Where Data Science and Creativity Meet

Data science has become an essential part of modern marketing, but it doesn’t negate the importance of creative, emotionally-sensitive storytelling.

The torrents of data that emerged in the wake of the digital revolution have reshaped a wide variety of industries. According to the Harvard Business Review, “Companies in the top third of their industry in the use of data-driven decision making [are], on average, 5 percent more productive and 6 percent more profitable than their competitors.”

That being said, creating a sustainable, data-driven decision-making infrastructure is easier said than done, and roughly 60 percent of big data projects are abandoned after the pilot phase. In the marketing world, these failures almost always stem from an overeagerness to embrace data science and all its concomitant benefits at the expense of what has always been — and what remains — the core of effective marketing: creative storytelling.

“Data is fundamentally important in the building of a market,” writes advertising executive John Hegarty for the Financial Times. “But what it cannot do is create an emotional bond with the consumer. Data does not make magic. That is the job of persuasion. And it is what makes brands valuable.”

In other words, the best data-driven strategies don’t abandon creativity, empathy, and emotion — the cornerstones of compelling storytelling — but figure out ways to integrate data-driven insights into fundamentally human marketing campaigns.

Learning the Lessons of Behavioral Economics

For marketers, success in the digital age pivots on providing comprehensive customer experiences that are optimized at every touchpoint on every channel. Intuitive, user-friendly interfaces from the likes of Starbucks, Apple, and even Sephora have conditioned consumers to expect high ease-of-use and consistency from every one of their digital interactions, regardless of the context. In fact, 82 percent of consumers report having stopped doing business with a company as a result of a “bad experience,” including things as seemingly trivial as a subpar social media interaction.

But crafting and delivering a top-notch brand experience is complicated by the seemingly obvious fact that audiences are people before they’re consumers. While traditional rational choice theory dictates that consumer decisions are guided first and foremost — or even entirely — by utilitarian cost-benefit analyses (however subconscious), the reality is far more complex.

Pioneered by Nobel Prize-winning economist Richard Thaler, the burgeoning field of behavioral economics challenges traditional assumptions about consumer behavior by claiming that consumers frequently act in ways that defy economic rationality. From a marketing perspective, this means that giving consumers what they want is a fundamentally unpredictable — and always changing — endeavor. If consumers act according to their whims or emotions as much as their rationality, predicting — and, more critically, shaping — their behavior becomes a less formulaic exercise.

Accounting for Consumer Irrationality

Increasingly, facilitating these interactions falls to both media buyers and media sellers, as both are involved in content creation and, as Anush Prabhu writes for Adweek, “equally responsible for delivering innovative ways of driving and lifting impact, engagement and conversion.”

If, following Thaler, we accept that emotion and other non-rational factors significantly influence consumers’ decision-making processes, the question then becomes how, precisely, marketers should approach the structuring of their campaigns. Circumstantial particulars will require any number of slight variations, but there are certain overarching principles that all modern marketers — on both creative and data teams — would be wise to consider.

For example, sophisticated “social listening” can provide marketers with valuable insights into how consumers are making their decisions, rational or otherwise. This is especially useful in the medical marketing sphere, as it offers access to the kinds of conversations that formerly only took place offline and were thus incredibly difficult to track, let alone analyze.

By strategically monitoring social media feeds, online forums, and even exam-room conversations, marketers can deduce what percentage of a patient group is talking about their shared condition, how many patients are discussing a particular treatment option, or what kind of questions repeatedly pop-up among patients. And while data analytics is necessary in capturing and organizing this real-time consumer feedback, it cannot — regardless of an organization’s sophistication — tell show marketers how to engage with these patients. Data reveals who marketers should talk to and where these conversations should take place, but it remains the marketer’s job to talk to their audience in human, emotive ways.

This is where, as paradoxical as it may sound, the human side of data analytics becomes increasingly important. In the same way that consumer behavior cannot always be rationally accounted for, data analysts themselves are also prone to frailties in judgement. As much as we may want to rely solely on statistical techniques to cut through swaths of data and identify meaningful patterns within that data, human fingerprints appear all over the analytical process. Once we see patterns we begin to create stories to explain them — and that’s ok. The challenge comes in identifying our impact on the process and leveraging it toward improved consumer experiences.

Striking the Right Balance

This “division of labor,” so to speak, is the foundation of the increasingly popular problem-solving technique known as “Design Thinking.” Within a strictly scientific, data-driven problem-solving framework, a solution emerges from tests performed on a closed, fully-defined set of premises. Conversely, as summarized by the Interaction Design Foundation, “Design Thinking investigations include ambiguous elements of the problem to reveal previously unknown parameters and uncover alternative strategies.”

This is the fragile balance for which modern marketers of all backgrounds must strive. Data science is unparalleled in its ability to pinpoint problems like ineffective — or entirely absent — touchpoints at critical junctures along the consumer journey, but it is more or less incapable of solving these problems itself. Solutions require the kind of creative, emotionally-attuned problem-solving of which only humans are capable. Creative storytelling and the emotional connections it can foster remain powerful tools to engage customers — specifically within the context of social media platforms that in turn produce data upon which future campaigns may be modeled. Ultimately, data analytics help marketers find problems, but it’s up to us — in all of our emotional, irrational humanity — to solve them.

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