Marketers must look beyond engagement in order to realize the immense potential of descriptive and predictive analytics in marketing.

In summing up her company’s adoption of predictive analytics in 2014, Cox Communications Director of Advanced Analytics Parimala Narasimha explained, “What we are really trying to do is to provide business value…The [predictive] model guarantees that by reaching only a small section of your customer base — say 30 percent — you can reach something like 80 percent [of the people who make purchases].”

As this explanation highlights, predictive analytics tools provide marketers with a viable solution to the problem posed by the Pareto principle, that is, the idea that roughly 80 percent of a company’s business comes from roughly 20 percent of its customers. At any given time, only a fraction of a company’s audience of potential customers is primed to convert, just as only a fraction of its current customer base is at risk of churn, and pinpointing which consumers fall into which categories — and tailoring communications with them accordingly — is essential to raising the bottom line.

For a variety of reasons — complex customer (i.e. patient) journeys, convoluted purchasing decision structures, etc. — this targeting precision is uniquely elusive in my field, healthcare marketing. This complexity is why predictive analytics holds such promise for our vertical in particular. That said, realizing the full potential of predictive analytics in healthcare marketing will require paying closer attention to what we are using our tools to predict.

Foresight for foresight’s sake amounts to little more than a party trick; what we need to do is focus on predicting behaviors that are not only specific and actionable, but actually impactful in improving patient outcomes. This will require a reconsideration of the way we leverage our predictive analytics tools. In addition to using them for proven use cases like improving patient engagement, we must now strive to use our predictive analytics to drive tangible improvements in healthcare outcomes, as well.

Parlaying Engagement into Outcomes

As Narasimha pointed out, generating real business value should be the impetus for adopting predictive analytics tools. Unfortunately, this is not the way things have always played out in marketing — particularly in my field of healthcare marketing.

Taking a cue from their counterparts working in the broader consumer sphere, many healthcare marketers have opted to use their predictive tools to maximize vanity metrics like impressions or click-through rates. These upper-funnel metrics help marketers get an idea of the reach and quality of a campaign, but they don’t provide much insight into whether the campaign is actually changing behaviors. In short, they speak to the breadth of brand-patient conversations, but they’re mute on each conversation’s depth and quality.

This is not to say that upper-funnel, engagement-level behaviors are irrelevant. Far from it. For example, at Saatchi & Saatchi Wellness, we’ve built tools that leverage a combination of  machine learning and natural language processing to predict the the level of engagement our clients’ social content will generate (for example likes, comments, and shares) prior to going live in- market. Tools like this enable our creative teams to fine-tune our social strategy with unprecedented precision, which in turn helps maximize our investments in those channels.

Deploying predictive tools in this manner is not itself problematic, but marketers run into trouble when they call it a day after simply maximizing likes, comments, or shares. In healthcare marketing, for example, our success hinges on the extent to which we’re able to nudge our target audiences into taking a concrete action to lead healthier lives— whether it’s visiting a healthcare provider, filling a new prescription, or adhering to a therapeutic regimen. In the general consumer sphere, broad-based “brand awareness” is valuable unto itself (because just about anyone is a potential consumer of, say, candy or toothpaste), but in healthcare, brand awareness only matters if it causes the right patients to act on the information they’ve been given.

Generating this very particular kind of action-inspiring awareness among the specific set of patients to whom a drug is relevant involves reconsidering the way we think about leveraging predictive tools. In short, successful healthcare marketers use the insights they generate in predicting — and subsequently measuring — engagement-level behaviors to inform the way they go about predicting outcome-level behaviors.

Taking Predictive Analytics to the Next Level

“We have to go one step above reporting. We need to understand what our customers are going to do next,” Narasimha concludes. Ultimately, adopting a service-oriented mindset — that is, one designed to preemptively address consumers’ problems by focusing on driving concrete actions and outcomes instead of mere brand awareness — will enable marketers in every industry to increase the tangible business value they achieve with their cutting-edge, albeit not panaceaic, predictive analytics tools.

Written by Kevin Troyanos

I lead the Analytics & Data Science practice at Saatchi & Saatchi Wellness. I have focused my career within the healthcare marketing analytics space, empowering healthcare marketers with data-driven strategic guidance while developing innovative solutions to healthcare marketing problems through the power of data science. I’ve worked to measure, predict, and optimize marketing and business outcomes across personal, non-personal, digital, and social channels. I’ve led engagements with brands that span all stages of the product lifecycle, with a particular focus on established brands. My role is to guide the departmental vision and lead innovation initiatives, effectively positioning marketing analytics as a competitive differentiator and organic growth driver for the agency at large.

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