As we continue to explore the importance to be bold enough to trust the counterintuitive advice that data can provide, life science companies should deploy advanced analytics techniques to generate actionable predictions.
We recommend deploying a machine learning-powered “inside-out” approach to sales force sizing. By leveraging machine learning to properly segment health care professionals and determine the call volume needed to generate sales (as a company would during a segmentation and targeting project), commercial leaders can determine how many field sales representatives they need to maximize results.
Machine learning techniques can supercharge a life sciences company’s sizing efforts by helping it identify the health care professionals it should call on who aren’t yet on its radar. From clustering analyses to classification trees to complex algorithms like neural networks, life sciences companies can deploy many machine learning techniques to generate reliable and sophisticated predictions about a health care professional’s prescribing behavior. In addition to providing actionable predictions, machine learning also helps the company determine which analytical model(s) to use to generate the most reliable results. Therefore, once a company builds a machine learning model, it can deploy it continuously instead of having to build a new one every quarter.
Further, as the amount of data life sciences companies can access continues to increase rapidly, life sciences companies will have no choice but to adopt machine learning. This data proliferation means the performance gap between machine learning models and more analog, intuitive approaches will expand. At some point, there will simply be no other way to process and pull insights from the massive amount of data in the industry.
This data explosion is a positive development for the industry. Now, with increasing access to claims data, electronic health records, and more, companies can not only see how many prescriptions a health care professional writes, but also the surrounding circumstances and timing. These insights can help a life sciences company hyper-focus its sales and marketing outreach.
With the opportunities for more accurate and granular insights growing, it’s up to life sciences companies to take the next step. By deploying machine learning for sales force sizing projects, commercial leaders will arm themselves with sophisticated predictions. The only thing left to do at that point is to put those predictions into action, execute the play call, and optimize sales force structure and performance.