By employing increasingly sophisticated customer targeting methods, supported by market research data and technology (such as CRM applications, customer profiling and quantitative segmentation techniques), pharmaceutical sales and marketing organisations have become adept at targeting “high value” prescribing physicians with product-oriented messages.
The key limitation of these customer targeting approaches is that they still focus primarily on a single sub-population of individuals (prescribing physicians) and miss out on engaging with important customers by foregoing a systematic understanding of the broader context of the healthcare stakeholder environment.
For years, it has been enough to purchase and use physician prescription data as the primary input by which to value and segment customers. Critical contextual elements, such as social and professional networks, role-related influence, and the restrictions on choice resulting from policy and budget restrictions, have been used sporadically or experimentally by a few innovators to develop a more complete picture.
In today’s changing healthcare landscape, the trends toward increasing private care provision, growing market competition, new influential stakeholder groups, and evolving policy are all contributing to the increasing formation of formal and informal communities of practice, alliances, and collaborative networks. In this new reality, network insight generated through capturing, processing and analyzing network information becomes essential and needs to be performed routinely and regularly.
What is necessary, however, is a systematic way of gathering customer intelligence on the networked environment surrounding the most important stakeholders for a pharmaceutical company’s brands. The roles, relationships, memberships, affiliations, etc., which comprise the complex web of interactions between key players and decision makers, when captured and analyzed, can provide profound new insights into the preferences, needs, orientations, and conflicts of, and between, individuals and groups. It is this enhanced level of contextual understanding that we refer to as “network intelligence”.
Network intelligence provides detailed insight into the power structure, dynamics and interdependencies within and between organisations, their key members, committees and decision making authorities as well as associated parties, offering a holistic understanding of the big picture from a number of perspectives (geographical, institutional, therapy area, peer network, etc.).
Network intelligence can reveal key insights into how communities are interconnected, the roles individuals play, their relative influence within a therapy area and the degree to which they are influential. Ultimately this intelligence enables more precise customer segmentation, more value adding customer interaction, the development of new customer collaboration concepts and the build up of new networks whose members will have a greater influence on brand objectives.
Network intelligence also can provide a more accurate picture of the changing landscape of the local/regional healthcare economy, as well as critical insights into each customer’s specific environment, in order to respond and engage more effectively. There is increasing evidence that pharmaceutical companies who develop new capabilities, and adapt their marketing and sales approaches to target the customer network as a whole, create an advantage compared to competitors who continue to drive the “share of voice” model with physicians.
In this series of articles, we will explore in more depth a number of key aspects of network intelligence and how it is a critical enabler of new promotional strategies, including topics such as key account management, market access programs, organizational effectiveness, and value-added service development.
Network intelligence is more than KOL influence mapping
The practice of mapping and understanding networks is not new, but for pharma it has remained almost exclusively relegated to the domain of Key Opinion Leader (KOL) influence networks, relying on survey-based methods, as well as Internet-based publication and event tracking, for identifying local and regional KOLs. This has mainly been due to a lack of both network-oriented data and the graphical network analytics solutions to enable sales and marketing personnel to make sense of complex customer networks. Network data and network analysis tools are now becoming available, but business still needs to develop a strategic approach to harness the intelligence to effectively engage with networks.
Assembling a comprehensive overview of healthcare networks is difficult due to the wide range of stakeholder groups involved (payers, providers, professionals, patients, and policy makers) and the variety of network types (e.g. treatment/referral networks, integrated care networks, buying/commissioning groups, hospital networks, etc.). Also, there is currently no de-facto taxonomy for describing healthcare networks, since the discipline is still quite new, and because healthcare systems vary widely from country to country it is impossible to develop a single generic network model.
On the other hand, it is not necessary for a pharmaceutical company to embark on a massive program to exhaustively map the entire complex web of interconnections present at all levels of the healthcare system in order to achieve business benefits. By starting simply with an initial small-scale pilot, focused on a limited number of customer groups, and taking a systematic approach to capturing relevant network data, it is possible to considerably enhance the understanding of the broader customer context and generate significant benefits from the resulting insights.
Patient-flow potential drives network-based targeting
To illustrate one application of network intelligence, let us look at network-based targeting using patient-flow potential data. Network intelligence allows identification and targeting not only of individual physicians, but also of physician networks related to a specific disease or therapy area. This type of targeting leverages referral-based connections drawn from patient-flow potential analyses, enabling pharmaceutical sales representatives to simply validate the referral data as a key input to refine the targeting model, enabling a precision focus on the most influential physicians and/or clinics in a given network.
Instead of targeting physicians on an individual-by-individual basis, this approach recognizes that physicians interact with each other, and therefore aims at groups of physicians, focusing primarily on the physicians with the highest patient potential, as well as the most influential individuals in the network cluster.
Such an approach requires an accurate mapping of the relationships that exist among physicians, within a specific therapy area (supported by network intelligence technology), and thus, has been difficult to achieve until recently. With the advent of patient-flow potential data from data mining specialist providers like Mederi Research, the referral patterns with the highest statistical probability can be identified up-front.
Conclusion
The data and technology are becoming available to support marketing and sales organisations in developing a far more detailed and integrated understanding of their local / regional healthcare environment and who their key customers are, as well as their contribution within relevant communities. This situation will allow early adopters of network intelligence to achieve a significant competitive advantage versus companies that stay with the traditional approach to the market.
In upcoming articles, we will focus on various applications of network intelligence as a critical enabler of customer strategy and new promotional models.
By Robert Mark, Partner, Executive Insight
The way forward
This is an excellent article which explains exactly what pharma should be doing and how they can align themselves closer with their customers. And in the light of healthcare changes in Europe (Germany specifically) this kind of information is going to be invaluable.