Analytics has been at the core of sales force strategy for a while now. Most of the top pharma companies have been mining data to get direction on sales targets, sales force size, structure and activity planning.
At the core of all commercial insights, however, is identifying the source of business. Twenty-five years ago, pharma companies focused their analysis on identifying the right physicians to engage with, based on the volume of sales or prescriptions a doctor generated. Things are no longer so straightforward for them. Regulations have been driving structural changes in the healthcare market since the turn of the decade, making selling and marketing pharmaceuticals a lot more challenging, even as operating margins have come under pressure.
According to a 2014 IMS Health survey1, over 40 percent companies expected to reduce their commercial expenses by 6-10 percent in 2017
Current challenges faced by the sales and marketing function in the pharmaceutical industry include:
Loss of exclusivity of blockbuster drugs around the beginning of this decade (patent cliff reached its peak in 2012) has led to a sizeable drop in the profitability of major pharma companies. This has enforced a stringent cost control mindset within the companies
Globally, there has been a focus on healthcare cost containment, with many governments encouraging the prescription of generic drugs. This has put pressure on pharma companies to prove the efficacy of their drugs with data and analyses. Decision-making has shifted out of the hands of physicians too, with policymakers and the payers (government as well as private) practically mandating what a doctor can prescribe
Acess to physicians is on the decline, largely due to the consolidation among providers influenced by healthcare reforms such as the Affordable Care Act in the U.S. This has meant that the purchasing of drugs is centralized for a hospital chain or health system
The need of physicians to meet with sales representatives has gone down as digital technologies give them access to drug-related information that was earlier sourced primarily from the representatives. Patients too are more knowledgeable today due to healthcare-focused websites and social media forums. They now have a say in the discussions on the course of treatment with care providers
While the prescription of personalized medicines and specialty drugs is on the rise, there is no single syndicated source that aggregates the data from these areas comprehensively. Companies cannot understand the source of business accurately as they need to combine the data from multiple sources such as syndicated sources, payer network and specialty pharmacy hubs. Additionally, these sources do not follow the same standard for data aggregation
As a result of these challenges, pharma companies are looking at earning more with less, and the evolution of analytical technologies has given pharma companies the capability to do just that.
Advanced analytics tools can integrate and process millions of records of structured and unstructured data including physician notes, clinical trial data, medical transcripts, claims, patient records, and even social media posts, faster and more cost effectively. The deep and often unique insights generated allow pharma companies to develop more precise and differentiated sales force strategies in the areas of distribution, market opportunities per therapy area and innovative value generation, to name a few.
Let’s explore how advanced, intelligent analytics can drive pharma’s commercial success, given the changes and challenges shaping sales and marketing in the industry.
Advanced analytics can help pharma companies devise differentiated sales strategies
Better Insights, Greater Benefits
At the heart of the sales and marketing challenge for pharma is the need for the commercial strategy to become more aligned with the true buyer forces in the market today. The rise of newer influencers has meant that sales representatives need to engage with decision makers beyond physicians. This is bringing into focus the Key Account Manager (KAM) role that targets health systems, Integrated Delivery Networks (IDNs) and payers.
An in-depth analysis of managed care data, along with an analysis of the healthcare delivery network through physicians’ affiliation mapping, can help KAMs understand the scope of each player’s influence. They can also identify where the true purchase influence lies; whether it is the healthcare payers, IDN decision-makers or physicians themselves.
Also, the team’s targets, pricing strategy and messaging can all be refined based on insights on past transactions, behavior and latent needs of different players in the buyer ecosystem. Most pharma companies are already using predictive and prescriptive modeling to forecast either revenue or customer lifetime value.
However, to improve sales force design and effectiveness, analytics applications and solutions can enhance the relevance and impact of pharma commercial strategies. Here are a few examples.
Customer Segmentation and Targeting
Not all physicians who write more prescriptions are of importance to pharma companies. Customer segmentation and targeting algorithms can help identify physicians who serve patients fitting the company’s relevant patient profile. They can identify, with greater accuracy, doctors who are likely to switch from, to, or continue with the drug.
Analytics can also identify physicians’ preference for specific channels and frequency of communication. With significantly reduced physician access, these inputs can help design interactions that are tailored, on point and convenient – thus building physician satisfaction and loyalty toward the brand.
Managed Market Analysis
The majority of drugs sold in developed markets are channeled through payers and managed care organizations. Payer analysis can offer pharma companies important insights on brand affinity, predicted revenue, cost to benefit assessment of a drug, and so on. These insights can inform KAMs of the appropriate pricing and reimbursement strategies to be negotiated with a payer.
In some cases, payer insights can also lead to opportunities for joint value creation in the market. For example, a U.S.-based pharmaceutical company joined forces with its Health Maintenance Organization (HMO) customer and a large teaching hospital to develop a patient registry and outcomes-monitoring program.
Patient Data Analysis
Advanced analytics run on terabytes of patient data (electronic medical records or diagnostic data) with the help of machine-learning techniques can deliver unique insights into a drug’s effectiveness. For example, the identification of a drug being preferred because it does not have averse reactions with certain chronic illness medications can help the sales representative position it strongly to the relevant specialists.
Medical Science Liaisons (MSLs) are the new generation sales representatives with higher acumen in medical sciences. They are often required to interact with doctors to explain the drug beyond what is available on the internet. They are also required to hold informed discussions with Key Opinion Leaders (KOLs) to justify the inclusion of a product in the formulary. Patient data analysis provides the right pharmacoeconomic data to support such strategic discussions.
Enabling Patient Wellness
In order to establish trust and win brand awareness in the minds of increasingly aware patients, pharma companies need to go beyond just selling the product. Engaging with patients through health advocacy or wellness programs helps win customer mindshare. It also increases adherence to medication leading to higher sales.
A piecemeal approach to implementing analytics will not bring in the desired outcome for
An analysis of patient profiles can give sound insights on what sort of patient advocacy program would be needed. Additionally, integrated Business Intelligence (BI) tools can help track the impact of those advocacy programs on a brand. For example, Merck, one of the largest pharmaceutical companies in the world, launched a patient assist program in conjunction with Cigna, a payer, to improve medication adherence levels for patients taking its diabetes care drugs. Digital patient engagement programs are also on the rise globally to help patients take greater ownership in improving their health.
Social Media Analytics
Patients are the final consumers of pharma products, albeit through recommendations by providers. Social media analysis is thus important to understand the sentiment of this group better – not just the extent of positive or negative comments, but why they are upbeat or have negative sentiments.
While pharma companies have been largely cautious about venturing into social media, social media analytics tools can help guide their engagement efforts with greater certainty. Natural language processing capabilities in conjunction with sentiment analysis algorithms deliver real-time brand engagement insights and relevant context to each social media mention. Trend analysis highlights the topics that are interesting to patients in a particular therapy area and can help pharma companies post content that will get positive traction.
Infrastructure Before Insights
To truly achieve a transformation of its commercial strategies, the new pharma commercial environment requires lots of data — structured (syndicated prescription and patient data, CRM data) and unstructured (electronic medical records, labs and diagnostic data or social networking data). Handling and processing these different kinds of data requires a big data management framework with built-in capabilities for advanced analytics.
Over 85 percent of pharma companies responding to a WNS DecisionPoint™ survey in 20152 had indicated that they planned to increase their investment in analytics infrastructure, with majority investment in the development of operational analytics and BI systems.
The effective use of analytics also requires its integration with core work processes. This ensures that insights are generated at every point of impact and can enable decision making within the shortest possible time.
With high implementation costs and the lack of top management buy-in for a more centralized approach, most pharma companies only adopt a standalone implementation of analytics tools. This fails to impact their strategy and decision-making processes. According to the WNS DecisionPoint™ survey, only 11 percent of the pharma companies studied had adopted a centralized operating model for their analytics infrastructure.
Establishing the right analytics infrastructure would, therefore, be the starting point for achieving commercial excellence. Pharma companies also need to move beyond using analytics for forecasting and modeling revenues and targets. They should explore its more complex capabilities in identifying gaps and newer opportunities in buyer engagement.
Enabling sales teams to execute focused strategies in a precise and impactful manner is possibly the only way for pharma companies to break out of the margin pressure and cost containment deadlock.