RESEARCH PAPER - Simple and efficient metrics for Pay-As-You-Go companies

Background

Pay-As-You-Go, or PAYGo, is a business model or payment system in which customers pay for goods or services as they use them. The model is often used in developing markets as it allows customers who may not have the means to make large upfront payments to make small and manageable payments while already having access to the goods or services they need. The market is expected to grow as more people in developing countries gain access to mobile technology and the need for affordable, accessible energy solutions increases.

Despite its success in reaching millions of energy-poor, the PAYGo market has proven to be complex and challenging to navigate, both in terms of understanding the financials of individual companies and developing proper market standards. To address this, stakeholders in the PAYGo industry are working together in an attempt to standardise and improve industry metrics. This paper is written in the context of these efforts.

Objective

The paper aims to spark a conversation on the simplification and improvement of financial metrics in the PAYGo industry. It builds on the industry’s efforts to standardise metrics by suggesting alternatives to some that require a large variety of data yet remain open to misinterpretation and can be misinformative. The intended result is to help provide stakeholders with efficient metrics to improve financial decision-making and increase financial transparency in the PAYGo industry.

Paper Summary

An efficient metric can be defined as simple, accounting-independent, and with outcomes that are consistent with the underlying data it seeks to showcase. While efforts to standardise and streamline PAYGo metrics are moving the industry in this direction, some metrics still require a wide range of data yet remain open to misinterpretation and can be misinformative. This can lead to limited financial transparency, inadequate accounting practices, and poor financial decision-making. To properly assess a PAYGo company’s financial health and portfolio, it is essential to understand and monitor the following key elements:

• The variation in actual and future payment profiles,
• The speed at which the future payments take place,
• The total value of the future payments (actual receivables),
• The unit economics implied by the payments,
• The future payments’ capacity to cover the existing debt.

Consequently, the financial metrics and covenants used by the sector should be able to efficiently convey this information. This paper suggests that most of the key information needed to perform a robust financial analysis and comparison between companies can be extracted through cohort analysis using only two straightforward data points that are easy to report:

1. Total cohort contractual value,
2. Actual monthly payments per cohort.

These data points are the most essential for a PAYGo company, and leave no room for interpretation. Additionally, because they focus on cash repayments, they are comparable and independent of accounting decisions.

The paper illustrates how to use these data points to build a full actual and projected cohort payment table. It proposes a methodology for projecting cohort payments that involves using a moving average that gives more weight to recent cohorts and accounts for specific trends within the cohort being projected. From the cohort payment table, metrics can be developed to perform a robust financial analysis and comparison between companies. The paper recommends that when a metric is being considered, it should always be tested using identical client repayment profiles in four specific scenarios to ensure that it accurately reflects the underlying data it aims to showcase. This test is referred to as the Static Test and is used in the paper to help demonstrate the inefficiency of certain commonly used metrics, and identify efficient metrics that cover all the PAYGo financial focus points listed above.

Paper suggestions

1. The two data points: Total cohort contractual value and Actual monthly payments per cohort, should serve as the foundation of financial reporting.
2. The metrics presented in the paper should be adopted as reporting standards as they are deemed efficient and cover the key financial focus points.
3. Any additional metric should be subject to the Static Test defined in the paper to ensure that it accurately reflects the underlying data it aims to showcase.
4. Revenue and receivables should be provisioned for upfront based on what is projected to be received rather than being booked at full contractual value with no provisioning.

Acknowledgements

I would like to thank Andy Keith, Solar Panda’s Founder and CEO, for developing the cohort prediction methodology that inspired the methodology outlined in this paper, as well as for highlighting some of the deficiencies with current metrics and introducing important concepts such as the Static Portfolio Test and Calibration Period. My gratitude extends to Madeleine Gleave, Nithio’s Chief Data Scientist, for the vibrant discussions held on PAYGo metrics and the comments received that have helped inspire this work. Lastly, I am grateful to Drew Corbyn, Bill Gallery, Oliver Reynolds, Lucia Spaggiari, Alasdair Lindsay-Walters and the rest of the PAYGo Perform Initiative team for their ongoing trial on the cohort analysis approach. Their work is anticipated to supplement and expand the conversation around this methodology.

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About the author:

Guillaume Cruyt
Investment Associate – EDFI Management Company
g.cruyt@edfimc.eu
+32 474.57.00.64