While business intelligence and analytics have been with us in varying forms for decades, they’ve never had the potential to transform business as much as they do with the latest advancements. In the past few years there has been a lot of hype around big data, artificial intelligence and the Internet of Things (IoT). In addition to these, there has been exponential growth in data mining of both structured and unstructured data. Analytics teams need to mine this data to gain insights that drive efficiency and effectiveness of their efforts.
In the old days, you needed to create a hypothesis and then the statistics or mathematics to test the hypothesis. No more. Now a branch of artificial intelligence – called machine learning – can create hundreds of millions of models in a day and identify what the key components are for marketers to improve their results by a specific amount.
Machine learning is a subset of artificial intelligence based on algorithms that learn from data without needing rules-based programming. It is designed so that the machine itself can learn to identify patterns humans cannot see and solve problems without human intervention. With machine learning, computers can process and mine enormous quantities of data to discover insights and generate predictive models. Unconstrained by preset assumptions required in statistics and other types of mathematics, machine learning can yield insights that statistics and humans cannot feasibly attain…and it does so with far higher degrees of accuracy than is possible with other approaches.
Real world Applications for Pharma Marketing
AI and machine learning based analytics are superior for Pharma marketing, in particular, because success often requires many ongoing complex decisions containing a large degree of judgment. The dramatic increase in the number of channels used, the complexity of these channels, the fast pace of change in our market environment itself, and the complexity of the decisions we make every day, makes it very difficult to get this consistently right without the intervention of something as sophisticated as artificial intelligence, which can distill the noise and match financial goals with the accurate marketing decisions to attain them. Therefore, using machine learning based artificial intelligence is superior for Pharma marketing departments because it can undertake large volumes of interconnected and complex judgmental decisions by sieving through a multiple of seemingly unrelated datasets, and this with a high degree of accuracy.
Whilst start-ups and large corporations are making amazing strides with these new techniques, mathematical and computing science departments within universities are the ones continuously delivering cutting-edge advances. Using these techniques in Pharma to drive extremely differentiated results will redefine the future of
Pharma marketing analytics.
To that end we have been working intensively with a number of leading professors in the fields of artificial intelligence, and machine learning specifically, for the past 18 months. The result of this collaboration is that Eularis have now developed a unique artificial intelligence, machine learning powered platform that is able to identify “the wood from the trees” with an astonishing level of accuracy. It can determine the best strategies as well as individual tactics for a brand, whether it is just emerging, growing, defending or failing.
Using the artificial intelligence machine learning generated insights, Eularis can provide pointed recommendations to help achieve optimal marketing and sales results. Within our E-VAI growth accelerator platform, a group of clients have been testing the recommendations with their real-world results, and we are very happy to say that the trials on the platform provided an accuracy of 97.3% in our 2015 trials so far. We are now releasing it to all clients to use in our platform for their projects in any country – even shallow data markets.
In addition, the interface is still simple and intuitive. See the Budget Allocation Tool powered with AI in the image below. With such easy-to-use interfaces, Pharma marketers can also test scenarios and see what the real revenue and market share impact of these changes will be. The test results from our current work have been impressive so far. As soon as we finish our ‘clinical trials’ on real-world data using this platform, we will release the accuracy we have found with this technology. Stay tuned.
What Lies Ahead?
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 take boxes of legal documents and figure out which ones are discoverable. Now that job is being done faster and more accurately with machine learning than it ever was with humans. Pathology is another example. Machine learning has been found to be more accurate than Pathologists with years of training and experience in identifying where the active parts of a breast cancer tumor were. You can even put machine learning in a car and it can drive, without a human, safely down a street without hitting anyone or anything despite a constantly changing environment.
The super ability of these algorithms is increasing exponentially and the more data you give it, the better it works. So each time you put new data in, you get even more insights out…which make it ideal for big data. Our clients get answers to the following types of questions:
• “What is the maximum revenue brand x can get with x budget?”
• “How do we allocate the budget to achieve that?”
• “What mix of messages will give the maximum market share?” Or “What is the maximum market share?”
These questions are also commonly asked of older linear approaches – specifically ROI, correlations, multivariate regression analysis, promotional response curves, marketing mix modeling and multivariate statistics – but with disappointing results, as anyone relying on these approaches but not getting the growth they want can tell you. Utilizing AI within Pharmaceutical marketing will simplify the Pharmaceutical executive’s ability to process large volumes of doctor and patient data, and derive accurate and consistent findings from that data to improve patient and doctor outcomes, as well as real-world financial results.
