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

Jacob Winiecki
Mar 14 · 10 min read

STEP 1: IDENTIFY A GOOD PROBLEM

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
  • They use ML to feed into a process/decision that’s important for a priority problem: With the excitement and hype around Artificial Intelligence and ML, it is tempting to jump in looking for a place to use machine learning. This approach is a mistake. First, providers need to find a problem in their business that matters immensely, and could have a high likelihood of being solved. Ask yourself: What mission-critical information are you dying to know? What problems could you solve “if you only knew ___?” Minimizing non-payment and customer churn are critical, ongoing challenges for PAYGo operators that dramatically reduce expected revenue if left unchecked. So for ZOLA, we identified a clear area that could be significantly improved with accurate and timely predictions of repayment behavior: The decisions of who to target for better repayment, at what point in the customer journey to reach out to them, and with what tools.
  • They have the potential to grow revenue and/or reduce costs: It’s important to start with an initial assessment of the ways in which the proposed ML model might deliver cost savings. These might include: lowering the cost of prediction and forecasting; managing repayment risks and lowering defaults; expanding serviceable customer segments; identifying and delivering on new up- and cross-selling opportunities; and delivering differentiation — i.e. through automation, customized communications and offerings — along with a better overall customer experience. It’s also important to think about how these financials could evolve in the next two to three years, considering your growth and anticipated changes in the market. At a later stage with a first iteration of the model, you can use these identified revenue and cost opportunities as inputs into a return on investment calculator. Losing customers is expensive for any business. Like most PAYGo operators, ZOLA manages post-sale interactions with customers through call centers and a network of agents, who are focused on post-sales service at the customer’s doorstep. Prior to their work with FIBR, ZOLA’s post-sales interactions were generic and applied uniformly across the portfolio, without regard for the product, repayment behavior, age or churn risk of the individual customer. We saw significant opportunities to both reduce outright churn as well as eliminate interactions with clients who do not need them (because they would likely pay on their own), by better targeting the right people at the right time in their customer journeys.
  • They can be tested quickly, using proven, widely-available AI tech: In ZOLA’s case, there is a large, existing literature and experience base on how to increase customer retention. Churn prediction is a common ML use case in financial services, telco and digital technology companies, so ZOLA could take advantage of existing wisdom and lessons learned. For decisions/problems where there is not a relevant base of experience, being the pioneer can be more costly and difficult.
  • They are addressing problems for which the solution is not urgent: Specific timelines will vary by provider and use-case, but we have found that it’s safe to budget four to six months to deploy a new ML solution. Any less and the solution will not be properly implemented or tested. If the problem being addressed requires a decision in a shorter time frame, such as a few weeks, ML is not a useful tool. It requires cross-functional team collaboration, multiple model iterations, and often changes to company processes or decision-making — all of which require sufficient time and development to be useful.
Caption: Machine learning use cases relevant for PAYGo-based businesses

STEP 2: NARROW YOUR FOCUS TO A SINGLE PREDICTION QUESTION

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.

STEP 3: HAVE A PLAN FOR IMPLEMENTING PREDICTIONS

  • Churn predictions are used to create a list for call center representatives to conduct daily outbound person-to-person payment reminders and/or lease renegotiation conversations.
  • Field agents receive a list of predicted-to-churn clients each evening, which is used to plan phone calls and customer visits the next day, prioritizing high-risk clients with targeted messaging and offers.
  • Finance departments use the predictions to determine whether to remove clients at risk, evaluate if the hardware can be refurbished and redeployed, or make special offers to recuperate high-risk clients.
  • Churn predictions are also used to automate the decisions of who receives an intervention, at what point in the customer relationship and in what format (i.e., outbound calls, robo calls, automated and custom SMS interactions) the intervention happens, and the type and timing of visits by a ZOLA agent.

NOW WHAT?

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.

  • Collect more data at point of sale, and ensure product offering and customer journeys are tracked closely — and that data related to these are structured to enable future analysis.
  • Establish processes for ensuring the validity, accuracy, completeness and consistency of data. For example, sociodemographic data collected in the field by agents using a smartphone app might be audited by a phone call from a PAYGo operator’s call center rep to the end-customer.

View the PAYGo NEXT Innovation Gallery Demo Presentations

Click to view demo presentations
  1. Robotic process automation (RPA) to streamline time-consuming backend processes and examples of chatbot for agents
  2. Geospatial maps to visualize potential markets for expansion using predictive analytics and public data set
  3. An app for optimizing field operations for agents to reduce cash transfer risk

Finance for Life

BFA specializes in using finance to create solutions for low-income people around the world. We bring deep expertise in customer insights, business strategy, inclusive fintech, policy and ecosystem development to enable the next billions to strive for better lives.

Jacob Winiecki

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Posts on energy access, digital finance, pay-as-you-go solar and financial inclusion at @BFAGlobal. @SimpaNetworks co-founder #paygo #solar

Finance for Life

BFA specializes in using finance to create solutions for low-income people around the world. We bring deep expertise in customer insights, business strategy, inclusive fintech, policy and ecosystem development to enable the next billions to strive for better lives.