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'Batch and Blast' vs 'Always-On' : A Segmentation Showdown
Healthcare marketing is somewhat, let's say, 'behind-the-times.' Constrained by small marketing teams, government regulations, and slow adoption to modern marking practices, healthcare marketing is in need of a re-vamping. The advent of Eloqua within my organization is being implemented to address that issue. A transition from old 'mail shop' practices of batch-and-blast, into the more modern marketing practice of always on communications. A new way of thinking; a new way of marketing.
So what's the goal? What are we trying to solve for?
The goal is to communicate the right message to the right person at the right time. A transition from 'speaking' to 'listening.' A transition from all 'push' communications to a healthy balance of push, pull, and engagement based communications.
Our proprietary CRM is loaded with a myriad of predictive modeling capabilities / algorithms that take in patient data and output various patient scores and risk levels. These risk levels, and a healthy dose of other demographic data points, were the primary data points used for campaign segmentation criteria. The individuals that matched the specific criteria were pulled into a list, uploaded to our former automation platform and sent (batch-and-blast style.) Sometimes, our IMM's would include a simple 'drip' follow-up e-mail a few days later if no action was taking on the first e-mail send. Once that follow-up e-mail was sent, the individuals were exited from the program and were never communicated to on that service line again.
There are many things wrong with the approach stated above, but this was the crawl stage of the transition from a mail-shop to a digital marketing agency. With Eloqua, not only can we use the predictive models to inform who is at risk for certain service-line messaging, we can reinforce the messaging by capturing additional data points such as 'digital body language.' For example, who is more likely to engage in an e-mail communication: An individual that is pulled solely from predictive models based on clinical data for the cardiac service-line? Or an individual that is pulled from predictive models based on clinical data for the cardiac service-line AND within the past month has visited at least 2 pages on the hospital.com that were tagged with the 'cardio hvc' ? This is what we tested and the results were obvious; the latter won.
As you can see in the diagram above, leveraging online (digital body language) data and reinforcing that with our predictive models eliminates non-engaged targets. This minimizes the targets that will not engage and hyper-focuses on converting / nurturing those asking for messaging. This is the fundamental shift from 'batch-and-blast' to our newly founded 'always-on' methodology. By utilizing this type of segmentation, our engagements rose by 10%. As far as the campaign flow went, nothing had changed. We simply sent a single e-mail to the list, waited 3 days, and sent a follow-up to those who did not engage in the first. This was important as we were testing the following hypothesis: Refining campaign segmentation by leveraging digital body language data point will increase the CTOR by +5%.
Let's talk baselines:
The numbers / results stated above were calculated by taking the engagement baselines garnered from 'like' campaigns within the old marketing automation platform. We took cardio campaigns that utilized the 'batch-and-blast' methodology with very similar designs / CTA's across 7 clients. The data point we used for our major KPI was the CTOR. The industry average for CTOR in the healthcare space lands somewhere between 6-7%. Our baselines were a bit lower than that. They sat around 4-5%. The targeting of all individuals (engaged in cardio content or not) that match solely CRM criteria garnered high numbers of targets with lower e-mail engagement rates. By leveraging one additional data point (digital body language), we were able to contact lower numbers of engaged individuals increasing CTOR immensely.
By communicating with engaged / predictive model reinforced individuals, we are able to contact less / higher qualified individuals. We lessen the cost of marketing while increasing the amount of downstream revenue attributed to these marketing initiatives. A jump from 4% CTOR to around 14% CTOR in the healthcare marketing space is a very big win. As we move forward, we will work on optimizing the balance of engaged individuals (digital body language) versus crm informed individuals (predictive models) by testing additional hypotheses. Currently, the data is trending toward an ROI of 15:1. There is data lag associated with the downstream dollar spend in hospital, so the trend analysis was completed using past outcomes.
The B2B Luminary Targeting training was very crucial for this type of integration, specifically, the web profiling aspect of the course. This was a net-new capability within our organization. It set the groundwork needed to integrate this capability.
So in conclusion, we can see that the effect of one singular data point can have a great impact on your business. There is great importance in leveraging the Eloqua web tracking script, identifying individuals on your web entities, and using those additional data points in your segmentations. The enhanced segmentation practices help us engage the individuals who are looking for the service-line information. By reinforcing their risk levels with our CRM data, we are able to increase the messaged individuals' utilization within the hospital. Which is what hospitals pay us to do well!