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Simply knowing who’s included in our most underserved populations can be a challenge that makes public sector outreach nearly impossible. To overcome these obstacles and better serve consumers, we must first identify the underserved and their needs. Public agencies can use two powerful tools to help reach the most vulnerable: credit and alternative data.
Defining underserved populations
The first thing agencies must do is understand what an underserved population is. The Department of Health and Human Services (HHS) has a good definition specific to healthcare: individuals who have experienced healthcare disparities. Healthcare disparities can manifest due to a lack of available services, difficulty accessing care and limited knowledge about navigating the healthcare system or finding providers. Agencies looking to define underserved populations can adapt this definition to their specific fields.
The Federal Reserve also has a good working definition: people who don’t have access to a bank. This lens is handy because a lack of access to essential banking functions is a significant barrier to receiving other public services. Without a bank account, options to cash checks are limited and often come with additional hurdles like stricter controls, timing requirements, increased fees and more. Those without a bank account also can’t receive direct-deposit benefits or savings interest rates that could help them get ahead. Identifying the unbanked or underbanked first is an excellent way to use data to find and reach more individuals likely underserved by public benefits.
Research shows underserved populations regularly fit into specific demographic groups. These groups include the unemployed and elderly, veterans, disabled persons, those living below the poverty line and those residing in rural areas. Through a combination of factors, these groups are at the highest risk of needing government benefits while often participating in assistance programs at lower rates.
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Out-of-reach insights
As it stands, government agencies could better understand who uses their services. A lack of comprehensive understanding is partly due to outdated privacy laws and red tape; until recently, government websites weren’t allowed to collect cookies on their visitors. Of course, there’s a fine line between privacy protection practices and using data to reach underserved populations better. Still, many government agencies can be more effective in using the data they have at their disposal. Crucial insights may remain out of reach for agencies that struggle to analyze the reams of data that can exist across systems.
Public sector executives must meet this pervading problem with a viable solution. Veterans are one significantly underserved group — often because states don’t have access to a robust database covering their veteran populations. However, they’re only one of the groups often overlooked by public agencies. And while many agencies are getting better at using digital tools and data analytics, there’s still work to be done. Improving outreach is one way to close this gap, and we can do so through the judicious use of good data.
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Data unlocks doors
The private sector is good at leveraging data to identify and reach its customers. Most brands and companies know the demographic data of their typical consumers — and they’re experts at turning that knowledge into profits. Data can reveal where a company’s target market lives, how it responds to advertising and other key behaviors that better enable retailer outreach. Public sector agencies can operate in the same way.
For example, take the bus system in Montgomery County, Maryland. The county’s Department of Transportation redesigned its bus system to introduce the Flash. That redesign happened because the agency looked at its proprietary data behind its typical user. Before the redesign, bus riders often had to make multiple transfers, adding inconvenience to their lives.
The Montgomery County Department of Transportation (MCDOT) reviewed whom this problem impacted, the peak times it affected them, and how the city utilized the busing system. Then, it created new routes, resulting in significantly improved and efficient customer experiences. Innovations like these are precisely what other public sector agencies need to embrace to serve constituents more effectively.
Related: Redefining Customer Engagement in a World Where Data Privacy Reigns
Taking action
Good data is essential to determine the best way to connect with consumers. But how exactly do busy public sector leaders begin implementing a more robust data analytics strategy? External data is readily available through many public sources. Companies like credit reporting agencies have access to a plethora of information on underserved populations. They can help pinpoint the most vulnerable audiences — who they are and what they need — to maximize the good a new outreach program can do.
Internal usage data may also be key to determining the highest area of need. Public transit is an excellent example: Adding a bus route in an affluent suburb may not be as important as expanding or optimizing routes in a high-density metropolitan area because most suburban people have cars. Agencies will only discover information like this by leveraging data.
Analytics are particularly valuable when they inform the best strategy to reach those in need. Not all methods work for all audiences; one group may be best reached via email, another may be more open to television ads, and yet another may be most receptive to telephonic outreach. Analytics can provide valuable insights that keep agencies from wasting resources on dead-ends or unnecessary services.
Quality service begins with informed outreach
Public services are intended to help the people who need them most. But to meet the mark, we must first know their needs. Improving the customer experience begins with a solid outreach strategy guided by both external and internal data and analytics.
Modern tools can help us close the gap in need, enhancing the quality of life for the most vulnerable and elevating our society. Data is the engine powering the train toward that goal.
