May 15, 2019
Turning Data Into Action
The key lies in using information obtained from reputable sources to fill in some of the gaps in the data you are already gathering.
Over the past decade, insurers have focused heavily on improving the customer’s journey. This task can be particularly challenging because a customer’s engagement with them could be as little as one annual wellness visit with no other claims for that year.
In an effort to create engagement and build loyalty while working toward better health status, insurers have gamified biometric device interactions, launched semi-automated communications platforms and established group wellness challenges for employer groups and individual coverage plans.
But here’s the challenge: If the data gathered from these engagements that is fed back to insurers is not clean, readable and available in the format and time in which it is needed, then a carrier is unable to optimize its application. If this challenge can be solved, high-quality data that does meet those parameters can be used for CRM modeling tools, experience and loyalty measuring systems, enhanced communications applications, cross-sell offers and lifetime customer value formulas.
So how does one begin to solve this challenge? The key lies in using information obtained from reputable sources to fill in some of the gaps in the data you are already gathering.
See also: How Agencies Can Use Data Far Better
Here are some of the benefits of using third-party data to inform your analytics:
- You can enhance the bland data you already have. You could fill volumes with the amount of information you have about your customers’ basic demographics such as age, geography and household income. But what about their risk for certain health conditions and their history of disease? Including these details can support better communications, closer engagement and efficient transaction processing with care providers and administrative systems managers.
- You can improve both the quantity and quality of your data. Quality of data can make or break processing and downstream analytics. When you use a third party to obtain your data, you may experience a more reliable return on investment in your marketing and communications spend. You can also make more informed decisions when you are pricing the risk of catastrophic losses. High-quality data can mean the difference between automated workflow decision making or manual and costly processes. It does not have to be a lot of data — but it does have to be clean, understandable, reliable and available when needed.
- You can diversify ways of turning data into actionable insights. Information might be engineered or derived from big datasets that are curated in a way that a payer can ingest, making it useful for activities including workflow automation, risk management assessments, price modeling exercises, population health management or sales and marketing activities.
Of course, it’s important to be able to efficiently manage data from multiple sources. To do that, you need to create a master data management plan. Often, a centralized location for several datasets makes sense, although a connected, decentralized arrangement can work, as well. Establish a standard data dictionary within your company to ensure that your staff understands external data in the right way and can more precisely define even internal data. In other words, break down data silos and functional barriers that may be preventing a standard dictionary that all can leverage.
How can you determine whether you are getting the most out of your use of data? A three-step approach may be helpful:
- Evaluate the data you have and verify whether it is clean, reliable and accessible in the manner you need it.
- Identify the areas in which external data could complement your own and structure a data management approach for all of your data — both internal and external.
- Establish a cross-functional executive team that can prioritize where you need the data most, and start on one initiative now. If you are not doing something, your competitors most probably are.
See also: Role of Unstructured Data in AI
Well-organized data can help you engage your current customers, attract new customers and ultimately improve your company’s bottom line. But too much data, that is not optimized for your business needs, may not help the organization meet its goals. When you focus on high-quality and reliable data, you can see some tangible results when you adapt its use into platforms all along the lifecycle of your business.