Artificial Intelligence: Changing the Rules of Marketing

AI is a game-changer in transforming pharma marketing.



For a number of years all eyes have been focused on the powers of Artificial Intelligence (AI) in business and for good reason. AI has the potential to solve complex business problems and transform your company’s destiny. Pharma executives seeking effective ways to get the most out of their company’s data should understand what it is, what it can do, and what to watch out for when using it.

Think of AI simply as a branch of mathematics designed for a world of big data to solve real problems. It is used to recognize behavioral patterns through computational learning. One game changer within AI is machine learning, whose algorithm learns by making predictions from large amounts of datasets - either structured or unstructured.

Why is AI a game-changer?

I am sure you have heard of the famous Google driverless car. Did you know that the car is operated with machine learning based AI? Complex datasets are processed to ensure that the car makes the best driving decisions. So far, the vehicle has driven over 1 million miles without incurring one single accident. It is also able to determine what a tree, a building or a pedestrian is. It assesses what the vehicle should do next and how it should respond to unexpected events.

The Google driverless car is not the only extraordinary machine learning example out there. This emerging approach also allows businesses to truly determine people’s preferences from a massively large crowd of choices. Amazon, for instance, uses machine learning algorithms to identify consumers’ preferences to suggest new products. Even more impressively, from a single user account, it can pick up multiple different users and give the right preference for the right user. So, if a family had an Amazon account, it would be able to tell if it was the mother looking for books for her child, or if it was the child looking (and which child), and provide perfect recommendations every time – despite all being logged in as the same user. Uber, the $50Bn valued venture, uses it too to progress their mission of bringing safe, reliable transportation to everyone, everywhere.

How AI can transform Pharma marketing: providing the power to know

Artificial intelligence and machine learning based analytics are superior for 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 make it very difficult to get this consistently right without the intervention of something as sophisticated as AI, which can distill the noise and match financial goals with the accurate marketing decisions to attain them.

Therefore, using Artificial intelligence powered analytics is perfect 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.

Older linear approaches - meaning ROI, correlations, multivariate regression analysis, promotional response curves, marketing mix modeling and multivariate statistics - had their place in business for years, as that was all there was to use. However, in today’s multichannel, and highly digital, environment with so many stakeholders, messages and channels to contend with, a more advanced non-linear approach to providing actionable insights is crucial. Because of today’s fast-moving technological advancements, Pharma companies can actually reduce their reliance on traditional analytical techniques, and deliver real-world value by utilizing new advanced AI techniques.

AI will help simplify the pharmaceutical executive’s ability to process large volumes of doctor and patient data, and derive from that data accurate and consistent findings resulting in improved patient and doctor outcomes, as well as real-world financial results. AI can actually drive simple and accurate results. It facilitates the use of new ways to help the least scientific crowds digest the findings from the newest data-rich analytical techniques.

A client recently told me they were hiring for a position in their analytics team, and one interviewee claimed to be an expert in pulling out insights from big data. My client responded, “Great. How do you do that?” The interviewee responded, “I put it in an Excel spreadsheet.” You can see where this is going. Big data does not fit in an Excel spreadsheet. Even the data we used to create linear modeling ceased fitting in Excel macros around 2005. So, if you are relying on any kind of macros in Excel, you are really not in the big data league. To conclude the story, the client gave the interviewee the biggest Excel file he could find and asked him to find some insights by the next day. As expected, he came up with nothing of significance.

The types of artificial intelligence that are actively in use today allow not only prediction of future results but also provide prescription on how to change those results to achieve expected outcomes with unsurpassed accuracy, and this by changing specific leading components.

We recently ran a project with a few brands, and our machine learning AI analytics platform created several hundred million options to examine, analyzed them all and pulled out critical elements for the client brand to succeed. This included changes in the focus of sales and marketing messaging, changes in sales force focus, and changes in both focus and budget for marketing channels. It then can predict with extreme accuracy exactly what financial results those changes would have for the brand or portfolio.

Conclusion

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. It’s time that Pharma companies took on that role.

The types of artificial intelligence that are actively in use today allow not only prediction of future results but also provide prescription on how to change those results to achieve expected outcomes with unsurpassed accuracy, and this by changing specific leading components. And if you can add to that a very simple user interface, Pharma marketers can then test scenarios and immediately see what the real revenue potential and market share impact of a change might be.

Try it. Who knows? You may well be able to unveil unexpected outcomes and change next year’s growth trajectory.


For more information on use of Artificial Intelligence in Pharmaceutical marketing, including Machine Learning, please contact the author - Dr Andree Bates at Eularis: http://www.eularis.com


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