The Search for a Predictive Indicator
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Post originally appeared at blog.liftcommunities.org
Imagine there was a question you could ask your spouse that, if answered, would correctly predict the success of your relationship? Or, imagine there’s a question your children’s teacher could ask that would accurately predict the student achievement outcomes of the entire class? Here at LIFT we are asking ourselves an equally audacious question: is there a question we could ask our Members that could predict the likelihood that they will be able to lift themselves out of poverty for good?
For corporations, predictive questions like these have been around for ages. Corporations routinely solicit customer feedback and use it religiously to improve products, customer service, strategy, and everything in between. There is even an “ultimate question” – how likely are you to recommend a company/brand/product to a friend or relative – that is famously and reliably predictive of sales, profitability, and growth.
In the social sector, though, we don’t have predictive questions and similar feedback systems don’t exist because incentives are not properly aligned. The folks who fund interventions (e.g., foundations, donors) are rarely the intended beneficiaries, so we too often pay more attention to those who pay the bills. At LIFT, a nonprofit working in some of the poorest urban communities in the country, we are trying to turn this reality on its head. We believe that we have to listen to those we serve. And we also believe that when we do, we will discover powerful insights to accelerate our impact.
Systematic Listening
A few months ago, I was introduced to David Bonbright, founder and CEO of Keystone Accountability, who travels the world evangelizing the revolutionary idea that interventions should be responsive to the people they are intended to help. Keystone developed a simple methodology called Constituent Voice (CV) that takes proven customer feedback techniques from the corporate world and adapts them to the social sector. It requires social change organizations like LIFT to ask program participants – i.e., our Members – what they think about program design, plans and implementation, and puts that feedback at the heart of how we think about, innovate, and implement our strategy. At LIFT, we collect that feedback by administering short surveys at the end of each meeting on things like relationship quality (“People at LIFT treat me with courtesy, dignity, and respect”), service importance (“LIFT helps me with the goals and priorities I think are most important”), and overall satisfaction (“I plan to come back to LIFT again”). We analyze and make sense of the data internally, close the feedback loop externally with Members to gain their insight into how we can do better, then actually make changes to improve the way we operate. Theoretically, at least. Having just launched our full surveys in mid-October in two of our six cities, we are preparing to conduct our first external CV loops next month, so we can put our Learn & Improve cycle to the test.
So where does the elusive predictive indicator come in?
For us, we’re taking subjective data from the first six weeks of our CV pilot and are triangulating it against objective economic data (e.g., jobs or housing secured) to see if and how Member feedback correlates with our intended impact. In other words, we want to know whether Members who give us higher scores are more likely to make progress on their own goals, like finding a job or securing a safe place to stay. Our hope, of course, is that the two are related and that relationship metrics prove to be predictive of development outcomes and impact.
For an anti-poverty organization like LIFT, where true economic mobility can take years, this would be huge. If CV really does prove to be as predictive for LIFT’s outcomes as customer feedback has been for corporations, it can be a powerful tool to help LIFT anticipate member outcomes, get a sense of where there are opportunities to improve our program, and make appropriate changes while we’re in the process of delivering the intervention.
No Time to Waste
For our Members, there’s no time to waste. In the six months since I started working at LIFT, I’ve spent most of my Friday mornings volunteering at one of our DC service centers, where I work one-on-one with local community members to achieve self-defined goals, so they can lift themselves out of poverty for good. I’ve been privileged to work with a young woman named Janelle who shared that, as a child, she spent ten years in the foster care system, working with case workers that were supposed to help her, but who she said looked right through her. She heard from one of our Members that LIFT was different, that we treated people nicely and really tried to help them. So, here she was, looking for help finding a doctor to look at the wrist that had been stabbed when she was a teen. She wanted help finding her own place, so she could move out of her friend’s apartment in a rough neighborhood. And, while she was saving up her paychecks to get an apartment, she was hoping to find some free clothing to supplement her current wardrobe – all of which fit in a single bag.
That was two months ago. Since then, Janelle survived violent attack in the apartment building where she was staying with a friend and has had to move into temporary housing. Unbowed, she has started seeing a therapist to help her recover from the attack, is working with police to find her assailants, used a LIFT referral to get free clothing to wear at her restaurant job, and even leveraged a LIFT-edited resume to get a second job. And, she continues to return to LIFT and has made progress on her goals each time. We know that Janelle first came in because she heard we treated people nicely, but what keeps Members like Janelle going? Can CV prove that LIFT’s work has caused her to make progress? Maybe not, but we believe it can give us good insight into why she’s making progress and, through feedback from Members like her, help us predict our overall impact. And, as David Bonbright often says, who needs causality when you have predictability? Here’s hoping he’s right.
