Although my colleagues and I have long believed that Living Goods was doing a good job of expanding access to health care in the poorest communities, in reality, we struggled to prove that with any empirical certainty. And so, when I first learned about Metrics for Management’s (M4M) EquityTool, I was struck by how simple and efficient this tool could be for obtaining and analyzing demographic data about the population served by Living Goods-supported Community Health Workers (CHWs). We began discussing the EquityTool with our technology partner Medic Mobile and the M4M team, and agreed that the ability to embed a simple survey into the Smart Health mobile app used by Living Goods-supported CHWs – without any separate workflows or analytics – made it a truly compelling solution.

Once the survey had been fully rolled out and administered across our operations in Kenya, we were eager to dig into the data. We were very pleased that the results showed we were largely reaching a population that was on par or slightly poorer than the overall population. In addition, we were encouraged that about 44% of the households we served were in the lowest two wealth quintiles and that 73% were in the lowest three. At the same time, we noted that the lowest wealth quintile was a bit under-represented, which reinforced ongoing internal discussions about how we can do an even better job of reaching the poorest members of the communities in which we work.

For Living Goods, the EquityTool has exciting benefits that extend beyond understanding who we are reaching at a macro level. While that is an important first step, we have no intention of stopping there. A deeper analysis of our data has enabled us to see how we’re performing on interventions around specific health interventions, and how those vary by wealth quintile. This has generated some great insights – for instance, pregnant women from the lowest two quintiles have a far lower delivery rate at formal health facilities than those in higher wealth quintiles. (That said, we were pleased to see that even along these lower wealth quintiles, their delivery rates at health facilities were still far higher than national rates.) This is an actionable and potentially very impactful insight for our teams so that we can create interventions that better target poorer pregnant women through our work.

These types of insights have tremendous potential when it comes to identifying households at risk for specific negative outcomes. Knowing which households fall into which wealth quintiles, combined with the rest of the data our Smart Health platform generates, will allow us to develop data-driven targeted interventions so that the CHWs we support can continue driving exponentially greater impact. As an organization obsessed with numbers and impact, Living Goods is just getting started when it comes to fully leveraging the powerful data that the Equity Survey has enabled us to collect.