With the ever-increasing volume of real-world, customer-level data generated daily, it isn’t surprising that life science companies have been accelerating investments in data intelligence solutions as a critical component of clinical and commercial model transformation.
A recent report by Research and Markets indicates that the pharmaceutical industry will spend over $3.6 billion on AI technology by 2026, with diverse applications supporting drug discovery and development, clinical trial optimisations, forecasting and planning.
One ongoing quest in the life sciences sector is to determine how to effectively utilise AI capabilities to plan and execute customer-centric marketing strategies. While quality customer service and personalisation are core to customer connection for many industries, the complexities of an individual’s healthcare and treatment journey make this even more critical for pharma.
At the same time, industry marketers have long struggled to activate advanced data and technology innovations due to a lack of true connectivity across disparate data sets, a lack of guidance on how these insights impact cross-functional communication planning, and uncertainties with how to rapidly navigate evolving data privacy regulations.
Today, many brands’ marketing automation and next-best action strategies remain heavily influenced by algorithms that respond to promotional engagement rates and the time it takes to move HCPs through a sequenced messaging journey. The challenge is that the “customer journey” is not a linear and time-boxed experience and the factors that influence clinical decision-making are diverse.
While advancing segment profiles and corresponding messaging needs are core to moving towards personalised HCP engagement, it’s also important to layer in variables that give a more holistic view of the customer than writing behaviours and promotional responses, along with incorporating methods to understand and respond to individual customer support needs in real time.
Progressive Segmentation: Building Individual Target Profiles and Information Needs Over Time
Treatment decisions are influenced by many factors that go beyond an HCP’s clinical prescribing profile. Influencer circles, behavioural prescribing trends, hospital and health system affiliations and patient demographics are just a few of the factors that can be integrating into a progression segmentation strategy that helps brands move towards a more personalised communication strategy that delivers better business outcomes.
Machine learning algorithms can be used to create and evolve more complex identity graphs, paired with other factors like frequency, messaging, and overlay with patient promotion. The challenge is often the availability of the longitudinal “big data” required to reliably power algorithms.
For brands that don’t have sufficient data, whether this is due to lifecycle or lack of historical data aggregation at the physician level, one way to avoid over-emphasis on channel reach, frequency and engagement as the primary optimization driver is to systematically layer on addressable customer attributes that correlate to marketing-driven behaviour change.
Typically, this involves developing target profile and message need hypotheses built around a combination of both validated and prospective influencing variables. Combining these with a channel delivery and impact assessment strategy that’s executed over pre-determined timeframes, and layering on additional variables in a controlled manner, can help marketers progressively hone-in on the addressable characteristics that correlate with promotional response.
Self-selection In Addition To Prediction: Addressing An HCP’s Needs
While advanced segmentation and needs analyses are prime tactics to enhance the HCP experience, even the most contextualised segments will not always lead to reliable prediction of the information and support a brand’s customers require in the critical moments of patient care. This is where virtual assistants can function as AI-enable customer service and sales extension. When virtual assistants are planned and implemented with clear cross-channel use cases, they can serve as an excellent mechanism to respond immediately to custom, individual needs while also gathering critical insights on additional informational, behavioural, attitudinal drivers that strengthen marketers’ abilities to better understand and serve more holistic customer needs.
Evolution Is the Key: Transforming from Brand.com to an On-demand Resource Engine
While pharma marketers are still in the infancy of this AI revolution, the pharma industry should not underestimate the need to update even the most fundamental of HCP resources: Brand.com. Incorporating a well-thought-out chatbot experience can both personalise and improve the customer experience. Taking it a step further, the future of the Brand.com could evolve from a largely static set of content to a dynamic informational resource, including product support but also expanding to an intelligent hub connecting individual HCPs with appropriate medical education, patient support tools, condition management resources, external news, and more.
To conclude, if we gauge the multifaceted prowess of AI and machine learning in the modern marketing era, there are many opportunities to help pharma marketers improve the relevance and accessibility of how they engage with HCP audiences. As HCPs engage with pharma through diverse channels, we must complement real-time data collection with seamless customer feedback mechanisms so that the “voice of the customer” isn’t just represented in ‘moment-in-time’ surveys disconnected from the clinical experience but rather is integrated into the knowledge resources HCPs are using every day to manage their practice and deliver high-quality care to their patients.