Wednesday
March 16
2016

Emily Tucker

Mobile Data Has the Answers, But First, We Need to Ask the Right Questions

How two social enterprises are using mobile data and analytics to transform their business

 

While I worked at Amazon, a running joke was that no employee could sneeze without first performing data analysis. In reality, that’s how the company actually functioned; every decision had to be supported by data that included testing, validating and improving a current system.

When I made a career change to focus on economically developing and emerging contexts, I had to reorient my thinking, as decision-making in these contexts often happens without analyzing data. Over the past 13 years, I have been working to change that approach. However, I’ve realized that when guiding social enterprises, nonprofits and international NGOs to begin using data, it’s helpful to focus on a key, sometimes obvious point: data should actually answer a critical business or programmatic question. Mobile data collection for the sake of collecting data isn’t enough. That’s why at TaroWorks, we focus on making analytics easier. (Note: The author serves as CEO of TaroWorks.) Below are two mini-case studies that illustrate this point.

 

Proxy credit scoring

In January 2014, TaroWorks began working with the social enterprise Iluméxico. The company helps households, schools and hospitals across Mexico in lower socioeconomic demographics that do not have access to the electrical grid, buy solar panels through loans. When the company launched it kept track of clients by using a system of paper surveys in the field and Excel spreadsheets in their headquarters. But this system quickly broke down. It took weeks to compile data from the field and without timely visibility into field operations and repayment rates, they were unable to address issues as they arose. They implemented TaroWorks to solve a critical business need: To gain visibility into their repayments by understanding who will be able to pay for a solar panel.

As they began collecting data and analyzing repayment rates, their executive management team realized that a few key factors correlated with higher repayment rates, including the amount of time a client spends in an Iluméxico-targeted community area, their level of community engagement, access to multiple income streams, and if they own a phone. Using the TaroWorks platform, Iluméxico then created a mobile survey that asks about each factor and calculates a proxy credit score. Now, a field sales agent can simply key in a few inputs and instantly see whether a client is likely able to maintain the cost of a solar panel. In doing so, both Iluméxico and its potential stakeholders have a better understanding of whether a solar panel will add value or will indebt a client.

 

Resource optimization

Another analytics success story is Komaza, a Kenyan company that helps smallholder farmers grow eucalyptus trees as a cash crop. As with Iluméxico, Komaza relied heavily on paper surveys before joining forces with TaroWorks. Their field staff work in remote, semi-arid areas, which led to compromises in data integrity. Paper surveys were hard to keep dry, there was no mechanism to ensure individual sites had actually been surveyed, and manual data entry is inherently risky with human error. Over time, the Komaza management noticed that some of their trees were failing in areas with reportedly good soil and rainfall. This led company managers to believe that either the reports were inaccurate or that their field staff were not training their farmers properly.

Komaza thus decided to focus their data collection around maximizing tree growth for the benefit of the farmers and the company itself. They tracked tree mortality rates, tree height and diameter over time. With the time required to answer surveys drastically reduced, and with surveys coming in with verified GPS locations, Komaza was able to parse which areas were underperforming because of natural or staff constraints, and then narrow in on how to improve the performance. In turn, Komaza was able to better allocate overhead, plan better logistical coordination and rotate planting schedules more effectively.

In the case of both the case of Iluméxico and Komaza, it’s important to note that their success with mobile data collection came from having a clearly defined problem that needed to be solved. Both organizations were thus able to collect the necessary data from the field, use our dashboard to analyze the information, and outline the best way forward. That’s why it’s our goal at TaroWorks to help social enterprises understand the value of their data collection efforts quickly, easily and intuitively. After all, analytics should be as natural as a sneeze.

 

Emily Tucker

Categories
Energy, Impact Assessment, Technology
Tags
big data, Impact Assessment, metrics, mobile applications, research, social enterprise