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Most emerging pharma companies still struggle to amass the computing power and assemble the teams to make use of the vast amounts of data now available to them, write Beth Beghou and Patrick Lezark.
Big Pharma is on its way to mastering big data. But most emerging pharma companies still struggle to amass the computing power and assemble the teams to make use of the vast amounts of data now available to them – from claims to electronic health records.
In fact, Beghou Consulting’s 2019 Emerging Pharma Pulse Report 1 found that nearly 60% of emerging pharma companies only use advanced analytics “somewhat” or “to a limited extent” to generate commercial insights. But that won’t cut it. To build commercial strategies that will maximize a new therapy’s market potential, emerging pharma companies must fully embrace data and the advanced computing tools that will help them analyze it.
With the right data at their disposal and properly channeled computing power, companies can deploy more sophisticated techniques – such as machine learning – to uncover more and better insights. The challenge lies first in mastering all the capabilities afforded by advanced analytics and then in applying analytics-generated insights to the commercial strategy.
There’s no question it’s tough for emerging companies to process in-house the large volumes of data necessary to perform advanced analytics. The data sets can be very complex and difficult to efficiently combine with other data sources. Indeed, emerging pharma companies responding to our recent survey identified organizing and storing data as one of their top challenges. Further, truly integrating advanced analytics into commercial operations requires commercial teams to dedicate time to build models, interpret data and stay up to speed on the latest analytical techniques.
Yet if these teams don’t commit to making advanced analytics a core piece of their commercial efforts, some emerging pharma companies will cede ground to competitors. While traditional analytics may provide enough information to commercialize a product, only advanced analytics will give commercial teams the power to develop hyper-targeted strategies and tactics. A robust advanced analytics effort can help uncover valuable information about the patient journey and prescriber activities that can enhance every element of the company’s commercial strategy – from forecasting to segmentation and targeting to incentive compensation.
Before an emerging pharma company can fully deploy advanced analytics, it must put in place a strategy that governs how it collects and organizes large volumes of patient claims, prescription and sales data.
Emerging pharma companies must consider three key foundational elements to create a strong data management strategy:
• Data expertise: Health care data can be complex. So, emerging pharma companies need to tap data professionals who understand how to organize health care data and account for its many nuances and restrictions.
• Access to cloud computing power: Emerging pharma companies can access a variety of data, but to prepare for and manage increasingly large databases they need to invest in scalable computing resources only available in the cloud. In addition to the power to process all available data, cloud computing allows the commercial team easy access to updated data and analyses. Further, emerging companies can avoid many technology management headaches by investing in a cloud platform, which eliminates troubleshooting requirements of an on-premise server and allows for quick, easy updates.
• An internal champion: Emerging companies operate with limited resources, which usually requires each employee to wear several hats. As a result, some companies sacrifice data management efforts in favor of other commercial activities. To avoid this, companies should designate an internal champion to lead development of the data management strategy and implement clear data governance rules.
The sales forecast serves as a roadmap for the entire commercial process, guiding commercial teams as they optimize sales force size and alignment, targeting, incentive compensation and more. The commercial team must use as much data as possible to inform the forecast. Advanced modeling can help them go beyond a straight-line analysis and develop a more accurate forecast.
First, emerging pharma companies can use advanced analytics to assess market potential. Analytics can help the team uncover key data points – such as the addressable patient population and how often to call on key physicians to maximize the chance of success. This results in a more informed forecast that is linked to the most effective sales force size. Additionally, commercial teams can mine data to predict uptake, troughs and peaks in their products’ sales, and more, which can help them shape the rest of their commercial strategies and minimize surprises down the line.
Segmentation & Targeting
Advanced analytics, such as machine learning and predictive modeling, can help the commercial team target the right customers and determine the optimal commercial tactics by predicting patient behavior and physician actions. Most companies determine their target lists before launch and update them annually or quarterly. By capitalizing on the power of machine learning, commercial teams can consider implementing more dynamic targeting to quickly adjust to new data points and shifting marketing situations.
Pharma companies have a near-constant intake of customer activity data. Using advanced analytics, they can harness that information to better anticipate and respond to customer behavior and adjust their targeting efforts accordingly. For example, if a specific segment of physicians responds more favorably to digital promotion compared to in-person sales rep visits, the home office will recommend fewer in-person visits for this segment and more details to physicians who respond to face-to-face calls. By using advanced analytics, the home office can gain a stronger understanding of the most effective sales and marketing tactics.
Nearly one-third (31%) of survey respondents told us that retaining top sales reps after launch was among their toughest tasks. Further, more than one-third (36%) of emerging companies experience annual sales force turnover of 20% or more. These statistics provide reason to place additional emphasis on creating a quality, motivating incentive compensation plan. Emerging pharma companies need the information and insights that come from advanced analytics to ensure the plan keeps reps engaged and is financially sustainable.
Here’s why: At launch, emerging pharma companies often implement commission-based plans to motivate reps and drive sales. But, continuing with a commission-based plan for more than a year after launch is often unsustainable. The commercial team can deploy advanced analytics to simulate sales and payout outcomes, further understand how reps’ activities impact sales, and more accurately predict new customer acquisition and existing customer retention. These efforts can help the company smoothly transition to a quota-based plan that accounts for territory-level nuances.
Patient Journey Mapping
With advanced analytics, a commercial team can gain deeper insight into the patient journey across physicians, tests and treatments. Commercial teams can then aggregate these insights about individual patients to formulate perspectives on entire patient populations. These, in turn, can inform the sales strategy.
Commercial teams can use the predictive power of advanced analytics to create patient cohorts based on common characteristics. This segmentation enables the commercial team to better understand patient types and informs a sales rep’s next best action. For example, predictive analytics can help the team identify a segment of patients whose insurance may not cover the product. When calling on physicians who treat patients in this cohort, the sales force can then prioritize messaging about patient assistance programs.
In some cases, commercial teams launching a product with little or no market data can use the patient journey to identify their potential market. By analyzing claims data, commercial teams can find patients most likely to benefit from their product. Then, they can target physicians who treat the identified patients with the understanding that they will likely treat additional patients who could benefit from the product. Further, the patient journey-derived targets may provide guidance on sales force size and alignment.
While the advantages of using advanced analytics are clear, emerging pharma companies will squander them if they fail to plan ahead. Emerging companies must arm themselves with the necessary data and have the computing tools in place to fully embrace advanced analytics as a key part of their commercial strategies. Strong data management strategies and robust analytics operations will equip emerging pharma companies to uncover the insights they need to devise and execute more precise commercial tactics and remain a step ahead of their competition in the advanced analytics arms race.