This is our latest blog in a series on Customer Science, an emerging business discipline that integrates data science, behavioral science, and AI to gain a deep understanding of the customer experience and what motivates HCP and patient decisions along the customer journey.
In our first article, ODAIA Co-Founder, Helen Kontozopoulos, explored the rise of Customer Science as a new paradigm for pharma sales managers to better understand HCPs and their patients, and drive engagement.
In this blog, we’ll take a deeper look at the AI revolution that has made Customer Science achievable, industry dynamics driving the need for change, and lessons from past disruptions so we don’t repeat history.
AI is propelling Customer Science forward
The life sciences industry is not short on historical data. The problem hasn’t been finding the data, it’s been the inability to go through the data and make sense of it. But machine learning and AI are changing everything.
In many ways, AI is the secret sauce to Customer Science. Because of AI, Customer Science isn’t something that companies start from scratch. Instead, it is built on cumulative history and information from decades of life sciences data.
With machine learning and AI, we can scour internal company data and publicly available information to get insights and predict actions faster than ever before. We are no longer limited by the capacity of human thinking. Algorithms can be trained quickly in any therapeutic area to understand HCP and their patient decisions along the entire customer journey.
That’s why no single technology innovation has done more to advance Customer Science than AI. Data science gives us statistical analysis on HCP experiences and behavioral science interprets the data for insights on HCP behaviors. But AI makes reason of “what’s happening and why” to predict what will happen next.
AI is still just a consulting project in commercial
AI finally gained broad acceptance in life sciences because of the results it achieved in areas like clinical development. Early innovation labs explored AI for drug discovery and clinical trial patient selection. Now there is recognition that AI can drive faster vaccine development and patient understanding.
We saw this firsthand during the pandemic. AI found vaccine and drug targets quickly,1 speeding clinical development and getting a vaccine ready in just over 12 months, a process that typically takes 10-15 years.2
In commercial operations, analytics are entrenched across organizations, but AI is still largely relegated to a consulting project. After months of development, consultants hand sales teams a top-decile list of HCPs to target and engage. But because of the manual effort and time lag still involved with these projects, these insights and suggested actions quickly become outdated and go-to-market processes remain broken and inefficient.
The cloud advanced things further. Instead of bringing consultants onsite, organizations can now leverage an off-the-shelf, packaged SaaS platform to analyze and draw insights from massive amounts of information, process it, and automate go-to-market workflows more efficiently and effectively. Even with the cloud, many sales processes are still highly manual and slow.
Productizing AI will transform sales and marketing
Sales managers typically maintain spreadsheets with their business parameters and objectives, including target HCPs, prescription volumes, competitive information, and various territory details. Targeting segmentation and pre-call planning are painfully cumbersome to update regularly and data is often outdated when execution starts.
For reps, they’re overwhelmed with data and reports that aren’t easily discernible to determine high-valued, priority HCP targets. Insights are typically always backward looking and based on business rules that simply predetermine a conclusion of what actions to take and fail to adapt to market changes. Reps are left with lack of understanding on which activities are most effective with which HCPs and why, and call plans that are subjective according to a rep’s preferences.
In other words, there is a desperate need for machine learning and AI to make the entire HCP engagement process, from segmentation and pre-call planning to outreach, much more timely, efficient, and effective.
Now, a productized, AI SaaS platform can do the work for sales managers and reps. Evaluate hundreds of thousands of physicians across many regions to determine the best HCPs to target and engage, understand their prescribing behavior, and discover what outcomes were for patients. Examine millions of patients to learn which HCPs prescribed what drugs and therapies after a certain diagnosis.
One top 10 pharma company turned to ODAIA to find, prioritize, and target the right HCPs, drive more meaningful interactions, and derive real-time predictive insights from multiple data sources. Over a six-month period, users saw a 6% uplift in new patient starts and were better able to accurately anticipate HCP needs. Sales reps saved an average of 70 minutes per day during pre-call planning, and top users reviewed an average of 50 HCP profiles per week.4
That’s just one example of how leading biopharma innovators are leveraging AI and machine learning. Companies are using ODAIA to bring all their commercial data together in one place and drive predictive insights to their field teams so they can anticipate each HCP’s journey with greater accuracy, improve omnichannel engagement, and personalize their outreach.
Commercial teams are just scratching the surface of AI’s potential. Some of the biggest brands in pharma are innovating their go-to-market approach and commercial strategies using ODAIA. But if life sciences history has taught us anything, it’s that change is slow.
We learned from the decline of the blockbuster drug model and digital disruption during the pandemic that there is a risk of being caught flat-footed and falling behind. That’s why leading companies are proactively preparing for the impending technological shift and moving beyond AI consulting projects.
Patent cliffs and precision medicine are increasing the need for AI
A lot has changed in life sciences over the last 10 years. Growth of blockbuster drugs has steadily declined as patent protections have expired. Products have become commoditized and pressures from generic competition have increased. And things don’t appear to be changing anytime soon.
Over the next decade, the industry is facing another patent cliff, as nearly half of the world’s top 20 best-selling drugs will lose market exclusivity.3 With the decline of blockbuster drugs, companies are focused more on innovative personalized medicines and rare disease treatments for smaller, more targeted patient populations.
These two shifts – the fall of the blockbuster model and rise of personalized medicine – are significantly impacting commercial operations and go-to-market strategies. Teams are rethinking how they market and sell highly commoditized products to a broad patient group in a highly competitive environment. And there is a significant need to drive personalized engagement in the era of personalized medicine to connect with doctors and smaller patient groups.
The same top 10 pharma mentioned earlier was facing this very situation. One drug was about to lose market exclusivity with several generics becoming available. And another one of their drugs was a market newcomer.
Based on market changes, in less than two days the company quickly adjusted business objectives to prioritize the new drug and enable a fast rollout to the field force. ODAIA unified their commercial teams with actionable, predictive data insights to drive brand alignment. Over a 12-month period, they saw a 62% lift in new to brand prescriptions (NBRx).
Lessons from past industry disruptions
Industry dynamics have made digital a necessity in life sciences to reach and connect with the right HCPs and patients. But as recent as a few years ago, this wasn’t the case. Many companies hadn’t made the leap to building out a more extensive mix of digital channels to engage HCPs. Business was still largely done in-person and sales rep access to physicians and hospitals was unlimited.
It took a global pandemic to push commercial teams toward a digital-first strategy. Before COVID-19, digital was treated like a project, much like AI is today. A digital strategy mostly consisted of thousands of websites or portals. The state of digital across the industry was broken channels with few insights being extracted. And there wasn't a broad understanding on how to drive commerce through digital channels, from providing patients and HCPs information to enabling digital samples or prescriptions.
Because of a lack of digital channels and the ability to glean broader HCP and patient insights, we’ve had decades of sales managers over relying on syndicated data from a handful of companies to create targeting segmentation and drive pre-call planning – and then patiently waiting 3-6 months before getting a refresh of that data.
Fast forward to today, omnichannel digital engagement is now the way of doing business because many doors to HCPs and hospitals remain closed to sales reps. With more digital channels comes more opportunities to collect data, gain insights, and use the power of machine learning and AI to predict future HCP actions.
Consequently, Customer Science is more important than ever before because of the need to get a more complete picture of HCPs, their behaviors, and make correlations from data captured from many different digital channels.
AI is ready for primetime
AI has become fundamental to Customer Science, which is now paramount to gaining a better understanding of HCPs and patients and educating physicians in the commodity drug and personalized medicine era. AI and machine learning are powering never-before-seen insights and dramatically impacting sales manager decision-making.
Innovative sales managers are starting to make the shift to AI. For others that wait, there is a risk of finding themselves where they were during the decline of blockbuster drugs, the rise of personalized medicine, and when the pandemic hit and digital disrupted operations – they simply weren’t ready.
Why wait? There is significant potential for organizations to leverage machine learning and AI to improve their commercial processes and gain an advantage in predicting the right HCPs to target with the right message in the right channel and, ultimately, get the right treatments to the right patients – which is the goal of everyone in life sciences.
The future in life sciences looks bright because of AI. We are entering a new golden age of drug discovery and commercialization that will be powered by AI and machine learning. In our next blog, ODAIA Chief Product Officer, Mark Zou, will give you a sneak peek into his crystal ball and what lies ahead in the development of Customer Science.
With over 25 years in Life Science, Pete is a respected leader, known for his market influence and expertise in Business Intelligence, Information Management, and Customer Science. Pete's thought leadership as an industry speaker has been quoted at numerous media outlets in analytics and cloud computing. Formerly a Partner/Managing Director at Deloitte Consulting, Vice President at Veeva Systems, and General Manager at IMS Health (IQVIA), Pete has impacted 100+ Life Science companies across 36 countries.