Diagnosis: Misattribution. 3 Reasons Why Healthcare Lead Attribution Is Suffering

Mediahawk’s Natalia Selby discusses why healthcare lead attribution is often inaccurate – and why call tracking is vital

Patients are accessing information about healthcare through more channels than ever before, including mobile, social media, voice search and paid advertising to name just a few. Although providing prospective patients with more ways to access a business is a positive step towards more leads, it also means that marketing teams have the difficult task of determining where the leads are coming from and which channels are working. This is where lead attribution comes into play.

Attribution is a vital step in marketing strategies across all industries. How can you tell if your marketing efforts are successful without being able to attribute conversions to particular activities?

Marketing teams in all industries will have some variation of an attribution model in place allowing them to optimise profitable channels and prove the return on investment (ROI) of the campaigns they are running. However, are they using the correct attribution model for the type of business and are they sure they are capturing every touchpoint in the patient’s journey? The answer is probably not.

And here’s why…

1.      Crediting the wrong marketing source to the lead

Until recently many marketers were using a last click attribution model. This assigns credit for the conversion to the final touchpoint in the patient’s journey before the conversion was made.

Crediting only one step in the patient’s journey is a common mistake healthcare (and other) marketers are still making today which could lead them to assign credit to less profitable channels or eliminate any better performing supporting channels.

Consider the journey of a patient. They are searching for a particular cosmetic procedure online and they come across an ad for the procedure in the search results. They click on the ad but don’t have time to contact them. Later in the week they are presented with a Facebook remarketing ad and click again and call the company to book a consultation. In this case marketers may only attribute the lead to the remarketing ad, but in actual fact both channels played a critical role in the patient’s journey.

Without the paid search ad the prospect may not have found the company, but without the social remarketing ad the patient may have forgotten to contact the company altogether or visited a competitor.

Similarly, consider if a company uses offline marketing materials, such as flyers or a newspaper ad, and a prospective patient calls the number to ask a question, later they visit the website and book an appointment. The conversion will be attributed to a direct visit, rather than the initial flyer and phone call both of which played important steps in this customer’s journey. With a last-click attribution model offline material will almost always be discounted, restricting marketers from being able to see the ROI from this activity.

This is why multi-channel attribution is the most accurate model. Marketers are able to view the entire journey, from the first touch point, supporting touch points in the middle and the final touchpoint before conversion, all of which played a vital role in converting the lead.

There are different types of multi-channel attribution models:

  • Linear Attribution model: This model credits each touchpoint in the journey equally for the conversion. This model is ideal for those just wanting to understand the customers’ journey, engagement and for those that have a shorter sales cycle
  • Time Decay Attribution Model: This model assigns more credit to a touchpoint the closer it is to the conversion. Unlike last-click attribution it still credits all the touchpoints, but assigns more credit to the final action
  • Position-based Attribution Model: This model gives more revenue credit to certain stages of the buying journey. It gives more credit to the important touchpoints, such as first-touch and last-click, but still assigns some credit (but less) to the other touchpoints in between
  • Custom Attribution Model: Similar to position-based, a custom attribution model allows the marketers to determine which touchpoints they want to assign the most credit to where you might have more stages in your customers’ journey than is given in position- based models like W-Shape, U-Shape or Z-Shape
  • Algorithmic Attribution Model: This model creates rules for allocating revenue credit based on an examination of all your past touchpoints data and determining which touchpoints are predictive of revenue.


2.      You’re not using the right tools

Google Analytics, when utilised correctly, is a great free tool for attribution, if the majority of your conversions happen online that is. In the healthcare sector this is unlikely to be the case.

Those in healthcare are likely to find that the phone is still the patient’s preferred method of communication. Despite offering online booking forms, contact forms, apps and webchat features, Inbound MD found that 65% of all patients polled still preferred to book an appointment via the phone.

This is largely because of the delicacy of health-related matters, patients want to ensure that they are seeing the right person, are getting questions answered and that the booking is correct. It may not always be the last touchpoint in the patient’s journey to conversion, but phones are definitely a vital step in many patients’ healthcare journeys.

Unfortunately, Google Analytics does not automatically track calls, but additional software, from companies such as Mediahawk who provide call tracking solutions to healthcare providers, is necessary  to be able to accurately attribute phone calls to specific marketing activities.

Call tracking gives marketers the ability to view the touchpoints before and after the phone call was made. For example, a patient clicks on a paid search ad, is taken to the website, calls to ask a question, then visits the website directly later to book a consultation. Call tracking will be able to tell the marketers what keyword led the patient to the paid search ad, would show they made the phone call, record the conversation and see if/how the patient converted after the phone call.

With other attribution models, in the example above, the patient’s conversion would have been attributed to a direct visit to the website; however, the paid search ad played a key part in the conversion, and therefore had a high ROI. With call tracking in place marketers will know this and be able allocate budget to all the most profitable, supporting channels, not just the last touchpoint in the journey.


3. You’re not using network analytics to optimise multiple touchpoints

Marketers shouldn’t rely on Google Analytics alone to track the effectiveness of online activity. Many online channels have their own analytics data, social media in particular, that can be utilised in conjunction with call tracking and Google Analytics to get the full picture across all touchpoints in the patients’ journey.

For example, a patient may see a Facebook ad for a free download of an eBook about a procedure they are considering, they click on the download button which directly downloads the eBook onto their device and once they have read the eBook they call the number at the end to book a consultation.

If the download button does not redirect to the company’s website, Google Analytics will not pick up any of this patient’s journey. The Facebook Ads Manager will be able to tell you the number of people who have clicked on the download button on the ad and, from here, call tracking will be able to tell the marketers the call was made from the eBook through the use of a number specific to this marketing material.

These separate network analytics also help to collect more data about converting patients, i.e. Facebook ads may collect different demographical data to Google ads, but this information can all be put into the CRM (Customer Relationship Management) which will allow marketers to tailor future activity to be more appropriate to these patients and prospective patients coming from the same channels.