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How to tackle the Achilles heel of CX in life sciences with content analytics

17 Oct 2022

Leading life sciences organizations are moving away from a product-first mindset - where customer communications mainly focused on information about a drug's clinical efficacy, safety, and superiority over alternatives. Personalized customer experience has taken centre stage and organizations have started thinking of ways to personalize patient and prescriber experiences by addressing pain points along their individual journeys.

This transformation (to a customer-first experience) is highly dependent on the quality of content distributed. When content experiences do not meet customer needs or expectations, they push your customers farther away and eventually dilute the brand experience. This makes the content piece itself the Achilles heel of customer engagements.

In a panel discussion at the Indegene Digital Summit 2022, industry experts from Janssen, Gilead Sciences, and Pfizer highlighted that the life sciences industry is still at a nascent stage of customer content personalization. This is primarily due to challenges around building highly effective common data models, integrating people and process operations, understanding key metrics, knowing how to use them, and eventually translating campaign insights into actions.

What does it take to tackle these challenges?

1. Focus on data

It is paramount for organizations to gather as much data as they possibly can about their patients and prescribers to understand individual historical behaviors and patterns. This helps organizations accurately profile customers into micro-segmented cohorts. For example, knowing which customers engaged with your web assets (and when) can help you map them to the relevant content and channel bucket. Doing this for all your customers unlocks a powerhouse of customer knowledge that empowers you to plan appropriate and meaningful interventions along the customer journey.

“Focus less on seasonality and use data on a daily or weekly basis to create agile content,” Elliot Antrobus-Holder, Executive Director of Digital and Innovation at Gilead Sciences, said.

He added that organizations should leverage as much data as possible to effectively map content, attribute an activity back to a single profile, and drive more relevant engagements. It then becomes paramount to store all this data and make it easily accessible to marketers and customer-facing teams so they can leverage that in their engagements.

“The key to all of this is to build the right kind of data model, followed by the application of predictive and behavioral analytics,” Elliot said.

2. Invest in the right journey orchestration tools

The panel echoed the importance of adopting journey orchestration tools to achieve dynamic content relevancy and meet customer requirements as they evolve. AI-driven capabilities, along with machine learning and deep learning techniques, make it possible to predict the kind of content that would be relevant to a customer at a specific touchpoint. Organizations should also leverage technology enablers (like Digital Asset Management tools) to modularize content and test them across micro-segmented cohorts in an iterative and agile fashion to see whether the customer advances to the next stage.

“Maximize the value of content by investing in the right kind of journey orchestration tools,” Aaron Foster, VP of Business Analytics and Insights at Pfizer, said.

However, the panel also highlighted that not every use case within the realm of personalized customer experience requires the application of machine learning or analytical models. Organizations must be nimble and apply appropriate business rules to effectively respond to competition and market dynamics.

“We need to have a plethora of tools in our toolbox - with deep learning models and convolutional neural networks at the far end and simplistic tools (such as business rules) in the near end - enabling us to respond iteratively and dynamically as we continue to move and engage customers along their journey. Over a period of time, this will translate to a prescription uplift,” Ajit Menon, VP of Customer Engagement and Digital Transformation at Janssen, said.

3. Glocalizing content

Glocalizing the process of personalized content creation is extremely critical. Organizations need to focus on co-creating content with key stakeholders across all targeted markets to ensure that the content resonates with local customers. Modular content plays a critical role here as it enables stakeholders to customize content appropriately based on specific customer and market trends.

“The important element (for glocalizing content) is metadata. Organizations should build data around their existing data, focus on the key components of the content distributed, understand the type of content and information used, and then create a feedback loop across market stakeholders to share insights into what is working, what is not, and how the content format and type can evolve across markets for better engagement,” Aaron said.

4. Measure in rapid sprints

To effectively measure content performance, organizations should look at campaigns as a journey (instead of a one-off activity). This helps them understand what content engaged customers and how many of these customers eventually moved to the next stage of their journey. The application of business analytics here can offer insights into what is driving these conversions in terms of behavior, attitude, and more. Organizations should run these measurements in two- or three-week sprints to effectively monitor and optimize performance on the go based on what content worked and what did not.

"Organizations should measure progress and impact in rapid sprints. That's where A/B testing is crucial because it allows you to try out different variations of content to find the most suitable fit for your micro-segments," Ajit said.


As organizations move from a product-out mindset to a customer-in mindset, data, tools, co-creation processes, and an agile measurement framework will pave the path for better, more personalized customer experiences across the life sciences value chain. Natural language processing and other deep learning techniques, coupled with traditional analytics-based recommendation systems, behavioral targeting, and more, are powerful tools for life sciences commercials to have in their arsenal for creating contextually relevant customer experiences.