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Experimenting in the Commercial Investment Lab

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Pharmaceutical companies are transforming their commercial capabilities to align to a market where the buying process and key decision makers are dramatically changing.

By Faruk Abdullah, Vice President, Applied Predictive Technologies (APT)

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There has been a proliferation of diverse and complex strategies designed to improve commercial performance: Key Account Management (KAM) models, sales roles, multi-channel strategies, non-personal promotion, localized marketing, mobile marketing, and patient engagement programs.

While scientific experimentation has long been the norm in clinical trials, pharmaceutical companies have an opportunity to bring a test & learn approach to their commercial strategies.

While each of these is useful, not all investments will generate the desired outcome. Some will work only with a portion of physicians, patients, or employees, and will need to be fine-tuned before rollout. For example, deploying additional sales force efforts where a company has low share may work only with physicians who have certain characteristics.

All changes bear risk; rolling out new, previously-untested strategies is especially risky. To reduce the risk inherent in innovation, organizations are applying commercial strategy experimentation, similar to the kind of experimenting that has always gone on in the scientific labs. Business experiments enable companies to accurately determine the impact of each strategy before taking on the risk of a rollout. Further, they enable companies to understand the adjustments necessary to maximize ROI. An experimental approach involves taking an action (e.g., increasing non-personal promotion budget) with one group and comparing the performance of the group that received that action (either purposefully through a designed “test” or naturally, as a normal course of business) to a “control” group, which is ideally similar in every respect except for the action taken. This test vs. control comparison, when executed correctly, will determine the initiative’s incremental impact. We define this method of experimentation and analysis of the outcomes as “testing and learning.”

While scientific experimentation has long been the norm in clinical trials, pharmaceutical companies have an opportunity to begin testing and learning from their commercial strategies. Using this approach, companies can answer three key questions about any proposed initiative:

• What is the true incremental impact of this idea on key metrics, such as prescriptions, revenue, patient adherence, and prescriber loyalty?

• How can we target rollout of the idea, by market, health system, physician, sales rep, or patient type for the best results?

• Which variation of the idea (e.g., different call scripts, frequency, creative, etc.) will generate maximum impact?

By testing and learning, pharmaceutical organizations can benefit in three important ways:

More successful innovation: Companies improve their innovation success rate by rolling out the winning ideas, discarding the losers, and tailoring and targeting implementation for maximum impact.

2. Accelerated innovation: Testing and learning helps businesses get innovations into market much more quickly.

3. Breakthroughs: A testing and learning capability establishes the basis for breakthrough outcomes on mission-critical issues.

How to Begin Testing and Learning

Though some companies have already begun to conduct test vs. control analysis on the commercial side of their business, they are finding that executing tests in a manner that rapidly and consistently drives meaningful results is complicated. To overcome these challenges, pharmaceutical companies can take the following approach:

1) Understand where your company stands today. Executives can begin by assessing their company’s current commercial innovation capabilities with the following questions:

• How structured is our process for testing commercial strategies?

• How consistent is our methodology for designing tests and analyzing results?

• How detailed can we get in our understanding of test results (e.g., do physician segments respond differently to the program)? Do we roll out programs to the entire network or to a subset of the network?

• Can we understand the statistical significance for all of our results so that we can make well-informed decisions, while accounting for risk?

• Are we able to apply the testing process across the entire commercial organization, throughout the lifecycle of a drug?

Advance your testing capabilities. Isolating the cause-and-effect relationship between initiatives and key performance metrics is both analytically challenging and time-consuming. First, there is a broad range of external phenomena that influence performance – from changes in market access to competitor actions and concurrent internal strategies. Outside the lab, it’s a noisy world. Test groups and the rest of the population also generally have different characteristics, making it hard to find a representative control group for accurate comparison. With these analytic concerns in mind, there is a range of possible strategies for analyzing an initiative, and it is often unclear which strategy is most accurate. Adding to this challenge is the time required to collect (e.g., query a wide variety of time series and attribute data), cleanse, and integrate multiple data sources before analysts can even begin working.

Testing and learning in an accurate manner requires specific, complex analytics that mitigate noise (e.g., daily volatility in script trends) to isolate a small, but potentially highly profitable signal. With the right analytics in place, executives can achieve alignment around a consistent, rigorous methodology to use for decision making. To develop an efficient process, companies should also integrate data into one central repository so that disparate data can be rapidly analyzed to find deeper insights across the wide expanse of products, markets, sales reps, and customers. With data in one place, and a highly automated system to execute test analysis, executives don’t have to restrict experimentation to only their moonshot projects.

Find practical means to begin testing and learning.. Organizations can immediately begin testing and learning using two primary methods: analyzing past events and proactively designing tests. Identifying past, often naturally occurring, events enables companies to begin analyzing more initiatives today.

For example, companies can find past situations in which sales reps had an increase in live dialogues with certain physicians and compare performance (e.g., scripts) of those physicians to highly similar physicians that did not experience the same change. By measuring the incremental impact of past actions, companies can generate actionable insights for future campaigns.

Companies can also begin proactively designing rapid micro-tests to analyze new commercial innovations. An advanced testing capability enables companies to minimize the number of physicians, patients, or employees needed for the initial test, while also ensuring that the test group is large enough to measure meaningful results. Over time, companies can continue to drive low-cost, low-risk innovation with this type of disciplined approach for each stage of the testing process, from test design to analysis and rollout planning.

Develop your long-term commercial innovation pipeline. Though executives have many good commercial ideas, it is often the case that a number of these ideas will not turn out to be effective. Companies that rely on a short list of ideas will lack alternatives if testing shows that they are not valuable investments.

To continually improve commercial success, pharmaceutical companies need to develop a robust pipeline of ideas to test through the entire lifecycle of the drug, from launch through patent expiration, and across all commercial areas. With this list in hand, executives can then prioritize their innovation agenda, considering the following factors:

• Economic: Test has a potentially sizable financial impact

• Strategic: Test addresses the strategic priorities of the company

• Decision-focused: Test affects a near-term action

To continually improve commercial success, we need to develop a robust pipeline of ideas to test through the entire lifecycle of the drug, from launch through patent expiration, and across all commercial areas.

• New learning: Test creates new learnings for the firm

Pharmaceutical companies that use this type of testing agenda to prioritize and plan many tests each year have the opportunity to reap the benefits of ongoing, profitable innovation.

Case history:

To illustrate the value of testing and learning, consider this example in which a leading pharmaceutical company with multiple specialty drugs wanted to evaluate a new non-personal promotion strategy. The company was particularly interested in understanding the incremental impact of investing in a contract sales organization (CSO) to replace costly sales force investments for one of their specialty drugs. The brand and analytics team employed a variety of analytic approaches to measure the effectiveness of investing in the CSO, but the low-volume nature of the drug, competitor actions, and local market factors made it difficult to achieve consensus on ROI.

Using testing and learning, the company was able to accurately isolate the signal of the CSO initiative amidst the noise caused by factors unrelated to the investment, finding that the CSO led to a clear and statistically significant increase in prescriptions. However, the impact was not enough to meet the company’s desired success hurdles.

The company was able to discover hidden profit opportunities by segmenting the results across various activities and physician characteristics. This process showed the optimal number of live dialogues as well as which physicians responded best – specifically, those in higher income areas and those who had lower prescription volume of the drug.

This process showed that investing in a CSO for only a small percentage of physicians, tailored with the right content and delivery, could drive a more than 50% increase in ROI per dollar spent. More broadly, understanding the true ROI of the investment gave the company a framework for optimizing other non-personal promotion investments across brands.

As the healthcare landscape continues to change, only those companies that can innovate rapidly and invest in successful commercial innovations will survive. Testing and learning from initiatives enables pharmaceutical companies to understand which strategies really accelerate commercial innovation and build competitive advantage. As the first step, think about any commercial initiative that you are considering. Before you roll it out, can you test it? •


faruk Abdullah, Vice President, Applied Predictive Technologies (APT), leads the company’s efforts in the life sciences and healthcare industries. Mr. Abdullah has helped companies optimize a wide range of commercial strategies, including marketing investments, sampling initiatives and patient support programs. He helps clients leverage APT’s software and analytic best practices to support their top priority strategic objectives. Prior to APT, Mr. Abdullah was an Associate Partner at the Monitor Group, where he focused on developing growth strategies for life science and healthcare clients. Applied Predictive Technologies (APT) is a leading cloud-based analytics software company that enables organizations to rapidly and precisely measure cause-and-effect relationships between business initiatives and outcomes to generate economic value. APT’s software suite is revolutionizing how executives leverage their data to improve commercial performance. It helps organizations determine cause-and-effect relationships between changes in business strategy and KPIs. APT has offices in Washington, D.C., San Francisco, London, Bentonville, Taipei, Tokyo, and Sydney. Visit www.predictivetechnologies.com  to learn more. FAbdullah@aptmail.com

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