Posts Tagged ‘data’

The Quiet Digital Revolution: Indigenous Innovation in Intelligent Information Systems

Big data, machine learning, and artificial intelligence are the buzzwords of the day, along with the obligatory blockchain and bitcoin. Much is being written on their potential to solve Africa’s problems, or India’s challenges. In turn, each has been promoted as the next big thing to address poverty and its discontents. Yet, we note, that all of them, without exception, assume implicitly and some go as far as to articulate explicitly, that these future and potential solutions are the sole purview of the first world’s silicon centers. “We know best, and we are the experts in this, as in so many other things, when it comes to the context and conditions of developing countries.”

However there’s a quieter digital revolution taking place, using much the same cutting edge technologies and techniques. One which is emerging from the deep contextual knowledge of local needs and local challenges, tapping into opportunities in relevant and accessible ways. I found two exemplars of this ongoing trend worth highlighting here, one from Kenya and one from India.

From Kenya, technology enabled livestock insurance

Andrew Mude, a senior economist at the International Livestock Research Institute (ILRI), created a program that protects pastoralists against losses from drought, an increasing scourge for nomadic communities in northern Kenya and southern Ethiopia. The index-based insurance uses satellite imagery revealing how much foliage has been lost to calculate the projected impact on the herds. It eliminates the need for an actual census of dead animals. More than 3 million pastoralist households in northern Kenya depend on goats, cows, sheep, and camels, and the high rate of livestock losses during droughts is a major cause of childhood malnutrition. With their households constantly on the move, the payments give families enough money to survive economic downturns without having to sell off their herds. Foreign aid programs from several nations help subsidize the cost of the insurance.

Mude, 39, says his interest in finding new tools for economic development comes from his parents, who were the first boy and girl from the Marsabit district of northern Kenya to attend high school and who later helped other villagers acquire an education.

Dr Mude won the 2016 Norman Borlaug Award from the World Food Prize for his innovative program that provides pastoralists with livestock insurance.

From India, Data Intelligence Drives Microtargeted Development in 290 Villages

SocialCops partnered with the Tata Trusts and Government of Maharashtra to drive rapid development in Chandrapur. The story behind this pioneering initiative of the Maharashtra government required the data-mapping of three blocks of the district at an unprecedented level. In this remote, inhospitable setting, a mammoth task was conducted —a survey to gather data in villages on every single individual.

The objective: setting up a real-time data system that can help the authorities and communities plan at the local level according to their specific needs.

Computing power and data intelligence allows for a customizable, human centered approach to social and economic development at scale, and India, with her vast population and their myriads of unmet needs, is showing us how to do it right, for future scale.

As their blogpost says, a quiet digital revolution is underway.

How do we make a business case for an innovative concept given the data scarcity for the African mass market?

Anzetse Were writes some thoughtful points on the challenges facing private sector innovation in Kenya, and Africa. Two of her points caught my attention, in particular:

With regards to the private sector, an interesting point raised is that innovation targeting it must have a business case for adoption otherwise the innovation won’t be absorbed. Innovation must demonstrate that the short-term inconvenience of adoption will pay off in the long term.
[…]
We have a real problem with information asymmetry and data bias. [… ] strategies for market penetration and sharing cannot be rolled out since the lack of data means the private sector doesn’t know where the market sits.

While Anzetse has specifically focused on the interface between the private and the public sector with regards to innovation, the points she brings up are nevertheless a challenge for either or both parties.

Size and value of the market opportunity for an innovation when data is scarce

Investors in innovation for new and untapped markets need the numbers to make sense of the opportunity. A dollar value and estimated size of the market are among the conventional metrics used to provide evidence of a return on their investment. How substantial is it?

In the African context, the mass market where the volumes can be found tends to be heavily biased towards the informal sectors, and still for the most part based on cash transactions. Textbook approaches to sizing and valuing the market space fall short without accessible and relevant data.

A few years ago, we were faced with a similar challenge for Village Telco, a social enterprise launching an innovative ICT device for low cost voice and data communication. They had developed the Mesh Potato, a device for providing low-cost telephony and Internet in areas where alternative access either doesn’t exist or is too expensive. It is a marriage of a low-cost wireless access point capable of running a mesh networking protocol with an Analog Telephony Adapter.

They were looking to enter the Kenyan market, with the notion that the cyber cafe industry would make the best target audience for their device. Their investors wanted to know the size and value of the market opportunity prior to launching the product in Kenya. Although this happened just over 6 years ago, Kenya had already made a name for itself as a forward looking mobile phone market unafraid of experimentation.

Our challenge was two-fold: We were to look at 2nd and 3rd tier towns, not just Nairobi and Mombasa. Village Telco was looking to connect the unconnected. And we had to estimate the size and value of the market opportunity for a sector – internet cafes – that was primarily cash based and informal, particularly given the rural and small town geography we were considering. There was little or no data available to even get a handle on the number of cyber cafes operating in Kenya.

Secondly, we had to get an idea of the price point at which the product would be acceptable to this target audience. Keep in mind that the device was wholly unknown – an innovation – and there was nothing comparable on the market.

A qualitative approach to quantitative estimation

Given that this was not a conventional research project, and time and resources were constrained to a market analysis, we designed a minimal viable market discovery phase that would permit us to gather enough insights directly from the cyber cafe operators in order to estimate the size and value, as well as recommendations for pricing and market entry.

In late 2011, Kenya’s administrative divisions were still the original provinces.

Based on population density and relative income demographics, as well as an ICT gap analysis of voice and data services – reports available through Kenyan government institutions – we planned an optimal route that maximized exposure to the types of locations Village Telco had specified whilst sampling cyber cafes across a range of infrastructure access and regional income. This coverage was completed in less than 3 weeks.

Surfacing trends through indepth open ended interviews

Where we invested our time and effort was in identifying entrepreneurial and innovative cyber cafe operators in the smaller towns and villages we visited. The vast majority of internet cafes are run as side businesses by the owners who might be white collar employees or civil servants, and often managed by employees. It was the cyber cafe owner operator who saw their business as a growth opportunity that we were seeking.They not only knew their market but had seen the opportunities to grow and expand their services.

They were able to give us an idea of the future of the cyber cafe business in their region, a rough estimate (few businesspeople are willing to openly share revenue data) of the scale of their business, and the trends in decline or growth of the types of services they offered.

Through the data gathered, we were able to estimate the high growth regions for internet cafe services – Nakuru town for instance had seen the number of cybers grow from 10 or 15 in 2007 to upwards of 50, primarily due the increase in tertiary education institutions. Kilifi, on the Coast, had seen a doubling when a local university campus opened.

At the same time, we were able to gauge the value of the opportunity space by using the proxy of the proportion of owner/operators to manager/employees – the former were more likely to be interested in the Mesh Potato than the latter.

Our route planning also provided evidence of the pathways for innovation diffusion, outwards in a hub and spoke model from the central hub of Nairobi’s business district where new electronic products landed from the manufacturing centers of Asia.

Sitting down face to face with the cafe owners and showing them the product and what it could do gave us the insight on pricing and market entry strategy. By the end of 5 weeks from start to finish, we were able to make a business case for innovation meant for a data scarce environment.

Innovation means breaking new ground

While the effort on the ground was very different from a conventional market analysis exercise due to the need to elicit information directly on the market and the product, the time and resources invested by the client were no different from an analysis based on secondary sources and accessible data flows.

The nature of the African mass market is such that pioneers entering the market will have to break new ground, not only with their products and services, but also their approach to analyzing and evaluating the business case for investment. It is not an impossible task and should not be considered a barrier to entry.

Key Competitive Advantage for Frontier and Emerging Markets

There’s a nuance, I’ve discovered, in the application of user centered design methods for entering the frontier and emerging markets of the developing world where a significant proportion of economic activity is confusingly labeled “informal”, rather than unformal as the case tends to be.

In more advanced consumer market contexts, where there are umpteen data flows, and decades of consumer research and insights to draw upon, the unquestioned assumption is that user research tends to level the playing field among contenders.

However, this doesn’t hold true, in my experience, in the emerging and frontier markets, such as those in East Africa. Simply basing one’s product development and market entry strategy on even the most rigorously designed user research program does not suffice. At the frontier, competitive advantage boils down to how well you interpret your data so gathered using design ethnography methods and quantitative surveys.

The biggest and best data collection in the world cannot help you if it’s not answering the right questions, nor the insights drive design if there are underlying biases filtering the inputs.

The key, as any trained anthropologist will inform you, is in being able to shift one’s perspective sideways, enough so as to perceive the landscape and the context from the viewpoint of the users being researched. And, perhaps, that is why increasing the diversity of experiences and perspectives of your team can make or break your new product introduction and/or your competitive strategy.

An amusing example of this kind of problem is one I discovered yesterday when poking around my twitter profile after the sudden change in UI that took place without warning. It seems that because I hadn’t input my real gender in the system, Twitter’s data analytics designated me “male” based on my tweeting behaviour.

Their age range is vast enough that they cannot go wrong, and besides, a lady never shares her real age. In the grand scheme of things it doesn’t matter if I’m considered male or female in the system. What is of concern is the underlying assumptions that the designers of the system have made when assigning behavioural choices to one or the other gender.

Now, if we were to extrapolate this relationship between initial design settings in the system, and the inaccurate output – as clear a case of their assumptions being rooted in stereotypes as any that I’ve seen – imagine for a moment what would be the case if the same sort of unthinking, unquestioned stereotypes were applied to the interpretation of user research data collected from a geography or context vastly different from one’s own?

What if this same approach was used for the system of designating assumed behaviours and user needs meant to guide the design of solutions for the rural African market woman?

If the most modern and global social media messaging systems of Twitter are unable to distinguish something as basic as gender – they state based on your profile and activity – they’d do better by stating they are unable to distinguish gender based on these factors than to make gross assumptions on “What do women tweet?” in 20 foot pink letters.

I’d have more respect for them tbh instead of feeling I’ve been put in some fluffy fuschia box, as a woman, just because the stuff I do (my profile is professional) and the stuff I tweet about (business, trade, economics, and design strategy) flags me as a male?

Extrapolating this challenge further, in the context of frontier and emerging markets, where the markets are not crowded with competitors at this early stage, nor is your brand recognized, is this the first impression that you can risk making?

I’ve often said that these are some of the most challenging markets, and affordable connectivity is only making it harder – word of mouth now flies at the speed of silicon, and a new entrant must stand up to social media scrutiny.

Frankly, in my own discipline and field of focus, it only makes me more confident of my team’s ability to offer a distinct competitive advantage.

Prepaid Mobile: The Business Model that Empowers

It feels like a long time since I last pondered the nuances of the prepaid business model, until I came across some words written by Indian social media researcher Swati Janu. She documented her observations on the infrastructure of insecurity from the tenements of New Delhi. There’s value in reflecting on how our understanding only increases over time, and we can never say that we’ve stopped learning

This sentence caught my attention:

From a rural population that is fast going online to the resourceful teens in urban slums, the lower income demographics are choosing to buy internet, through small but recurrent amounts, which enable them to straddle the line between affordability and aspiration.

The small but recurrent amounts – the Rs 10 mobile recharge Janu writes about – are the lifeblood of the prepaid payment plan for voice, text, and data (airtime) for the now ubiquitous cellphone that has changed the landscape of the developing world.

To enable the lower income demographic’s ability to straddle the divide between their aspirations and their ability to afford them is empowering. One could say that:

Prepaid is a business model that empowers aspiration, through affordability, incrementally.

Instant gratification has never been within their purview.

Detailed breakdown of Uber’s business model in Kenya puts spotlight on weaknesses

Latiff Cherono has just published an indepth analysis of what exactly it takes for an Uber driver in Nairobi to cover the cost of doing business. Here’s a snippet,

In this post, I try to understand the root cause of the disconnect between how the customer (who defines the value), Uber (the service that controls the experience) and the driver (the one who provides the service).

He accompanies his analysis with a detailed breakdown of costs and revenues, such as the table below, and others in his post.

And concludes:

The incentive for any person who starts a business is to maximize their profits. As such, we should expect that Uber drivers will approach their business in the same vein. However, the data provide by Uber to the driver is limited and prevents them from making informed decisions about generating revenue. For example, drivers do not know the estimate distance of a new trip when they accept it via the app. They are also penalized for not accepting rides (even if that trip may not make financial sense to the driver). All this is by design as Uber wants to maintain a steady supply of “online” vehicles on their network. One may argue that Uber is not being transparent enough with its independent contractors.

My thoughts:

Nairobi, Kenya isn’t the only ‘developing’ country context where Uber is creating unhappy drivers (and customers, one assumes) due to the design of their system. While most of the first world challenges to the company have come from the perspective of the formal economy and its regulations and laws regarding revenue, tax, employment status et al, the same cannot hold for the entirely different operating environment where the informal sector holds sway. And taxi driving is one such service.

Kampala, Uganda has it’s own challenges for Uber, including:

Uber drivers are reportedly leaving the service, switching off the Uber apps or not picking calls from corporate clients and those paying with a credit card. For the first four months after its launch, Uber was offering drivers incentives that saw them earn between Ush200,000 ($57.1) and Ush350,000 ($100) a week.
With increasing competition, drivers say that Uber’s incentive structure has been changing. In the first four months, Uber drivers were getting Ush15,000 (about $4) per hour, but this has since been scaled down to Ush10,000 ($2.9) and to Ush4,000 ($1.1) in incentives.

There is so much to be unpacked here, including the entire section on Uber’s own perception of how the market works, upto and including how to introduce time limited incentives, that I’ll follow up on it subsequently.

In this post, I wanted to highlight Latiff’s analysis and hard work pulling together the operating costs data, even as I leave you with this snippet from the article:

Uber’s commission in Nariobi was reduced from 25 to 20 per cent following protests by drivers in August, accusing the taxi hailing service of working them like slaves.

As I wrote earlier in the year, Uber could have done so much more in these markets, particularly on the path to formalization. Instead, they’re continuing on their journey as yet another smartphone app making life even easier while squandering the potential for real world change for the less privileged members of our societies.

Africa’s Middle Class: Development economics and marketing demographics conflating the holy grail

The most developed nation on the African continent, south of the Sahara desert, is considered to be South Africa with its financial and transportation infrastructure and systems, a legacy from history. In the first decade of the 21st century, the black middle class – known as Black Diamonds in marketer jargon – came into prominence on the back of numerous economic initiatives after the fall of apartheid.

The rise of the Black Diamonds was meant to be the signal of a changing rainbow nation, one whose peoples would finally be included in the social and economic advancements long enjoyed by a privileged minority. This emerging middle class was also among the first to be noticed as African consumers in their own right, and their discovery pioneered the subsequent search for the now mythical African middle class. Even then, their total number was under scrutiny for its aspirational inclusivity versus actual households fitting the conventional definition of a middle class. From The Economist writing in 2007:

The University of Cape Town’s Unilever Institute of Strategic Marketing says there are now 2.6m “black diamonds”, as it calls the black middle class, a 30% increase in less than two years. Included in the definition are working professionals; those who own things such as cars, homes or microwave ovens; university students; and those who merely have the potential to enter these categories. The survey estimates that these black diamonds represent 12% of South Africa’s black adults, and make 180 billion rand a year ($26.2 billion), or 28% of the country’s (and more than half of all black South African) buying power.

For some, such as Lawrence Schlemmer, a sociologist in Cape Town, this definition is far too broad to be meaningful. He agrees that numbers are rising fast but argues that they are still tiny. Last year, he says, only 322,000 black South Africans (less than 1% of the black population of 38m) could be deemed “core” middle class, a far cry from 2.6m black diamonds.

Still, whatever their size, the buppies are affecting the economy and the political landscape.

This week, a comprehensive new survey by the South African government shows the on the ground reality in 2016. The National Income Dynamics Study (NIDS)‚ launched by the Department of Planning‚ Monitoring and Evaluation (DPME) in Pretoria surveyed 28‚000 people who were tracked every two years from 2008 to 2015. Very similar in fact to the recent household panel survey completed in India. Even their conclusions resemble each other:

According to the study‚ those in the middle class have a tendency to drop in and out of poverty.

And the size has not actually changed much since 1993 – the year before the fall of apartheid and the election of Nelson Mandela.

The study also shows that the South African middle class is much smaller than estimated‚ sitting at around 14.5% of the total population in 2014. Women are more affected by poverty, and even those who manage to climb the ladder may slip down again.

“…It has not grown much since 1993 — growing its share by only two percentage points in the past 23 years…”

And, perhaps, the real challenge we face with the ongoing search for Africa’s middle classes is the conflation that took place back then between a consumer marketing segmentation and a socio-political demographic. By allowing the aspirational reach of the consumer marketing driven research to inflate the size of the segment classified as middle class, it has given rise to an ongoing and complex muddle across teh entire continent. As the AfDB’s former president Donald Kaberuka said last year:

“I think we are wasting too much time on the definition of the middle class and the cut off point, it is a sterile debate.

“A dynamic middle class that rises with the sea increases domestic demand, the diversity of the economy, [its] resilience, and they also stabilise the politics of a country as well, since they have a stake in the system.”

He has a point. But perhaps not the one he intended to make. Instead, if we consider disentangling consumption and demand for consumer products from the increase in political voice and “stake in the system”, we may in fact discover that there is indeed a sizeable bourgeoisie emerging even though they may not possess all the qualifying criteria traditionally attributed to a middle class per se. (Previous posts on this topic have been tagged informal bourgeoisie)

There’s the demographic segment which is the middle, and then, there’s the conceptual body of solid citizens invested in the democratic stability and economic growth and development of their countries. As Jacques Enaudeau wrote in 2013:

But fixated on wealth, the discussion on middle classes in Africa misses out on the other two pillars of social stratification: social status and political power.

As soon as those two are factored in, discussing the “African middle class” as a homogenous entity seems absurd, and so it should. Thinking that what separates the senior civil servant from the street hawker or the country head of a multinational from the shop owner is a matter of daily expenditure amounts to looking at their reality through the wrong end of the telescope: the bigger picture is that they live in different worlds.

In the developing world, the formal sector with its white collar jobs populated by university graduates may jostle cheek by jowl with the informal economy’s life lived on the street but that proximity might be on the only thing they have in common.

For here lies the rub: the material culture that the notion of “middle class” posits as shared consciousness is articulated to a strong sense of individualism, which is borderline contradictory with the idea of class. All the more reasons for the analysis to consider the representations which members have of themselves as a group and the historical context in which such groups are being shaped.

This, however, is not the post to unpack those complexities of self image and collective consciousness. It’s one which pauses to ponder the newest set of findings on the dynamic nature of poverty and wealth in the more uncertain and volatile operating environments of the still developing world. And considers the South African example introduced today:

There has, however, been considerable demographic transformation within that band of the middle class, with Africans now outnumbering whites by about two to one, the report said. Factors driving the surge include greater access to credit, improved education levels, BEE and improved economic growth until recently.

Transformation of societies is underway, just as the Indian researchers concluded in their analysis. This might be a much larger global trend underway, whose weak signals we’re just beginning to pick up now. I’ll be following up with these musings on the blog. The people with the real problem on their hands are the consumer companies looking to justify entering the African markets, and perhaps that’s a topic to take up in the next article.

Poverty is Dynamic and Flexible, Just like the Informal Economy: Evidence from India

…the concept of poverty today is fundamentally different from that of poverty three decades ago, and that safety nets need to be tailored to meet the needs of a society in transition.~ The Hindu, 2 Aug 2016

When quantitative data provided by the India Human Development Survey (the first large panel survey in India) provokes the academics involved to question their fundamental assumptions and premise of what poverty is, and what it might mean, its a noteworthy moment.

The survey, conducted by the University of Maryland and the National Council of Applied Economic Research (NCAER) for the same households at two points in time, viz. 2004-05 and 2011-12. Their analysis has led them to say:

Once we recognise that poverty is dynamic in nature, and that as per our conventional definition of poverty, poor households may move out of poverty and the non-poor may become poor over a period of time, we are forced to question the veracity of our fundamental assumptions about poverty. Perhaps poverty occurs not simply due to the accident of birth or as defined in terms of where and in which family people are born, but also due to the accident of life caused by the occurrence of disease, disability and unemployment. Achieving this recognition entails a complete transformation in our mindset.

I will leave them to their explorations from the perspectives of their disciplines, and explore the broader implications of their findings.

A few years ago, as part of my discoveries from more qualitative user research in the field on the informal sector’s financial context and operating environment, I had had my insight on the dynamic nature of poverty as it was conventionally defined.

It was when attempting to clearly distinguish between patterns of cash flow in the formal vs the informal economy, using the concept of the degree of control granted to the end user over the variables of time (duration, frequency, periodicity) and money (amount, cash or kind), that it struck me what kind of difference does control over timing mean for money.

That is, there is a complex value processing underneath each of the decisions on allocating available cash money, particularly in rural areas where cashless transactions can tend to be more common.

When one can control the timing of one’s payments – such as the advance purchase of airtime minutes to use a mobile phone – one’s income could be called dynamic. Within any particular set of calender based time eg a week or a month or a quarter; a vast majority of the lower income bracket cannot predict their total cash income nor feel confident enough to claim it. It can be affected by seasonality prevalent in their region, or it can be purely random volatility, one’s workshop burns down in an accidental fire.

Static income is that which is stuck, such as a fixed salary paid every calender period, regular in frequency, amount and periodicity.

As cash flows tend to be volatile, fluctuating with seasonal influences, chance, and the vagaries of daily life, those whose incomes are not as predictable as a periodic paycheck, are more often than not unable to clearly state (or even know) their monthly or weekly income.

That is, even as data gurus in development banks seek to segment people into neatly defined ranges such as $2 to $4 a day or whatever, it is neither a given that people will remain within this range over the course of the natural year, nor can it be a reliable and consistent indicator of their income level – Below Poverty Line (BPL) is the concept used in The Hindu’s article above.

Therefore, if the survey studied households in an agricultural region during its fallow season the first time, and then went back to study the same households during the post harvest season the second time, that simple little factor of calender time alone can create a difference of as much as 100% to the incomes being claimed during that period. If the study does not follow up the income question to ask if there was seasonality in their cash flows over the course of the natural year and if this question was being asked during the high season or the low season.

When I did the original fieldwork for the prepaid economy project on an IDRC grant, looking at the rural household financial management behaviour in rural India, Philippines, and Malawi, I found that depending on the local region’s primary cash crop harvest patterns over the natural year (say monsoon to monsoon, or Christmas to Christmas) the entire local economy felt the impact of the difference in cash flowing through their ecosystem during the high and the low season. Or, the wet and the dry season.

It was not the naming of the seasons that is important. It is the ability of the people to forecast known fluctuations in their income streams based on patterns recognized from experience and local wisdom. Within the context of an environment of uncertainty and volatility, it offered them some anchors for planning and financial management.

Given that the vast majority of the poor in the developing world, like in India and across Africa, are dependent on irregular, often unpredictable cash flows from a variety of sources, in an environment of higher risk and uncertainty, their incomes can confidently assumed to be dynamic, rather than a static salary.

And the dynamic nature of the informal sector precludes conventional classifications and categorizations of poverty, especially by any stated amount of money mapped against a particular duration of calender time. Time and money are themselves the uncertain elements requiring flexibility built into the systems if they are to work properly in this operating environment.

Thus, I can confidently state that what the Indian data is finally providing the evidence for are the findings from my qualitative research among the same segments of the population, using design ethnography methods. That is, we now have the quantitative data to support the insights derived from the qualitative research.

Full Stop.

Is Uganda’s rural, informal economy helping people climb over the poverty line?

I stumbled across this dataset on the World Bank’s open data website yesterday, and couldn’t resist making a table to convey the message. Uganda’s poverty headcount halved in the decade between 2002 and 2012. Their statistics are rated well enough that this doesn’t seem to be too far off the mark. In the three years since, one can imagine it has only dropped a wee bit further. For context, the poverty headcount in the United States is officially 14.5% – not too far away from 19.5%.

This intrigued me enough to go through the data for the greater East African region. The first table is sorted in order of GNI per capita, with Kenya leading the pack, while the second table is sorted by the least proportion of the population below the poverty line.

Here are some visual outcomes of my playing around.

Though Kenya is the “richest” country, its poverty headcount is more than double Uganda’s. What’s interesting is that Uganda’s per capita GNI (Income) is around half of Kenya’s. Uganda is heavily dependent on agriculture, and not as urbanized. In fact, the urban poverty headcount is a wee bit higher than the rural.

Given that rural economies, especially in East Africa, are technically part of the “informal economy”, I wonder if looking closer into that might offer some insights on how a “Low Income” country can slash its poverty level so dramatically? It might help explain why the per capita GNI is so much lower (Kenya is far more industrialized) yet far less people are living hand to mouth.

Dynamic vs Static Metrics: Attributes for an African Measure of Competitiveness

“No Data Available Gray Area”

For analysts everywhere, the challenge of considering each economy in its own right seems to be far too much trouble, and so they tend towards sweeping generalizations which lump all metrics under one label – “Africa”. Some find even that far too exhausting and aggregate Africa along with Europe and the Middle East.

These regional groupings might be fine for executive Vice Presidents responsible for regional sales in a globe spanning multinational but for anyone seeking to assess and evaluate the emerging opportunities sparking interest in the continent, these aggregate metrics only serve to obfuscate and confuse the issue.

Static vs Dynamic

What distinguishes the majority of the emerging African economies from the more established ones is the prevalence of informal business activities, in addition to agriculture.

As I wrote previously, from my research on the underlying rhythms of the informal, there are two forms of income – one that is static, and thus predictable, like a regular monthly salary, and one that is dynamic i.e. volatile, such as the irregular cash flows that those in the informal sector tend to rely on for their household expenses. For many households, their cash flows have a combination of both forms – a predictable static paycheck from formal employment as well as bits and bobs from informal livelihood activities.

One can extrapolate the presence of this dynamism into the larger context of the entire operating environment – when there is a significant component that is irregular and unpredictable i.e the cash flows from the informal sector, and consider this as a key attribute that distinguishes these economies.

That is, instead of seeking metrics which maybe static, could we perhaps instead seek those that convey a measure of the dynamism that’s best characterized by the hustle of the informal marketplace?

Acceleration and Growth Trends

A great example is the rate of mobile phone penetration. Here is a snippet of data extracted from the GSMA’s statistics showing just a couple of years of change in phone penetration. Can we see how fast Ethiopia’s subscriber numbers grew, almost doubling in just 2 years?

Here’s another chart that truly visualizes this dynamic activity

And if this doesn’t suffice to convey the rapid pace of change happening on teh ground, then lets take a more detailed look at trends related to this mobile phone penetration activity.

The point is that measurements that are static, or slow to change over time, aren’t conveying dynamism of the African markets nor their opportunities. With such a low base of development, static measurements lead to African nations being ranked low on indices. But when we consider the rate of change or the acceleration of growth, we see entirely different trends than if we were looking at absolute numbers alone.

I chose these measures because mobile phones are rapidly evolving into powerful and portable computing devices, while the proliferation of mobile money solutions reek of business activity, transactions, payments and the flow of cash circulating in an economy.

In this table, for example, both Egypt and South Africa lead the pack in terms of size but are they the leaders in terms of opportunity for growth or ROI?

Nigeria’s e-commerce sales grew 400% in the same time period as it took for South Africa to double and for Egypt to grow by 80%. Ghana and Ethiopia grew 300% while Kenya came in close enough. Where would you place your bets for e-commerce investment?

Connectivity and Communications

The final attribute that emerges from the patterns I’ve seen in the ‘prepaid economy’ and the informal and rural markets is that of flexibility and facetime. This isn’t the post to get into those details, which are available on demand, but the point here is to look at local and social activities, fuelled by the phone, that are hallmarks of the increasingly connected emerging consumers.

Source: http://www.biztechafrica.com/article/mobile-africa-2015-african-phone-use-decoded/9962/

“You’ll find me on Facebook” is the de facto business card of the African informal sector and/or startup, SME, telco or bank. The ability to communicate, thus negotiate, is key to the flexibility of the informal, and the perceived intimacy of social media mimics the hyper-local, social trusted networks of transactional flow and culture. As I write these sentences, I realize that embedded within each is volumes of densely packed insights that I promise myself I’ll return to in subsequent posts and articles.

These are the trends that drive adoption of Uber in Lagos and Nairobi, and the emergence of local variations for informal services. These are also connected to increasing visibility of geek culture and tech savviness among that favourite metric of demographers – the African youth.

An African Index of Competitiveness

Biashara is teh Swahili word for business, and a better descriptor of the informal trade and business sectors, as it covers the smallest livelihood activity that every family must conduct. A Biashara Competitiveness Index that could reflect the true picture, incorporating as it would the dynamic aspect of the informal sector, one that has failed most other attempts to measure and define.

Are the metrics I’ve displayed necessarily the ones that would contribute to this index? I don’t know, at this point, but I do know that when we look at the opportunity space and overlook the changes taking place and the innovative solutions in industries like financial services, cross border transactions, e-commerce etc we’re missing out on the ground reality by relying on metrics more suited for formal and/or developed economies for comparison.

If we can find a way to convey the pace of change, the acceleration of innovation and the flux, to capture and communicate the dynamism of the operating environment, we’d be better able to assess which markets offer us the best opportunities or where future growth may lie than static indicators. I’ll continue working on this.

Questioning Convention: Comparison Metrics for Competitive African Markets

Taking the question of appropriate and relevant metrics by which to assess competitiveness (rather, attractiveness) of the emerging African consumer markets further, I decided to dig up some analytical infographics to compare and contrast their approaches.

Urbanization is a current favourite, and here are two similar looking visuals from two different perspectives. The first is Knight Frank’s report on real estate opportunities on the continent, while the second is from PwC’s African section of the WEF Global Competitiveness report.

Knight Frank 2015

PwC 2015

Leaving aside the question of whether Dar es Salaam will show greater than or less than 120% growth in the next 15 years, here’s a clear indication of how choice of metrics impact outcomes. Granted, PwC selected countries on which to focus, thus the cities they list differ from Knight Frank’s, and each report has a different emphasis. Otoh, should there be a difference of ~ 10% in growth estimates for Nairobi, for instance, or Ibadan? No wonder these reports lead many to decry the quality of statistics and data from Africa.

Anyway, the point isn’t to debate whose method was better or if Dar will be the fastest growing capital on the continent or not. Until the dust settles down in the current scramble for African reportage, its best to take multiple sources of data into consideration and triangulate on the most reasonable estimate.

Questioning Convention and Convenience

The point is to ask if the conventional way we approach assessing the size and value of a market opportunity might itself need to be questioned when it comes to the African market?

For decades, South Africa was the closest thing to a developed economy south of the Sahara and until last year, the largest and strongest of all. This led to it becoming the de facto frame of reference through which to evaluate the others. PwC’s report shows this heritage in these analytical charts which compare regional (and continental) economic powerhouses of Nigeria and Kenya against South Africa.

In today’s world, you’re highly likely to be looking at Nigeria in West Africa and Kenya in East, if you’re looking at Africa at all. What you’d want is a means to compare the two, or more, rather than compare each against a third country whose operating environment you may not be familiar with.

This choice of metric – the lens by which they assess competitiveness – seems to make sense at first glance. But is it helpful in any way, shape or form to any organization without experience of the South African context by which to judge the relative rankings of the others? South Africa’s historical economic development lends itself to favourable rankings on the conventional metrics used for a globe spanning index while much of the others fall behind in contrast.

Yet they are distinguishing themselves in unique ways, contradicting what the metrics seem to imply – we saw the same challenge, in different form, with the E-commerce readiness index proposed by UNCTAD. South Africa’s current economic trajectory as compared to projections for either Kenya or Nigeria (or quite a few of the others) is quite dismal and the outlook gloomy, quite unlike the healthy exuberance of these two – compare SA’s 2.1% with Kenya’s 6.2% or Nigeria’s 7.3% – like I said, I’d be wanting to compare these two against each other, and maybe Ghana or Ivory Coast or Rwanda etc .

Is it time to think about developing metrics that better reflect the complexity and potential of the African operating environment?

Older Entries