How AI can help drive biopharma marketing ROI

Turning missed opportunities into targeted results

Biopharma companies are heavily investing in digital marketing, but are those dollars being put to good use? With multiple audiences and a wide spectrum of channels, the experience can be inconsistent or irrelevant for both health care providers and patients. See how cloud-based AI solutions can turn these lost opportunities into tangible wins.

Increased marketing spend ≠ better results

US health care and biopharmaceutical companies have significantly increased their marketing investments in recent years, with spending across advertisements, health care provider (HCP) engagement, and disease awareness almost doubling to $29.9 billion in 2016 from $17.7 billion in 1997. Not surprisingly, digital channels have become an increasingly important part of the media mix: In 2020, health care and biopharma companies spent an estimated $9.53 billion on digital ads, up 14.2% from 2019.

Despite increased spending, ROI may be falling short of expectations—although some organizations likely will never know if they are wasting their brands’ marketing dollars. According to the most recent CMO Survey of marketing executives across industries, only half of pharma and biotech respondents use quantitative metrics to determine the long-term impact of marketing. Respondents in the pharma/biotech industry reported the lowest current investment in AI and machine learning as part of their marketing analytics capabilities. This suggests a notable gap, but it also presents a significant opportunity for biopharma companies to explore how AI can address some persistent marketing challenges.

The limits of a disconnected marketing ecosystem

Biopharma companies typically market their products to a broad audience across a wide spectrum of channels—TV, print, web, and social media—and through sales representatives. But this omnichannel marketing ecosystem is often disconnected and episodic. Many marketers have limited visibility into what’s working and what isn’t, making it difficult to answer an essential question: Are we sending the best message to each audience through the appropriate channel at the right time?

Process and system limitations can create an inconsistent or irrelevant experience for HCPs and patients alike, leading to a low return on life sciences companies’ marketing spending. These limitations include:

Siloed marketing and sales functions
“Stovepipe” patient marketing, HCP marketing, and sales functions contribute to disconnected engagement.

Data access and integration challenges
Promotional data comes from a variety of sources, making it difficult to identify, integrate, and leverage data from impression through to revenue increase.

A priori business rules
Engagement strategies are often based on rigid and simple if-then business rules, making it difficult to identify and optimize engagement drivers.

Loosely coupled decisioning and engagement layers
Omnichannel engagement isn’t tied to a robust decisioning layer that can be fine-tuned to support marketing execution.

Transforming customer engagements through AI predictions

Cloud-based, omnichannel AI engagement solutions that analyze multiple points of data—including socioeconomic, demographic, location, medical history, and sales data—can help biopharma companies predict the best ways to interact with patients and HCPs. This can help transform engagement from episodic and transactional interactions to personalized, empathetic dialogs that strengthen ongoing relationships.

See it in action: Marketing to patients

AI could enable personalized, just-in-time content suggestions—backed by scientific evidence—to a patient’s smartphone when they enter a pharmacy or read a WebMD article.

See it in action: Marketing to HCPs

AI could provide recommendations to marketing and sales representatives, suggesting which channels and personalized content will be most relevant. A few companies are already experimenting with AI to optimize interactions with HCPs by providing tailored, product-related information.





When integrated with existing marketing platforms, omnichannel AI solutions can combine clinical and campaign data to generate insights that optimize marketing spending. With the capability to create more precise, personalized customer interactions, biopharma companies can:

  • Make informed interventions across channels, aligned to the disease journey, to drive medication loyalty
  • Coordinate timely marketing messages and behavioral nudges across the customer journey
  • Connect online and offline measurement capabilities to provide a holistic view of the customer
  • Target personas and predict behavioral responses across channels
  • Detect signals to predict the next best action based on attributes and variables
  • Develop customized content that’s relevant and authentic to the patient or HCP
  • Improve ROI by understanding what’s behind conversions—and the amount of revenue they generate

Anticipate, connect, and grow with AI

The technology, restaurant, and retail industries, among others, use AI and analytics to anticipate customer preferences—for example, identifying which people prefer self-service over live agent assistance. Life sciences companies that follow their lead may similarly find that AI can be a fast and direct path to strengthening HCP and patient engagement, improving marketing return on investment, and remaining relevant in an ever-evolving, increasingly virtual health care ecosystem.

The article was published on Wall Street Journal on March 23, 2021

Get in touch

Mark Miller
Managing Director
Deloitte Consulting LLP

Sriram Ramamurthy
Senior Manager
Deloitte Consulting LLP

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