In an increasingly competitive pharmaceutical and biotechnology landscape, maintaining and growing market share requires knowing how to effectively use customer data across multiple distribution channels. Health care groups, physician networks, and consumers are all sources of valuable data — each group constantly generating new data that grows and changes over time.
In order to best target key decision makers, and know where business/sales opportunities exist, it is important to have a way to keep customer data accurate and up-to-date across all distribution channels. Customer mastering is an essential tool for getting the most out of customer data by managing the data behind these relationships. Customer mastering takes disorganized, incomplete, and unconnected data sources and masters them to create a unified and accurate resource that improves downstream activities like analytics and marketing strategies.
We will cover some of the current customer mastering challenges in biopharma marketing, sales, and distribution in a series of articles. Each article will explore how human-guided machine learning can help organizations get a 360-degree view of customers in their distribution channels and maximize their market potential.
This first article will dig into what customer mastering is, and why it is an essential tool for biopharmaceutical organizations today.
What is Customer Mastering?
For pharmaceutical and biotechnology companies, the term customer might have multiple meanings. Customer data can involve:
1) Health care groups,
2) Physicians and providers
3) Consumers and patients
Having accurate and up-to-date customer data is critical for delivering robust insights and driving optimal business decision-making in each scenario.
Despite customer data being a high-value asset, this data is often locked in silos where it is difficult to access and use in conjunction with other relevant datasets. These data sources can make up an overwhelming amount of data with an unknown number of redundant, out-of-date, and incomplete or inaccurate records. And it is impossible even to begin analyzing that customer data without also establishing how a customer fits within a business or account hierarchy. The goal of customer mastering is to solve these kinds of problems and deliver data that organizations can trust in the form of golden records: unified, accurate, and up-to-date, with a persistent identifier that can be used to track customer data across multiple systems and complex hierarchies.
Mastered customer data from all of a company’s data sources and business groups can be fully utilized in companies’ operational and analytics systems, correctly attributing customers to their site and region — without including duplicate or inaccurate customer records.
Modern Customer Mastering Requires Machine Learning
Traditionally, companies would use a manual, rules-based approach to try and integrate data sources. However, those methods are time-consuming and expensive, without ever providing a system that can truly scale and adapt as needed. Manual processes also require dedicated specialists to update and overhaul the system when it inevitably changes.
In contrast, a human-guided machine learning approach can solve these problems at an enterprise scale and be informed by subject matter experts, saving companies time and giving their teams access to the data insights they need. Machine learning can handle diverse data sources rapidly, with considerably less manual effort. Humans are kept in the loop as subject matter experts, guiding and tuning the process as needed, which means you will always have input and the flexibility to organize your customer mastering effort to meet your needs. No matter how distribution channels are organized or what systems are used, a human-guided machine learning approach can deliver results faster. And because customer mastering can be done in the cloud, with people who know the data best guiding the process, businesses can start mastering their customer data right now.
In the next part of this series, we will look at specific ways to take complicated customer hierarchies and find the connections that you need for your customer mastering and analytics.