Leveraging Analytics Throughout a Fluid Product Lifecycle

Most marketers recognize the value of data analytics as a retrospective tool, but far too few extend their analytics programs across entire product lifecycles.

One of the many benefits of working on the analytics team at Saatchi & Saatchi Wellness (SSW) is our unique position at the center of a massive marketing organization — Publicis Groupe, SSW’s holding company, is the third-largest advertising and public relations company in the world. By virtue of this positioning, we are able to work with our clients not just on a project-by-project basis, but throughout their entire business — from ideation and R&D to market launch and beyond.

This level of end-to-end service can be difficult to provide, but we have the resources and experience necessary to offer our clients strategic guidance regardless of their place in the market or their stage of product development. This begins with communicating to our clients that data analytics are less a one-off, perfectly timed analysis than an ongoing process that takes place at every stage of the product lifecycle.

The Breadth of Data Analytics’ Potential

For the most part, healthcare marketers have traditionally viewed analytics as a tool of retrospection — an exercise that helps them understand what happened with a particular campaign and why. Marketers tend to pose questions like, “Did our campaign reach the right people?” or “Which targeted segments engaged with the campaign?” or “Was engagement higher at a specific time or in a specific geographic region?” While these are important questions to ask, they barely scratch the surface of what modern analytics can do for us.

In fact, analytics are just as powerful of a mechanism for looking forward as for looking backward. By leveraging a host of cutting-edge tools and techniques — machine learning algorithms foremost among them — healthcare marketers can shift their analyses from the realm of the purely descriptive to the realm of the predictive (and even prescriptive).

Sophisticated predictive analytics enable marketers to craft and deploy hyper-targeted campaigns that forecast both business and healthcare outcomes at both the patient and physician level, giving them insight into everything from the likelihood that a physician will try prescribing a new therapy to the probability that a patient will adhere to a therapeutic regimen once it has been prescribed.

An Analytics Program for Every Kind of Question

This, ultimately, is how the SSW analytics team approaches our work. As part of a recent episode of NYU’s Data Science Demystified podcast, SSW Group Director for Analytics & Data Strategy Allegra Mira pointed out, “In our capacity as ‘analytics folk,’ we think about our work in terms of the product lifecycle.”


Under the SSW umbrella, we’re constantly working with numerous brands and products that fall into a wide variety of categories. Each of these categories has its own set of dynamics, meaning each of our clients has different concerns and different goals. “Let’s say we have a brand that’s in the process of going to market,” Allegra offered. “The brand stakeholders are going to want to understand as much as they can about the market niche they’re attempting to enter. They’re going to want to understand who the other players are and how they’re investing in tactical strategies and promotion. On the other end of the spectrum, they’re also going to want to understand how likely the target patient group is to seek treatment, what kind of physicians or other HCPs [healthcare practitioners] work with this patient group, and how likely these physicians or other HCPs are to prescribe certain kinds of treatments.”

With the right predictive analytics tools and techniques, we are more often than not able to deduce nearly all of this information before a client goes to market, empowering them to tailor their rollout strategy to the exact audiences they’re after. Of course, category dynamics change, patient and HCP priorities shift, and major new players enter the marketplace, but a strong analytics program is agile and adaptive enough to provide close to real-time adjustments to previous conclusions.

In short, effective data-driven marketing is inseparable from deep, creative, and ongoing analysis. Sometimes this analytical component is descriptive — how a product rollout affected a particular market niche, for instance — sometimes it is predictive — which HCP group is most likely to prescribe a new product — but whatever shape it takes, it should be the driving force behind brands’ marketing strategy from start to finish.

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