Improving Financial Inclusion for the Informal Traders in Tanzania

“Human Centered Design is about designing for inclusion. So using design as a way to bring together different disciplines and people from different backgrounds and whether you have a university degree or not, it doesn’t matter. You can still acquire, you can still apply the tools and you can still learn certain skills for you to be able to navigate your way in life and solve problems in your own context. So for me, it’s about the democratisation of the design discipline and making it more accessible and using it as a strategic tool to solve problems”. – Dr. Keneilwe Munyai

Design is indeed an innovative problem solving tool, but unfortunately not everyone understands it or applies it in their problem solving approach. In this article Dr. Keneilwe Munyai shares her biggest dream which is to bring HCD into the schools so people become aware of design early in their study. She strongly believes that all students should be exposed to design earlier in their education. She also shares a unique case study that helped improve financial inclusion for informal traders in Tanzania.

Case Study

Majority of people make use of informal methods for their savings. And these could be people either working in the transport space or informal hair salons, which are very popular in Africa, or farmers even. We were working with financial institutions to get them to move away from informal savings to saving with the financial institutions. Financial institutions usually charge a monthly service fee for customers who earn monthly income. This becomes a challenge for informal traders whose income might not be regular on a monthly basis. For example, the income of informal traders is seasoned most times. Why would financial institutions give them the same kind of product as somebody who earns income on a monthly basis? The goal here, or the purpose of this project, was to use human centered design to support these financial institutions to first collect data for research and understand who these people are and understand the context.

Informal Trader in Tanzania Source: The New Humanitarian

The Comfort of Large Data

And as you can imagine, financial institutions are very big on data. And for them, data means quantitative data. This project was an opportunity to marry quantitative and qualitative datasets because often in the human centered design space, we shy away from quantitative data because we know that quantitative data is not going to give you rich insights that you get from qualitative data. But in reality, you can use both methods, you can use both data sets to inform whatever decisions that you are trying to make. That was the goal, to basically use both quantitative and qualitative data and generate insights that can then be used to develop solutions that are meaningful and that matter to the people that they are designed for.

The research started off with quantitative data where surveys were sent out to thousands of people, because that’s something that makes financial institutions feel more comfortable. If you’re saying in a country of 60 million people, only ten people are interviewed, that sounds like nothing to financial institutions. Sending out 5,000 to 10,000 surveys and analysing them gave them a sense of comfort because now they have larger data that can guide their decisions. For the designers, the goal was how do we actually translate that data into meaningful tangible outcomes? Because it’s not enough to have all this evidence and have all this data and not use it to make decisions, but how do you actually turn it into something tangible that is meaningful not only for the financial institution, but also for the people that are impacted by the problem. After the quantitative data, we also did the qualitative data collection which were interviews.

Collecting and analyzing data

Different categories of informal traders were identified. So from shop owners to transportation to farmers. Interviews were conducted with all of them to understand the context and their challenges. What was interesting with all of this was that there were a lot of rich insights and that’s what qualitative research actually allows you to do. Because by doing the quantitative research first, at least it helped to narrow down, in terms of the areas to focus on. And then you can take those areas and dig deeper, which is what qualitative research enables you to do. After the research was done, we spent time analysing the information. So I was taking the interviews first and then analysing and synthesising the research. And this was basically an opportunity to really spend time and immerse ourselves into the information that we got so that we could understand the real problem and pick out the real interesting insights which then would be a springboard for the extra innovation. So spending a lot of time understanding the problem and understanding what the real challenges are for the people that we were interviewing. So that when we move on to trying to co-create a solution, at least we have spent time and we understand what the data had told us, we understand what was contained in the data and we did this in collaboration with the employees of the financial institution. What was interesting there was that we had quite a diverse team from product developers within the financial institutions but also the regulators. These are the people that ensure whatever is being developed meets the requirements in terms of regulation. Which was quite interesting because often we don’t think of bringing in the regulators and only later we realise that actually this is not going to work from a regulation perspective. So it was quite interesting to have people in the same room that can actually help us flag certain things and flag certain solutions as we were developing them as well.

In terms of the outcome of this whole process, it was basically tangible solutions that the institutions can now develop further and implement in the context and solutions that were tested. Before we handed these solutions to the financial institutions, we did rounds of testing with the people that were impacted in order to understand that we are going in the right direction. So we didn’t want a situation where the financial institutions end up spending time and resources on a solution that is not going to work. Testing was critical as well in this whole process and I think it was valuable especially for the employees of the financial institutions as well because they saw the value of doing that upfront as they didn’t have to spend any money developing low fidelity prototypes and taking them out to the communities to get feedback quickly.

The Synthesis Tool

Synthesis is organising, pruning, and filtering data in the context of a design problem, in an effort to produce information and knowledge.” finding clarity in chaos” to transform the data into insights. The most basic principles of making meaning out of data is to externalise the entire meaning-creation process as well which is achieved through a systematic synthesis process to search for consensus as a team (convergence). It is the process of finding relationships and patterns within the data collected. It is less important to be “accurate” and more important to engage with the data, and encourage thoughts and reflections. Often, teams will either have done the interviews or either create themes and label those themes and then choose the theme that they want to take forward in terms of the innovation, which I always felt, doing that doesn’t necessarily help engage more with the data. And so I started applying this tool which consists of four quadrants: connections, contradictions, tensions and surprises. And basically after the team had unpacked their interviews, they had to physically look at the interviews that were unpacked and start forging connections. So looking at the information from across the interviews that they’ve anticipated and also within the interviews themselves, look at what are the connections, is there anything that the people that we’ve spoken to have said that were connected? Is there anything that people have said that were contradictory? And that requires a lot of focus and it requires you to really pay attention to the data. It also requires you to listen to other people, to listen to each other as a team because the information won’t end up in the quadrant where it needs to end up unless the team has discussed it and come to a decision that actually, it belongs to a particular quadrant.

Why use the synthesis tool

  • Clients need to see the relationship between design research and research synthesis and design ideas. Synthesis is frequently relegated to an informal step in the overall process, it is practiced implicitly in the privacy of our own thoughts, and performed only rudimentarily.
  • Not having a visible connection between the input and the output of the synthesis process makes it difficult to articulate exactly why the design insights are valuable and this increases the risk for the rejection of the insights.
  • Creating a systematic way for synthesising data helps the team go from group knowledge to start building a common understanding of the insights and reach a consensus about what are the most compelling (user) insights or consistent problems the people are facing.
  • Synthesis also helps the team better realise how life experience from the interviews drive design decisions systematically.

How to use the Synthesis tool

  1. After unpacking interviews using a different colour for each person interviewed.
  2. The team creates four quadrants that consist of connections, contradictions, tensions and surprises.
  3. The team then looks at the unpacked interviews focusing on the what they told us column.
  4. The team then picks, discuss and physical moves the sticky notes and place them within the relevant quadrant.
  5. Once the groupings begin to emerge through the process of organisation, the groupings can be made explicit by labelling them within the quadrant.
  6. The grouping label captures both the literal and the implied contents of the group—it makes obvious the meaning that has been created through the process of organisation.
The Synthesis Tool

Explanation of the Quadrants

  1. Connections: The team identifies the elements/insights they see connected to each other within one interview or connections across the interviews.
  2. Contradictions: These are insights that are contradictory within the same interview or across the interviews.
  3. Tensions: The team focuses on elements of data that show elimination of options such as safety vs. Cost. (for example the interviewee emphasises safety when it comes to public transportation, but the options available at a reasonable cost to them are unsafe)
  4. Surprises: These are some unexpected insights

Conclusion

  • This process creates an appreciation for the data and lived experiences of the people who gave the data.
  • This process is a difficult mental task which requires the team to focus and to meaningfully engage with the data to work out what connects to what.
  • When synthesis is given its due the results can be powerful.
  • Working with quantitative and qualitative data sets can help make richer innovative decisions.
Recommend0 recommendationsPublished in Social Entrepreneurship, Tanzania

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