Information Into Action

Drugs do not become blockbusters by accident. Rather, phenomenal successes are the result of product managers' careful analysis of market information, detailed knowledge of target consumers and competitive positioning, and execution of targeted messaging. That's why many pharma companies are using analytics and the application of business intelligence (BI) to drive marketing decisions, brand management, and cus-tomer relationship management (CRM) programs.

Steps to BI Success

BI Basics

BI and analytics are discussed so often that their meanings have blurred. (See "Myths of BI," ) For the purpose of this article, BI is defined as the discipline of collecting and analyzing information—including competitive intelligence, market research, and customer behavioral data—and effectively executing new business strategies based on it.

A plethora of software applications and consulting services for enterprise reporting, analytics, and other functions claim to provide "BI solutions." But those are just tools used to execute a BI strategy. BI is a process that requires human thought and execution to produce effective results.

Though software and back-end integration are vital, BI success is the result of using of advanced tools and information to create an integrated intelligence pro-cess. A lack of information is not generally the problem. Pharma companies are often drowning in raw customer data. The key is to convert data into actionable intelligence that can enhance operations and improve decision making.

For data to reach maximum value, they must develop from the point of collection to business execution. This process occurs in three main areas:

Content. This is the raw, event-oriented data, the "who/what/when/where" details captured during activities such as physician encounters, marketing campaign releases, and seminar attendance. Content is typically captured in internal systems such as sales force automation (SFA) or CRM applications.

Context. Context data provide a more robust view of the conditions under which events occurred. They contain nuances such as timing, physician demographics (specialty, geography, age), prescribing habits, psychographics (attitudes toward details), and other attributes, such as past participation in clinical trials. Rich context includes a wide variety of internal and external information sources.

Analytics. This is what results from analyzing content data and contextual relationships. It is the evaluation of data in aggregate that adds meaning to the volumes of individual data collected. Given a robust information architecture, there are many software tools available to assist with analysis, from data mining and predictive analysis to trend evaluation and reporting.

Beyond Information

Through analytics, product managers can leverage internal and external information to move beyond reports of past results and figure out dynamics such as:

• Why did it happen?

• How will results vary if we vary messaging?

• How will product adoption vary by geography? By physician specialty? Consumer demographics? Message delivery?

• Who will be most receptive to our messages, and why?

Individual results must be reviewed in the context of all of the factors that may have contributed to the success (or failure) of the campaign. One pharma company used BI to divide its marketing teams into two divisions: macro and micro. The macro division focused on major market influences such as competitive drugs, clinical positioning, and lifecycle concerns. The micro division looked at individual demographics; prescribing patterns, such as early adoption and switching; and corporate influences, such as sales force numbers and distribution.

This approach has led to improved messaging based on prescriber segmentation, as well as the ability to better coordinate campaigns with sales force readiness. That, in turn, can lead to significantly improved sales performance.

Marketers for another major pharma spent four months on pre-launch customer segmentation and analysis of prescribing patterns to determine early adopters by market. The result? The company beat its sales projections by 200 percent in the first six months of product launch.

Using BI and analytics to enhance its customer-focused strategy, another pharmaceutical company consolidated more than 250 data feeds into one common customer repository where 1,000 field reps could access information more efficiently. True BI is enabled through the combination of information architecture, appropriate analytics tools, and an effective implementation strategy. This holistic approach enables brand teams to better understand their own behaviors (internal operations) and the behaviors of their target providers.

BI as a Sales Tool

In pharmaceutical sales, the traditional approach for increasing market share has been brute force—more reps, more calls, and more talk time. But now, the sheer number of reps has caused physicians to severely limit access.

Myths of BI

As a result, sales strategies must evolve from quantity to quality. Brand managers must provide targeted messaging that will be heard by providers andattract a diverse consumer population.The message communicated at each encounter must be more compelling for the providers and consumers and more cost efficient for the company. Smaller pharmaceutical companies are using BI andanalytics to gain an edge over larger competitive sales forces. Detailed analysis of their target markets and individual prescribing patterns helps these smaller companies leverage their limited sales forces by maximizing each provider encounter.

In one case, a small pharmaceutical company leveraged BI to create "top-prescriber target" lists that drive sales force alignment, geographical sales rep positioning, and call detail planning. Using this strategy, the company was able to grow market share in older products without expanding its sales force or its marketing budget.

Applied Analytics

Without discrete analytics for the evaluation of physician behaviors, product managers may not understand the true impact of their messaging. Only when they analyze the breadth of cause/effect data, from call details, automated marketing, and call center systems, tied with informational context—and the latest competitive news, FDA bulletins, and clinical research—is it possible to efficiently and effectively target physicians.

Information must be accurate, complete, and current. Otherwise, brand teams may focus on the wrong factors, overemphasizing, for example, specialty-specific messages when fact-based drug information is more appropriate. Or they may spend too much time on already loyal high prescribers, instead of cultivating high-potential prospects, who are key to revenue growth.

Information Lifecycle

Information is the engine of BI, and itis important for product managers to work with the information technology (IT) organization to define information needs. In addition to having the right data available, product managers need to evaluate the types of analytics software available. It is important to pick the right tools for the right function, thus maximizing the effectiveness of the investment.

Even if the company uses external consultants to develop a BI strategy and select the appropriate software, these decisions must be coordinated with company IT so that it becomes a part of the overall information acquisition and management strategy. This is critical for compliance, security, and cost efficiencies.

Information resources, especially external data sources, can be expensive. It is important to make sure these resources are used as effectively as possible throughout the enterprise. An effective BI information strategy can make information widely available to meet many different business and analytics needs.

BI solutions offer a wealth of opportunity for enhancing sales and marketing processes. Effectively leveraged, BI can help product managers segment their mar-ket and create highly effective, targeted marketing communications to position products. Success is the difference between understanding sales performance and improving that performance. A holistic approach to BI will help product managers close that gap.