A pharma sales representative visits doctors with varying ability to prescribe a drug to their patients. A TV commercial blankets 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 engage with them. Wasted commercial spend and missed opportunities keep life sciences companies from reaching their full business potential. How do these misspends still happen, and how can companies address them?
Examine commercial spending habits
For decades, companies in the life sciences industry have invested their sales, marketing and advertising budgets uniformly across US geographic regions and channels. They struggle to optimally reach healthcare providers and patients. They overspend just to maintain the status quo, missing scores of unseen opportunities. Instead, companies need to target and invest strategically in geographies and channels with the highest potential return.
For example, many pharmaceutical companies still invest a high percentage of their overall budget on sales and marketing initiatives in geographies where their brands do not have a significant market access position. Additionally, significant investment is made in regions dominated by integrated delivery networks (IDNs) such as Intermountain Health, Kaiser Permanente, and Advocate. These organizations have a decision-making structure driven by their internal pharmacies and therapeutics (P&T) committee — not the individual health care providers (HCPs) — that determines whether a brand can be administered. It is therefore imperative for marketing, sales and market access to coordinate in tandem along with their center of excellence (CoE) support teams, such as commercial operations and analytics, forecasting, finance and contracting, to most efficiently deploy promotional dollars.
Use data and AI to optimize spend
Life sciences companies have a significant amount of data, more than enough to drive optimal commercial investment. But the data is complex, messy and decentralized, and 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, co-pay, claims data
Government data: TRICARE, CMOP, TMOP, FSS, VA
Internal promotional data: details, samples, speaker program, omni-channel promotions
To make sure all this data is usable, companies need data analysts to architect and engineer the data, business rules and assumptions.
With the right mix of integrated data, an understanding of historical performance and implementing AI to get a forward-looking view, the life sciences industry can make far better decisions about securing contracts with key payers and determine which promotional channels are most effective for each geographic region. Differentially using promotional channels such as peer-to-peer, sales rep visits, tele-detailing and digital libraries will ultimately lead to optimal commercial spend across channels and geographies.
This idea is easy to grasp: Use the data to understand how best to distribute investments and resources, such as brand marketing and sales outreach. But because the data is so diverse, its value is not always immediately clear. It takes focused effort and expertise to cleanse, categorize and bridge this data effectively.
Managing and exploiting this data becomes much simpler with a data fabric. Instead of laboriously pulling all their data into a centralized location, life sciences companies can tie various elements together by using that data wherever it resides within the client ecosystem. Specifically leveraging data fabric across the hybrid cloud will enable companies to knit together complicated and diverse commercial data sets. After pulling the elements together, companies can analyze and benchmark the data by geographic region, promotional spend and discounts to provide historical insights on performance and cause and effect.
By leveraging AI and machine learning-driven insights and pathways in 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 their therapies and medications are finding their way to the patient markets that need them the most. AI technology can optimize for differences in patient socioeconomic needs, enabling life sciences companies to target areas with pricing that aligns with the geography.
Optimizing commercial spend by geography informs brand, therapeutic and company strategy
…you can look up what geographic and brand mix drives the most profitable growth?
…you have an omnichannel view of which promotions are most effective in each geography?
…you have a framework that helps align all major organizational commercial stakeholders on brand, portfolio, and strategic execution to grow your business?
Want to know how you can pull together a capability like this? Reach out to us today. We can help create commercial insights that learn and adapt — helping you optimize your commercial spend and maximize your profitability.
The post Optimize commercial spend and profitability in the life sciences appeared first on IBM Business Operations Blog.