Pharma Marketers: Big data, Artificial Intelligence & You

Even though we don’t think a lot about it, everything in our lives is driven by data. We make decisions about how, or even if, we should market a drug based on clinical data. We use historical data to make assumptions in our business and personal lives. Heck, we even use data when deciding what to have for dinner. If three quarters of your family dislike fish, it probably won’t be on the menu.


Big Data: Dilemma in Pharma Marketing

Business intelligence and analytics have been analyzing data for decades. With recent advancements in technology, an immense quantity of data is being produced at a vertiginous speed. Now we have big data and with it the potential to transform businesses.

Doug Laney from Gartner predicts that:
    •    By 2017, more than 30% of enterprise access to broadly based big data will be via intermediary data broker services, serving context to business decisions
    •    By 2017, more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the Internet of Things
    •    By 2020, information will be used to reinvent, digitize or eliminate 80% of business processes and products from a decade earlier

It’s no surprise, then, that board members and CEOs of Pharma companies are rushing to get big data on their strategic agendas in order to grow, innovate and compete. However, when faced with big data, we find our old ways of analyzing data insufficient.

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 Excel and linear approaches – meaning ROI, correlations, multivariate regression analysis, promotional response curves, marketing mix modeling and multivariate statistics, which tend to deal with aggregated data. They are simply not granular enough to really allow us to look ‘under the hood’. Therefore, they cannot extract the much needed, critical information that will transform our results, or even tell us about complex variable interactions. Simply put, they cannot give the depth of information needed in today’s complex Pharma environment.

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. That’s the dilemma facing Pharma leadership. There is more data available than ever before to drive intelligent decisions; however, collecting, mining, and analyzing that data is more challenging than ever. Pharmaceutical executives are now seeking transformational ways to unlock value from their data.

Enter Artificial Intelligence (AI)

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

Simply put, AI can be seen as a branch of mathematics designed for a world of big data to solve real-world problems, used to recognize behavioral patterns through computational learning. Within AI, one game-changer is machine learning. Its algorithm learn by making predictions from large amounts of datasets – either structured or unstructured.

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.

AI in Pharma Marketing: the Power to Know

While AI can be used for many business challenges, it has tremendous specific value in marketing in particular, which requires a host of ongoing complex decisions containing a large degree of judgment.

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.

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.

What Eularis have been doing in AI is to simplify the Pharmaceutical executive’s ability to process large volumes of doctor and patient data, and then derive accurate and consistent findings resulting in improved patient and doctor outcomes as well as real-world financial results for our pharmaceutical clients. The unique machine learning powered platforms such as E-VAI – are able to identify “the wood from the trees” for both marketers and C-suite executives. It can determine the best strategies as well as individual tactics for a brand – whether it is just emerging, growing, defending or failing – as well as for a full brand portfolio, or even a company.
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.

Integrating Artificial Intelligence based analytics with your offering can provide substantial benefits including, among other things:
    •    An understanding of optimal strategic direction
    •    Enhanced value proposition identification
    •    Superior competitor differentiation
    •    Optimal resource and budget allocation for maximum market share gain, revenue and profit
    •    An ability to see which segments offer the most value to your brand as well as what levers to pull to maximize growth from them