If you read our first article on customer mastering in biopharma marketing, sales, and distribution, you know how important customer data is and that machine learning holds the key to unlocking its potential value. This article will focus on how customer mastering can classify even the most complicated and large-scale customer data into flexible hierarchies, which will help you get the most out of your data analytics.
Managing Healthcare Hierarchies
Multi-channel customer data contains many complicated business relationships, and managing all the data from these relationships can be a real challenge. Particularly in healthcare settings, reaching customers often requires navigating vast and complex organizational networks. And because biopharma customer channels are constantly growing and changing, not understanding these relationships can result in missed opportunities. Customer data becomes disconnected and difficult to interpret without a framework for these relationships and keeping them up to date. Therefore, an essential part of customer mastering is classifying customer relationships into hierarchies to get a full 360-degree view of how each customer interacts within their region, associated clinics, hospitals, and larger healthcare organizations.
Adapting to Hierarchies That Evolve
Healthcare hierarchies can evolve in a multitude of ways. Healthcare organizations and data are in constant flux because of acquisitions, mergers, consolidation, reorganization, rebranding, and expansion. As a result of these changes, information becomes outdated and obscures the true connections between the parent organizations and their business entities that span multiple sites and regions. This problem requires a solution that works at scale because even a single parent organization might contain everything from community health clinics to large hospital networks and international subsidiaries. Creating a hierarchical classification of all your customer data must be a broadly adaptable process and handle large-scale data — an excellent use case for machine learning.
Making Flexible Hierarchies Using Machine Learning
Within healthcare groups, a network of sites contains multiple groups and practices, forming a complex organizational chart. Teasing apart all the relationships between healthcare groups manually and updating them whenever they change is a nightmare. Machine Learning can help you classify each relationship to create a hierarchy for multiple sites, groups, and practices that places customers in the correct context. A cloud-native, machine learning approach can make this both easy and scalable.
For example, Mass General Brigham is a major hospital network consisting of Mass General Hospital, Brigham and Women’s Hospital, and multiple subsidiaries and sites with variations on the Mass General/Brigham and Women’s name. In the future, many of these sites and facilities were rebranded to Mass General Brigham. In the context of this hospital network, and hospital networks with similar shared branding, organizing a hierarchy from hospital name alone would be a challenge using rules-based logical methods. Also, the whole process would have to be checked, updated if that information changed, and repeated manually for each large hospital network, each of which can contain hundreds of hospitals, clinics, and other facilities. In contrast, machine learning can classify these parent organization and subsidiary relationships quickly and accurately as part of a repeatable process, using a cluster of data associated with each record, and for as many networks as there are in your customer data.
Beyond healthcare organizations, these same principles can be applied broadly throughout distribution channels. Whether you are dealing with wholesalers, specialty and retail pharmacies, or any of your payors — you can manage pricing strategies and distribution relationships better when you have the flexibility to create hierarchies that meet your operational and analytics needs.
Using Flexible Hierarchies to Power Your Analytics
Whether you want to focus on regional sales groups or a subset of hospital clinics with specialty drugs, you can guide the machine learning process to create a hierarchy that will power your analytics. In some cases, you may want to identify a broad regional hierarchy for marketing that lets you assess market penetration and growth potential. At the same time, your team of sales representatives also needs a more strict hierarchy that saves time and money when they engage with customers at specific sites. In both cases, large overlapping sets of data can be analyzed and brought into a workable framework. Using machine learning, you can classify these relationships into functional hierarchies and bring them directly into your analytics tools to find opportunities and drive sales.
Not only does customer mastering demand accurate data, but it also needs a framework for understanding the relationships that connect customers across marketing, sales, and distribution channels. Organizing these relationships into hierarchies, including domestic and international business groups, will let you think big and manage global strategies while also drilling down into the data when needed. As part of your customer mastering process, managing these hierarchies brings you one step closer to having the full benefits of mastered data so you can understand where problems are and opportunities exist.
Our next article will focus on connecting with the people in these hierarchies: provider networks, how they dynamically change, and how to reach them better.