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William King offers five predictions on evolution of data and predictive insights that could be critical for pharma go-to-market strategies.
William King offers five predictions on the evolution of data and predictive insights that could be critical for pharma go-to-market strategies.
With 2018 off to a roaring start, now is the time to reflect on the journey the life sciences industry has taken over the last 12 months and chart the trends we see on the horizon for 2018-especially those on big data and predictive insights.
The healthcare data landscape is more complex than ever, with exponential growth in the sources and types of data available. Extracting insights-the right insights, at the right time-from the available data is crucial to the kind of personalization that is now required for commercial success. At the heart of these insights, however, is the data-as is the ability to connect, link, and interrogate big data, little data, and disparate data, ensuring that the insights are multifaceted and multidimensional.
Historically, the task of connecting and analyzing data fell on humans such as analysts and consultants. Now, the body and diversity of the data is so significant that it’s necessary to utilize computed analytics and machine learning to manage and link the data for timely and actionable insights. Artificial intelligence, and in particular machine learning, will be a core underpinning of any winning big data or digital transformation strategy in 2018.
Another important step in the digital transformation journey is breaking through the data silos that exist between business units and technology teams, and gain efficiencies from the data being created and purchased.
Life sciences companies are increasingly looking at ways to build value “beyond the medicine” and offer complementary services and value-added, integrated care solutions. Frequently these include service centers, where patient-reported outcomes and other feedback create insights into the care continuum and all that it entails, from pricing to delivery.
These data points-patient experience, disease progression, treatment, disease management, preventive measures, and beyond-are crucial to commercial initiatives, as a growing number of payers are asking for the comprehensive impact of medicines on patients or mandating that companies track the patient journey. Capturing this data, and integrating it back into the company’s strategic go-to-market approach, however, is not so simple. Each point of engagement creates “digital exhaust.” Just as a wisp of exhaust trails a car’s tailpipe, new data elements generate an important digital trail that can be used to create a more personalized approach to commercialization and customer engagement.
Our personal consumer experience with technology is setting the standard for technology with ease of use and intuitive, relevant delivery of information and insights. Companies like Google, Facebook, and Amazon collect enormous amounts of data to analyze our historic activity and predict our future preferences. They position recommendations, in the form of products or advertisements, in a highly targeted way, wrapped in a seamless user experience. We are served with what we need next, before we even know we need it.
This is the north star for life sciences companies; as they implement more comprehensive go-to-market strategies to address pressures in the healthcare ecosystem, they also need to reexamine the technology platforms and applications their commercial organizations are using, and provide a powerful, “programmatic” delivery of the customer journey to sales and marketing teams.
It is now critical to engage with, and influence, a broader group of decision-makers to successfully create access to, and adoption of, a medicine or therapy therapeutic. Additionally, it’s important to understand where “customers”-providers, payers, patients-are within the buying cycle and treatment journey. This extensive and complex go-to-market approach demands that life sciences companies make better use of a broader body of data and use predictive, programmatic insights from this data to guide their strategy. For 2018, it will be a business imperative to connect more disparate data sources, process and analyze them, and operationalize these with programmatic recommendations to deliver an experience akin to Amazon’s product recommendations.
Leading life sciences companies are taking steps to analyze and leverage networks within the healthcare landscape to maximize the impact of their commercial efforts and ensure improved returns on their investment in go-to-market initiatives.
The number of stakeholders shaping the decision-making for appropriate treatment and intervention continues to grow, and the relationship between these players-prescribers, physicians, payers, organizations-influences these decisions, positioning network-based decision-making as the new normal.
Whether with structured networks, like integrated systems of hospitals, physician practices, health clinics, and provider networks, or unstructured networks, it has been difficult for life sciences companies to develop a commercial approach that can successfully navigate these networks. In fact, these relationship networks can be defined and connected by subtle factors, such as physicians being co-authors on publications or guidelines, co-investigators on key clinical trials, common education or residency at medical schools, or shared patient management due to referral or management of multiple comorbidities.
Effectively understanding these networks of influence requires a sophisticated approach that goes beyond information captured from field personnel and taps into the multiple, external data sources that can collectively populate any gaps. In a recent survey of more than 110 biopharma leaders in sales and marketing roles, 71% said they believe that the behavior of a healthcare provider (HCP) or organization (HCO) is heavily influenced by their relationships with other HCPs/HCOs, but less than 30% of them are using, or are even able to analyze these relationships. In 2018, organizations that leverage technology to identify and analyze these relationship networks, and integrate the resulting insights in their go-to-market approach, will see their competitive advantage accelerate.
The life sciences industry is highly skilled at understanding the return on investment for discrete engagement initiatives targeted at a specific stakeholder group, such as non-personal promotion to physicians or direct-to-consumer campaigns focused on specific geographies and demographics. Today, marketing leaders must optimize the channel mix, investment, and messaging across multiple stakeholders and networks.
A data-driven go-to-market strategy requires broader use of derived insights to determine the best multichannel strategy, spanning both sales and marketing. For 2018, improved personalization of both content and delivery channels will be top of mind for biopharma marketing leaders. This means they need more dynamic, real-time feedback data from these same channels-field force and non-personal.
Additionally, companies need to coordinate, measure, and enhance activities across multiple internal customer engagement teams. This includes market access teams targeting pharmacy benefit managers or health insurance companies, national account teams interacting with integrated delivery networks, sales representatives detailing physicians, and all the complex interactions that span across these networks. At the campaign or product level, this translates to additional complexity for sales and marketing programs that span field activity and multichannel marketing.
Just as machine learning is paramount to connecting the multitude of data sources that underpin these efforts, augmented (or artificial) intelligence will support the increasingly dynamic, data-driven evolution in multichannel go-to-market.
While 2017 gave us a glimpse of the potential for technology to integrate broader data and extract deeper insights, 2018 will be the year when leading life sciences companies take the next, most crucial step in digital transformation, and put data and predictive analytics at the forefront of their go-to-market strategies.
William King is Founder and Executive Chairman of Zephyr Health.