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For Pharmaceutical Marketing Analytics

7 Ways to Leverage AI for Stronger Pharma Marketing Results

Posted by Dr. Andrée Bates | Jan 15, 2018 10:14:51 AM

Pharmaceutical marketers are starting tentatively to utilize AI in their sales and marketing activities which is a big improvement on 5 years ago. At that time, we were utilizing AI with some clients and I would ask others how they saw AI as having a role in their work and the response was usually ‘It doesn’t, does it?’. It does. There are so many applications of AI in pharma and every project we do unveils new and interesting applications of AI to enhance our pharma client results. Here are a few examples.

1. Automation

Any task that is repetitive and requires sifting through large amounts of data can be automated with AI. Think about Key Opinion Leader (and Up-and-Coming Key Opinion Leader) Identification and Mapping which is constantly changing. Consider all the journal articles coming out each week in your field, and all the clinical trials ongoing in your field and how changes and new additions can impact your KOL database. This is a perfect task for AI. At Eularis we have carried out KOL analysis with a higher accuracy of identification of highly influential KOLs (and identification of up-and-coming KOLs) than humans doing this task have been able to uncover. This has resulted in stronger earlier relationships and stronger ability to influence results with the help of the right KOLs. Another task that can be automated is sifting through medical records. One client discussed how they were manually going through medical records to identify rapidly progressing patients in their disease area. They were then supporting a specific blood test for these patients which would identify whether they had a specific marker which would determine whether their drug would show excellent outcomes. This manual task would be much faster and more efficient with AI and trillions of patient records could be scanned and logged in a minute once the AI was set up. This could be set up to do this in an ongoing way so each new patient progressing rapidly could be identified automatically.

2. Precision Targeting and Segmentation

This can be done with both patient identifications in rare disease and physician targeting identification. In rare disease patients Euaris have been able to identify specific rare conditions using face recognition from photos uploaded online with far greater than expected results. In physician targeting we can see which physicians are the ones more likely to be needing to write scripts for your condition and which are the most appropriate for discussions on the suitability of the product for those patients. In addition, if we take this to segmentation, we can see how the physicians group into segments as well as the dynamics across segments, and how to influence those dynamics with sales and marketing levers.

3. Faster Reimbursement

By combining natural language processing and machine learning, we can identify which combined factors will influence access to different formularies thereby speeding up access. Pharmaceutical Executive reported that the average cost to a company every day a drug is delayed in reimbursement is $15m per drug per day. The return on doing this kind of approach is significant.

4. Stronger Customer Engagement and Personized Multi-Channel Marketing

Using AI to uncover individual content preferences, channels and timing of information leads to allowing personalization at scale and ensuring every individual is receiving what they want, when they want, and in the channel they want. Eularis have done this across multiple brands and countries for one client who achieved spectacular return on this with vast incremental profit per brand.

5. Value Pricing

At Eularis, we are able to identify what pricing and contract deals are best by institution to ensure your brand attains and retains formulary status by combining data from Government regulations and reimbursement, hospital revenue models, (Pay for Performance, Value Based Care), Insurance payment models, Capitation + Pay for Performance, Bundled care, Centers of Excellence, Accountable Care Organizations, Market pressures and cost containment models, Range and size of hospital, Leadership priorities in hospital, Group Purchasing Organizations, and Patient treatment journey s. We are now doing this for biosimilar entry (both for originators to retain share and for biosimilars to enter their market).

6. Patient Adherence

With lack of adherence being responsible for 125000 deaths a year in the US alone, adherence to medication is critical for patient health. Additionally, if a medicine is not being used properly it is not doing what it is meant to be doing. With pay for performance on the rise, it is now up to pharma to ensure that the medications are adhered to in order for optimal patient outcomes and business performance. Several Eularis projects have utilized the power of big data and AI to identify which patients will cease adherence and how this can be addressed. AI is the perfect tool for this kind of project as each person that ceases medication does so for their own reasons. Studies about adherence have identified over 250 different reasons that patients stop taking their drugs. It is important to understand these in individual patients in order to address their personal reasons.

7. Precision Sales Force Messaging

Using big data and AI, custom messaging can be created for sales reps to use for an individual physician based on what that physician needs at that particular moment in time. Use of customization based on AI analytics have been shown in Eularis projects to increase prescribing by 43% for sales reps using these compared with sales reps not using them.


Although pharma are now embracing AI, it is still utilized in the discovery, R&D and clinical phases more than sales and marketing arena. There are also significant advantages to using it in sales and marketing widely. However, AI is only as good as the data and algorithms that make it up. There is no one-size-fits all and the process requires highly skilled people. AI is not plug and play. Companies cannot simply “buy intelligence” and apply it to their challenges. Although elements of AI are available in the market, the hard work of managing the interplay of data, processes, and technologies happens in-house.

By combining pharma strategic thinking, good data and strong algorithms, anything is possible – the rest is up to you.

For assistance in planning the optimal AI approach to overcome your challenges and meet your goals, contact Eularis for a confidential discussion at any of our offices.

Topics: Marketing Insights, Business Analytics, Big Data, Artificial Intelligence, Marketing Effectiveness

Written by Dr. Andrée Bates