Much has been written about the challenges facing the pharmaceutical industry over the past several years. Blockbuster patent expirations, force reductions, and the recent downsizings at Novartis and Astra- Zeneca are examples of the industry’s need to evolve and maximize their remaining resources. On the positive side, pipelines are diversifying and expanding, and many companies are looking forward to the next several years as being full of opportunity. As the industry evolves, marketing and sales leaders need to incorporate new tools into their brand plans in order to compete more effectively. Advances in technology, including social network analysis, are truly game-changing, offering new ways in which companies can market to or educate physicians.

We live in a world of interconnected social networks and communities. The people who make up our social networks can influence our behavior, and we, theirs. Groups of physicians are no different.

Targeting The Right Influencers and Knowing Their Connections

Social network analysis confirms that clusters of physicians will adopt the behavior of the most influential physicians, whom we call Key Action Leaders (KALs), who are not to be confused with traditional Key Opinion Leaders (KOLs). KALs exert influence for a variety of reasons including day-today interactions with other physicians, referrals, shared patients, coverage, and more. In addition to targeting traditional KOLs and high prescribers, brand managers can add to their targeting process information about influential local physicians who, because of their connectedness in clusters of physicians, and their own actions, are likely to influence the decisions of colleagues. We find that these influential KALs have great impact on the day–to-day prescribing decisions of their professional colleagues.

It is also very important to note that influence moves through networks and communities in multiple directions, so people with high influence are also able to be influenced. Think of this as influence bouncing around communities as opposed to it only moving in one or two directions. Network science allows us to analyze and identify key physicians in networks based on their own behaviors, their location, and type of relationships with other doctors in their network as well as those other doctors’ behaviors.

Furthermore, by knowing both the KALs and also those specific doctors connected to those influencers, brand and sales teams can plan interactions and know better where successful behavior changes will spread. One important note: physician networks extend well beyond group practice, referral patterns, hospital affiliation, etc. Influence travels through strong professional ties out to three degrees of separation, which means that without the kind of map that is generated by a social network analysis of physician relationships in a region, physicians or field reps may not know all of the network ties that exist, or the geographic alignment implications (cross- territory or district for example).

To demonstrate the results of existing social influence within a community of physicians, we highlight two case studies of market events showing that KAL behavior drives prescribing changes within their local network of prescribers.

Case Study #1: Product Launch

The first case study shows the power of social network analysis within the context of a product launch. The process starts by mapping networks of physicians in the launch brand’s therapeutic category and by creating a scoring value or Influence Index for all of the physicians. Analysis then showed how adoption occurred from day one of launch onward. There were several findings based on the analysis:
• In network clusters where the influential physicians adopted the new therapy, there was consistent and broad adoption of the new brand in the cluster or community. Adoption can also happen faster in connected communities of practice.
• In network clusters where the key influencing physicians did not adopt the new drug, the adoption across the cluster was lower.
• Early adopters, while important, did not routinely influence broad adoption. Only early adopters who were also KALs (i.e., in key influence locations in their networks) were able to stimulate adoption in the broader cluster of physicians.
• In retrospect, targeting physicians in key central influence locations would have allowed greater productivity of promotion. Focusing on them earlier in the product launch is also important because we all know that the first several quarters of a launch are critical to establishing product adoption and penetration levels.

Network analysis makes visible an important distinction between KALs (who are in key network locations and are highly influential) and early adopters (who may or may not be either high influencers or in key network locations). While early adopters are important, if they are not influencers, then their adoption does not help accelerate implementation. Conversely, when physicians who are influencers become early adopters, this adoption does help accelerate implementation within their social network. (Figure 1.)

Case Study #2: Generic Enters the Market

The second case study focuses on the introduction of generic simvastatin in the dyslipidemia market. This is particularly interesting, given the fact that Lipitor is now also off patent. In a study of physician prescribing behaviors following Zocor’s loss of patent, network effects predicting changes in prescriber behavior were found to be statistically significant.

We observed that whatever path the KALs took, on average their connected clusters followed. This was true regardless of the KALs’ prior prescribing preference, be it for Lipitor, Crestor or Zocor. Once a generic option was available, various physicians shifted behavior to Lipitor, Crestor or the generic simvastatin. Network connected physicians followed the same behavior pattern as the KAL physician. This shows strong consistency between the leaders’ choices and their connected network cluster. (Figure 2.)

The Importance of Social Network Analysis

Both case studies demonstrate that network influence is real and actionable. If pharma or device companies know which physicians are most connected to their peers, and who among these physicians is most influential, targeting and promotion efforts can be modified to prioritize these physicians. Research has shown that if the causes the KAL’s behavior to change, then the physicians whom they influence adopt that behavior more rapidly. Additionally, in clusters where the central influencer doctors do not adopt a new behavior, the case studies show that adoption in the broader cluster is hindered. The takeaway is that a network informed strateg y is an improvement over targeting using current methods only, and should be added to traditional volume-based or segmentation-based targeting.

By knowing both who the KALs are, as well as who they are connected to, brand and sales teams can work more effectively on call, promotion and education efforts. This also provides insight for alternative call strategies when faced with no-see or limited access doctors. The brand and sales teams who add social network analytics to their tool kit have a competitive advantage over those who do not, and pharmaceutical companies who realize that physicians are part of an interconnected community will be the most successful in the coming years.

Figure 1. The Effect of Network-Aided Targeting

 

 

Figure 2. Following theLeader After Zocor went off patent, Physician 734 (Case A) and Physician 2643 (Case B), replaced Zocor with a specific pattern of other statins. Other physicians closely connected to the KALs mirrored their behavior. Source: Activate Networks, Inc. 2012. Influence Index score based on proprietary analysis.

 

 

 

 

 

 

  • Greg Gallo

    Gregory Gallo leads the Life Science Services practice at Activate Networks, Inc. Activate Networks is a network analytics company that identifies and understands the key social connections that drive commercial, organizational, and health results. He can be reached at ggallo@activatenetworks.net.

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