Machine Learning in PAYGo: What You Need to Know Before You Jump In

Authored by: Jacob Winiecki
March 14, 2019 - 8 mins read

Last summer, we published a post that described how ZOLA Electric uses Machine Learning (ML) to predict which of their customers are likely to stop paying. The post was informed by the work BFA’s FIBR Project has done with ZOLA, a leading PAYGo solar operator in Tanzania, Rwanda, Ghana, Nigeria and Ivory Coast. Since that time, many PAYGo operators in the solar, cooking and water sectors have contacted us asking for more information about how ML actually works in practice, and for guidance on how to get started.

In this post, we’ll describe three concrete steps to help your PAYGo enterprise prepare to implement ML. Before we get started, remember that ML is not a magical solution to business model problems. Rather, it is a statistical process that your team can use to learn from your experience. Simply put, ML allows you to put all the data you have collected to work by using a computer to search for patterns that could help with segmentation, and that you can use to predict key customer behaviors.


PAYGo businesses are data-rich — products, payments and agent touch points generate information that machine learning can use for informing key decisions. As a first step, it’s important to identify areas of the business where an existing or potential prediction or decision is complex enough to warrant ML in the first place. The best ML opportunities are those that have a strong combination of the following characteristics:


Source: Catalyst Fund

To get you started, here are a few examples of machine learning in practice that are relevant to companies deploying a PAYGo model. For each potential ML use case, we have identified the core problem that would be addressed, described what might be predicted and the type of data that might be used for a prediction, and discussed what process or decision-making the predictions could improve:

Caption: Machine learning use cases relevant for PAYGo-based businesses


To begin exploring AI/Machine Learning and data without a concise question in mind is akin to acquiring a room full of tools and materials without knowing what you’re going to build. Before jumping into data exploration and modeling, it is crucial to establish a primary question that will guide your efforts. This question needs to be driven by the specifics of your individual business.

In ZOLA’s case, with the PAYGo solar systems it sells on a three-year lease-to-own model, the company allows customers to frequently make payments in amounts of their choosing. But it strongly advocates for monthly installments to improve portfolio predictability, and to minimize friction in making payments using mobile money. While payment flexibility may be attractive to end-consumers, ZOLA and other PAYGo operators need to keep periods of non-usage within reasonable limits to ensure that leases are completed in a timely fashion. To this end, ZOLA deploys escalating interventions after a solar system is “locked” due to lack of payment. The company typically deploys an agent to a customer’s home to follow up on non-payment, and sometimes repossesses the asset after it has been locked for a certain number of consecutive days. So after reviewing the studies ZOLA had conducted internally and considering the business context, we determined that our prediction question should be: “On any given day, predict what inactive customers are at risk of x consecutive days locked.”

This question served as our initial guide for ZOLA’s churn prediction effort, because it’s the timeframe that is most relevant for their business model — it matches their billing preference for clients, it’s a timeframe in which the company can take action, and research and exploratory analysis indicates that once a client is locked for this period, their likelihood of reactivating and catching back up on payments becomes much lower.



Just as it is important to determine what decision you want to consider, you must also decide what you are going to do with your insights. Once you have identified a prediction/decision where ML can add value, and organized yourself around a single prediction question, you will need to think ahead a few steps to build a shared vision of how the prediction will feed into processes, product changes and decision-making. Think about who will receive the prediction the ML process delivers, how they will receive it, and what action they might take as a result. Otherwise you’ll end up with a cool technology that doesn’t address a real user need.

In ZOLA’s case, predictive modeling and machine learning ultimately reduced churn and could deliver a positive ROI only once it was paired with interventions that we designed and field tested to reduce repayment risk. Here are some examples of operational uses of churn predictions from our brainstorm with ZOLA that could be equally relevant for other PAYGo businesses:


Once you have finished these three steps, what should you do?. We’ll answer that question in an upcoming post, in which we will explore how to: organize and create new features from your data, perform exploratory data analysis, set up in a live test environment, and build and iterate on an ML minimum viable product — that is, a first version of a predictive model whose results can be evaluated against the baseline.

What if I’m not ready for ML yet?

Don’t worry! Most businesses will find that maybe five to 10 percent of their data is correct and useful for an ML push. ML might not be easy to set up, but the potential efficiency gains are so substantial that it is not an approach many businesses can leave off the table for too long. Here are two timely steps you can take now to get yourself ready for ML in the future.

Keep reading about how to use AI in your business:

Start to organize yourself to be data-driven and data-ready:

This blog post was originally published on NextBillion

View the PAYGo NEXT Innovation Gallery Demo Presentations

Click to see demo presentation


See BFA’s demos for PAYGo operators such as:

  1. Churn prediction models to help identify customers at risk and better target customers for repayment
  2. Robotic process automation (RPA) to streamline time-consuming backend processes and examples of chatbot for agents
  3. Geospatial maps to visualize potential markets for expansion using predictive analytics and public data set
  4. An app for optimizing field operations for agents to reduce cash transfer risk

Download demo presentation (PDF)

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