Health Data in the Programmatic World

This blog post is the fourth and final of a four-part series which will help you better understand the world of programmatic, how it works, and how it applies to the health industry. For more information on programmatic for health marketing, listen to our Programmatic Podcast.

In the last post of this series, we covered the different programmatic buying options and their pros and cons. In the final post of this series, let’s talk about the buzzword that you hear every day in marketing: Data. 

Data is driving marketing forward into unprecedented levels of relevance and usefulness, especially in the Health sector. Patients can discover medications, healthcare providers (HCPs), and other Health-related information before they even realize they need it. This is the power of data when combined with programmatic technology for Health. 

One of the primary benefits of programmatic advertising is the massive amount of inventory available to reach wide audiences at scale, while also being able to hypertarget within that audience to find the right patient, provider, or other desired audience member. This means that Health brands can reach the audience segments that are more likely to perform a specific health action, like treating a specific condition such as Type 2 Diabetes. When you are hitting these segments, you are also reducing media waste and inefficiency, which is great for everyone involved — your budget is kept tight, your ads are more effective, and users are seeing ads that are actually meaningful to them.

An issue that faces direct-to-consumer (DTC) Health marketers is the abundance of regulations regarding privacy and sensitive conditions. All patient targeting needs to be fully HIPAA compliant and follow these regulations. The same goes for marketers targeting HCPs. Be sure to fully review these regulations when planning your programmatic media strategy, and get more information on regulatory practices in the Programmatic Health 101 white paper. 

Still, programmatic technology and audience data offer an opportunity so beneficial to all parties that the preparation required is clearly worth it. So, when you’re ready to determine what types of data you need to use to reach your target audiences, find partners who can connect you with the right data sets. While you may have a good start with your own first-party data, there are numerous sources of third-party consumer and HCP targeting data. 

Below are some of the key types of data used for DTC and HCP targeting:

• First-Party Data: Information owned by the advertiser, often provided by consumers or HCPs when registering on websites or other similar actions.
• Contextual Data: Information obtained based on the specific type of content a consumer or HCP is exposed to upon visiting a website (e.g. an endemic web page with information about Type 2 Diabetes). 
• Demographic Data: Information such as age, gender, ethnicity, and more (Note: Not available for HCP targeting).
• Geo Data: Information such as geographic region, DMA or zip code (HCP example: conference attendees).
• Behavioral Data: For consumers, information such as online visitation, engagement, ad exposure, and purchasing behavior across desktop and mobile sites. For HCPs, information like script-writing behavior, diagnosis behavior, and more.
• Modeled Data: A combination of various data sets to pinpoint consumers or HCPs who are more likely to take a specific health-related action.
  

Okay, so you know what data is out there for you to use, but how do you manage this enormous amount of information? Your answer is a data management platform (DMP). A DMP is a digital tool and resource that acts as a warehouse, collecting data from first-, second-, and third-party data sources. It analyzes and organizes the data, providing insights and defining audience segments toward which you may push your future marketing efforts. 

Don’t worry — this DMP isn’t rivaling any demand-side platforms (DSPs) you may be using. In fact, DMPs and DSPs often go hand in hand, and most DSPs include some DMP functionality. Typically, you can develop custom audiences and lookalike audiences by using DMP functionality within a DSP to expand the scale of any audience you are targeting. You can also build out your first-party audiences with audience collection mechanisms in DSPs and easily transfer audiences between standalone DMPs and DSPs. Together, they’re a powerful pair for audience segmentation and targeting. 

Utilizing a cross-screen vendor amps up the power of a DMP even further, allowing you to continuously identify and target a unique user across all of the devices they use. Having cross-device unique user reach lets you better control frequency and messaging to individuals you want to reach. 

Another relevant term you should be aware of when working with DMPs is data onboarding. When you onboard your data, it means you are transferring offline data to an online environment for marketing purposes. 

Two common use cases are:

• Pixel or Javascript code: Tagging first-party data to collect key elements from advertisers’ digital touchpoint, across desktop/mobile web pages and other tactics like email.
• Offline Data and CRM: Managing offline actions like in-store purchases and loyalty cards, as well as Customer Relationship Management (CRM) data.
  

To bring data online, a “match” must be made between offline profiles and online profiles using cookies. With offline and online data matched, marketers receives a more holistic view of their consumers. 

Here’s an example of why this is so helpful in Health: Say you have a target list HCP audience, and you want to segment the HCPs on that list based on their prescribing behaviors from other marketing efforts. This segmentation occurs prior to onboarding, and DMPs make is easy to apply this segmentation throughout the lifespan of a digital marketing investment. Once this is done, the data is onboarded, and your marketing efforts can be accomplished in a much more effective way. 

Data and DMPs give marketers a leg-up in their efforts to target specific audiences and deliver messaging that will be as efficient and meaningful as possible. In Health, there are many examples of how data is already changing the way marketers find success with programmatic. There are claim space data providers who model audiences based on specific disease states or ICD9 codes. Lab-based data providers establish custom target lists of doctors. 

The most unique challenge for healthcare marketers when it comes to data is scale. You’re not looking for a hundred-million people, but a couple of million at most, usually. However, it’s not just that you may have found the users you want, but it’s asking if you found them in the right context, or in the more premium spaces where you can present them with a 30-second video ad versus a basic 300x250 banner ad. 

The other challenge goes back to the regulations that hamper Health marketers’ efforts — many marketers who advertise drugs need to determine if a disease state is considered sensitive. If it is, you shouldn’t be retargeting based on it. The best plan is for every brand to make a clear decision on this matter and think about how the patient might feel if they are retargeted for a specific disease state. Be sure to review the Network Advertising Initiative’s (NAI) “Sensitive Data” definition in their Code of Conduct, which can be found in the Programmatic Health Council’s Glossary. 

Programmatic is always advancing, but it’s also becoming more complicated and more sophisticated. We hope that this blog series has helped to introduce you to the basics of programmatic technology and its usefulness for Health marketing. To keep up with what’s new in programmatic for health, stay tuned to PulsePoint’s blog by subscribing to our newsletter. Thanks for reading, and be sure to check out the Programmatic Podcast.