September 3, 2021

Introducing the Fit Factor: A new impact measure

In this blog post we introduce The Fit Factor: Matching Loans and Savings to Cash Flows, a new paper from Gojo’s R&D team.

Typically, when we set out to measure the impact of a loan on someone’s life, we tend to look at what happens after they take the loan— for instance, whether their income increases, whether they are able to spend more on education, or whether they acquire more assets. However, there are a couple of limitations to this approach:

  • Outcomes data taken following a loan tends to be a snapshot of a certain point in time, and is usually collected at a time pre-determined by the service provider, rather than at a time that makes sense for the borrower in terms of when they expect to see results from the loan
  • Changes in income and increased spending on education can be influenced by many different factors in a person’s life, not just their access to finance. For instance, we see in financial diary research that unexpected events happen more often than we might think, such as accidents, sudden medical needs, new opportunities, and other events which may disrupt the original plans a borrower had for their loan

While outcomes data is useful, it cannot give us the full picture of the utility provided by a loan or other financial service. What if, in addition to outcomes data, we considered the impact of a financial service from a cash flow perspective? In other words, what if we could see how loans or savings fit with clients’ real cash flows and affect their day-to-day money management?

Using data from Stuart Rutherford’s Hrishipara Daily Diaries project, we would like to introduce a new impact measure which looks at how financial services either reduce or add to the volatility of a person’s cash flows. Our working assumption is that services which increase cash flow volatility generally make money harder to manage, and vice versa. We call this measure of impact on volatility the Fit Factor.

Read our paper setting out the concept, its applications as an impact measure, and its limitations here.


Yoshinari Noguchi is a researcher at Gojo and Company. He works in Gojo’s R&D team, and is currently looking into new ways to understand and support money management for low-income people, as well as analysis of data from the Hrishipara diaries and Gojo’s own financial diary projects.

Cheriel Neo leads impact measurement at Gojo and is also a member of Gojo’s R&D team. She is setting up and running Gojo’s financial diary projects in Cambodia and Sri Lanka, and is interested in using data to better understand Gojo’s current and target clients.

September 3, 2021

Introducing the Fit Factor: A new impact measure

In this blog post we introduce The Fit Factor: Matching Loans and Savings to Cash Flows, a new paper from Gojo’s R&D team.

Typically, when we set out to measure the impact of a loan on someone’s life, we tend to look at what happens after they take the loan— for instance, whether their income increases, whether they are able to spend more on education, or whether they acquire more assets. However, there are a couple of limitations to this approach:

  • Outcomes data taken following a loan tends to be a snapshot of a certain point in time, and is usually collected at a time pre-determined by the service provider, rather than at a time that makes sense for the borrower in terms of when they expect to see results from the loan
  • Changes in income and increased spending on education can be influenced by many different factors in a person’s life, not just their access to finance. For instance, we see in financial diary research that unexpected events happen more often than we might think, such as accidents, sudden medical needs, new opportunities, and other events which may disrupt the original plans a borrower had for their loan

While outcomes data is useful, it cannot give us the full picture of the utility provided by a loan or other financial service. What if, in addition to outcomes data, we considered the impact of a financial service from a cash flow perspective? In other words, what if we could see how loans or savings fit with clients’ real cash flows and affect their day-to-day money management?

Using data from Stuart Rutherford’s Hrishipara Daily Diaries project, we would like to introduce a new impact measure which looks at how financial services either reduce or add to the volatility of a person’s cash flows. Our working assumption is that services which increase cash flow volatility generally make money harder to manage, and vice versa. We call this measure of impact on volatility the Fit Factor.

Read our paper setting out the concept, its applications as an impact measure, and its limitations here.


Yoshinari Noguchi is a researcher at Gojo and Company. He works in Gojo’s R&D team, and is currently looking into new ways to understand and support money management for low-income people, as well as analysis of data from the Hrishipara diaries and Gojo’s own financial diary projects.

Cheriel Neo leads impact measurement at Gojo and is also a member of Gojo’s R&D team. She is setting up and running Gojo’s financial diary projects in Cambodia and Sri Lanka, and is interested in using data to better understand Gojo’s current and target clients.

June 4, 2021

Data as (s)oil in microfinance

Farmer in the field in Anad, India. Photo by Nandhu Kumar on Unsplash

Data is the new oil?

In the past decade, the phrase “data is the new oil” has become hugely popular, with hundreds of articles and talks using this metaphor. And there is good reason for this: many see data as the “fuel” which is giving energy to the 21st century economy. 

Data on its own has no, or little value - similar to crude oil, it needs refining to become a useful and valuable resource. Only after we process our data, put it into the right context, and use it for decision making, do we get the real benefits (in the same way that oil is much more useful when turned into petrol, asphalt or plastic). To do so, we need infrastructure for collecting, storing and processing the data - which is another similarity with the oil industry.

But is this metaphor really fitting?

First, oil is a finite resource, consumed over time, and rarely reusable. This is very different from the nature of data- data  is (almost) unlimited, reusable and multiplies whenever we cross it with other data (we create information, rather than using up data). This gives us unlimited opportunities without having to worry about running out of “fuel”. 

Second, with oil you need to start big - the infrastructure is very complex and it requires a huge investment. Again, data is very different - you can start from simple data analytic functions, gradually developing your capabilities and outreach. It is also possible to test solutions and pivot if the chosen path does not fit your business. 

Third, you can store crude oil and it will still keep its value, whereas data very often loses its value over time. For example, records of some events age very quickly, and can only bring benefits if used immediately.

Finally, in case of leaks, oil can be cleaned up (although the damage to the environment is done and not always fully reversible), while in case of data it is impossible - leaked/stolen data can damage businesses and people’s lives for many, many years.

(S)oil

While listening to The Data Strategy Show1 podcast, I encountered for the first time the idea of “data as a new soil”. In the episode they mention the soil metaphor in passing, in contrast to oil. I found the soil metaphor to be much more accurate and decided to extend this thinking further.

First of all - you need to work patiently with data/soil to bring value. To grow crops, you need to know the quality of your soil well (explore your data), understand what crops you can expect to grow on this type of land (understand the business context), prepare the soil for agriculture (prepare data), sow seeds (run analytics), water crops and look after them (enrich your data, observe the results, improve analytics), protect from pests (ensure data security), harvest crops (make use of ready information), and… iterate or improve on the process.2

Moreover, the soil metaphor is useful to show that without previous experience it might be better to turn your enterprise into a data-driven one gradually. As with soil or land - if you are new to agriculture, you can start with a small plot, learn, experiment, pivot, and progress with time. Unlike with oil, you don’t need to build the whole operation from day one, but can start small and keep gradually improving.

You also need to be patient - careful preparation, good understanding of data and business context are key to obtaining the best outcome and should not be hurried. Data projects you start now might bring value after a few years - crops you planted today will not grow in a few days.

Of course, the examples above do not exhaust the similarities between data and soil, but they demonstrate the usefulness of the soil metaphor.

Woman working on a rice field in Chiang Mai, Thailand. Photo by Eduardo Prim on Unsplash

In the context of microfinance

In the microfinance context this metaphor is even more appropriate- and it is not only because the low income households we serve very often make a living from agriculture or animal husbandry. 

The microfinance sector has not usually been associated with being “data-driven”. Access to data has historically been limited, the need for data analysis has not been recognised, and the lack of proper infrastructure for data was very common. With growing usage of smartphones and tablets, however, the situation has slowly started to change, this change has rapidly accelerated under COVID-19 - more and more microfinance institutions are starting to implement better data collection methods, build (or outsource) data analytics, and use data more often in decision making or product development. But (almost) everyone proceeds in the same way as a farmer starting to cultivate a plot of land - start with a small project, learn, experiment, and then pivot or scale. And just as with growing crops: for some outcomes we will need to wait a little while.

Finally, one important point: data belongs to the people, to our clients. They give us access to their personal information and in exchange we improve our operation, pricing, and product fit. Together, we are cultivating the soil and sharing the fruits of our labor. Sometimes literally.3


Tomasz Ociepka works on data analytics at Gojo. He is currently working on setting up Gojo's data lake for the secure storage and easy analysis of data from Gojo's partner companies.

June 4, 2021

Data as (s)oil in microfinance

Farmer in the field in Anad, India. Photo by Nandhu Kumar on Unsplash

Data is the new oil?

In the past decade, the phrase “data is the new oil” has become hugely popular, with hundreds of articles and talks using this metaphor. And there is good reason for this: many see data as the “fuel” which is giving energy to the 21st century economy. 

Data on its own has no, or little value - similar to crude oil, it needs refining to become a useful and valuable resource. Only after we process our data, put it into the right context, and use it for decision making, do we get the real benefits (in the same way that oil is much more useful when turned into petrol, asphalt or plastic). To do so, we need infrastructure for collecting, storing and processing the data - which is another similarity with the oil industry.

But is this metaphor really fitting?

First, oil is a finite resource, consumed over time, and rarely reusable. This is very different from the nature of data- data  is (almost) unlimited, reusable and multiplies whenever we cross it with other data (we create information, rather than using up data). This gives us unlimited opportunities without having to worry about running out of “fuel”. 

Second, with oil you need to start big - the infrastructure is very complex and it requires a huge investment. Again, data is very different - you can start from simple data analytic functions, gradually developing your capabilities and outreach. It is also possible to test solutions and pivot if the chosen path does not fit your business. 

Third, you can store crude oil and it will still keep its value, whereas data very often loses its value over time. For example, records of some events age very quickly, and can only bring benefits if used immediately.

Finally, in case of leaks, oil can be cleaned up (although the damage to the environment is done and not always fully reversible), while in case of data it is impossible - leaked/stolen data can damage businesses and people’s lives for many, many years.

(S)oil

While listening to The Data Strategy Show1 podcast, I encountered for the first time the idea of “data as a new soil”. In the episode they mention the soil metaphor in passing, in contrast to oil. I found the soil metaphor to be much more accurate and decided to extend this thinking further.

First of all - you need to work patiently with data/soil to bring value. To grow crops, you need to know the quality of your soil well (explore your data), understand what crops you can expect to grow on this type of land (understand the business context), prepare the soil for agriculture (prepare data), sow seeds (run analytics), water crops and look after them (enrich your data, observe the results, improve analytics), protect from pests (ensure data security), harvest crops (make use of ready information), and… iterate or improve on the process.2

Moreover, the soil metaphor is useful to show that without previous experience it might be better to turn your enterprise into a data-driven one gradually. As with soil or land - if you are new to agriculture, you can start with a small plot, learn, experiment, pivot, and progress with time. Unlike with oil, you don’t need to build the whole operation from day one, but can start small and keep gradually improving.

You also need to be patient - careful preparation, good understanding of data and business context are key to obtaining the best outcome and should not be hurried. Data projects you start now might bring value after a few years - crops you planted today will not grow in a few days.

Of course, the examples above do not exhaust the similarities between data and soil, but they demonstrate the usefulness of the soil metaphor.

Woman working on a rice field in Chiang Mai, Thailand. Photo by Eduardo Prim on Unsplash

In the context of microfinance

In the microfinance context this metaphor is even more appropriate- and it is not only because the low income households we serve very often make a living from agriculture or animal husbandry. 

The microfinance sector has not usually been associated with being “data-driven”. Access to data has historically been limited, the need for data analysis has not been recognised, and the lack of proper infrastructure for data was very common. With growing usage of smartphones and tablets, however, the situation has slowly started to change, this change has rapidly accelerated under COVID-19 - more and more microfinance institutions are starting to implement better data collection methods, build (or outsource) data analytics, and use data more often in decision making or product development. But (almost) everyone proceeds in the same way as a farmer starting to cultivate a plot of land - start with a small project, learn, experiment, and then pivot or scale. And just as with growing crops: for some outcomes we will need to wait a little while.

Finally, one important point: data belongs to the people, to our clients. They give us access to their personal information and in exchange we improve our operation, pricing, and product fit. Together, we are cultivating the soil and sharing the fruits of our labor. Sometimes literally.3


Tomasz Ociepka works on data analytics at Gojo. He is currently working on setting up Gojo's data lake for the secure storage and easy analysis of data from Gojo's partner companies.

Newsletter

Sign up to receive news from Gojo here.