Competitive network data is valuable to a variety of people in the employee benefits industry. The most common uses we see are sales people comparing networks for a current or prospective customer or broker, or network managers building recruiting lists. Some marketing departments use competitive network data in collateral and strategic planners use it to help chart the way forward.
This wide variety of users has an equally wide variety of preferences. Like many other systems that include geographic analysis, NetMinder uses standard groupings – from the whole country down to a single five-digit ZIP code – to make it easy to match up with other datasets such as procedure-level cost data, membership counts and employee populations. But sometimes you need to slice the data a little bit differently. Maybe you have sales regions that include several states or underwriting zones made up of three-digit ZIP codes. That’s why we added custom geographies.
Here’s what custom regions (groups of states) look like in NetMinder. In this example, the client included 6 states in their Mid-Atlantic region:
You can also set up groups of three-digit ZIPs as markets and groups of counties as territories. There’s no limit to the number of custom geographies NetMinder can support.
Customers love the flexibility custom geographies add to NetMinder. The business information lead for a large national dental insurer tells us: “It is helpful because we are able to focus our reports to match our internal geographical breakouts. We could run the same reports by choosing the 3-digit zip codes that correspond to each area, but having them already grouped for us saves a great deal of time. It also allows us to combine multiple geographical groupings into one report. It is a very beneficial tool for how we do business.”
Some other uses for custom geographies that we’ve found are:
- Focused recruiting efforts
- Evaluating network capacity and competitive position to support sales prospecting programs in defined geographies
- Reporting on network size or makeup in service areas for regulatory and compliance needs
How do you group geographic data when you compare provider networks? What other pieces of data are important to your analysis and how do you match the datasets up?