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Digital attribution models have transformed the way e-commerce companies measure and optimize ad spend along the path to purchase. So why have medical marketers been slow to adopt them? Kevin Troyanos reports.
Digital attribution models have transformed the way e-commerce companies measure and optimize ad spend along the path to purchase. So why have medical marketers been slow to adopt them?
Imagine you’re scrolling your favorite online news outlet and a banner ad for a shoe retailer catches your eye. You’re intrigued by the pair of shoes on display, but you’re running out the door to work, so you make a mental note to check out the store’s website during your lunch break. After browsing the site later in the day, you find the same pair of shoes and place it in your virtual shopping cart, but decide to hold off on making the purchase until payday. Several days later, you receive an email from the retailer reminding you about your pending purchase and end up completing the transaction on the retailer’s website.
In a situation like this, which touchpoint should get the most credit for driving your purchase? The banner ad, without which you might have never discovered the item in the first place? Or the reminder email, without which you might have forgotten to finish your order? In reality, of course, both ads played a critical role in your consumer journey. The challenge for marketers is to determine exactly how much credit each advertising touchpoint should receive, and to assess how these channels work in concert in order to optimize their overall channel strategy.
Until a few years ago, most e-commerce marketers were measuring conversions using “last click” attribution. A last click attribution model assigns credit for the conversion to - you guessed it - the final touchpoint delivered before the conversion occurred.
But last click attribution lacks the sophistication necessary to assign credit where credit is truly due, which is why it’s largely fallen out of favor in e-commerce marketing. In recent years, online retailers have embraced the full complexity of the consumer journey and begun to turn to more nuanced attribution models that are capable of assigning partial conversion credit, powered by user level data, across dozens (often hundreds) of marketing channels and touchpoints.
But while e-commerce marketers continue to explore new and increasingly sophisticated attribution models, I’ve found that healthcare marketers have been slow to adopt these techniques to optimize their digital marketing mix.
Admittedly, the healthcare decision journey is exponentially more complex than the average e-commerce path to purchase. Rather than browsing a shoe retailer’s website for a style that catches their eye, healthcare consumers are processing a diagnosis, researching a disease, and wrestling with questions of long-term impact and cost. Newly diagnosed patients are searching for answers to questions like, Will this diagnosis negatively impact my quality of life? What kind of management will it involve? How will this impact my insurance premiums? The process of “conversion” then, is understandably less straightforward than that of your typical online shopper.
The intersection of multiple stakeholders in the healthcare decision journey compounds this complexity. Decisions are influenced by not only patients, but physicians, payers, caretakers, and in some cases, the government. Furthermore, unlike the e-commerce model driven by digital product pages, shopping carts, and direct shipments, the “point of purchase” in healthcare commerce (with few exceptions) is not captured via classical digital conversion metrics.
But this doesn’t mean that attribution modeling is completely impossible, or irrelevant. In fact, it means just the opposite.
The process through which patients become aware of different treatments can be lengthy and complicated, and there are countless attribution models that marketers can use to gain insight into the influence of each unique channel or touchpoint. At Saatchi & Saatchi Wellness, my team has found success in leveraging network analytics to interpret, visualize, and simplify cross-channel insights into how patients engage with various touchpoints along their path to treatment.
Attribution may not yet be a perfect science, but a network analytics-oriented approach allows marketers to achieve an understanding of how various marketing channels and touchpoints work together - and how they get stuck in isolation. Some channels tend to work best at early stages in the patient journey - in other words, they raise awareness about a given therapeutic category, or increase knowledge of a specific product. Other channels are more well suited for later stages in the patient journey - for instance, email reminders about support programs that may reduce costs (to ultimately maximize patient outcomes).
The beauty of network analytics is that it can expose the influence of multiple touchpoints across the entire journey. For example, in most cases, general awareness like display rarely lead to last click conversions (say, co-pay card or doctor discussion guide downloads). Traditionally, the assumption would be that display is an underperforming marketing channel and thus isn’t worthy of heavy investment. What network analytics does is drill down beneath superficial - though admittedly important - metrics like last-click conversions and examine the ways in which channels like display advertising are connected to and influence other marketing channels and touchpoints.
In many cases, network analytics reveals how certain early-journey-stage touchpoints actually have tremendous success in priming consumers across other touchpoints. For example, the likelihood that a consumer will click through and take the desired action on a social ad or a paid search ad may be much higher if the consumer has already been exposed to a display ad. Channels like display often do the “dirty work,” if you will; it helps increase the number of exposed consumers at the early stages in the patient journey. Display, then, can be just as important as the other touchpoint delivered throughout the patient journey, even if it’s rarely responsible for a conversion or patient start on its own.
Treatment decisions in healthcare are highly complex and fragmented, and attempting to truly understand a patient’s digital journey from something as rudimentary as a last click orientation ultimately results in a limited understanding of why patients and HCPs behave as they do.
Network analytics does not represent a perfect solution to the challenge of tracking patient journeys. But insofar as it shows us what’s going on at the top and in the middle of the sales funnel, it represents an excellent way for medical marketers to achieve a more nuanced understanding of the complex path to purchase or product adoption in healthcare settings.
Kevin Troyanos leads the Analytics & Data Science practice team at Saatchi & Saatchi Wellness.