The Power to Know and Change What Your Customers Will Do

What if you could predict what your customers will do (before they themselves know), as well as how to change that behavior in advance. Such as, predicting behaviors like prescribing your drug or switching to a competitor drug – before they even happen?


For example, if you knew why specific doctors (and who) would prescribe a certain brand of drug over others, you could target your marketing efforts to reach them with personalized messages that play upon their perceptions to convince them about the value of your own product over their current preferred drug and influence them switch to yours.

How would that change your organization?
That’s the role of prescriptive analytics. You’re probably aware of Big Data — though you might not quite know what it means. In simple terms, it’s a lot of data – so much that it would crash a regular computer if you tried to open it. It can contain trillions of data points that can be analyzed using Artificial Intelligence to reveal causal relationships unseen by traditional statistics due to the more complex nature of the relationships. Prescriptive analytics involves artificial intelligence powered techniques to sort through that Big Data and provide predictions based on the relationships found in the data. You’ve encountered similar technology in to the recommendation systems used by Amazon and Netflix. 


Within pharma marketing, prescriptive analytics can predict which will be the most profitable sales calls, and which physician is most likely to prescribe your medication. As a result, you can focus on providing improved personalized services, and marketing while saving time and money.

Prescriptive analytics is an important part of the pharma sales and marketing process because it helps you refine your sales and marketing and can stretch limited resources. Incorporating artificial intelligence (AI) makes gives a high degree of accuracy going far beyond the statistical analyses of market research data common in pharma marketing.

Here Are 8 Steps to Getting results from your big data and AI analytics:

1.Plan The Specific Behaviors You Want To Predict and Change
Who will buy, when, and why are good starting points. Why a customer would switch to competitor drug? Who will switch without intervention? How can you change that outcome? When you define your business objectives, you are guided to the specific questions to ask and that guides the models that are built.

2. Get the Right Data
This is probably the most difficult part. We find we have to think laterally about data collection and do data partnerships in order to get the right data. e.g. Boehringer Ingelheim and patientslikeme.com both benefit from their data partnership as do numerous other data partnerships. Eularis have several data partnerships with various oncology data providers, respiratory data providers, diabetes data providers and more. Just thinking about old world data options such as IMS and CRM and market research are not enough to gain a real competitive edge anymore. You need to go wider and deeper. For example, you may want real world information on patients with COPD and how they manage it in their daily lives and what unmet needs they still have. This gives you insight for gaps in the market or ways you can improve your existing marketing.  Your business objectives determine the type of data we want to analyse in order to have the most successful outcome.

3. Clean the Data
Incorrect, duplicate or corrupt data won’t do you any good. Data scientists will always have to first clean the data to make sure it’s useable and clean. The quality of your data determines the quality of your outcomes. This is not a glamorous step but sadly a necessary one.

4. Track Your Customers Over Time and Leverage Real Time Data
When Customer A achieves a desirable outcome, prescriptive analytics can identify similar customers and make accurate recommendations of actions that will achieve a specific outcome for them. Part of the magic of prescriptive analytics using artificial intelligence is that you have the right information to make relevant offers with relevant messaging through the right channels to the right people. It improves your sales and marketing and customer service all in one.

5. Dynamically Segmenting Customers
Your customers are individuals and wish to be treated as such. When you have prescriptive analytics in place, you’re able to cater to smaller groups of people (sometimes to a segment of one) and deliver personalized messages that engage them at an individual level.

6. Create the Right AI Powered Model to Predict and Change Behavior
There are many types of AI approaches that can be used in building a behavior prediction model and it is always important to examine which type of AI model will work best with the data combination. Which is the best will depend on your data and your business objectives.

7. Train and Test the Accuracy of The Model
Do this by running the training data sets through the model and train it on that data. Then tune the model with the validation set of data, and then test the model with the test data. Then you can deploy the model with the real time data. Once you’ve deployed the model, you can review the insights and iterate as needed. It’s normal to tweak and refine the model over time.

8. Adding Data to Get the Model to Learn And Become More Accurate
Like anything, optimizing your outcomes is a process of refinement and the more you engage in it, the better results you enjoy. Due to machine learning, the algorithms get smarter the more data you add to them.
In addition to pharma marketing the healthcare industry is now using prescriptive AI powered analytics to predict patient outcomes, help doctors diagnose diseases and identify at-risk patients even before they show symptoms or contract a disease.

At Eularis, we build live real time prediction engines for our clients. We help our clients devise strategic business objectives, gather the data, build the algorithms and feed them into visualization dashboards, or even into your CRM or marketing automation systems and help you put the data to use. The result is you create strong engagement and customer satisfaction for an improved bottom line.

For any questions, please contact the author at Eularis: https://www.eularis.com.