Empirical Marketing: Combining Behavioral Insights and Marketing Analytics to Optimize Pharmaceutical Marketing Effectiveness

Contributed by:

Brad Davidson, General Manager, Ogilvy CommonHealth Behavioral Insights and Iyiola Obayomi, Senior Direct of Analytics, Ogilvy Healthworld

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Behavioral research and marketing analytics typically function as independent, data-driven marketing disciplines. Integrating these offerings should yield significant, tangible increases in marketing effectiveness. A Tale of Two Data-Driven Worlds Marketing and communications have long relied on market insights to help focus their activities. To generate these insights, departments with names like “market research,” “consumer insights,” “marketing science,” “campaign analytics,” and “database analytics” have become part of the landscape of any large advertising agency or pharmaceutical company. These disciplines can be roughly divided into two categories, which we will refer to as “insights” on the one hand and “analytics” on the other. Insights are the in-depth qualitative research disciplines, which focus on human behaviors and small-n studies of how people talk, act, live, and think, and which are used to identify marketing opportunities and challenges. Analytics refers to the quantitative disciplines that apply mathematical analyses to large-n datasets, to capture and interpret large-scale patterns of behavior, identify marketing opportunities, and quantify advertising effectiveness. In short, analytics frequently is best suited to answer the “what” while insights answers the “why.” The two have many similarities, evolving as they have to serve similar marketing masters, and having at their core a commitment to both the scientific method (hypothesis, experimentation, analysis) and also empirical observation as the appropriate means to inform that method. However, for many years now, the two have been seen as different, if not diametrically opposed, approaches to shaping marketing efforts. Many potential causes explain the separation of these groups: divergent disciplinary and methodological history, differences in the core methods and approaches, differences in training and self-selection based on qualitative versus quantitative preferences, discrete buyers of the services, as well as non-paired demand for these services. These factors help explain post-facto why we do not combine insights with analytics more routinely, but none of them explain why we don’t work harder to overcome this division. The historical separation of marketing insights from marketing analytics has led to a fossilization of a divide that has no real reason to exist — we don’t work together because we simply don’t work together. As far as we can tell, that’s the only real reason for the division. When insights and analytics have been well-combined, both disciplines provide a mutually reinforcing, empirical perspective to the same problem. It’s the difference between looking at an object with one eye closed, or with both eyes open. Two eyes, two different viewpoints, provide perspective and depth to the same object of study that would have looked deceitfully one-dimensional otherwise. In this article, we are proposing that the healthcare communications industry would be well-served by a more deliberate and organized pairing of insights research and quantitative analytics. Specifically, we believe that designing marketing campaigns that leverage both insights and analytics will create better, stronger campaigns that make efficient use of shrinking communications budgets. We don’t want to exaggerate — there are examples, all over, of conjoint qualitative and quantitative studies, but more often than not these are discrete components of a larger “cafeteria” plan of research, where components are selected for a “best-in-class” rather than “best-system” approach. Our goal is to make planned collaboration between insights and analytics the norm, not the exception. Designing Research to Create Complementary Insights We can pair insight and analytics in many ways, in terms of timing and ongoing data generation, but coordination and planning are key right from the start, to ensure that there is indeed a plan and that communication between sets of researchers and data takes place effectively. It doesn’t help to look with two eyes if one eye is wearing sunglasses and the other isn’t. The examples below are by no means exhaustive, but are some obvious (to us) ways that insights and analytics can be combined: 1. Operationalizing campaign ­optimization: Insights informing ­analytically driven marketing ROI ­studies Ultimately, pharmaceutical marketing campaigns and communication objectives intend to change audience behavior. Pairing quantitative analytics with qualitative insights allows us to both create materials that are likely to hit at the right place and the right time (aiming before we shoot) and then determine what has worked and what hasn’t, to optimize our work in an ongoing fashion. This one is almost a no-brainer, and is discussed frequently as a model to follow — rarely, however, is the proposed collaboration laid out in a planned iterative fashion, which is what we believe is the best way to apply this approach. To create the best initial campaign and to see how well a campaign has achieved its goals, insights conducts original research on a representative sample while analytics evaluates large data sets of activities and behaviors across online and offline channels. Analytics conducts its analysis more frequently because its datasets are typically captured within campaign data streams that occur frequently and at relatively lower cost. The solution for bridging the cost and frequency gap in order to provide the analytics and insights perspective has resulted in the identification of leading indicators: analytics metrics that have been found to correlate well with perceptual metrics. In lieu of conducting perceptual analysis frequently, the collaboration of insights and analytics in creating these leading indicators provides a stronger program performance metric than either discipline can provide alone. The important lesson here is that we are not guessing as to what the numbers mean, or making inferences based on nothing other than speculation (“They drop off right here —perhaps it’s because they got bored…”). Rather than have analytics optimize campaign behavior on response data alone, insights allows us to understand the “hows” and “whys” of the campaign impact on behavior, which in turn allows us to react and maximize impact for round two. Integrating these two disciplines to identify the most important leading indicators for behavioral outcomes yields better results. 2. Modeling economic implications of qualitative consumer research: ­analytics providing context to ­ in-depth qualitative insights Predicting the economic outcome of marketing and communication efforts during the campaign planning phase requires appropriate forecasting and modeling. Large variations in brand perception and adoption rates could translate into economic gains or losses. As a result, insights formulates specific hypotheses and explores the intent and likelihood of adoption as well as other underlying perceptual maps. The outputs feed into the analytics economic model, spitting out ranges of economic scenarios and performance hurdle rates to meeting specific ROI targets. Post launch, insights conducts in-market studies to understand the actual rates, which are feedback into the analytics model for an updated in-market ROI performance as well as potential interventions. For the numerous pro-forma models we have built for clients, including several that estimate the economic potential of DTC advertising, the models have relied on knowledge from the insights team. Such pro-forma models have included anticipated behavior and ad-response impact with the ability to calibrate the expected along with the aggressive and conservative scenarios. It’s important to update these models throughout the campaign’s duration with in-market research to track progress on the predicted ROI outcome. 3. Total segment understanding for ­investment priority and message ­relevance Segmentation, the categorization of an audience into business-relevant, homogenous, addressable groups, has been a fertile ground for integrating analytics and insights. Effective segmentation should combine a quantitative value-based approach with a qualitative attitudinal (belief/motivation/perception) based approach. The value-based segmentation provides the current and potential value segments to help allocate investment and priorities for a marketing program, while the attitudinal dimension provides the needed qualitative perspective to inform communication, frequency, channel preferences, and tone. Although after the segmentation, ongoing response-based analytics can help adjust the program on an ongoing basis, the initial program design is more robust when built on both the qualitative and quantitative dimensions. For example, the analytics team will segment a group of physicians based on prescribing habits, office size, and the number of patients seen, the insights team will improve the segmentation by layering on behavioral characteristics like willingness to adopt new medications or valuing safety of a drug over efficacy. The combination of these two valuations enables a strategic, custom look at the target audience, ultimately making a campaign more effective. As mentioned earlier, these are by no means exhaustive examples of the chance collaborations of these disciplines. But we argue that rather than relegate such collaborations to chance and “work together if you like,” marketers and their consultants stand to gain significant effectiveness by institutionalizing the pairing of insight and analytics. Transitioning from Incidental to ­Deliberate Collaboration Adapting research to aid ongoing marketing effectiveness will be a priority for the pharmaceutical industry for many years to come. Almost nothing can be taken for granted in terms of channel impact, message targeting, or even who is going to make what healthcare decision. In this new world of shrinking budgets, customer empowerment, and fragmented channels, marketers who can take advantage of the combined power of the empirical marketing disciplines of insights and analytics stand to realize better marketing effectiveness. To make close coordination of insights with analytics the norm will require a fundamental shift in placement of these offerings, in both the mental and physical landscape. Scopes should be built as a coherent whole, which is difficult in a piece-work environment but which would help dramatically increase the efficiency of market research spend. Put another way, two eyes are better than one. Empirical Marketing: Combining Behavioral ­Insights and Marketing Analytics to Optimize ­Pharmaceutical Marketing Effectiveness In this new world of shrinking budgets, customer empowerment, and fragmented channels, ­marketers who can take ­advantage of the combined power of the empirical marketing ­disciplines of insights and ­analytics stand to realize better marketing effectiveness. Experts Brad Davidson, General Manager, Ogilvy CommonHealth ­Behavioral Insights — part of Ogilvy ­CommonHealth Worldwide. Ogilvy CommonHealth Worldwide — the health behavior experts of Ogilvy & Mather — is committed to creativity and effectiveness in healthcare communications, everywhere. For more information visit ogilvychww.com. Iyiola Obayomi, Senior Director of Analytics, Ogilvy Healthworld — part of Ogilvy ­CommonHealth Worldwide. Ogilvy CommonHealth Worldwide — the health behavior experts of Ogilvy & Mather — is committed to creativity and effectiveness in healthcare communications, everywhere. For more information visit ogilvychww.com. Ogilvy CommonHealth Worldwide — the health behavior experts of Ogilvy & Mather — is committed to creativity and effectiveness in healthcare communications, everywhere. { For more information visit ogilvychww.com.

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