The Rise of Customer Science in Pharma to Predict HCP Journeys

Over the next several weeks, we’re dedicating space on our blog to explore a fast-growing business discipline called Customer Science.

What is Customer Science? And more specifically, what is Customer Science in Pharma?

Customer Science is an emerging practice that integrates data science, behavioral science, and AI to gain a deep understanding of the customer experience – or in the case of life sciences, HCPs and their patients – and what motivates their decisions along the customer journey.

The shift to digital channels has radically changed how HCPs and patients connect and engage with biopharma companies. At the same time, the rapid development of technological innovations like machine learning and AI are providing never-before-seen insights into HCPs and patients, and dramatically impacting sales manager decision-making. All of this has created a perfect storm for commercial organizations to use Customer Science to better understand their audiences and deliver more valued experiences to meet their needs.

Customer Science is at the center of our work around customer journeys, combining data science, behavioral science, and AI. 

In the coming weeks, we’ll look at the AI technological revolution that has made Customer Science achievable, life sciences industry dynamics that are driving the need for change, lessons from past industry disruptions, and what lies ahead in this new discipline.

First, we want to begin by looking at the rise of Customer Science as a new paradigm for sales managers to better understand HCP and patients and, consequently, drive engagement. To do that, let’s go back to the start of ODAIA.

‍ODAIA’s beginnings in Customer Science

When we founded ODAIA six years ago, our focus was to better understand the customer journey using machine learning, AI, and data science. We wanted to help companies move beyond post-it note and whiteboard mapping exercises that didn’t accurately reflect reality, only showed what was known about customers, and plotted what an ideal journey might look like.

But what about the things a company doesn’t know and can’t see about its customers? Or the fact that not every customer journey is the same? What if we could not only show past and present customer experiences, but also predict future ones?

We believed it was the underlying organizational processes that created the customer journey. If we could analyze these processes and all the data behind them and use machine learning, we could understand the journeys of multiple people, see roadblocks, identify patterns, and use AI to predict future journeys.

We knew the implications in life sciences could literally be life changing. We can get a complete view of a therapeutic journey, including HCPs and patients, and accurately forecast changes over time – what HCPs are prescribing, in what regions, for which sets of patients. Ultimately, biopharma companies and their sales reps could get life-changing therapies to the people that need them faster.

This seed of an idea sparked the ODAIA vision and mission. Fast forward to today, Customer Science is at the center of our work around customer journeys, combining data science, behavioral science, and AI. 

Applying Customer Science in Pharma

Data science is statistical analysis on the HCP experience and their many touchpoints with a pharma brand, including people and channels. Behavioral science interprets the data for insights on HCP behaviors. And AI makes reason of what’s happening and why, to predict what will happen next with HCPs and their patients.

In other words, Customer Science is layer upon layer of understanding of any therapeutic area to get a clear picture of the HCP and their patient decisions along the entire customer journey. We’ve been applying Customer Science for six years. Now, we have 2- 3 of the top 15 largest pharma companies innovating their go-to-market approach and commercial strategies using ODAIA.

The rise of Customer Science is being driven by the need for a more complete picture of HCPs, their behaviors, and the ability to make correlations from data captured from many different channels. Moving ahead, Customer Science will be the new paradigm for sales managers to better understand HCPs and their patients to enhance customer engagement.

Top-decile targeting is no longer enough

For decades the industry has been trained that syndicated market data is the only data sales managers need for HCP engagement, including targeting segmentation and pre-call planning. If sales managers know the HCP and their address, medical license number, the volume of prescriptions they write, and get a refresh of this data every three to six months, they can drive effective customer interactions.

But what does this data truly tell us about the HCP and their patients? Not enough. We still don’t know the “why” behind the data. There are no forward-thinking KPIs about which HCPs might be better to engage compared to others. We may know which HCPs are high prescribers in a therapeutic class, but not understand which have a greater propensity to switch to a new treatment option that their patients may need.

Decile-based targeting according to prescribing volumes was perhaps the last major innovation in customer engagement – and that was over 30 years ago. Companies brought in (and still do) hordes of consultants to manually decile groups, which takes time. It’s the reason why call planning and targeting segmentation can only be done every few months – it is the limit of what is humanly possible.

Top-decile targeting remains the industry’s standard mode of operating. The conventional thinking is to chase the highest-deciled prescriber groups with more promotional visits and calls. And, if you need to, add more reps to drive more engagement and, hopefully, sales with these groups. Companies define successful patient outcomes on this strategy. It’s been the prevailing wisdom for decades.

The Emergence of Customer Science in pharma to deliver more meaningful HCP and patient experiences

But Customer Science is changing the game once again with a new paradigm to target the right doctors and patients and drive engagement. Sales teams can look beyond past HCP trends and usage data in picking the right physicians and leverage AI algorithms to score and rank HCPs based on prescribing potential. 

Also, sales managers can understand HCP behaviors, picking up signals about why they’re writing prescriptions and what changes their mind about what they’re writing. With better insights, sales can educate physicians on new therapies more efficiently and get the right message to them at the right time in the best channel. The best part? Instead of waiting for new data every quarter or bi-annually, sales managers can get weekly or monthly predictions. 

The innovative sales managers of the future will leverage Customer Science to improve HCP and patient experiences and make data-driven decisions to fulfill HCP needs more effectively and efficiently. They’ll be able to spend less time analyzing data and more time seeing customers. Most importantly, they can shrink the time from symptoms to treatment to improve the health outcomes of patients.

Perhaps no single innovation has done more to propel Customer Science forward over the past six years than AI. It was the final piece to the Customer Science puzzle.

In our next blog in our three part series, Peter Harbin, VP of Industry Solutions at ODAIA, will take a deeper look at AI, the life sciences industry trends that are driving the need for change, and lessons from past industry disruptions so we don’t repeat history.

Learn more about ODAIA and Customer Science as a new paradigm for sales managers to drive HCP engagement at our upcoming webinar called “The New Science Behind Meaningful HCP Engagements”. Learn more and register here.