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How the “Big Data” Revolution Can Generate Value

DATA ANALYSIS

It isn’t just accumulating data: it’s identifying, accessing, interpreting and applying it that differentiates you.

By Dan Twibell, Managing Director,
Skysis, LLC

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Sales and marketing executives in the life sciences industry have massive amounts of data—some proprietary, some licensed from 3rd parties, and unfortunately, much of it siloed in a manner that’s not necessarily streamlined and accessible. But you know that.

What you may not know is exactly how the smartest companies are starting to draw meaningful intelligence from these massive silos of data, resulting in a higher yield on their sales and marketing investments and distancing themselves from less sophisticated competitors.

How are they establishing a competitive advantage? What can we learn from their performance? The following 4 Key Themes tell the story:

Identifying Obvious and Non-Obvious Sources of Data

This seems easy enough, but the majority of companies lack both a systematic approach to reviewing and assessing data, and a comprehensive data plan that bridges departments and prioritizes investments. Data sources may include:

• 3rd party prescriber data — most companies subscribe to this data but many fail to maximize the value associated with their subscriptions

• Specialty pharmacy data—the level and value of this data depends on multiple factors and the data is sometimes incomplete and inconsistent. That said, meaningful intelligence can be drawn from careful analysis and mapping of this data by specialized analytical approaches

• Primary research conducted by the company—This can include traditional market research but also more in-depth sales operations and customer relationship management information relating to physician preferences, historical call patterns, sample ordering/usage, etc.

• Managed markets data—Frequently an undervalued set of data, but when rigorously mined and mapped, can significantly enhance sales force targeting and promotional efforts. This data can include:

• Market volume

• Managed market/payor coverage

• Historical prescriber patterns, adoption rates

• Historical prescribing patterns of competitive products

• Brand/generic prescribing behavior

• Group practice affiliation

• Promotional preferences, and where viable, qualitative feedback from the sales force

The majority of companies lack both a systematic approach to reviewing and assessing data, and a comprehensive data plan that bridges departments and prioritizes investments

Accessing & Sharing Data Across Internal/ External Silos

Organizations differ with respect to their level of transparency and willingness to collaborate across functional areas, but in general very few companies share information as well as they could. “Hoarding data” is a common complaint among many sales and marketing organizations. A few common challenges and recommendations for improvement include:

• Senior executives who could draw meaningful insights do not review data in sufficient depth—It may be a reliance on less experienced staff to “synthesize everything into 4 or 5 slides,” or an unwillingness to allocate sufficient time to poring over data. But, let’s face it, few senior level sales and marketing execs are drilling down into customer data to the degree that they could. This prevents them from using their in-depth knowledge for informed decision-making. Ask yourself: How familiar are your senior and mid-level execs with the actual data sources? How can you better use their insights? How can you more effectively aggregate data?

• Employees with exclusive access to data are not sufficiently rewarded for collaboration with colleagues—If your employees feel “unique” and “valued” when they have the key to the data vault, why should they share? They see exclusivity as job security and an opportunity to build a reservoir of favors. Changing this behavior isn’t simple, but allocating discretionary funds to be used for recognition of collaboration across functional areas and teams can go a long way toward driving alignment

• Headcount reductions result in too few available FTEs with the knowledge and bandwidth to drive initiatives and analyze data —Talented, hard-working employees are frequently marginalized because of the excessive demands on their limited bandwidth. A shortage of people to develop the technology and analytics to extract maximum value from existing data is forcing many companies to rely on contract resources so that they can maintain a static internal headcount goal while getting access to the data and insights they need to make more informed decisions

Interpreting Data & Drawing Meaningful Insights from Analysis

Raw data, in and of itself, is meaningless unless the user can extract relevant insights that are measurable and actionable. Some companies excel at interpreting a single data set but fall down when it comes to aggregating information from multiple sources. Other companies can bridge and map data from multiple sources, cross-link it efficiently and create visually compelling analyses that unfortunately are useless because they lack domain context and product/ category-specific expertise. To develop a unique competitive advantage, you have to master both the organization of consistent, reliable information and the ability to distinguish what is relevant and actionable from what is simply interesting.

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How are some of these successful companies leveraging data?

• Predictive modeling—A select few companies in the life science space are using software applications to predict performance, monitor anticipated performance and adjust investments as needed on a regular basis to incorporate learnings. This requires discipline, investment and a willingness to learn from failures as well as successes

• Crosslink and integrate data—Frequently data sources are incomplete and incompatible. But companies can build bridges between data sources, create or modify elements so that they are compatible (or sufficiently compatible) to draw directional insights that can be validated through proprietary research. Building data sets that are consistent, reliable and well-linked takes substantial time and effort but can yield tangible results. Particular emphasis on enhancing data linkages can pay dividends when it comes to deploying sales and marketing resources. As a reference point, a recent McKinsey & Company and Massachusetts Institute of Technology Report shows that companies “that inject big data and analytics into their operations outperform peers by 5% in productivity and 6% in profitability.”

• Applying Data Insights, Modifying Investments & Measuring Performance

Even if you are successful in the above, applying data insights that go against historical practices can be daunting. The challenges of gaining senior management acceptance, demonstrating the superiority of new analytical approaches and being willing to acknowledge failures prevent many companies from innovating and embracing new models. A few thoughts:

• Embrace retrospective analysis—It is far too easy to gloss over performance that is sub-par in an effort to deflect blame and diminish second guessing. Unfortunately, this tendency precludes sufficient learning and adjustments to improve in the future. Diligent mining of performance data, evaluation of achieving key objectives (or not) and the application of lessons for future planning should be the goal of every management team

• Put in place a common data platform, communications system and governance process—This requires executive management buy-in and advanced planning (think three-year time horizon as opposed to a quick fix). Establish who owns what data sources, how this data can be optimally shared/integrated, what data should be withheld or shared and how to ensure that the data is being kept fresh

• Automate reporting where possible—The creation of a smart visual dashboard (where data and graphics can rapidly be included into management presentations and updated regularly/consistently) is essential if the goal is to change behavior. This doesn’t mean that management should rely simply on consolidated summaries. But these summaries do play a valuable role in communicating across an organization, and investing in a system that makes the process as easy as possible will pay dividends

The Payoff—An Illustrative Example

Let’s take a look at one isolated example of how a company could apply advanced data analysis.

• Situation—A traditional approach is employed for sales force targeting and performance management, leveraging historical and current prescription data to decile physician targets based on perceived potential value. While multiple variables are utilized for this alignment initiative, the current approach relies heavily on 3rd party prescription data only.

• Advanced Analytical Approach—While no additional “unknown” physician targets are identified (hence the universe of prescribers is unchanged), a re-prioritization and re-deciling of targets based on an accurate depiction of the potential cumulative value of a given physician is put in place. This revised potential value is established by incorporating a much larger and more robust data set, including:

• Managed care data summarizing the associated payor mix of a given prescriber and the relative favor-ability of formulary status for the target product within the prescriber’s patient population

• Historical utilization of samples as recorded in an internal CRM system

• Historical adoption patterns for launch products in the therapeutic category

• Group practice affiliation (where restrictions on brand prescribing may exist)

• Impact—The company launches with the same 40 sales representatives calling on the same universe of 6,000 prescribers, but now with a reconfigured deciling that incorporates targeting of prescribers whose patient population skews to plans where favorable formulary status exists and where other factors influence prioritization. The adjusted deciling decisions, influenced by more advanced analytics, lead to a more rapid adoption curve, a shorter time to peak sales and the ability to significantly drive market share within select payor accounts

Data can be a very good friend – but only if you really work on the relationship. •


Dan Twibell is a co-founder of Skysis, where he has supervised over 100 assignments encompassing strategic planning, brand management, new product planning and business development/ licensing for small, mid-size and global pharmaceutical and biotech organizations. Previously, he was in executive positions with inVentiv Health, Eli Lilly and UCB. Dan is a published thought leader and frequent speaker on emerging trends and their impact on the commercialization and marketing of prescription and non-prescription healthcare products.

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