiveness. The IAB, meanwhile, has forecast a nearly 18% year-over-year increase in how much brands are spending on buying, managing and analyzing third-party audience data, or $19 billion.
In other words, it’s not that companies lack availability of, or access to, data. The problem is what they might be overlooking when they see it — and that they might not be looking hard enough in the first place.
How marketing data has evolved (and keeps evolving)
To be fair, it can be difficult for anyone to keep on top of all the potential data sources that are being added to the mix that marketers and other teams within an organization might use.
While programmatic advertising was among the first technologies to offer brands an unprecedented ability to manage where their messages were seen online, almost as much is now happening without consumers sitting quietly in front a screen.
Just think about fitness trackers, smart homes with devices embedded within them, or the growing number of objects with voice-controlled features. All these things represent additional mechanisms to collect information on consumer behavior that could make brands present more relevant, contextual and personalized marketing.
With all that data at their fingertips, then, why aren’t brands able to extract the value they need? One explanation might come from looking back to the ad industry’s early days — some might now say its “Mad Men” days — when much of marketing was focused on “the big idea.”
The stereotypical scene from those days is probably familiar, even if you didn’t live through it personally: an agency exec shows a new piece of creative on an easel, which represents the big idea or theme that will become the focus of a brand’s campaign. It will be original, thought-provoking, colorful and possibly funny.
The client may or may not like the big idea being presented, but if they’re smart they’ll ask a key question: “Why will my customers care?”
As compelling as the big idea might be, in other words, it merely represents the “what.” To be effective, a big idea has to tap into something that speaks to its audience’s sense of purpose — the “why.”
In a similar way, marketers can’t be successful simply by amassing large amounts of data and hoarding it within analytics applications. They need to improve their abilities to identify which data points apply to their organization’s “why” — its reason for existence, its mission and the objectives that contribute to furthering that mission.
This is obviously easier said than done, which is the reason we have increasingly turned to machines to help. Merely purchasing a particular product, however, isn’t the answer.
The quality data that drives quality marketing results
Theoretically, most of the tools on the market should allow brands to better understand things like the path to conversion, if specific messaging or creative resonated, if a particular time of day proved to be a better performer and so on. You should be able to look at what happened in the past to get a reasonable sense of what trends might play out in the future. If the data those tools ingest is incomplete, error-prone or disconnected from other critical sources, though, it will be like wandering through a library that hasn’t been properly catalogued.
Even when the quality of information is there, of course, we sometimes need help navigating it. In a library, we turn to a librarian who has the skills and experience in organizing all the books and other resources. Marketers are now looking more closely at ways to hire data scientists or others who can fine-tune applications and algorithms to assist them in synthesizing raw data into something meaningful.
The end result will be something brands have been using all along to communicate with their target customers: stories. Analytics is not just a matter of pressing a button or hoping an algorithm will do the work. It’s a process of thinking analytically — through a deeply human perspective — to marry data with the nuances of consumer needs and desires that might only have been learned through day-to-day, on-the-job experience as a marketer.
This first phase of data-driven marketing has been largely about showing brands a whole lot of the “what.” Whether they hire data scientists or not, the best of them will continually develop their abilities to get closer to the “why.” And it’s going to make the “how” of marketing – optimizing approaches to targeting and message personalization – more dynamic, more effective and ultimately more fulfilling than ever before.