Marketing requires numerous complex decisions that have traditionally been made based on a great deal of qualitative and quantitative data analyzed with linear statistical approaches… and, let’s be honest, gut feeling.
That simply doesn’t work in today’s data-driven environment. Artificial Intelligence, however, provides a way to distill the ‘noise’ into actions and match financial goals with the marketing decisions necessary to attain them.
Specific Marketing Challenges That Machine Learning Algorithms Can Address
1. Financial Results
Questions to ask include:
• What is the maximum market share and revenue I can achieve given the market and competitors, and what do I have to do to get there?
2. Customer Segmentation
3. Strategic Direction
4. Personalized Driver Messaging
5. Rx Switch Prediction
6. Resource Allocation
7. Discovers all critical relationships in the data – both linear and non-linear relationships
What about customer segmentation analysis as another example? Using linear approaches you can test your segmentation hypotheses, but you cannot uncover segments you had not already considered or identified. However, by using Artificial Intelligence, you will uncover both physician and patient segments that are high growth segments which linear approaches would have missed.
8. Deep, accurate insights to create real-world improvements and results
We are now creating billions of data points and combinations. Statistics and other older analytics techniques simply don’t have the power to provide the level of meaningful information that machine learning offers.
Machine learning can churn through hundreds of millions of data points to provide answers you can trust with an accuracy that is in a different league to older approaches. For instance, we’re finding that machine learning analytics are actually better than experienced Pathologists at identifying certain Breast Cancers.
9. Sample data
Linear approaches using sample data require robust sizes of data, but AI can provide good insights even when only a sample dataset is available. These limit the results in linear models.
10. Exponential advances and continually increasing power
We are constantly innovating as the pace of knowledge increases in this domain. For instance, the machine learning algorithms Eularis uses today did not even exist three years ago.
Today’s machine learning algorithms are better than humans at things once thought to be the unique domain of humans. For example, lawyers used to painstakingly read through boxes of legal documents to develop their case. Now, the documents are uploaded and sophisticated algorithms are used to identify useful material.
How long does your analytics staff spend blearily staring at spreadsheets to identify trends and pull data that, as you know, doesn’t provide the expected return? The kind of computer analytics we’re talking about is capable of identifying trends and patterns in minutes, even seconds, providing more timely business intelligence than even your best analyst.
Data is dynamic, and the amount and type of data available changes on a daily – even hourly – basis. Machine learning can integrate such data in real-time, even incorporating events outside your company such as economic issues, weather and natural disasters, to provide the most accurate, comprehensive results possible.
AI-based approaches are far superior for solving real-world problems, including impact on revenue of specific marketing actions and their synergistic impact.
Once you plan and implement these appropriately, they learn when new data comes in and adjust their own algorithms to take the new data points into account. So you can keep adding data, and the more data you put in, the more they adapt and learn; millions and billions of data points can be processed quickly and easily.
If your competitors are all using linear, they will not be getting the richness of the data, nor the accuracy, and you will have access to more information which will allow you to succeed before they do – hence our results.
So, what do you want to achieve? Mediocre results with linear approaches or outstanding results with AI approaches? The choice is yours.
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To learn more about how Eularis can help you find the best solutions to the challenges faced by healthcare teams, please drop us a note or email the author at abates@eularis.com.