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?

African E-Commerce: Successfully Leapfrogging The Metrics of Fail

Postal networks are critical elements of the e-commerce chain, a UN report said, including home postal delivery as an indicator in a new global index to measure countries’ readiness to carry out business-to-consumer (B2C) e-commerce. ~ source

By these metrics, countries on the African continent such as Nigeria rank 101st on the global index, far below South Africa at 67th place, and Cote d’Ivoire isn’t even on the readiness list. Why should this matter?

Jumia, one of the rising giants of pan African e-commerce, just opened 6 new hubs across the Cote d’Ivoire, and happens to be headquartered in Nigeria. On the other hand, South Africa has been struggling to get its e-commerce industry off the ground to meet its full potential.

While the reports such as these may indeed be organized collections of tastefully analyzed data and well presented charts and graphs, are they able to offer any meaningful insight? This report presents The UNCTAD B2C E-commerce Index as a means for countries to assess their readiness for e-commerce and identify the areas that need further development and investment.

Konga Warehouse, Lagos, Nigeria

Yet, on the ground, the “least ready” countries seem to be leaping forward, building brands and developing ecosystems for the emergence of supporting services, employment opportunities and even, niche platforms. How does all the hard work that may have gone into the creation of such reports help them?

Metrics – the attributes by which to rank or measure – may not always be universally appropriate, nor will they always represent the real world operating environment. As African economies emerge onto the global platform – both real and virtual – they may require new ways to measure opportunity and success.

Metrics that can realistically reflect their unconventional characteristics of cutting edge communications commingled with undeveloped infrastructure. Else the growth opportunities such as those in Cote d’Ivoire, which isn’t even listed in the UNCTAD B2C E-commerce Index may pass under the nose of international players.

Yes, Africa is starting from a very low base, but early investors like Rocket Internet’s Jumia know that its only here that one can show results like 900% growth in sales in as many months.

The difference between what and why

Today’s meeting threw up an interesting observation that made me think about problem areas, how they’re identified and how they may be deconstructed. In simpler terms, the difference between the “what” and the “why”.

Take two regions in a country, one far more fertile and having a better overall economy than the other. Yet both areas face the same lack or unmet need. Take a product which fills this need. Yet it’s sales in the far more economically challenged area are more than double that of the first region. Why?

The numbers provide the managers a means to identify a problem. But they are not able to provide any explanation for the discrepancy. It was the numbers themselves that originally identified the first region as one which would be a good location to launch a product – average income was X, unmet need was felt by almost 90% of the population etc etc.

This is where putting people first, followed by supporting metrics (data) makes sense. Or rather in the case of those who attended today’s meeting, where their data now needed answers that only the people generating those numbers could answer themselves.

Data, charts, graphs, metrics and numbers all have a role to play but when they are about human beings (and not just the number of cars per minute produced in an automated factory line) I believe that role is a supporting one, not the Oscar winning star of the show.