Digitally Tracking Consumer Engagement
WebMD partnered with Luth Research to better understand the new patient journey and the unique contributions of WebMD, Search and Social Media that guided a consumer to a particular health system’s site.
Most of WebMD's health system clients and programs tend to market around the patient journey. Since this journey can take several months and include multiple touchpoints, our client needed an approach that could measure WebMD’s unique contributions relative to the broader media mix employed by most health systems. An approach that better helped them connect the dots around patient journey timing and ultimately attribution for the individual health system.
Luth Research’s consumer research products allowed them to create a map of a patient’s path to care. The map helped WebMD understand many things. It was behavioral, attitudinal, and had a lot of implications around audience duplication and attribution.
Discovering the Big Picture of Consumer Behavior
WebMD’s goal was to provide support for a bigger picture of how, where and why they fit into the path to care for health systems. These included both behavioral claims—about what people are doing online—and attitudinal claims—about why they are doing what they are doing—and our study was designed to include an online survey in addition to historical digital data.
This study differed from other digital studies we are likely familiar with – and use for retailers and or brands. Path to purchase studies utilize digital data to follow a consumer’s journey to purchase, be it toilet paper to a new refrigerator. In doing a path to purchase study, a relevant activity—such as shopping in-store or online for a particular type of product or visiting a retailer of interest, is often inherently relevant, and we define the pathway forward from the first activity until we drop off within our time of reference or purchase.
Brands typically are trying to understand how consumers are shopping their category, what’s working with their retail partnerships and what can be improved upon, and where to insert themselves along the consumer journey. If you are a retailer, you’ve likely used this research for benchmarking against your competitors to stay ahead of the curve by knowing what new features on competitor sites are most effective, help your shoppers convert better and eliminate friction points during shopping. In essence, how are people shopping your sites and others – what’s driving conversion or adding friction?
For this project, we had to go backward:
- Started with the end in mind (visit a health system site)
- Developed Hypothesis
- Determined the path to care
- And ended with WebMD proving its importance on the journey
This made it clear where and how WebMD is a valuable source of traffic (digital behavior) and information (survey data) on the way to patient care in a health system. Sounds easy right?
This process, on our end, required a lot of judiciousness and work. Defining a hospital visit involved some simplifications because most members in our sample made more than one hospital website visit. So, we defined the first hospital visit they made as a reference or anchor visit and used the activity in the 6 months leading up to this single visit as our focus of analysis.
The value of this approach was its ability to capture time-based, behavioral insights. By looking back on our defined “conversion” - a visit to a Health System’s universe of sites - we were able to capture key insights such as:
- How long was the overall journey?
- What were the first, middle, and last touchpoints?
- How often did they use each touchpoint during the journey?
This time-based behavioral approach also allowed our clients to gauge attribution in a way they had not been able to do before. They were able to analyze the patient journey and gauge attribution by:
Identify unique and duplicated audiences
Analyze search terms to identify patient intent
View how and where they learned about specific hard-to-pronounce/spell surgical treatments
Analyze how they came across the actual doctor’s name that they then typed in the search engine
These are all important attribution-related insights. By identifying platforms such as search and social, we were able to identify that each played an important role – and because the behavioral analysis helped benchmark patient volume, we were able to provide insights around campaign allocations.
To sum it up we started with the end in mind, developed hypotheses that mapped back to the Patient Journey to Care, and focused on the unique contribution WebMD delivers as part of the overall media mix.
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