As life sciences companies seek to increase the volume and quality of healthcare provider engagement with advanced analytics, there is a lot that this industry can learn from the way sales and marketing has evolved in both retail and consumer products. Currently, we see many of our LS clients using basic claims data for targeting, using a scatter shot approach to engage those targets, and then tracking the success of those efforts through multiple KPIs. While these efforts have been effective, there are simple and complex ways to utilize data improve this process.
A small step to improve the targeting process is to use basic HCP information, claims, and relevant testing data to create clusters of HCPs through unsupervised machine learning. This provides a data-based view into who an HCP is and how they see/treat patients so you can better focus and prioritize your current targeting strategy.
For a more advanced look into targeting, consider utilizing a knowledge graph to better understand relationships among all the HCPs in your network. This approach requires additional information to be ingested to make sure we are providing the 360 view of the HCP, including data on: publications, clinical trials, Medicare, social media, etc. With the data seamlessly consolidated into one location, it is easy to understand an HCPs influence, and who they are influenced by, which allows for more personalized targeting strategies.
Healthcare Provider Engagement
When improving engagement, you want to optimize who you are trying to engage, through what channels, and with what content. While the target list might be an obvious answer for who to engage during the year, it does not give good enough insight into who you should be engaging right now. We could use the clusters generated during targeting to identify a group HCPs with high patient potential, but low product adoption. This gives us a clear focused goal to increase engagement among HCPs with a high opportunity for growth. We could also use additional advanced techniques to identify HCPs who are likely to switch to a competitor based on their overall product spread or who are likely to start a new patient based on their historical engagement patterns.
Once you understand who to engage, you will want to know the best channel to contact them in right now. Historical engagement data alongside the marketing team’s expertise can help you provide this recommendation. Marketing expertise can be used to generate a rule-based decision tree, but data can add confidence to the results. For a simpler solution, data can determine the optimal inflection values of the decision points, like: “if an HCP has received more than X face to face interactions within the last Y time period, the next best channel is Z”. If you use more advanced techniques where you can predict the outcome of an HCPs engagement in each channel, you can instead use optimal probabilities as the inflection values of decision points, like “if an HCP is at least X% likely to attend a speaker program, send them an invite”.
Lastly within engagement, you want to understand what content will most likely result in a positive engagement with this specific HCP within the recommended channel. The clusters generated during targeting gives us a great start to understanding the type of content that an HCP may be interested in. The HCPs with higher adoption will likely not engage with disease education materials as much as the HCPs with less experience in the disease state. For a more advanced solution, we can identify similar HCPs and provide a content recommendation based on what those similar HCPs have engaged with. This is similar to the “what others have bought” section when checking out online.
Tracking Healthcare Provider Engagement Success
There are many KPIs that can help track the success of marketing efforts. As long as you are consistent with those metrics, you can have a basic understanding success siloed within each channel. However, it is very difficult to determine attribution of success without a controlled environment. Using the technique of AB testing can provide a deeper understanding of what makes an engagement successful. For a more comprehensive understanding, you can create an algorithmic engagement score that combines the successes of each channel into one value that can be easily compared. This will help you to realize how an HCP is engaging holistically.
There are a lot of opportunities for life sciences companies to use the data available to enhance the way they are engaging with HCPs, and we have just scratched the surface here today. Being able to scale these efforts and continue to incorporate new data and platforms, like social media, will continue to be a vital part of the growth of sales and marketing in the life sciences industry.