Mastering the Market: Innovative Strategies for Identifying Customer Needs and Securing Market Dominance

Damilare Adekunle at UXDX Community: Mastering Design and Market Dynamics. Video: https://youtu.be/x46kWecZwKY

Readable transcript: edited from the recording's captions for readability (fillers and false starts removed, punctuation and section headings added). Wording is the speaker's own. Timestamps are positions in the video. Names marked [?] could not be verified against the audio.

Impact, not profit, at Data Friendly Space

[00:00:00] Hello, good evening, everyone. It's evening here in Nigeria. I am Damilare Adekunle, and I'm the UI/UX product designer at Data Friendly Space, based in Lagos, Nigeria, where you are hearing me from this evening. I want to appreciate Rory and the UXDX team for the opportunity to be speaking here this evening, because this is my first time, and I appreciate the opportunity.

[00:00:34] This evening I'll be talking about mastering the market: innovative strategies for identifying user needs. If you check my slides, it's not securing market dominance but creating significant impact, and you're going to know the reason for that shortly. At DFS, that's Data Friendly Space, where I'm the product designer, our mission is that we provide digital tools and actionable data for social impact organizations to get things done. What we do is make tools available, and we also deal a lot with data, for social impact organizations to get things done.

[00:01:21] We believe that there's too much on the hands of social impact organizations, and we want to take that out of their hands. We want to help them solve the tools problem and the data problem so that they can focus on humanitarian action. Some of our partners include the UNDP, that's the United Nations Development Programme, the UNHCR, the IFRC, the International Federation of the Red Cross, then USAID, AER [?] and a lot more in the humanitarian field.

[00:01:54] For us, the measure of a product's success is not in profit but in its impact on society. Because we focus on impact and not profit, it affects how we start our product and how we go through the product life cycle. So let's go on that journey of discovery and innovation in the humanitarian sector.

DEEP and the Nepal earthquake

[00:02:19] One of our standout products is DEEP. DEEP is a platform that was developed as a response to the challenges faced by humanitarian responders in tracking, translating, storing, verifying and sharing critical data when there's a disaster. In a nutshell, what that means is that for humanitarian actors to carry out action, they need credible data. They need proper data at the right time to be able to know how to come in in the case of a disaster or a humanitarian crisis. That's where DEEP came in. DEEP stands for Data Entry and Exploration Platform.

[00:03:13] How did DEEP come to be? DEEP came as a result of the 2015 Nepal earthquake. Because of the magnitude of the earthquake, humanitarian actors needed information, credible data, in order to know how to take action. They needed things like: what was most impacted? How many children were impacted? What do people need most? Is it food, is it shelter, is it water, and all of that stuff. DEEP came to sift through all of the information that was available about the earthquake and then help let the responders know where help was needed the most.

[00:04:02] It's a platform designed to centralize, accelerate and strengthen inter-agency response to humanitarian crises. Because in a humanitarian crisis, you find that what is most needed is always inter-agency response. You see UNICEF coming in for the children, you see other agencies coming in for whatever they represent. So DEEP was that platform where everybody could collaborate, get actionable data and sweep right into action.

[00:04:38] Just like I've said, when we are thinking about that kind of product, we are thinking in terms of what exactly is the topmost need of our users. Their topmost need is accurate and credible information as fast as possible. Humanitarian workers need to rapidly assess situations, understand the scope of the needs, and then determine the most effective intervention. DEEP was that platform that helped them sift through all of the data that was available concerning any humanitarian crisis and then let them know the actionable insight, the actionable thing to do to help with the crisis.

[00:05:38] The necessity of the humanitarian actors underscores the importance of DEEP, which helps to centralize and streamline the process of data collection and analysis, and it helps to facilitate a more efficient and impactful response to humanitarian emergencies.

Empathy at the core of design

[00:06:01] Having said all of that, how does that affect how we carry out design at DFS? Because we know that we are dealing with human lives, and the tool that we provide affects the response that people get in critical times, we have empathy at the core of our design. We infuse empathy into our UX design process to create impactful and compassionate solutions. Empathy stands as the topmost strategy that we employ to identify our users' needs. We try to deeply understand and share the feelings of our users, and then we are able to uncover their true needs.

[00:07:05] We know, for example, that in an emergency scenario, which is the scenario where we are always trying to help, speed is of utmost importance. So our platform is built with that in mind. We know that credible information is important, so we also put in steps for information to come into the platform. We allow the users of the platform to let other users know how credible the information is. We have a scale, for example, where people can let others know how credible the information is.

[00:07:49] Beyond that, we also have other steps that allow for quality assurance of whatever analysis has been made. Because for us, it's not just bringing information in and then taking information out. Whatever people do on these seemingly internet-based platforms goes on to affect people in real time, and it's always people who are in need of assistance. Moving on, this is a quote from me, based on what we do at DFS: for us, empathy is not just a buzzword; it's actually at the core of our design decisions.

Use cases: assisted tagging, generative summaries and collaborative analysis

[00:08:42] Some use cases. On our platform, before now, the way it worked is that we have a platform connected to different news outlets and information sources. What would usually happen is we have analysts who would read through the secondary data, mostly articles and publications, and then tag the part that is needed. Imagine that a news outlet like CNN writes an article about the earthquake in Nepal, for example. An analyst will read that article and look out for the information that is necessary for responders to take action.

[00:09:37] Mostly you see us deal with numbers. You see things like 1,000 people don't have a place to sleep, or 10,000 people don't have food to eat. Our analysts would tag that information, put it in a category, and then at the end of the day give a summary. We do that across different news and articles, so we are able to get to the point of what action is needed, where, and how severe.

[00:10:11] But over time we found that in a crisis scenario it took our analysts time to read through thousands of articles. So what we did was bring in assisted tagging. We infused artificial intelligence and natural language processing into the system, and what that did was, instead of the analyst reading through a thousand articles, the natural language processing would do that and then make suggestions for the analyst. So we significantly reduced the time the analyst needed to go through the articles that were available in order to carry out action.

[00:11:03] Another use case is generative AI for summaries. After all has been said and done and the analysis carried out, the truth is that the people at the top of the chain who will carry out action are not always interested in all of the stories. They just want to know: is this information credible, and what do we need to do? So after all the analysis has been done, we have generative AI summarize the detail of the report for the people who carry out action, so that they know exactly what to do, what is needed and how to go about it.

[00:11:44] We are looking at the use cases of empathy in our system, so another use case would be collaborative analysis. Just like I said before, in a crisis scenario what you find more often than not is that it is always inter-agency. It's not only one agency that needs to step in. Because of that, we had to build into the platform the ability for agencies to collaborate on analysis, on reports and all that, so that action can be carried out. Because in the humanitarian world, what we are out for is action carried out for the people who need help, and that action as soon as possible. Those are some use cases of how empathy affects the way we carry out our design.

Measuring impact instead of market dominance

[00:12:47] So how do we measure impact? Since we're not making money or profit from our systems and our products, we measure impact by how much agencies are able to carry out action and how much people who need help get the help they need. One of the things that has stood out for us is how we've been able to significantly help agencies and organizations collaborate, because that's such a big deal.

[00:13:24] Let me give an example. Imagine that an agency is doing its own independent research on a humanitarian crisis, and another agency is doing another independent research on that same crisis, and they end up getting different results. It affects how fast they are able to go out and carry out action where action is needed. But what tends to happen, because we have a platform where agencies can collaborate, is that everybody is there when the analysis is being carried out, so everybody is seeing what needs to be done. The result is unified, and action can be carried out immediately. That inter-organization collaboration is a measure of impact and success for us.

[00:14:20] Because we facilitate seamless cooperation and collaboration among humanitarian organizations, the DEEP platform has become an indispensable tool in collective efforts to address global crises, and that underscores our pivotal role in leading the charge towards more unified and impactful humanitarian action. This enhanced connectivity has not only improved the efficiency and effectiveness of humanitarian response, but has also solidified our position of influence within the sector. That's what in the commercial world would be termed market dominance. In our own case, we are really not looking for market dominance. We're looking at how we can unify the efforts of humanitarian organizations, and with DEEP we've been able to accomplish that.

[00:15:19] Some success stories. Since inception, DEEP has been used to inform more than 1,800 humanitarian projects whose scopes are estimated to impact more than 98 million people. For example, USAID used DEEP in their response to COVID-19, ACAPS [?] in its response to the Rohingya crisis, and the UNHCR and IFRC used DEEP in their response to the Venezuela migration crisis. That's together with, for example, the response to the Nepal earthquake that brought about the inception of DEEP. DEEP was also used in the analysis of the Ukrainian war. Those are some of the success stories of the DEEP platform.

Drawing parallels for other products

[00:16:20] Drawing parallels: somebody watching might be wondering, we are not in the humanitarian sector, so how does this help us? The strategy of infusing empathy into our design system should cut across many more organizations and many more people. That is, we must build products that are user-centric, products that resonate with people. Because when you win the hearts of people, then you have a product that wins in the market, a product that dominates.

[00:17:02] In the product development life cycle, in the product design life cycle, you must understand that if you pay attention to people's needs, that is, you empathize with them and put yourself in their condition, you can create products that truly resonate with people and win in the market.

[00:17:34] There's this story that I read on Twitter one of those days. A guy was using an app, and the app had a bug, or lacked a feature that he needed, and he kept writing to the company to add the feature or remove the bug, and that didn't happen. So what he did was apply to the company for a job as a developer. He got into the company, fixed the bug, and resigned. All that he did needed not to happen if the design team or the people at that organization had had their user in mind. Because if they had had their user in mind, they would have empathized with the user's situation and done something to fix the product.

[00:18:29] I hope that helps you in your product development, to empathize with your users, create a product that the market wants, and be able to dominate the market. Thank you. If you have any other questions, you can reach out to me through my email or on social media. Thanks for your time.

Q&A

[00:18:57] Host: Excellent, thank you very much. That was an interesting talk, because it's a little bit different from most. As you were saying, it's more humanitarian than the traditional corporate enterprise, but thanks for bringing it back with the parallels. If anybody has a question out there, please do write it in. My question, just because we had a talk a few weeks ago about jumping into empathy: there was a company dealing with therapy and difficult situations for people, and they found that some of their UX researchers were being quite impacted by the content they were trying to empathize with, because these were very difficult situations that people were going through. Have you found anything like that? Particularly in humanitarian situations, which are very extreme, people trying to empathize with what's happening on the ground can be overwhelmed by that.

[00:20:08] Damilare: Well, yes, empathizing can be overwhelming. But we always keep in mind that if we empathize with them, we have the solution. By coming in contact with what they are feeling and what they're going through, we know that the better we are able to do that... We are not just empathizing to sympathize. It's empathy, not sympathy. We are feeling what they're feeling because we want to proffer a solution. That's always at the back of our minds as we empathize. So yes, sometimes when you read reports and you hear about things that happen, it gets to you. But you know that your response to that should be to create a product that can help alleviate people's suffering and help them get the help and assistance that they need.

[00:21:03] Host: Great. Do you have any examples you could share where, through empathizing, you realized that you were going down one path, or you had some opinions of "this is what people need," but because you had spent all that time really understanding who they were and what their needs were, you realized you were going in the wrong direction and fixed it?

[00:21:26] Damilare: For example, on the DEEP platform, when we initially introduced generative AI, we found that our analysts were not really using it. It was empathy that made us put in the generative AI to help them, but we found that they were not using it because the way it was implemented did not work well with their existing workflow. And that's after we spent a lot of time and resources to get it done. But we still knew that they needed it in their workflow so that they could arrive at decisions quickly.

[00:22:16] So we went and did the traditional user interviews. We interviewed about 15 organizations, because we wanted to know why they weren't using it, and all that. What we did after was come up with a solution. In that scenario, we came up with a solution, and we did not implement it until we had ensured that our users had tested it and were comfortable with it, comfortable with switching over from what they used to do to the new solution we were proposing.

[00:23:01] Host: Excellent. I like that: take it much smaller and make sure it's validated before you invest in the technology. That's great. Is there anything that you're excited for? Generative AI is the current big buzzword, but where do you think the DEEP platform can go, and how do you think it will be able to help your customers in the future?

[00:23:25] Damilare: There are generative AI efforts going on now. At the moment, DEEP deals with secondary data, so articles and news sources. But what we are looking towards in our product roadmap is to implement primary data, so that people who are in the field can directly put in their data, and then we can also analyze that together with the secondary data we'll be having. And there's generative AI. Together with that, there's also a parallel platform to DEEP that we are not combining with DEEP but are making as a separate platform, which also deals a lot with generative AI. We think that what ChatGPT does is good, but humanitarians might need something more specific. Because at Data Friendly Space we are like the storehouse of humanitarian data, we have models that we are now training on the data that we have, which can help humanitarian responders get to the point of the information that they need.

[00:24:44] Host: Excellent. I love the move from secondary to primary, because that would make a world of difference, I'm sure. I think that brings us almost to time. Let's just see if there are any more questions coming in. No. We're getting a lot of love and applause on YouTube for you, so obviously there are people out there who enjoyed the talk. Thank you once again for sharing your insights.

[00:25:16] Damilare: Thank you. Thanks for having me. It's been a pleasure being on the platform with you.