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Five must-have AI capabilities to lead the commercial race in life sciences​

Executive Summary
Redefining the commercial model is undoubtedly a top-of-mind priority for life sciences leaders today, with many already expanding their investments in AI to meet the demand for modern healthcare systems and online engagements by customers. While there are a plethora of AI-powered use cases to leverage, it is crucial for life sciences organizations to prioritize those that drive immediate value and promise sustainable and scalable advantages in the long run. This article highlights those foundational AI capabilities that are fundamental to unlocking true commercial excellence in the life sciences space.
The life sciences industry has experienced transformative changes in recent years. The social distancing norms triggered by the global COVID-19 pandemic upended traditional commercial models abruptly, calling for an immediate shift to still-evolving practices such as virtual engagements. This brought dramatic changes in the way patients and healthcare professionals (HCPs) prefer to engage today.
HCPs worldwide prefer to consume medical and clinical information on their computers, laptop devices, or mobile phones1 - directly reducing the frequency of onsite, sales representative-led engagements.
Figure 1: Devices through which HCPs prefer to consume clinical, medical, and promotional information.
Patients have also reported that their favorite channels to interact with life sciences organizations are social media, sponsored forums, and company websites.2
Figure 2: Digital channels have definitively taken over as the primary source of pharma-created information for patients
As a result, life sciences organizations are re-thinking and re-engineering their commercial marketing models to hyper-personalize customer experiences in the digital world. They are turning to tech-enabled strategies to track ever-evolving customer preferences, increase operational efficiencies through digitally-enabled processes, and deliver better marketing campaigns through actionable and data-driven insights. Crucial to this success are advanced technologies, such as Artificial Intelligence (AI), that empower organizations to rapidly draw insights from massive datasets, automate commercial workflows efficiently, and strengthen commercial decisions effectively.
While AI offers a plethora of use cases, life sciences organizations must first focus on catalyzing the foundational few that are fundamental to unlocking true commercial excellence.
Through our work with leading life sciences organizations, we have identified five core AI commercial use cases that make all the difference. We outline each of these use cases in greater detail below.

1. Laser-sharp identification of Key Opinion Leaders (KOL)

KOLs are significant for life sciences commercial teams. They have access to huge audiences, wield substantial influence in the medical community, and create trust and credibility. Because of this, they can be highly effective at promoting brands for organizations. Aligning commercial strategies with the right KOLs is a sure-fire way for life sciences organizations to capture and sustain more customer mindshare. But this is no easy task. Scouting KOLs involves:
sifting through a copious amount of clinical literature, research publications, and medical journals published by hundreds of HCPs, analyzing several years of their data, and
understanding their national, regional, and local influence
...all of which is an extremely time-consuming process.
AI techniques simplify these tasks to a large extent. They speed up the process of spotting the right KOL influencers and ensure only the optimal targets for a specific therapeutic area are contacted and engaged.
By establishing pre-built AI connectors to public clinical literature libraries like PubMed and Scopus, organizations can simplify the task of manually searching and downloading publications on specific therapeutic areas (such as vaccines or immuno-oncology).
The output is sent to an AI engine that screens and validates the relevancy of all articles for a specific therapeutic area of interest - enabling companies to swiftly identify suitable KOLs, access their publications, and understand their research focus. Organizations can further augment this data by mining social media channels, such as Twitter and LinkedIn, to extract insights from relevant HCP discussions.
Since social media data is unstructured and fragmented in nature, it can be overwhelming for organizations to extract insights on time. This is where Natural Language Processing (NLP) plays a crucial role. NLP models convert large volumes of unstructured data into a structured format. They can be trained to recognize HCP sentiment in social conversations by identifying language patterns that reflect their opinions, interests, and expectations. Figure 3 breaks the NLP process down further.3, 4
Figure 3: NLP workflow
The structured data is then leveraged to generate insights into the current trends in HCP conversations, their expectations, and perceptions on a broad range of topics within the therapeutic area of interest. These insights help companies rank HCPs on specific attributes based on a scoring system, indicating whether they meet the required KOL criteria.

2. Data-driven omnichannel campaigns

Like every other consumer, customers across the healthcare value chain (HCPs, patients, payers, and beyond) expect similar personalized and contextually relevant experiences from life sciences companies.

The rise in demand for personalization across industries 5

Figure 4

HCPs echo a similar sentiment

HCPs surveyed by Indegene suggested that pharmaceutical representatives need to share only relevant content with them to make the interactions more insightful.
Therefore, there is a need for life sciences organizations to deliver the experience that customers seek and now demand across the digital ecosystem.
AI makes this possible. Customized AI algorithms can be used to recognize meaningful and recurring patterns in customer behavior across different points of interaction. This is useful to identify specific channels and content preferences that each HCP is drawn to, enabling you to group your audience into micro-segmented cohorts based on their similarities. For example, your algorithm could identify a group of HCPs who consistently engaged with your social media posts on cardiovascular treatments or those who have attended a series of your webinars on leading medical devices for cancer procedures. With this information, you can organize a highly-effective personalized campaign strategy to target different cohorts with different types of content and different outreach platforms.

In addition, AI algorithms efficiently gather information about preferred time and days of the week your audience is most likely to interact with your content. This enables you to identify the best times to trigger your campaigns on website, email, social media, and other channels to optimize user engagement.

With the right data-driven strategy and a robust AI framework, omnichannel efforts can help life sciences organizations achieve:

Up to 12%
increase in HCP engagement
Up to 20%
increase in annual sales conversions

Here are 8 crucial steps that go into building a hyper-personalized omnichannel engagement strategy:

Evaluate your data landscape and AI maturity through discovery workshops
Combine multiple data sources and integrate them using a cloud-based data platform
Leverage AI algorithms to build affinity scores by mapping HCPs’ historical activity across channels and content
Create micro-segmented models to define HCP personas across variables
Understand channel and content affinities among HCPs by executing controlled experiments through hypothesis testing
Design an integrated omnichannel call plan and activate them through integrations with downstream CRM systems
Automate salesforce recommendations with AI
Build a closed-loop performance measurement plan

3. Hyper-personalized content

For an omnichannel campaign to be successful, a customer-first content strategy is imperative. Poor content quality dampens customer experience and pushes them farther away from the brand.
HCPs surveyed by Indegene said they are overwhelmed by product promotional content pushed by life sciences companies on various digital channels
HCPs feel that sales representatives do not completely understand their expectations.
Figure 8: Illustrates what a hyper-personalized content journey looks like with AI

4. Automated content generation

Personalization demands relevant content - lots of it and at an accelerated pace. With the explosion of digital touchpoints, even seasoned life sciences marketers struggle with content marketing at the required pace.
This problem is far too common in other industries.
Marketers are under pressure to create assets or deliver campaigns more quickly
Marketers revealed they now need to create ten times as many assets as before to support all customer touchpoints
Thanks to technologies like Natural Language Generation (NLG), life sciences marketers can now let AI ease some of their content generation load.
NLG, a part of NLP, studies large datasets and converts them into plain natural language at an extraordinary scale, accuracy, and pace. NLG models can be trained to generate content for pre-defined templates, turning structured data like numbers, tables, charts, and graphs into descriptive reports aligned with the brand’s voice. It works best for long-form content when humans are an integral part of the process.
Figure 9: Illustrates the six stages involved in NLG.

5. Next Best Action (NBA) engine

Earlier in this article, we highlighted how a majority of HCPs have reported feeling overwhelmed with the volume of promotional pharma content they receive – calling life sciences commercial teams to shift focus away from a product-first mindset and deliver personalized and contextually relevant messages.
NBA engines enable just that. They flip the product-first approach (where the main goal is to find next best customer) to prioritize finding the next best proposition for a customer instead – one that will be relevant to customer needs in real time.
An effective NBA model, integrated with downstream systems, leverages AI and ML capabilities to recommend what to do next for a customer based on their past behavior, recent actions, interests, and needs – a crucial enabler for building contextually relevant and personalized experiences. These recommendations, delivered in real-time, can significantly optimize marketing and field-force effectiveness when it comes to personalizing engagements8. Sophisticated predictive and adaptive analytics lie at the heart of this. Predictive analytics use relevant data to predict the expected behavior of a customer, such as how likely will an individual engage with a specific content or campaign. Adaptive analytics learn from each customer interaction to refine predictions and continuously improve the success of propositions. Using these models, NBA guides every customer interaction and optimizes the marketing outcome, increasing customer engagement and sales conversions at scale.
Figure 10: Illustrates orchestrating a customer journey using NBA techniques.

Act now

Artificial intelligence is already shaping the future of healthcare, and it is time for commercial leaders across life sciences organizations to double up their efforts and investments in building robust AI-powered commercial capabilities that work well together. Identify your most immediate customer challenges, map them to your existing capabilities, and spot the gaps that AI can fill. Then pilot appropriate AI programs to bridge them, providing valuable insights along the way that indicate what works and what doesn’t so you can inform your long-term strategic decisions moving forward. Organizations that haven’t yet embarked on their AI journey will risk being severely disadvantaged in their race to commercial excellence. Now is the time to let AI pave your path towards a future-ready healthcare world.


The Digitally-Savvy HCP Report | Indegene
The State Of Customer Experience In The Pharmaceutical Industry, 2022: Patient Interactions
Social Selling for Pharma: Why It Is Important and How to Do It Right | Indegene
The State of the Connected Customer | Salesforce
Hyper-personalizing omnichannel engagements with advanced analytics | Indegene
Solving Content Challenges in Life Sciences with Digital Asset Management | Indegene
Omnichannel CX for life sciences: What do we need to get there? | Indegene


Gaurav Kapoor
Gaurav Kapoor
Vikas Mahajan
Vikas Mahajan