Optimize commercial spend and profitability in the life sciences

Apharma 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.

Differences in profitability observed across US geographies. Each bubble represents a blinded geography, sized according to revenue.

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

What if…

…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?

Source: ibm.com

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Customer-driven digital marketing: Generate incremental revenues through real-time AI-driven analytics and campaign steering

According to a 2021 study, 46% of marketing decisions are not yet influenced by analytics. Many marketing departments still need days or even weeks to compile reliable data. That is too long to make ad hoc, agile, and valid decisions in the post-pandemic new normal. Rather than making decisions based on trial and observation, all available marketing data needs to be compiled into a single dashboard.

This dashboard enables teams to monitor all KPIs constantly, optimize campaigns across all channels, and proactively identify trends and eliminate anomalies that could negatively affect the marketing campaign’s success. The combination of data from multiple sources and the improvement of cross-channel attribution is paramount to be able to fully understand the market and the customers.

Measure performance in real time with individual data sets

Measuring campaign performance channel by channel is not sufficient. With the increasing number of channels (the web, apps, CRM, social media, sales, paid media and more), it is just not possible to analyze results and to provide a holistic report in real time. Instead of creating dedicated data teams, data can be displayed in real time to meet the needs of each respective marketing team member. The individual data set, supported by AI, enables the individual to respond with agility to any event that requires an adjustment. A good system constantly monitors the results based on classic marketing KPIs, ROI and revenues. Team members can identify underlying negative trends before they have an impact on marketing campaigns, revenues or the business in general.

Augmented analytics allow for a highly proactive approach, applying machine learning to uncover deep insights within potentially vast amounts of data. This leads to a more objective and predictive approach to data discovery, automatically identifying patterns and trends that humans may never uncover. Additionally, this process provides insights into these patterns’ causes and relevance. AI can be used to identify highly specific audience segments, outlining their preferences and pain points, as well as predicting their buying patterns. It unveils bias within data sets stemming from unconscious human preconceptions or flawed data collection techniques, helping to avoid a negative performance impact.

Combine modeling with data analytics for quantitative insights

These meaningful analytics enable marketers to steer campaigns in a granular and revenue-driven style. But do they prove the effects of brand awareness and its conversion into revenue? To demonstrate the ratios between brand awareness, brand sympathy, willingness to buy, marketing campaigns and revenue attribution, teams combine modeling with data analytics. Attribution modeling mirrors the customer journey. It reveals which parts of the journey the customer prefers and which parts need to be enhanced. CMOs can extract the correlation between the multi-channel setup and customer touchpoints and show how they convert.

Many marketing budgets were cut during the pandemic. Thanks to the long-time investment in marketing digitalization, enterprises will be better prepared to manage future crises and make educated decisions about cutbacks. The goal is to be agile and able to re-prioritize quickly. Real-time 360-degree data that reveals the performance of all campaigns across multiple KPIs must be in place. These meaningful analytics provide quantitative insights that enrich and guide marketing team discussions.

By regularly analyzing data and taking action to adjust when needed to drive results, marketers can achieve desired ROI and efficiency. According to our IBM C-Suite study in 2021, only 9% of surveyed C-suite executives create high value from data and have a high level of integration. The most successful organizations will be those that are willing and able to adapt to the disruption caused by data-based decision making. The good news: If they act now, CMOs still have a good chance to surpass their competition.

Source: ibm.com

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