A recent project we did in predictive physician targeting, and discussions with several others, has made me realize how many companies are still relying on historical information to make physician targeting decisions. Up till now, the client had physicians in the call plan that are believed to be valuable, and historically they have been valuable. But the market is not about yesterday, it is about tomorrow. We are fortunate today that the data available allows us to make more precise predictions about tomorrow using AI.
Wayne Gretzky once answered a question as to why he thought he was so successful and he answered ‘I skate to where the puck is going to be, not where it has been.’ This concept is as valid for physician targeting as it is for ice hockey. When you put this concept into thinking about physician targeting, it is thinking about where the next script is going to be written for the patient condition type, what are the factors that could help us predict the probability of the next script written for our brand, and how we enhance that result.
Utilizing predictive physician targeting allows dynamic physician targeting, segmentation, as well as linking to sales force call list prioritization, personalized promotional activities by sales rep, switch prediction (both away from us or away from competitors), message recommendations by individual physician, custom detail aids, personalized multichannel promotional response and enhanced payer formulary inclusion.
How do we do this?
1. Gather relevant and appropriate data
We need unique physician IDs as a base, then from that we layer on as much details about those physicians as we can from big data sources, such as claims data, Rx data, CRM data etc.
2. Sort, clean, transform and combine the data
This data wrangling component is always the most time consuming part of any project. Data in big data projects has to be combined. You cannot simply drag and drop as you can with data visualization programs (that are not analytics – see blog 'The Difference Between Data Visualization and Data Analytics - and Why It Matters' and expect to get a sensible result. In data visualization programs without analytics you do drag and drop as you are simply visualizing data that is there, and not finding complex predictive relationships in the data. Once the data is cleaned, and coded (e.g. location data needs to be binary coded to be recognized in an algorithm while other types of data need to be normalized, features need to be selected (as leaving some features in would be detrimental to accurate results) and so on. Once the data set is ready it is typically divided into 3 parts; the training data set, the validation data set, and the test data set.
3. Create Artificial Intelligence algorithms that suits the needs of the data and objectives best
There are many techniques to choose from and even if using machine learning there are many different machine learning techniques to choose from. They could be decision trees, neural networks and many more. It is up to the data scientist to choose which techniques best suit the problem and data combination. Once they choose the technique there are many parameters to be created and tuned within algorithms to achieve strongest accuracy.
4. Train, Test and re-evaluate your model
Once the model is built it must be trained, and then re-evaluated for accuracy. Once a strong level of accuracy is attained, it gets tested on the test data set. That data set will have data unseen by the model previously so the accuracy can be assessed with new data. Getting the accuracy strong can be a time consuming process.
What can it tell you?
Numerous things can be identified that can create strong physician targeting and results. For example, on an appropriate timeframe we should be able to predict ‘Which doctor has the most potential to write a script for a patient appropriate for our brand, today?’ and to help the rep understand ‘What should be the priority, based on the most recent data, to gain more scripts of our brand?’ and ultimately with channel data added, we should also be able to identify, ‘What messages and channels and sales and marketing actions will enhance that outcome’. In addition, with appropriate patient data we could also identify maximum share per physician based on their patient population.
By continuing to target as before means that valuable scripts are being lost and it is not necessary to lose them in today’s age of big data and AI. These are areas made for big data and AI to enhance your sales and marketing results.
For information on how you can utilize predictive physician targeting, contact us at Eularis. http://www.eularis.com/contact