How AI can help drive biopharma marketing ROI | Deloitte US has been saved
By Mark Miller, managing director, and Sriram Ramamurthy, senior manager, Deloitte Consulting LLP
Biopharma companies increasingly rely on digital marketing, but they may be missing an opportunity to engage patients and health care providers with more relevant and targeted interactions.
U.S. 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 this increase, 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 across industries, only half of pharma and biotech respondents use quantitative metrics to determine the long-term impact of marketing. Furthermore, 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.
Connecting the dots
Biopharma companies typically market their products to a broad audience across a wide spectrum of channels—for example, via TV, print, web, social media, and sales representatives. But this omnichannel marketing ecosystem is often disconnected and episodic. In addition, many marketers currently have limited visibility into what is working or not, and are unable to answer an essential question: Are we sending the right brand messages to the right patient and HCP audiences at the right time using the right channels?
Current 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.
Cloud-based, omnichannel AI engagement solutions that analyze multiple data sources—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 evolve HCP and patient engagement from episodic and transactional interactions to personalized, empathetic dialogs that enable ongoing relationships.
For biopharma companies marketing to patients, for example, AI could enable personalized, just-in-time content suggestions—backed by scientific evidence—on the patient’s smartphone when he or she enters a pharmacy or reads a WebMD article. For 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 adherence
- 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 content-channel combinations
- Detect signals to predict the next best action based on attributes and variables
- Develop customized content that’s relevant, authentic, and based on a comprehensive understanding of the patient or HCP
- Improve return on ad spend allocation by understanding what’s behind conversions—and the amount of revenue those conversions are generating.
The technology, restaurant, and retail industries, among others, are using AI- and analytics-driven omnichannel optimization to anticipate customer preferences—for example, identifying which personas 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 strengthen HCP and patient engagement, improve marketing return on investment, and remain relevant in an ever-evolving, increasingly virtual health care ecosystem.
