• Sustainability
  • DE&I
  • Pandemic
  • Finance
  • Legal
  • Technology
  • Regulatory
  • Global
  • Pricing
  • Strategy
  • R&D/Clinical Trials
  • Opinion
  • Executive Roundtable
  • Sales & Marketing
  • Executive Profiles
  • Leadership
  • Market Access
  • Patient Engagement
  • Supply Chain
  • Industry Trends

Digital Transformation: A Guide for Pharma Marketers


Organizations need to be correctly set up to deploy AI and machine learning. A new approach to processes and content is often required, write Jan van den Burg and Abid Rahman.

Artificial intelligence (AI) and machine learning (ML) are not yet perfect sciences, but they are transforming life sciences companies in practical and meaningful ways. This includes improving targeting and relationships with healthcare providers (HCPs) and patients. 

However, organizations need to be correctly set up to deploy these new technologies. A new approach to processes and content is often required.

Cutting through the hype: What is AI?

To understand the role of AI, it helps to understand some of the common terminologies. Artificial intelligence uses machines to carry out tasks that require cognitive abilities. The concept has been around since the 1950s. However, its use, research, and development has escalated recently on the back of the following factors:

●      Digitization and vast amounts of data generated by smartphones and connected devices

●      Exponential growth in processing power

●      Falling price points in data storage

●      Improving speeds and internet connectivity

Machine learning falls within the field of AI. An ML-based system does not need explicit rule-based algorithms, as the machines can learn from data and continuously self-adjust based on new data. A vast and important area of AI, machine learning-driven solutions can be divided into two main categories: prediction and recommendation. Predictive systems are used to predict outcomes while recommendation systems propose actions to influence outcomes; like Amazon’s recommendation engine.

However, for ML-based systems to develop new insights and drive actionable outcomes, there is a growing recognition that high-quality datasets are needed. These must be regularly cleaned and organized in a standardized way to produce relevant results quickly. Here, we outline best practice for machine learning to optimize marketing for life sciences companies:

Creating an AI-ready infrastructure

The core advantage of AI is its ability to process large volumes of data, to identify patterns and reduce subjectivity. Automatically connecting the dots across an array of customer data enables efficient targeting of compliant content to meet customers’ needs. To get to that point, life sciences companies will need to ensure they have the right data, content, and organizational approach.

The right data is paramount

Many organizations are ill-prepared to leverage the benefits of AI and analytics. They are hamstrung by legacy systems that were not designed for masses of different data inputs. What they need is a cloud-based commercial data warehouse with an industry-specific data model and standard data connectors. This will help to collate crucial data sources from sales and CRM to formulary and claims data, and deliver new content experiences.

For AI to make next-best-action, content and channel recommendations, it requires seamless integration across the data warehouse, CRM, HCP-facing channels, and content management systems. This would also enable teams to assess content performance across channels and to link materials to business results, all directly within their digital asset management system.

The next step for AI/ML in commercial life sciences is to enhance the personalization of interactions between customer facing teams and channels, and healthcare providers. While life sciences companies have rich customer data and an in-depth knowledge of their customers, the challenge they face is unlocking it from silos and making it actionable, which is where AI comes in.

Not only can it provide recommendations on the best action and channel for following up, AI can also be used to customize content and sales aids. Drawing on knowledge of customers’ key areas of interest, affiliations and routines, the right digital content can be automatically generated and delivered at exactly the right time. Once the data foundations are laid, companies need to have compliant content available to feed the AI engine and provide highly relevant customer experiences.

Content is key

Given the regulatory requirements, it is harder for life sciences companies to bring content to market fast than it is for organizations in other industries. But, in the age of social media, building brand perception can hinge on the ability to join a conversation quickly. To address this potential gap, companies can create evergreen and modularized content that is ready to deploy at any time, like medical information, responses to FAQs, and digital sales aids. Using AI and ML can also help companies to speed up reviews and reduce compliance risk in the content creation process.

Streamlining content creation with AI

New technology offers a wealth of opportunity to tighten and speed up the traditionally cumbersome area of content review. Using ML, systems can be trained to identify claims-often safety or efficacy statements-and spot them in new documents. This can even extend to the system “knowing” which claims have been manually linked in previous pieces (and the reference used to support the claim), and using this information to screen new documents and automatically link claims to previously used supporting references.

By learning why content is rejected, AI can also potentially intervene to flag risky claims. This could help to ensure that the right level of review and legal consideration is given to potentially high-risk materials. Ultimately, AI can enable finely tuned messages to be created automatically, driving personalization at massive scale.

Scaling up personalization and predictive modelling

Technology companies like Netflix use ML to understand their customers and predict what products and services they might enjoy. Using predictive modelling enables them to personalize experiences in their bid to retain customers on their platform.

The same ML approach can be used by life sciences brands to scale personalization effectively, targeting customers with ads across platforms. The copy and imagery used in these ads may encourage HCPs to find out more about a particular treatment or help existing customers to continue with a treatment when appropriate. 

With AI using machines to carry out tasks that require cognitive abilities, the platforms built for life sciences today can provide intelligent banner services. This enables targeted search and opt-in resources for one-to-one communications via SMS or email.

Boosting customer services with automation

Combining ML with natural language processing can enable brands to build chatbots that respond to users’ questions via websites, social media, SMS, digital assistants and other platforms. 

Marketing professionals in the healthcare space are uniquely positioned to develop bots for customer service agents, since many already have workflows with very specific messaging approved by medical, legal and regulatory teams. Implemented correctly, ML can improve experiences for the brand and consumer, by delivering a faster, more cost-effective service and ensure adverse events or product complaints are understood and quickly addressed. 


To compete in today’s data-rich, real-time-intelligence environment, ML and AI will become an integrated part of your marketing business. While the prospect of implementing it may be daunting, due to the proliferation of data and content and efforts required to build out the systems, investing in the technology allows for stronger, more adaptive, directed marketing campaigns, and that can pay dividends.

Jan van den Burg is VP Commercial Strategy at Veeva. Abid Rahman is VP Innovation at Intouch Solutions.

Related Videos