Predictive Analytics and Rapid Cycle Evaluation to Improve Program Development and Outcomes

Predictive analytics and rapid-cycle evaluation not only describe the current status of programs; they also provide decision makers with guidance on what to do next.

Predictive analytics include a broad range of methods used to anticipate an outcome. For government programs, predictive analytics can be used to anticipate how individuals will respond to interventions, including new services, targeted prompts to participants, and even automated actions by transactional systems. Administrators can identify who is likely to benefit from an intervention and find ways to formulate better interventions. Predictive analytics can also be embedded in operational systems to guide real-time decision making—for example, in intake and eligibility determination systems, prompting frontline workers to review suspect client applications more-closely to determine whether income or assets may be understated or deductions underclaimed.

Rapid-cycle evaluation uses evaluation research methods to quickly determine whether an intervention is effective, and enables program administrators to continuously improve their programs by experimenting with different interventions. It leverages administrative data to assess large program changes, such as providing clients with a new set of services, as well as small program changes, such as rewording letters that encourage clients to take some action.

Predictive modeling and rapid-cycle evaluation— both individually and together—hold promise to improve programs in an increasingly fast-paced policy and political environment while efficiently allocating limited resources.

We propose that social service agencies take two actions:

1. Departments with planning and oversight responsibilities should encourage program staff to conduct a needs assessment. This assessment should identify where predictive analytics and rapid-cycle evaluation can be used to improve service delivery and program management. It should also evaluate whether the benefits of adopting these tools outweigh the costs, resulting in a recommendation of whether and how these tools should be deployed.
2. Federal agencies should take broad steps to promote the use of predictive analytics and rapid-cycle evaluation across multiple programs. These steps include investments in data quality and data linkage, as well as measures to support and promote innovation among agency staff.