Pharma's Customer Engagement Evolution: Is Your Data Governance Keeping Pace?

By Vignesh Ramesh, Bryan DeFoe, and Kunal Patrawala, PA Consulting

Changing healthcare provider (HCP) and patient expectations have disrupted conventional customer engagement methods across the entire healthcare ecosystem. The traditional reach and frequency model that has been extremely successful for pharma companies is no longer adequate because the customer of today prefers a tailored experience delivered through their preferred channels.

Recent surveys have indicated that sales representatives get 3x the promotional responses when they engage HCPs via a combination of in-person and virtual channels, while leveraging personalized digital content drives 2.5x new patient starts. Now more than ever, pharma companies must adapt their customer engagement methods to focus on hyper-personalization. This approach delivers personalized content such as educational material, clinical trial results, etc. through omni-channel interactions (e.g., e-detailing, face to face, websites, etc.) that account for customer preferences, product archetypes, and environmental factors.

How To Improve Data And Enhance Customer Engagement?

Achieving a high level of personalization necessitates investments in new and existing data, advanced platforms like a personalization engine and customer 360, and nuanced customer analytics for next best action and detailed customer segmentation. However, without deploying foundational data governance in parallel with these initiatives, pharma leaders risk wasting their investment and falling short of the desired level of customer engagement. Foundational data governance ensures data is standardized for cross-functional sharing, platforms are optimized with valuable metadata, and customer analytics are accurate to support decision-making.

Several near-term initiatives can help improve the data that is crucial to enhance customer engagement: 

• Data standards – pharma companies should define critical data elements (CDE), such as customer specialty and call interactions, develop a comprehensive data model, and adopt data standards for each CDE to create consistency across platforms and functions. This will enable data to be shared efficiently enterprise-wide while creating a holistic view of the customer.
• Data governance operating model – leaders should identify and assign governance bodies with a clear understanding of the marketing/sales organization to provide oversight across projects, functions, and systems. We recently worked with a global pharmaceutical company to instantiate a governance body with representatives from marketing, analytics, R&D, and manufacturing. The group was responsible for collectively governing the customer domain and ensuring data was of high quality and shared across functions. Defining and embedding roles and responsibilities into jobs that are supported with appropriately skilled resources will ensure data is accurate and of high quality, improving the ability to successfully engage with the customer.
• Data governance practices – cross-functional collaboration should be leveraged to define and adopt policies and procedures that use industry best practices with input from key stakeholders and end users. For example, a data quality standard operating procedure establishes a framework to formalize activities associated with the management, organization, production, collection, usage, storage, and disclosure of data. As a result, data becomes findable, accessible, interoperable, and reusable.
• Governance tools tailored for customer engagement – companies should make investments to implement and adopt tools such as data catalogs, data glossaries, data quality solutions and metadata management platforms that help manage data and metadata, automate governance processes, and collaborate on data governance activities cross-functionally. This will result in accurate and near real-time information being available to support customer interactions.

In addition to data governance, embedding data citizenship into an organization’s digital strategy will further enhance customer engagement. Although the timeline to achieve this type of transformational change is longer, it can build upon a model that is responsive, near real-time, and produces personalized and high-value experiences. Some key approaches to cultivate a data citizenship mindset include:

• Data-driven culture – executive leadership should champion an environment where data is valued as an asset, and everyone is accountable, which will enable pharma companies to trust their data and create a common understanding of data.
• Data awareness – pharma companies should launch internal training programs that focus on how to collect, store, process, use, analyze, and interpret different data types. This will improve operational efficiencies and strengthen cross-functional data sharing capabilities while leveraging data.
• Data accountability – create an environment where data is checked for accuracy, data duplication is prevented, and good data behavior is incentivized and rewarded, which allows organizations to leverage high quality data for decision-making.

Focusing on foundational data governance by implementing the right tools, operating models, and creating a data citizenship culture are at the core of adopting an effective engagement model and are critical components to transform customer engagement. By embracing a data-centric approach, pharma companies can maximize customer interactions and improve experiences in an increasingly data-driven world.

The authors would like to acknowledge the contributions of Veeral Solanki and Rupen Mehta to this article. Solanki and Mehta are life sciences experts at PA Consulting.

About the Authors:

Vignesh Ramesh is a life sciences expert at PA Consulting. He has 15+ years within the healthcare consulting industry and is experienced in leading multiple data governance, data standards, data quality/data management. He has led several multi-year data management programs across product/customer domain within pharma to facilitate high-value business use cases specifically focused on Next Best Action and personalized customer engagement.

Bryan DeFoe is a healthcare expert at PA Consulting. He is responsible for successfully leading business, strategy, financial, and operational management initiatives for health systems, community hospitals, and other types of healthcare organizations.

Kunal Patrawala is a life sciences expert at PA Consulting. He combines his pharmaceutical, digital, and innovation acumen to tackle an array of diverse projects in the life sciences and the healthcare sector.