The NEXT Pharma Summit webinar on customer experience (“CX”) was a great opportunity to speak and learn about the challenges and opportunities of enhancing customer experience with new digital technologies. I had the privilege to speak alongside Sudhir Mahakali, Global Head for Data Strategy and Advanced Analytics at Sandoz; Alexey Cherchago, Head of Sales Excellence for Europe at Sandoz; and Philippe Kirby, Global Digital Engagement Capabilities Lead at MSD.
Our panel covered a lot of different aspects of the problem, and everyone had their own perspective to bring, but ultimately, I think we all had similar conclusions about the challenges and opportunities surrounding AI as a part of digital transformation in the industry.
Here are some insights I took away from our conversation. You can also watch the session here.
Finding Insights in the Data Deluge
The Pandemic completely changed the world of the sales rep, and created a deluge of data in the process. As both reps and HCPs shifted to digital forms of engagement, they began to rely on an incredible array of online information. This has been both a blessing and a curse. In Phillipe’s words: “All of a sudden, we were confronted with all this data, all these insights, and the need to really exploit it, because all of a sudden digital was really the only means to communicate with customers.”
While pharma’s R&D side has been relatively quick to adopt AI and data science methods, the business side of the industry is still struggling to make use of digital tools for business planning and insights. While the immediate challenge might seem to be the problem of ingesting information and organizing it into useful warehouses (and scaling this will not be a trivial task), there are also bigger strategic questions.
What do we actually mean when we say we want to improve the customer experience? One important aspect is value. Customers need to find value in what they are offered; ultimately, this is the thing that drives them toward engagement. Again, Phillipe put it succinctly, emphasizing the value of content.
“The content they consume: that's where you get a lot of insights on what customers really value. [...] The ‘next frontier’ is really doing a proper job of understanding the content and what customers value, so that we can continue to return value to the customers.”
Finally, we need to be clear about who our customers actually are. In the mission to be customer-centric, we should consider the needs of internal customers such as front-facing reps alongside those of HCPs and patients. Ultimately, the tools reps use can help or hinder the people on the ground, who are tasked with meeting their HCP customers where and how they want to interact. If these tools are not used optimally, then the data ingested becomes marred or biased.
Augmenting Human Capabilities
While “AI” is a term with powerful connotations, the reality is more prosaic. In fact, I think we might be better off if we talk less about artificial intelligence, and more about technology as a way of augmenting human capabilities, rather than replacing people with machines. This viewpoint appreciates and complements the humanistic perspective of such tools, creating intrinsic value and freeing up time for people to act on insights that lead to high-impact engagements with their customers.
In practical terms, the proliferation of digital touch-points and hybrid digital/physical interactions has created a new and complex environment in life sciences. AI and ML (machine learning) are ways of tackling this growing complexity. As per Philippe Kirby, at MSD, marketers turned to omnichannel campaigns during the pandemic, but suddenly found that they needed AI support to manage the complexity of their new methods. This is the kind of use-case we should be paying attention to: a real problem, where technology provides tangible benefits, without removing human intelligence from the loop.
When it comes to implementing AI-powered tools, the most important thing is to take iterative steps via pilot programs, rather than try to make sweeping changes across a whole organization at once. Designs need to be validated by the user experience, then scaled up as the organization builds a stronger digital culture. This is also called “Design Thinking” in the tech world.
To do this, pharma will need to move away from the traditional culture of consultations with long intervals between iterations, and toward a more agile, data-driven culture with shorter decision-making cycles. Teams will need to be better integrated—for example, data scientists will need to learn about the business imperatives—and brand leads will need to be educated to incorporate data into their decision-making processes at the start of the planning process, not at the end.
When it comes to adoption, the technology itself matters. Users need to trust that the platform they are using is actually providing good insights. In fact, the user interface impacts the extent users adopt the technology. As an example, clunky and complex tools prevent sales reps from doing their jobs efficiently, taking away precious time they could be spending in front of their customers.
To have one pane of glass to the customer, department silos need to be managed, metrics/KPIs harmonized, and different platforms and parts of the business better integrated to keep the customer at the center, while respecting ethical and legal boundaries.
Finally, and once again, people need to see the value of new technologies and how they impact their daily work. As one speaker put it, users should see AI as a “golf caddy” that suggests a club, but leaves the final decision to human judgement. Reps and marketers need to have a chance to input and play with new technologies before they are mandated, and when they do, the business impact and value need to be clear. AI tools should delight, not burden, their users, so that people can and will use them on a regular basis.