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How AI can accelerate digital pharmaceutical marketing

By Harshit Jain, Founder and Global CEO , Doceree

Artificial intelligence is finally making its mark, coming a long way from the stage of experimentation to actual implementation across markets and sectors. As data is pivotal to craft effective AI strategies, its role is significant in the life sciences industry which historically has been a data-rich sector.

Technological advancements in the field have enabled data collection, processing, algorithms and improved computing speed, augmenting adoption of AI technologies at an accelerated rate for drug discovery, disease identification and diagnosis, patient identification for clinical trials, and predictive forecasting.

To pharma marketers, it provides them an opportunity to communicate with healthcare professionals (HCPs) at appropriate moments during their online journey. Especially after the pandemic, they have recognized the value of AI tools to share the right message with their target audience at the right time. Artificial intelligence undoubtedly is transforming the pharmaceutical industry in a lot of ways, including in the way life sciences brands approach marketing.

Let us discuss the five AI use cases that have great potential to transform digital pharma marketing:

Targeted messaging: It is as important to showcase a message to the right audience as curating one.

AI algorithms help analyze data of HCPs’ behaviour such as their browsing history, utilization of channels, view time of messages displayed, and where they spend their most time during their clinical journey so a more targeted approach can be undertaken. In digital pharma marketing, the usage of AI gains further importance as it helps ensure that messages are seen by the right HCPs and also which messages would most likely be of interest to them after duly analyzing their demography, interests, specialty and prescribing patterns.

Hyper personalization: Personalized messaging is the key to driving successful messaging campaigns and life sciences brands that have deeper access to their target audiences’ data and behavioural touchpoints can develop hyper personalized communication that their target audience can resonate with in real time. AI can help create detailed and unique profiles of HCPs and automatically serve them customized messages matching their preferences.

Predictive analytics: AI technologies can be used to predict which drugs are likely to be successful, which patient populations are most likely to benefit from certain medications, and which marketing strategies are likely to be most effective. This can help life sciences brands optimize their marketing efforts and focus their resources on the most promising opportunities. It further enables pharma marketers to make sure they are placing messages at crucial points in the clinical workflow of HCPs.

Next best actions: HCP journeys are designed to provide a broad view of how to engage prescribers. Combining the same with next best action insights drawn from the most recent data can provide HCP engagement predictions such as on right HCP, right channel, right message and cadence suggestions that can help make HCP engagement more dynamic. It allows sales reps to enhance their HCP interactions and enables learning and optimization.

Clinical trial recruitment: The proper execution and visualization of AI has the ability to accelerate drug development process by speeding up collection and management of data and cut down considerably the time required for clinical trials. Also, the technology can be used to identify

potential participants for clinical trials based on their medical history, past prescriptions, medication allergies, among others. This can help pharmaceutical companies recruit patients more efficiently. The effective use of AI in the process could not just save lives by providing patients access to safe medicines but could also reduce the cost of healthcare substantially.


Overall, AI is transforming pharma marketing by making it more data-driven, personalized, and efficient. As AI technology continues to advance, it is likely that we will see even more transformative applications in the coming years.

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