Many healthcare product companies continue to downplay the potential impact that investments in data analytics could have on their business.
Many healthcare product companies continue to downplay the potential impact that investments in data analytics could have on their business. While the payers and software companies make headlines for implementing robust data strategies, product marketers in maturing categories like implantables, radiology equipment, surgical tools, consumables, and DME (Durable Medical Equipment) debate whether the “big data” buzz is about a hammer in search of an elusive nail. However, category maturation and ever increasing cost focus should, if anything, be a driver of more, not less sophistication when it comes to understanding your markets and leveraging data.
“Most of the statistical techniques you see used in data sciences are old,” says Alex Moore, an independent data scientist who helps consumer products companies get started at adopting data analytics techniques. “It’s just now we have the computing power to do things we never could before.” Cloud computing, often priced on a usage model, is making powerful processing capabilities accessible to businesses that could not previously afford to make heavy capital investments. In addition, new database technologies and programming languages are helping reduce the investment that might otherwise be required to link different relational databases together.
At the same time, opportunities afforded by new data sources are more available than ever. Along with transaction-level data from accounting and CRM systems, many healthcare industries have the potential to access service and usage data that is continually streaming into the business from installed equipment, as well as anonymized third party sources on prescriptions and procedures globally. Here are five high-impact opportunities for you to benefit from step change improvements to your data analytics strategy.
Strategic growth drivers
Marketers in other mature categories, such as consumer products, have been taking advantage of data analytics for years in helping to make investment decisions. One example is to assess drivers of regional consumption variation. In addition, such insights provide a robust analytic foundation to simulate the effect of changes of macro demand trends and health-related conditions (e.g., chronic conditions, obesity, exercise, etc.). By fusing data from multiple sources, we can explore a comprehensive list of potential drivers to ensure that the impact of any individual factor is determined in the context of others. By understanding the factors that underlie consumption, companies can forecast demand more reliably, create scenario plans, and improve resource allocation.
The fundamentals of segmentation, customer prioritization, and positioning have become even more critical in slow-growth, cost-constrained categories. Sophisticated targeting in consumer goods now goes well beyond demographics and psychographics, incorporating analysis of consumer social media activity, store card purchase data, and market research panel data to identify consumers that would respond best to a new campaign, or unearth need states or situations that could be served by a new product. In healthcare, there is still no substitute for primary market research to determine the segmentation and customer prioritization approach. Once determined, the customer prioritization strategy can be made actionable by linking other data sources to the segmentation.
Across industries, sales organizations are driving their teams to adopt consultative selling, solution selling, and similar models that focus on what they can teach their customers. This is critical in healthcare, where physicians and purchasing managers demand evidence about the product’s efficacy, usability, and safety, as well as proof that the vendor has deep knowledge about their relevant purchase clinical situation. A rep that can come into the meeting armed with statistics specific to the customer about their clinical practices, or about their utilization of products in certain categories, combined with the potential upside specifically delivered by the product has a strong position. New data technologies can help marketers synthesize external and proprietary data sources, and deliver this level of customized information about specific priority accounts to their sales teams.
While accounting systems enable users to view their price and discount realization by customers’ basic traits, many struggle to integrate the data with CRM, or other sources of clinical data that could help extract insights from variations between customer groups. In an environment in which value-based care is taking roots with payers and providers, manufacturers can help their own cause with a deeper understanding of the value that their product is delivering to their customers. For example, a manufacturer of capital equipment with a remote service link can potentially learn about utilization rates and case mix across their customer base. Fused with the accounting data, one could answer questions like, “What level of discount is appropriate for customers that receive the greatest value from the product? Would alternative pricing structures or packages make sense for lower-utilization customers?”
Given the immense value that usage data can provide to product developers, any product that can be cost-effectively and securely connected to the Internet should be. Manufacturers of products from small tools to consumables should consider whether the product or packaging should have a mechanism to incorporate RFID (perhaps linked to the strategy for addressing FDA’s Unique Device Identifier requirements). Marketers of products with remote service and logging capability should be exploring means to parse, mine, and link the data to other sources.
“More complexity does not automatically translate into great results,” says Moore. Before making a big investment, start by understanding the resources you currently have available. Perform exploratory analysis on existing sources to get a feel for what insights are possible (Data Science PhD not required). Once the basics are complete, however, keep the organizational momentum lest the effort become “one and done.” Identify opportunities by looking at the critical questions that seem to repeat themselves every quarter, and explore working with external partners that can help identify solutions. Most importantly, take the long view, and start building a vision for how greater analytical sophistication and customer intimacy will become an integral part of your business in the future.
About the authors:
Dimitar Antov, Peter Killian and Jason Deeken are colleagues at The Cambridge Group, a Demand-Strategy consultancy firm based in Chicago celebrating more than 40 years of client impact. Jason Deeken is a healthcare executive with more than 15 years’ experience focused on strategic issues and product development, having worked as VP of Sales, Senior Director and Director/Manager at Western, Steris and GE. Dimitar and Peter are both Principals at The Cambridge Group, with 20+ years between them bringing the latest in data analytics and techniques to a range of clients in CPG, Retail and Healthcare sectors.