Are you ready to harness the power of analytics and predictive models? You want to understand where prospects are coming from, who your most effective brokers are, which products are bundled together the most, what percentage of policies have payments up-to-date, what your NPS is amongst your members, as well as key statistics around member satisfaction and retention to direct your marketing and sales efforts. But to many, getting those types of analytic results can seem a distant or even impossible goal without a clear roadmap to get there. Many organizations dive right into the analytics and attribution model design, but this step can actually lead you astray as your analytics and models will only be as good as the data used to populate them. Data must be collected before it can be analyzed. So, your first step should be to focus on collecting and collating information so you can build working analytical models.

Analytics projects are only as good as the data that feeds them. The simple fact is that the models can't be simply turned on one day and provide predictions the next. As insurance purchasing is an annual process, the sample size for purchasing data is low, necessitating several years of information to deliver the proper conclusions. If high-fidelity analytical models are desired at any point, then your organization needs to make the collection of behavioral, purchasing, utilization and relationship management information a high-priority now. As organizations like Google and Amazon have shown, you can never have too much information on your customers- and in the health insurance markets, this extends to your providers and brokers as well.

We at Colibrium use a simple three-phase process to describe the entire analytics lifecycle:

  1. Collect the data. This is easier said than done as this step includes both ensuring your externally facing systems are collecting all the necessary behavioral, purchasing, and utilization data needed for robust modeling, AND ensuring the centralized collation of that data (i.e., getting all the data to one location for analysis and use).
  2. Analyze the data. This analysis requires the building of models utilizing the full extent of the data you have collected, as well as bringing in other data sources (e.g., credit information, 3rd-party consumer data, benchmark data) to combine into predictive metrics and drive actionable conclusions.
  3. Act on the conclusions. Insights gained from your data and the entire modeling process are useless unless they lead to action. Adjusting and implementing best practices, such as proactive customer outreach, new product development, new broker incentive strategies, etc. is the essential last step in the process.

At Colibrium, we have found that a good foundation for your analytics efforts can be a properly configured and integrated CRM platform such as Microsoft Dynamics or Salesforce. Modern CRM systems can aid your organization with gathering and organizing data (both through manual outreach and constituent touch tracking); however, they need to be a central part of the enterprise platform and not a siloed, one-trick system. Our recommendation is to utilize the flexibility of these platforms to integrate across your front- and back-end systems to accelerate the collection and collation of information (the all-important Phase 1 outlined earlier). This makes both the sales and service process more efficient across all your external constituencies (members, consumers, providers, and brokers), as well as sets the table for future analytical capabilities.

The workflow and rules engines present in these platforms can be used to create your models (Phase 2), or the data can be integrated into other more-powerful analytical tools to let you generate the results and conclusions from your models, no matter how complex. And since these conclusions are immediately available in the CRM, the automated outreach and workflow tools present in the CRM platform can be used to turn those conclusions into concrete actions (Phase 3). The CRM is the glue that ties all these efforts into one unified process that starts with collecting data and ends with real actions. Automatically targeted and generated outreach can transform traditional customer experiences into new, lasting and meaningful connections across the lifecycle of their interactions with you.

As we get ready to dive into open enrollment season, make sure that your systems are ready to collect the necessary data to enable the analytics you want to have in the future.