The Power of Information: Revolutionising Market Intelligence in African Economies With Joseph Rutakangwa

[00:00:00] You're listening to the Unlocking Africa podcast. And went ahead and added now around 54 other languages in the app. Right now we have close to 100,000 mappers and they're spread across the continent. Stay tuned as we bring you inspiring people who are unlocking Africa's economic potential.

[00:00:47] You're listening to the Unlocking Africa podcast with your host Terser Adamu. Welcome to the Unlocking Africa podcast where we find inspirational people who are doing inspirational things to unlock Africa's economic potential. Today we have another special guest.

[00:01:07] We have Joseph Rutakangwa, who is the CEO of Ruazi which is a market intelligence platform that provides companies with actionable data from an emerging market on who is buying what, for how much, from where, when and why. Welcome, welcome, welcome to the podcast Joseph, how are you?

[00:01:29] Thank you for having me. I'm doing well, I'm doing well. How are you doing? I'm very, very well. Looking forward to our conversation today. Excellent. Likewise, likewise. Can't wait. Brilliant, brilliant, brilliant. So let's get started. I was hoping you could introduce yourself

[00:01:45] and tell us a bit more about Joseph Rutakangwa. Sure, sure. Yeah, so my name is Joseph Rutakangwa. I'm a founder and CEO of a company called Ruazi. I started Ruazi with Eric and we built a marketing agents platform which provides

[00:02:06] multinational companies with access to consumer data from emerging markets. And this is data on who is buying what products, you know, for how much, from where, when and why to hope these companies drive revenue and expand.

[00:02:21] And the way we capture this data is we use a mobile app and web app and equip that with our network of local consumers across all these emerging markets who then capture purchase data at buying locations when they're buying different products and services

[00:02:40] and also consumption data at home. The consumers get paid for every verify's data log and companies then have access to all this otherwise inaccessible on the ground data and that helps companies deliver hyperlocal packaging, hyperlocal pricing,

[00:02:58] as well as messaging to drive growth. Just a bit of background about myself. I'm originally from Tanzania. I grew up in so many different countries. I went to primary school in Uganda, spent some time in Kenya. Last year of high school was

[00:03:11] fortunate to become an exchange student in the US, in Iowa. There's an absolute excellent experience. After that, I never had the financial capacity to go to college in the US, despite having a bunch of presidential scholarships. And I didn't want to take

[00:03:29] debt. You know, I never believed that college education is something worth taking a debt over. And then being tied up to debt for 20 years or so. So I just always had my head around getting corporate sponsorships or full scholarships or anything around that lane. It didn't happen after

[00:03:47] high school. So I decided to start my business, my first business which was graphic design and video production. I was very successful with it. Earlier on in the US, went back to Tanzania. The problem was the purchasing power in Tanzania is 10x lower

[00:04:06] than what you have in the US, even more in some areas. So the amount that people were paying wasn't sufficient to cover my costs. I was doing weddings, church events, making logos, doing videos and so forth. But people would mostly pay 50% of funds. They wouldn't finish

[00:04:24] up the rest and so forth. So I got a lot of cash flow problems and decided that the business wasn't worth it. So I ended up closing it, tried to get a job in Africa. There's a huge thing of

[00:04:34] having a certificate, a degree certificate. So I didn't have a degree at a point. So I couldn't get a job despite having all the expertise and so forth. So I ended up deciding to move over into the development space where I started

[00:04:50] my own community development projects to help. Essentially the problem that I always saw since then was you have a lot of young people who have graduated from college or even some other tertiary education. They don't have jobs. It takes three to five years to get jobs in most

[00:05:08] countries in Africa and you have space. You have a lot of space, you have a lot of lands in countries like Tanzania but you don't have the resources for irrigation and other resources that's needed, supply chain resources and so forth to drive every culture. So I just focused

[00:05:26] on to that to build development projects. Didn't work for myself because I was young. There's a lot of ageism. So I had to join the UN as a volunteer which now started working my favor

[00:05:41] ended up doing a lot of development projects through the UN, from there moved to Indonesia and other countries training people to do the same. Subsequently was fortunate to go to Lehigh University through their postgraduate program which is where I found my passion for consulting

[00:05:59] and started consulting from there. Took eight years of consulting for multinationals and that's where I found the problem of consumer data, of lack of consumer data in market data in Africa that eventually drove me to starting RoAZ.

[00:06:14] Fantastic. So you've given us great insights into yourself and into what RoAZ does and how and why you started it. I was wondering if we could look specifically, you've touched upon this slightly in terms of can you tell us why you decided to create a

[00:06:30] platform to address the data gap in Africa? Yes, yes. So during my years in consulting, spent eight years doing this before starting RoAZ, the most common problem across different sectors consulting for companies in the beginning was energy, healthcare, transportation and then

[00:06:51] eventually started having a concentration in consumer packaged goods. The most common problem was that we didn't have access to consumer data. My job was basically find us a market in Africa and tell us what product form or portfolio we can roll out in that market,

[00:07:08] targeting what kind of consumer at what price point through which sales channels and so forth, right? And your distribution partners or local partners that they could work with, etc. The issue was that there isn't anything online that would give you any sort of indication

[00:07:28] for any metrics that you'd need to successfully decide on which market and to roll out what products and how to do that. So we subsequently turned to national bureaus of statistics, ministries of

[00:07:42] trade and commerce and so forth, but these bodies didn't have the data either. If they had any data it would be a five, 10 years old also it would be very limited or inaccurate, etc.

[00:07:55] So we decided to turn again to a private sector looked at traditional market research firms. The issue with market research firms, we had all the across the years I had all the top

[00:08:10] brands in that space. The problem is they use the panel model which is you'd get a panel of let's say 50 consumers in Lagos, you'd make sure you're something it well and then you'd use that panel to get the insights on what those consumers are experiencing and buying

[00:08:30] and so forth. And then extrapolate on a city of 15 million, right? And then a country of 100 million and so forth. The problem is that it's never intended for on ground use. So one

[00:08:43] you have, from there you have this 100 page report that tells you the macro trends and then tells you what consumers think and so forth. But that's not actionable. So an on ground sales representative or trade marketing coordinator can't actually use any of what you get from

[00:09:00] that kind of research from like panel based research to execute. It doesn't tell them where is the actual location over retailer where they need to put the product or where do consumers who buy a certain product size at a certain price actually leave?

[00:09:16] What are the actual price point across the entire range, neighborhood profiles? It doesn't tell them anything that's actionable. So after testing going over so different models for years along the way I met Eric we found that the only model that works is equipping consumers

[00:09:33] with a correctly set up that would enable them to log when they buy products at retail outlets and also log products they're using in the house and log it correctly. So there's a

[00:09:44] huge aspect, there's a huge like process innovation that you had to run in the background to make sure it works, make sure we can pay consumers correctly and on time to make sure we can capture data

[00:09:55] correctly and verify it and do all of that within minutes, etc, etc. So that's the model that we found that it works. Get the data from the horse's mouth and immediately the companies would access this data and execute. So you have the actual locations of all these

[00:10:12] important sales channels, you have actual locations based on pricing of heat maps, based on pricing and sizes and so forth. And that helps companies actually execute from the data perspective rather than just having a hundred page reports that's like text heavy and just focusing on macro trends.

[00:10:30] Fantastic. So you mentioned that you started Rewarzy to address the data gap you faced whilst working in consulting. So I was hoping you could elaborate on how this data actually helps companies to, I guess, drive revenue or expand in African markets?

[00:10:48] Yes, yes. So you have two different profiles of companies. So profile A is companies which already they have presence in African markets and this is just not limited to African markets. You have

[00:11:01] Africa, you have South Asia right now and Latin America. So for companies, they already have presence in African markets. The challenge is they're struggling in the market, they're struggling to capture market more market share. They are being outcompeted by either other multinational

[00:11:18] companies or local businesses and brands. So their need is they have the resources, right? They have the financial resources, they can add human capital and other resources to execute. They just don't know what levels to move to drive more revenue, right? Is it pricing? Am I pricing

[00:11:37] incorrectly in different cities and neighborhoods in the cities and so forth? Am I placing my products in wrong retail locations because they don't have visibility in any of that. Their visibility kind of ends for physical products, ends at large distribution centers,

[00:11:51] for services, they would be struggling to roll out competitive, for instance, in internet data packages. There's a huge data wall in countries like Nigeria and Kenya and so forth. So there's a lot of like, you don't have visibility on what exact value proposition consumers want

[00:12:11] and how much they're going to pay for and so forth. And you have the second group which is companies that do not have any presence at all. They have decided which countries they

[00:12:20] want to expand to or they want to figure that out and they have already substantial investments to do so. So in both groups, the data we provide is data from consumers. There's consumer inside data.

[00:12:32] So you have two groups, consumer inside data and then retail data for physical products. For consumer inside data, it's data on what consumers are using today and they're purchasing patterns where they buy products in terms of proximity, profile of the sales channels and so

[00:12:53] forth. Why they're buying products from those channels? Why they prefer certain brands? Why they buy on a practical level or you can find the distance from where they frequently buy X products, the other products that are available in those locations, the price points of the

[00:13:13] product they're buying versus others, the sizes, the frequency of purchase, the number of users in their homes that they have for that product. They're earnings and we have huge data points on these consumers, their earnings where they have kids and so forth and all of this is verifiable

[00:13:33] by images that are geo-stamped and timestamped and so forth. So you can have that weekly trend and understand their patterns and this helps companies to build and send their sales teams to important, if it's a retail data subscription to help them understand the important retail outlets

[00:13:55] that would sell their products. So these retail outlets would be selling competitor products. So now I know they're selling computer products at this price that's in the same range at the same size, same flavors and so forth. So I'm going to send myself team at these locations

[00:14:11] to do what they call listing in the CPG world. If it's a service business, for instance, some of our companies are global telecom companies, global credit card companies and so forth. What they look for is now I know that these consumers are using this mobile data package

[00:14:30] because of X, Y and Z. So now they can target, they can do hyperlocal messaging to target the value proposition that aligns with what consumers in that specific location value and that increases the marketing ROI because now you're not just messaging what you think based on 50,

[00:14:50] you know, consumers on your panel, but it's based on tens of thousands of consumers and more in a city, right? So you identify important retail location, you put your product there because you know consumers within 100 meters from that location are buying your competitor product because

[00:15:07] of the same reason that your product is actually greater. You're delivering hyperlocal messaging through marketing to drive conversions. And then the most important part there is you can adjust your prices and package sizes based on consumers in different localities. So for instance,

[00:15:27] right now, you know, since last year we've had for the last three years actually we've had huge supply chain disruptions and then last year inflation just hit so many African countries, you know, in the ranges of 30%, 40% and so forth. It's not that every when inflation hits, consumers

[00:15:44] will just go for cheaper products. Other consumers just prefer the same brands that they're using, but at a smaller size, right? But most of these companies, they just don't have the data on what correct sizes to send to which cities, which towns, right, which localities

[00:16:00] within those cities. And that gap, that mismatch of incorrect sizes at incorrect prices lets them, you know, like lose market share and so forth because now you have all these local brands that are just serving, you know, 500 meter radiuses and so forth capture those pockets.

[00:16:19] So with Razi companies are able to identify all of that and execute so hyperlocal messaging, hyperlocal pricing and packaging through identifying correct allocations for retailers and consumers. That's exactly the levels the companies are able to move with Razi data

[00:16:37] to grow their revenues. Fantastic. So you've clarified how you add value to companies and the type of data that you provide, which obviously differentiates what you do in comparison to your competitors. I know you use a model which incorporates, is it mappers? Yes. To gather

[00:16:58] the data. So for the people listening who are familiar with what that is, what is a mapper and what do they do for you at Rwazi? So mappers are local consumers in all the countries

[00:17:11] that will capture data. And in the end, all consumer facing companies are sending to end consumers. They're B2C. They're not sending to retailers, although, you know, in a general model it looks like the technical endpoint ends at retail, but they're targeting end consumers.

[00:17:30] So with our model, we identify that you want to get data directly from the end consumers for buying these products from retail locations or for service companies from the website. And what mappers are, they're all those end consumers for these companies. And because they're local consumers,

[00:17:49] where companies would be targeting specific demographics and so forth, we use mappers own default purchase, spending pattern and so forth to log that. And that gives a correct picture of where they buy products from, how much they spend, why they're buying certain brands, etc., etc.

[00:18:11] So mappers are not, we don't prompt mappers to go to, let's say, go to X retail location and check for brand X. No, no, no. It's as you go about your normal grocery shopping routine,

[00:18:25] let's say you decide to go and buy cooking oil or you decide to go and buy soda or powder milk and so forth. Then you log the product that you bought and other products that were there

[00:18:37] and prices and so forth at retail locations. And when you're at home, you log the products that you have, lotions, food products, beverages, etc., etc. And that is with images. So that gives us today these are the products you have in your home. This is how frequent you're

[00:18:54] stocking them and this is where you're stocking them from and the prices and so forth. So that's entire picture every month throughout the year is painted through the default existence rather than being prompted from our ends. So that's what the mappers are.

[00:19:11] Thank you for sharing that. So how many mappers do you currently have in Africa? So right now we have close to 100,000 mappers and they're spread in both Alban and rural areas in different countries across the continent.

[00:19:26] So would you say having mappers is essential for gathering data within African markets? Yes, you want to gather data from consumers. You can't gather data without consumers. Imagine Facebook without users. That's what this is. All companies want data from consumers,

[00:19:45] from the actual buyers of their competitor products or buyers of their own products. So you have to have consumers feed you with data on their purchases and so forth and fairly pay them so they have the correct incentive which is actually getting paid in exchange for that data.

[00:20:02] Right? So we acquire data directly from consumers starting from their profile all the way to their purchase patterns and the products they buy and so on, you know, and so forth directly from the consumers themselves. So you can't run

[00:20:15] a consumer data business without consumers. Otherwise, you'd be doing what most platforms have done traditionally which is get consumer data from social platforms from a third angle, right? Which is as Joseph looks at let's say Zara shared ads and interacts with them,

[00:20:37] then I deduce from there. So data is implied and you don't compensate Joseph and so forth. In this case, companies do not assign a lot of value to that data. They assign a lot of value to

[00:20:50] I want to know exactly how much Joseph is earning. I want to know exactly when they're making the move of buying, you know, in this case, let's say, I don't know, Jameson or any of those other

[00:21:03] products or Safari Con, you know, mobile data packages and so forth. I also know exactly when they make the moves and when they make the purchases. And then I know exactly

[00:21:13] how much they spend and I can put, I can align my product in their path to generate more revenue. It's first hand data from the actual passenger targeting to drive sales. Brilliant. So you mentioned that you incentivize mappers through payment. So do you find that

[00:21:29] these incentives for mappers differ in terms of mappers in regions outside of Africa? They're actually the same. We run a lot of tests in the beginning on the types of incentives we tested out the point model didn't work, tested out prizes doesn't work. We tried to

[00:21:49] give me find all sorts of way. None of that worked in most of the global south. The most common model that drives a lot of engagement is cash payment. And these payments, of course, you know, we use a

[00:22:03] lot of fintech payment infrastructure to get it directly to the consumers in the shortest period of time. Right now it takes around a day within 24 hours they have their payments for verified logs. They all have the similar, they assign the same value to cash. So whether it's

[00:22:23] India, whether it's Colombia, whether it's Nigeria, they value cash and they value cash when it arrives fast. They do not appreciate the traditional concept of, you know, you have a thousand

[00:22:37] points, you get a gift card from Amazon. None of that works. You need actual hard cash. It needs to be delivered, you know, through Momo or M-Pesa or any of that directly into their phones as

[00:22:51] fast as possible. Interesting. Thank you for sharing that. So if we look closer at the mappers outside of the incentives and look at the data that they gather, how do you ensure the accuracy

[00:23:03] and reliability of the data collected by your network of mappers? Yes. So we have a very, very, very long string of verifications. The first one when you're installing our app and signing up doesn't

[00:23:17] automatically qualify you as a consumer, capture data for once. Right. So you have to go through a qualifying process, which is verifying all your demographic data. This is, you know, education, employment, salary, you know, people you have in your home, etc., etc., etc. So verify that the same

[00:23:35] way you do a bank KYC. So there's a, you know, if you've used banking apps, they have a relatively fast KYC process, just a 15 minute process where you just upload your verified documents. We do

[00:23:51] image to text recognition. You know, you do facial recognition and a bunch of other things like that to verify, you know, your identity and, you know, whether what you're claiming is the case

[00:24:02] or not. Because otherwise it would be anyone can claim to end any amount and can claim to have a postgraduate in so and so and how do you prove that that's the case? Right. So we have

[00:24:13] that verification in the beginning to qualify the consumers. Then afterwards for every single log that's made, we have a very long string of revifications, heavily on geolocation data. So geolocation data is the heaviest because most of the purchase locations in the continent,

[00:24:34] 98% of them are informal retail outlets, right? Cash based informal retail outlets that do not have an address system that you find in the UK when the US and the like, they're completely invisible and they change all the time. So you'd have, I don't know, road constructions or the

[00:24:50] person just moves to a different pace and so forth. So that's the whole addressing infrastructure that we've built for ourselves that helps us find them updates the locations and so forth with high degrees of accuracy. So on every submission, retail locations that are geofenced,

[00:25:09] the geodata is collected for every single data point in a submission, timestamps are made, images are taken for products. We have image to text recognition for that, etc., etc. So that's all of those strings verified. So we have in up, that's done in up, then the submission

[00:25:31] is allowed to be made, then that's done throughout our back ends before it gets to the dashboard that the company's access through subscriptions. So we have a lot of strings, we have built of

[00:25:44] VPN blockers. So you can't be in Ibadan or Kano and then act as if you're in Abuja or Lagos, right? We have very, very tight VPN blockers, IP blockers, you can't have your phone and

[00:25:58] give it to someone else to act as if they're you. So there's a whole string of verifications that ensure that the data we're getting is 100% accurate. And that's what's driving our growth,

[00:26:11] because what consumers, I was the buyer of the data for eight years. So I know exactly what to look for. I've bought a lot of insufficient or inaccurate data from large U.S. government bodies

[00:26:25] or traditional research companies. So I know exactly what to look for in our customers. We built it directly from what our customers are valuing and what they're using it for. So the single verification is what our customers value, the most speed coverage,

[00:26:42] you know, verification. That's that's what they value the most. Awesome. So you've detailed how you gather and build the reliable data sets that you collect. So if you look at the data collectors, the mappers, what challenges have you faced in building

[00:26:58] and maintaining a network of mappers? Yeah, there have been a lot of challenges and the lots that we didn't anticipate. The biggest one being language. So in Africa you have a lot of English speaking countries and French speaking countries and then some Portuguese speaking, Arabic speaking,

[00:27:16] so forth. So for these countries, we assumed that if it's an English speaking country, then everyone speaks English so can have our app in English and voila, it's done. And then we started building our early network of mappers. It was working well,

[00:27:30] then essentially expands through word of mouth and then that was working well. And then last year we started expanding at a higher rate and then so that we're having limitations in the expansion of the network. Only to find out that limitations are because even people in English

[00:27:49] speaking countries aren't comfortable with English and the type of English isn't US English that you would find in the app. So language was the biggest barrier that we then identified and went ahead

[00:28:03] and added now around 54 other languages in the app and it will be my next month, we'll have more than 100 things around 147 or something around that in the app and that allows for

[00:28:15] all sorts of right now we have Uganda, you have Kenya Rwanda, you have all sorts of things Swahili if you want to all sorts of things so the app is localized and that enables consumers that enables

[00:28:28] our reach to consumers not just the ones who have undergrad and they're living in massive cities and so forth but all consumers of different levels and in townships, you know in rural

[00:28:40] areas and so forth. So greater expansion there. The other challenge was payouts, how do you roll out a complicated payout system across Africa where there's nothing more fragmented in the world? This is not actually just Africa in the world, there's nothing more fragmented than the payouts

[00:29:00] infrastructure and I never knew this but why what are all these vintage companies doing if payout is pure? Especially in Africa, everyone has raised through vintage, what are you doing? I can tell you right now the only vintage solution that's true, that's real and doing

[00:29:18] its job is Flutterwave. Flutterwave is the only vintage company that has the widest infrastructure covering all channels so this is banking, this is all mobile networks in all the countries where it's applicable, all banks in every single country where they have serious

[00:29:39] speeds like they're the only ones with an actual working payout infrastructure across Africa. All other companies I have no idea what's going on in that space, they don't have the like it's just nothing, nothing's working now almost all companies can do payouts in Nigeria right?

[00:29:57] But either Nigeria or Kenya or you know just talk at South Africa and that's it, people just can't do payouts like no one has figured out payouts other than Flutterwave which is on the

[00:30:08] correct coverage and correct expansion path. So that was our biggest problem right now we use Flutterwave primarily because they have the best infrastructure and works but we bandage with other payout solutions in other countries around the world so Asia has different ones so they're

[00:30:25] the ones that work in India, they don't work in Indonesia, the ones that work in Philippines, they don't work in Colombia so you have to like do multi-layered payout infrastructure, absolute nightmare right? For addressing system we had to build our own, so we have our own

[00:30:40] address system with AI coupled with like satellite imaging and so forth to correctly have our addresses that work and you offense every single location. So yeah so we have like a lot of infrastructural problems that we had to solve across the years and some that

[00:30:56] we're still solving to get this thing running properly at correct speeds. Thank you for that so you highlighted the challenges of various payout systems and various languages in Africa I know you have a focus on various industries could you provide some examples

[00:31:14] of say how Roasi has used its data to influence decision making in the sectors that you operate in? Yes, yes biggest base of our customers are coming from primary sectors that are consumer facing so you have consumer goods, consumer healthcare, telecom and financial

[00:31:34] services. These are like the key consumer phasing sectors where we have so our customers the large majority of causes and consumer goods we have a you know secondary majority in financial services telecommunications and consumer healthcare. The needs are the same they're all getting data on

[00:31:52] the same thing so what people are buying could be milk or could be mobile data packages or could be banning products right? It's the same it's just on one hand it's a product on the other

[00:32:03] hand it's a service and then you have you know where they're buying this from could be pharmacies or retail locations or online like for mobile data packages like or you're buying

[00:32:12] it either online or through usd which is like texts and so forth right mobile texts and so forth and then you have you know how frequent why and so forth which this is the same data points on

[00:32:26] usage patterns buying patterns pricing and so forth that help any of these companies in these sectors find the data that they need to execute. We have anomalies in our customer group where we have companies for instance that are tracking services but to build but

[00:32:46] they're non-profit organizations so they're tracking services to roll out products but without without the intentional profits for instance you know one of our customers Japanese non-profits tracking utilities so this is access to water electricity in rural Uganda

[00:33:04] they're rolling out you know the pay as you fetch and such sources in rural Uganda and they're financing that themselves. In the NGO world it's the exact same dashboards the exact same model

[00:33:18] the only difference is that for them they don't call them local consumers they call them target beneficiaries and you know they're tracking same services because in the end even if it's WHO you're still tracking medicine if it's the world food program you're still tracking

[00:33:34] food access to food and adequate prices and so forth. So the metrics the data points that are being tracked are the same regardless of industries and sectors but the use cases of this data are different. Our given example for instance we have financial service companies like PE firms

[00:33:52] that are tracking consumer products because they're tracking the demand of products for their portfolio companies so when they want to they are assessing which businesses to continue investing in and so forth they're looking at how their products are performing on the market and then deciding

[00:34:10] you know projecting how the businesses will perform and so forth. So there's a whole range of use cases on what businesses use this data for but the primary use cases is the one for

[00:34:21] driving on ground sales and increasing market chain so forth for the businesses that are actually selling these products. Fantastic so for the data to influence decision making it must be actionable I know you touched on this earlier but I was wondering if you could go into more detail

[00:34:39] in terms of how do you ensure that once the data is collectible it is also actionable in an African context? For us here the biggest thing is geolocation. If geolocation helps sales teams to execute or physically drive up to whether you're using motorbikes or DeFi teams and so

[00:35:01] forth to drive up to retail locations to actually execute a sale or DeFi product so you get correct geodata and the other reason for geolocation is to know where consumers are so you do targeted marketing campaigns this is whether it's on ground activations

[00:35:20] or trade marketing activities and so forth or it's online marketing activities because then you target the correct location based on the value propositions, the prices, what consumers have in their homes right because now you have geolocation data for that. So these are the two

[00:35:39] main things. The other actions you know our job is to provide the data right to provide it at speeds, great coverage and 100% verified. What companies are going to do with the data that's of course

[00:35:53] up to the companies themselves they already have all the subscribers already have their own angles on executing on this data. Executing windows are a week to a month or within a week to a month where they choose for instance the SKU situation where you have this one liter

[00:36:12] Fanta in an area which buys the 330 milliliters or 500 ml Fanta. It's a quick execution of changing distribution of SKUs to the 500 ml Fanta rather than the 1 liters in this specific area because that's what consumers are buying. People buying Fanta pineapple or they're buying

[00:36:33] either Fanta pineapple you better find a mirin that goes, that compares directly with that variant of Fanta right. So you have situations where people's flavors or sizes or brand preferences like I'll give you an example of what we found so interesting. For instance in the milk space

[00:36:54] people buy such as like 25 grams such as in this case it's mostly in place in congested places like Lagos. People prefer important brands but small SKUs like such as 25 gram packs in a belt.

[00:37:10] They have a string of users for that and the way they use it is certain farming profile that buys that etc. So you're a company you are exporting you're pushing 250 grams, 500 grams, a kilo of powder milk and no one is buying it and you're wondering what's going

[00:37:26] on and you're looking at your competitors they're moving volume what's going on well your competitors you use the data only to find out your competitors are pushing 25 grams in huge numbers. So if you don't have 25 grams in your portfolio you start pushing the smallest package you have

[00:37:44] while you make the 25 gram available maybe in three months or six and then you start switching with high companies do that and they immediately see like 35 growth in sales in those locations in between six months. So you see huge bumps because you're doing that

[00:38:01] hyperlope execution with the data. You've touched on this but I was hoping we could go into a bit more detail. So from the data that you've collected what's some of the most interesting consumer habits that you've observed from African consumers? Yes the first one I would say is

[00:38:19] African consumers they don't change their habits much as they earn more. Yeah so it's not that which is a massive misconception that most multinational companies coming from US Europe assume that if consumers are earning you know say $2,000 which is massive

[00:38:38] in most African countries and $2,000, $3,000 a month or even a thousand they will buy from supermarkets no they will not. They will still go to local markets and the neighborhood shop why do they still go to the neighborhood shop because I just want to buy from John because we

[00:38:53] go to church together or you know I just want to buy from you know so I'm buying from people I know I'm not going to buy from shop rights just because I'm earning more right now right

[00:39:04] and I don't want to drive miles so that's one two they still don't do bulk patches still at $2,500 a month or you know $1,500 consumers will not talk for a month right so then

[00:39:17] they're not going to say one Sunday or Saturday I'm just going to hit a department store and then buy all my groceries for the month no they're still going to buy a liter at a time

[00:39:29] they're still going to buy everything as they need them right so the patches frequencies is more often in small amounts as needed they still are not going to buy things like meat fresh foods and so forth from local markets because they value that more than packed processed

[00:39:50] heavily processed packed or frozen foods right so there's still like a huge inclination to organic foods and there's a very certain palette of taste that they have that doesn't change with increase in in earnings but you have other interesting aspects for instance in the

[00:40:09] aqua space for instance consumers will still go for very premium whiskey brands and and vodka brands and so forth that doesn't necessarily match the income levels and they estimate that same thing applies in mobile data you'd think someone who is earning

[00:40:29] they say $400 a month won't afford a $40 $50 per month data package well that's what they go for so the huge premium assigns to internet speeds the amount of gbs they get and so forth despite earnings so they assign huge huge proportion of their earnings

[00:40:47] towards those things and this depends on whether they're single or they're married whether they have kids or not whether it's a guy or 80% of purchases are done by women right for all these grocery products so that depends on all those demographic factors but there's still high

[00:41:04] assignment of value to things like mobile data packages you know for streaming and so forth for men in certain countries like Uganda and South Africa high premium assign to alcohol despite earnings and so forth so you have all these huge differences in behavior that if you copy paste

[00:41:24] they use behavior of a consumer in France or the US you fall flat on your face if you come in Africa and Africa max are different so the way Kenyan consumer behaves completely different from the

[00:41:38] way like a Ugandan consumer Nigerian consumer will behave yes and you know where they were frequent and so forth so I have a lot of that yes you've touched on some interesting consumer habits which you could say are linked to cultural habits given the diverse cultures and markets

[00:41:57] that you cover how do you navigate say cultural sensitivities and nuances while collecting data yes I think for us what we've built and what continuously iterate towards is making the app localized to different consumers based on their base right and the most important thing

[00:42:17] here is you have things like languages and then the way the data is collected changes so we use AI to get that done so it's not the manual process so the way the data is collected in different

[00:42:30] areas is adjusted changes based on the demographic and the demographic is tied to culture and and so forth is adapted based on their demographic and profile right demographic profile and that's tied to their culture and in nuanced patterns this is everything from

[00:42:50] religion everything from the type of products they buy and how they buy them how they use the product all of that you see their culture expressed in all those patterns and what we do is just

[00:43:02] we adapt our platform to just manage as much as possible even the way we collected data to manage as much as possible there are things that we're still working towards things like converting voice to texts which would enable certain consumers provide more data through voice

[00:43:22] so they'll provide more data with this product feedback and so forth through voice and that will be converted to text and data points for the companies and you have a lot of differences for instance you have consumers who are buying products from retail outlets on credit so that's

[00:43:39] a completely different avenue right different purchase style that's based on culture because that's like oh because why can they buy it on credit when they don't have any collateral or anything like that oh because they know john and yeah maybe they work together to go chat together

[00:43:55] so john knows that this person is a primary school teacher they end this much every month so they just repay at the end of the month and start a new account like a new account of that

[00:44:05] grocery debt so you have all of those completely new you don't you don't get groceries on debts in the u.s right so I've completely new behaviors that are specific and you have things like

[00:44:19] you know how sometimes coaching all they actually do not sell a pact so you have selling a one liter 500 m m and so forth and then your consumers buying coaching all buy like by a

[00:44:31] pint right so maybe it's 25 milliliters or 30 milliliters so how do you now measure that right so you have all of that you know all those nuances you have table tops which are not you know physical location shops these are just tables and someone can just change location

[00:44:49] tomorrow right so how do you account for all of that when you're building infrastructure and that those are the small very informal and on-ground granular nuances that were incorporated in our system amazing amazing so it's clear that your data is making an impact in the sectors that you

[00:45:08] operate what would you say is potentially the long-term impact of the data collected on the development of some of these markets in terms of understanding consumer behavior yes so one thing that's a big trend is that as you know Africa has the youngest population fastest growing population

[00:45:28] in the world one in four people will be African in the next 20 to 30 years this is very important thing the african middle class increasing isn't based on the growth of the local economies

[00:45:43] the relevant economies no it's based on the fact that they can now work online and end from outside the economies get paid by american companies european companies other african companies and so forth regardless of their location so that's what's driving the increase in earnings so there's

[00:46:01] increasing earnings but huge huge growth in population younger population so forth so companies are realizing companies in europe or you know japan and u.s and so forth and other multinationals in africa they are realizing that because the population is aging where they're at

[00:46:18] you see this is where there's a huge thing the whole one generation or two of consumers who will buy their products they're young so the products are relevant to them whereas baby products and so

[00:46:30] forth they have the earnings and that earning portion is growing and they have the numbers right so this is where the target is so because of that companies are now paying attention on the preferences of these consumers so the long-term consumers are now starting now and will eventually

[00:46:48] get more access to products that are tailored to them what has been happening is you have you know a french company sell a french product in senegal without changing anything about the

[00:46:58] product which you know people just are forced to just buy if they like the brand just buy whatever product the brand offers now the brand is spending an r&d to build products for those consumers because now they represent they're representing a growing chunk so consumers are

[00:47:12] getting products that are tailored to them at prices that are good for them at packages and sizes and so forth and the getting those products close at where they leave so you don't have to

[00:47:24] you know go all the way to a city center or you know big market and so forth find the product they're now bringing products close and close as they have location data the other thing is

[00:47:33] companies are now investing more in those markets so investing and you know hiring and so forth to just expand their operation in those markets which is driving jobs and a bunch of things around that

[00:47:45] area so for us that's what we see as a as a huge impact where you know the immediate impact consumers are getting paid to log the data and that's good driving you know getting all these multi-billion dollar companies to actually spend on data acquisition accurate data acquisition

[00:48:02] but the long term is on top of that you'll now get the product that you want that's tailored for you closer to your home at the correct price and so forth fantastic fantastic so you've touched on

[00:48:13] the impact of your data and also the current trends you're seeing from african consumers so if we look past current trends and into the future where do you see africans consumer data

[00:48:24] collection space in say five years time yeah i think the data space we are doing a huge huge change in how the data is collected there's already a high internet penetration and smart

[00:48:39] home penetration and app usage comprehension and so forth so we'll see more consumers tap into this to make their voices heads to completely influence how brands interact with them so we'll see a

[00:48:53] huge growth in that and that will drive more growth into other areas such as micro influencing and brand interaction and so forth so that's growing towards that trend and also like you know

[00:49:06] how we position ourselves in that is because we have the widest network of low consumers in all the way from urban to the most remote places we see ourselves as expanding farther to a million ten million plus consumers across all these countries and on the continent and

[00:49:24] making it easier and easier to log the data so we'll have new things coming up later this year next year where we're having more integrations into how consumers are logging data to enable them to look more data faster with less hassle so that's where the trend is going

[00:49:41] I look forward to seeing how things develop and also the part that Ruwazi will play in that space exciting thank you thank you for that Joseph awesome quote of the week as people we often

[00:49:55] have quotes mantras even african proverbs and affirmations like he was going when times are challenging or when times are good do you have one that you can share with us today yes yes

[00:50:07] I have a quote that I like it goes he who hesitates is lost the reason I love that saying is because in the startup world there's a lot of uncertainty and unknown variables and this would cause founders

[00:50:24] to hesitate and want to spend more time in market validation or product iteration or building correctly and so forth before launching and he was his days is lost is a lesson we had

[00:50:37] where we got to a point that we had to choose either not lunch and not do this or just go ahead and launch with whatever resources and and product we had at the time and we decided that after we

[00:50:50] just made decisions fast started to launch and got markets like extremely extremely fast early on and that's what enabled us to go to this point and you know throughout the years we've done this

[00:51:00] exact same decision speed which is get to crossroads you know you either do more with less and jump or you just wait and potentially die because you don't have resources and I think all founders can take from this and prioritize making decisions fast rather than

[00:51:20] waiting. I like that one thank you for sharing it with us Joseph so as we're coming to the close of today's conversation I was wondering do you have any closing remarks final course to action

[00:51:31] for people who are interested in the work that you're doing? Yes you know I invite them to contact us at hello at rozzit.com you know the most important thing I would mention is to not make assumptions about consumers and what consumers want just because you're a consumer

[00:51:52] doesn't necessarily mean a million other people are you know are behaving the same way just because you're from a culture let's say just because you're Nigerian doesn't mean you understand Nigerian consumers and likewise for all other you know countries and cultures it's important to use

[00:52:09] actual data from your target consumers and tailor your product offering pricing and so forth towards that that's how you fix this he who estates is lost it fix that kind of problem

[00:52:23] and get correct ROI for your business. So I invite them to contact us at hello at rozzit.com and we'll be happy to provide them you know help them access this data and grow their businesses. Perfect that has been an insightful informative conversation Joseph thank you for

[00:52:44] sharing light on the significance of market intelligence and driving positive change and growth I think your vision for a more connected and informed Africa is truly commendable and I look forward to witnessing the continued growth of RUWASI so thank you for joining us

[00:53:02] on the podcast today. Thank you for having me I appreciate it this was this is a great great interview. It's been a pleasure having you on the podcast and we will speak soon. Thank you. Cheers.

[00:53:14] Thank you to everyone who has listened and stay tuned to the podcast if you've enjoyed this episode please subscribe share or tell a friend about it you can also rate with viewers in Apple podcast or wherever you download your podcast thank you and see you next week

[00:53:31] for the Unlocking Africa podcast