The market engine behind our nonprofit service economy is the act of funders and donors “proxy buying” a service or program on behalf of a population. Analogous to “in loco parentis,” where a school plays the role of the parent while children are in their care, in the nonprofit sector, we have “in loco emptoris,” where the funder or donor plays the role of the buyer to a group of people in need of a program or service that they cannot afford. In everyday terms, we are talking about the act of charitable giving. While charitable giving will always be an unquestionably moral and societally important thing to do, there is a side effect of proxy buying that may be seriously hindering the nonprofit marketplace’s ability to innovate, evolve and expand.
In the private sector’s service economy, the buyer and consumer are one and the same, thereby placing the consumer in charge of the choice of outcomes, as well as the final judge of whether they were achieved. For-profit service business owners depend upon consumer purchases to sustain and grow. As a result, the consumer is the most important source of outcome feedback. The advantage of the for-profit service sector is that rigorous, valid outcomes metrics don’t have to be created because the “buy” serves as a universal proxy metric for consumer outcomes. More to the point, consumers of for-profit services provide real-time outcomes feedback when they continue to purchase (in order to get more outcomes); demand a refund (for not getting outcomes); and/or tell other consumers to buy (when they want the same outcomes). Businesses can and do use these real-time outcome feedback loops for R&D and market research purposes. The private sector is now going even further, leveraging real-time technology to collect and aggregate structured consumer feedback (data), as well as applying predictive and prescriptive analytics to continue to innovate, scale, and help every consumer achieve better results in the future.
Innovation in the private sector advances more rapidly because cause-and-effect is easier to establish. Due to individual consumers being in charge of the purchase, the outcomes they seek are almost always the shorter-term, more proximate changes and improvements that they need or want in their lives to get them to the next level. These more direct outcome goals drive a more common sense cause-and-effect feedback loop for business developers. Take a home visitation program for new parents as an example. In the nonprofit and private sectors there are services where a professional comes into the home after a baby is born in order to support the family; for those not in the know, one form of these services in the private sector are called “post-partum doula services”. I conducted a search of doula services and the outcomes that they promised, as well as outcome evaluations I and others have conducted of home visitation programs in the nonprofit sector. Here are just a few of the outcomes that consumers of doula services are buying, contrasted with the outcomes that funders are buying:
- Consumer outcome = less parental stress vs. funder outcome = reduction in child abuse and neglect
- Consumer outcome = better understanding of care and feeding of the newborn vs. funder outcome = the achievement of developmental milestones
- Consumer outcome = quicker maternal recovery vs. funder outcome = reduction in subsequent pregnancies
You will notice that the doula service’s proximate outcomes empower the paying consumer to serve as pretty accurate judges of whether the service worked. The funder-desired outcomes have to be evaluated longitudinally, likely with some rigor and control, and reflect longer-term population-level impacts that no beneficiary would likely articulate as the result that they expect for their payment. We all intuitively understand that the potential for cause-and-effect conclusion errors rise exponentially the further out in time we go from the end of a program or intervention; a life lived encounters new experiences that add to and subtract from the historical gains made during the journey. The real-time learning that can occur by measuring proximate outcomes through natural experiments, data-driven observational cohort studies and even randomized control trials are so much more rapid and directly applicable when anchoring studies in proximate outcomes. The key point here is that private sector service businesses receive more accurate, real-time cause-and-effect feedback because consumer-driven evaluations of outcomes focus on results in the here and now.
In the nonprofit sector the proxy buyer is not the consumer. As a result, the private sector’s very empowering consumer feedback role of getting to choose, judge and communicate their proximate outcomes is not realized in the nonprofit sector; there is no market exchange mechanism that forces a nonprofit organization to listen to the consumer because the buyers (funders) are not the consumers. When funders and donors sit in the driver’s seat and judge whether a program succeeded, they are taking on a role that would normally be the economic right of the beneficiary and/or their loved ones, had they just had enough resources to pay for it on their own.
Our nonprofit economy needs to formally convert the relatively mute service “beneficiary” into an empowered service “consumer.” By virtue of its safety net role within society, the nonprofit economy will likely never have the “consumer buy” as a naturally occurring universal indicator of near-term outcomes. So, if the nonprofit sector is to ever advance its services and practices we need to invest in gathering and using consumer-driven direct cause-and-effect feedback; specifically, we need much more real-time consumer data! The health profession is embracing the real-time consumer feedback model to research, learn, plan and evaluate existing and innovative interventions in ways that some sector leaders state will advance the pace of breakthroughs more rapidly than any time in history. They are leveraging big data, predictive analytics, and sharing economy possibilities through efforts like shared electronic medical records in order to make the dream a reality. We can and should now do the same in the social sector, turning the moments when consumers begin, go through and end their engagement into opportunities to learn how they are doing, what could increase their odds of success moving forward, as well as evaluate their direct results, all in real time.
Imagine what real-time feedback loops could do for our sector. How could funders, impact investors and donors enhance their efforts by leveraging real-time consumer outcome data, not to mention the power of next-generation predictive and prescriptive analytics? One clear added value of real-time outcome feedback loops are their ability to provide very accurate early indications of likely impact. Social innovation funds, foundation initiatives to scale programs and pay-for-success efforts are almost all conducting longer-term, rigorous impact evaluations. It takes a while for these evaluations to produce definitive findings. While they wait, real-time consumer-based outcome data could supplement everyone’s learning and accountability needs, with the bonus value of leveraging highly accurate on-demand predictive and prescriptive analytics to determine if and how to course correct. The potential complementary role that consumer outcome feedback loops can provide is a powerful addition to the impact measurement toolbox that to date contains output tracking and, for those rare few that can afford it, longitudinal comparison group studies of longer-term impact. Imagine our sector’s pace of innovation if we were to add consumer outcome feedback loops to current output tracking and rigorous comparison group studies!
At Algorhythm we are creating low-cost, shared impact measurement systems that provide consumer outcome feedback loops to organizations and funders working within fields of practice and/or networks. Our impact learning system – iLearning System – technology platform standardizes consumer outcome and program quality metrics; rapidly grows data across a network of participating organizations; applies predictive and prescriptive analytics for more accurate program and service planning; and automates the process of communicating insights and findings, all in real time.