Optimize commercial costs and profitability in life sciences

A pharmaceutical sales representative visits doctors of varying ability to prescribe medicine to their patients. A TV commercial covers a region where few people need the advertised drug. A hospital specializing in rare cancer treatments wants to consider a newly approved therapeutic product, but the life sciences company has yet to commit to them. Wasted commercial costs and missed opportunities prevent life sciences companies from reaching their full business potential. How do these abuses still happen and how can companies deal with them?

Explore merchant spending habits

For decades, companies in the life sciences industry have invested their sales, marketing and advertising budgets evenly across US geographies and channels. They struggle to optimally reach healthcare providers and patients. They spend too much just to maintain the status quo, missing out on dozens of unseen opportunities. Instead, companies should target and invest strategically in geographies and channels with the highest potential returns.

For example, many pharmaceutical companies still invest a high percentage of their total budget in sales and marketing initiatives in geographies where their brands do not have a significant market access position. Additionally, significant investments are being made in regions dominated by integrated delivery networks (IDNs) such as Intermountain Health, Kaiser Permanente, and Advocate. These organizations have a decision-making structure led by their internal pharmacy and therapy (P&T) committee—not individual healthcare providers (HCPs)—that determines whether a brand can be administered. It is therefore imperative that Marketing, Sales and Market Access coordinate in tandem with their Center of Excellence (CoE) support teams such as Commercial Operations and Analytics, Forecasting, Finance and Contracting to most effectively deploy promotional dollars.

Use data and AI to optimize costs

Life science companies have a significant amount of data at their disposal, more than enough to drive optimal commercial investment. But data is complex, messy, and decentralized, and it comes in many shapes and sizes. Some examples of this data include:

• Third Party Data: IQVIA (Xponent Plantrak, DDD, HCOS), PRA, Nielsen Advertising and Media Data, Social Determinants of Health (SDOH), Fingertip Formulary, Copayment, Claims Data
• Government data: TRICARE, CMOP, TMOP, FSS, VA
• Internal promotional data: details, samples, speaker program, multi-channel promotions

To make sure all that data is usable, companies need data analysts to design and engineer the data, business rules, and assumptions.

With the right combination of integrated data, understanding historical performance and deploying AI to gain forward-looking insight, the life sciences industry can make much better decisions to secure contracts with key payers and determine which promotional channels are most effective for each geographic area region. Differentiated use of promotional channels such as peer-to-peer, sales rep visits, teledetails and digital libraries will ultimately lead to optimal sales spend across channels and geographies.

This idea is easy to understand: Use the data to understand how best to allocate investments and resources, such as brand marketing and sales distribution. But because data is so diverse, its value isn’t always immediately clear. It takes focused effort and expertise to clean, categorize and connect this data effectively.

Managing and using this data becomes much easier with a data grid. Instead of laboriously pulling all of their data into a centralized location, life sciences companies can tie disparate elements together by leveraging that data wherever it resides in the customer ecosystem. In particular, the use of a data structure in the hybrid cloud will allow companies to bring together complex and diverse commercial data sets. After bringing the elements together, companies can analyze and compare data by geographic region, promotional spend, and discount to provide historical performance and cause-and-effect information.

By leveraging AI and machine learning-driven insights and pathways into revenue and profitability across channels, we can best predict optimal commercial growth. Brand leaders can then prioritize investments across the various promotional and payer and provider channels for each geographic region, ensuring that their therapies and drugs find their way to markets for patients who need them most. AI technology can optimize for differences in patients’ socioeconomic needs, allowing life sciences companies to target areas with pricing that matches geography.

Optimizing trade spend by geography informs brand, therapeutic and company strategy

What if…

…you can look up which geographic and brand mix is ​​driving the most profitable growth?

…have an omnichannel view of which promotions are most effective in each geography?

…have a framework that helps align all key organizational commercial stakeholders on brand, portfolio and strategic execution to grow your business?

Want to know how you can combo an ability like this? Contact us today. We can help you create trading insights that learn and adapt – helping you optimize your trading spend and maximize your profitability.

Gautam Nagabhushana, Partner, Data and Technology Transformation – Healthcare, Public Markets

Ric Cavieres, Partner, IBM Consulting – Life Sciences Commercial Practice, Head of Quantum Computing