has captured our attention and spurred us into action.
Advanced Data-Driven Targeting Can Help Pharma Marketers Think Outside the Box
Digital ad spending for the US healthcare and pharmaceutical industry has been on the rise in recent years and is projected to increase through 2017 where it’s expected to hit almost $1.5 billion. However, while the industry’s investment in this channel grows, its piece of the total spending pie is forecasted to remain fairly flat (source: eMarketer, Aug 2013). It’s no secret that this industry faces many obstacles, not the least of which involves data privacy. But advancements in data-driven targeting tools can help pharmaceutical and healthcare marketers overcome these challenges and reach more qualified patients safely and cost-effectively.
Traditionally, healthcare marketers have executed digital campaigns using health-specific publishers’ first- and third-party data, at least some of which is usually self-reported. Taking it a step further with lookalike modeling still often involves an endemic focus. While this approach is a way to be contextually relevant and stay within specific budget parameters, it limits reach and does not truly recognize that target populations come in all shapes and sizes. There are ways to harness rich data in house and externally to expand the opportunity while upholding a high degree of targetability, thus translating into bigger impact from and justification for the marketing spend.
Data modeling techniques are being used that identify consumer-level variables (non- prescription related) that are correlated to a person’s likelihood to take a specific health-related action – such as adopt a particular Rx medication. These models can be used to score and prioritize investment against the CRM database, target media buys, and prequalify third-party lists, as examples. While there certainly needs to be care in the creative delivered, privacy concerns are alleviated because only the demographic and psychographic data are used in the application of the model. Lookalike modeling leverages datasets of audiences that already exhibit desired behaviors as the basis for identifying other consumers with the same characteristics. Sample datasets include anonymized patient lists based on transactional, not declared, data or a segment of website visitors who completed a specific set of high-value actions. Overlaid with other profile, behavioral, and attitudinal dimensions, the resulting insights inform many aspects of the marketing program from targeting to creative to investment strategies.
From an infrastructure standpoint, data management platforms (DMPs) provide a centralized solution where data can be collected, compiled, analyzed, and triggered to find consumers, whoever they are, and engage with them wherever they are and on whatever topic they want. According to Forrester, half of US online adults use at least three Internet-connected devices and access the Web multiple times each day from multiple physical locations, and DMPs have the ability to reach them via their demand-side platform of online media partners. If the debate still exists as to whether healthcare-specific audiences are online, the Crossix Digital Impact Study, 2013 shows many therapeutic categories where online audiences over-index on prescription and OTC purchases as compared to the general population. These platforms go beyond digital, too, providing the capability to integrate real-time, in-depth insights from social data and leverage TV viewership to maximize the efficiency of your broadcast strategy.
There are targeting and measurement tools being used today that can help the pharmaceutical and healthcare industry activate and engage with more of their patients no matter where they are while adhering to privacy guidelines. With some planning and the right infrastructure, these relationships get off to the right start and will provide value for both the patient and the brand.
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