Archive for the ‘Strategy’ Category

2017 is the Year Mobile Service Operators Became Banks

South African business headlines read MTN takes on Vodacom for title of Africa’s biggest digital bank and usher in a whole new era for banking and finance on the mobile platform. Having watched this space impatiently for more than a decade, seeing this was a landmark worth noting.

The number of mobile-money customers in the region (Africa) is growing rapidly, having surpassed the number of traditional bank accounts in 2015 to reach 277 million by the end of last year, according to GSMA. ~ Moneyweb, 3rd November 2017

Here’s a curated selection of my journey watching the phone become a bank:

Photograph of Nairobi billboard taken January 2016 by Niti Bhan

Blowin’ in the Wind – perspective, May 2007

A User Centered Approach to Banking the Unbanked in Rural India (PDF, entire process) – January 2007

Pondering the Mobile Innovation Divide – perspective, December 2007

African Potential meets Indian Experience – perspective, May 2008

The Telco and the Bottom of the Pyramid – perspective, January 2009

Systems Thinking Applied To Why M-Pesa’s Economic Impact and Wealth Creation Lessons Affects the Entire Ecosystem – Afrinnovator, March 2012

What is The Prepaid Economy anyway? – 14.7.14, in response to Michael Kimani

Banking Opportunities in Africa – The Banker’s Association of South Africa, 2014

A bank meets a telco – how mobile banking is changing the landscape of financial services in Africa – The Prepaid Economy: African Edition, January 2016

Is Your Product Ready for Africa? Why Kigali’s “Smart” Project Faces an Unforeseen Challenge

However, KTRN boss agreed that they share responsibility since they never conducted a profound market research to determine whether the gadgets are compatible with African weather.

“We sincerely didn’t realize that the weather would affect the gadgets”~ Public Buses Wi-fi: Harsh Weather, Incompatible Gadgets Interrupt Kigali’s ‘Smart’ Project, KT Press, 16th October 2017

This isn’t the first time I’ve come across a Korean device manufacturer completely unprepared for the exigencies of the African operating environment. Do we simply hear less about the robustness of Chinese electronic devices, for instance, or do we hold them to a lower standard? That’s a conversation for another day as its an entire screed in itself.

Here, I’ll just introduce our simple framework for ensuring you’ve covered all the bases when developing a new product for a market with very different conditions from your existing ones. Perhaps, it may provide food for thought for both the procurement side of the equation, when thinking about technical specifications and requirements, as well as the potential supplier side, when thinking about entering the African market.

Place: Feasibility

…inadequate infrastructure is a fact of life. Whether is variability in electricity supply in the urban context or lack of it in the rural. Things we take for granted in the operating environment in which these lenses were first framed – pipes full of running water, stable and reliable power, affordable, clean fuel for cooking, credit cards and bank accounts – are either scarce, inadequate or unreliable for the most part.

Feasibility, thus, takes on an entirely different meaning in this context. Each location or region (place) may have different facilities.

This rather obvious oversight has tripped up much larger manufacturers than this. Consider Whirlpool.

Emerging new markets, such as Rwanda’s, are rapidly adopting the latest technology. Is your product up for the challenge?

Absolute Numbers 2007-2017: The “Developing” World Now Dominates the Internet

Source: http://tmenguy.free.fr/TechBlog/?p=161

Traditionally, the data on ICT usage across the world tends to be presented proportionally – per capita usage, or penetration in the form of percentage of population. This made sense 10 years ago, when the world had just begun to notice the rapid growth of mobile phone adoption in developing regions. The typical example shown above was extremely popular – many of you will recognize it – Africa was outstripping the world in phone sales, and the prepaid business model had opened the floodgates.

At this time, however, devices were still at the feature phone stage, and Nokia owned the market. Voice and SMS were the real time communication disruptors, and smartphones only just entered the public consciousness. Internet penetration was still in the future.

Recently, however, I came across current data on internet usage presented in absolute numbers – shown above – of people online. The difference is rather stark, when compared to the proportional representation – see below.

Not only are the next two billion online, but the absolute numbers re-order the regions in a very different way. Asia leads the world online, and even Africa ranks higher than North America. Here’s the same data presented, by region, as a pie chart.

The distortion created by proportional or per capita presented skews the true landscape of the actual human beings who are using the internet. Ten years ago, this might have made sense given the passive content consumption nature of much of the early world wide web.

Today, given the dominance of social media, and the frictionless ability for anyone to share their thoughts, their photos, or their music video, its the absolute numbers that actually make a difference. There is more content available in Mandarin than in English, though we may not know it, and there are more Africans talking to each other every morning than there are North Americans.

I’ll be following up with more writing on the implications of this historic decade in human history – between 2007 and 2017, the long awaited next billion not only came online, but began showing us how to disrupt everything from cross border payments, to cryptocurrency adoption. They are my hope for a more peaceful, inclusive, and sustainable future for our grandchildren.

Fundamental Elements of Informal Sector Commercial Activity

There are two key elements which underpin the dynamics of any business or commercial enterprise in the informal sector. These are Time and Money.

A generalized framework can be diagrammed, as shown above, where the dotted line denotes the degree of uncertainty and volatility of an individual’s cash flow patterns – whether from a variety of informal economic activities – such as for the farmer or trader; or from the salary received for a white collar job. The X axis – Time – denotes the increasing accuracy of estimating the Arrival date of a cash payment (from some revenue source), and the Y axis – Amount – denotes the increasing accuracy of estimating the Amount that will arrive. Their relative ability to estimate Arrival and Amount with any degree of accuracy is indicative of their ability to forecast and plan for expenditure.

Thus, at one end of the continuum, one can position an odd jobs labourer who may or may not get paid work on any given day, and is unable to predict with any degree of certainty what type of job he’ll get selected for, nor for how many days it will last. It could be as basic as loading a truck for half a day’s pay, which in turn might even be in kind, and not cash. And, at the other end of this continuum, one can position a the typical white collar salaried professional or civil servant who knows with certainty exactly on which day they will receive the salary and exactly how much will arrive.

 

Positioning and Location

Now, we can frame these two elements of the commercial operating environment in the form of a position map, as shown above, that maps the ability to plan expenditures against the stability of the cash flow. The red arrow is the continuum of certainty and stability of Timing and Amount of an income stream, anchored by the most vulnerable odd jobs labourer at one end and the relatively most secure salaried professional at the other.

Where it gets interesting is the relatively liminal space in the middle where the various economic actors in the informal economy constantly shift position as they seek to mitigate the volatility of their income streams, through a variety of mechanisms. Much of their decision making is related to their own perception of uncertainty and ability to forecast.

For the purpose of this explanatory diagram, I have selected 4 typical examples drawn from different sectors of the informal economy common in the developing country context. Each are at the more vulnerable end of their own segments i.e. a subsistence farmer, rather than one with an established cash crop; or a small roadside kiosk rather than an established general merchandise store in a market town; since they have not yet achieved the goal of their business development strategies to move their own entrepreneural ventures towards relative stability, and thus provide more insight on the relationship between cash flow patterns and investment and expenditure planning.

The hawker of goods at a traffic light or junction is in a comparatively more fragile situation than the kiosk owner with a fixed location who works to develop relationships with passing customers in order to convert them to regulars at her store. Unlike the kiosk, which might be located near a busy bus stop, or outside a densely populated gated community; the hawker cannot predict which cars will pause at the red light as he darts through traffic shouting his wares. However, compared to the odd jobs labourer, the hawker has comparatively more control over his income generation since his is not a passive function of waiting to be picked from the labour pool in a truckyard or construction site.

The smallholder farmer might actually be better off economically in many ways than his urban brethren involved in informal retail, being able to live off the land more cheaply than in the city. Experienced farmers, for the most part, are able to predict with reasonable accuracy, more or less the quantity of their crop, and the estimated timing of the harvest. However, his sense of uncertainty is often perceptually greater due to the unmitigatable impact of adverse weather conditions, or the sudden infestation of a pest or blight, any of which could at any time completely destroy his harvest, and thus, his expectations. This sense of insecurity in turn influences his decisions on expense commitments to far ahead in time, or too large a lumpsum at some point outside of his regional harvest season. The farmer’s income streams are relatively more out of his control than the disposable income in the pockets of the kiosk’s customer base.

The market woman with her display of fresh produce, at the entry level of inventory investment capacity, might only have one or two different varieties of vegetables or fruit to sell, and may not yet have established a permanent structure – a table, a kiosk – in the market. She might start off with only a tarpaulin on the ground with some tomatoes and onions for sale. Unlike the traffic intersection hawker, however, she is more likely to begin by assuming a regular placement and location as this establishes the foundation for her future business development, through the factors of discoverability and predictability among the customers in that locale.

That is, in addition to Timing and Amount of Income – the cash flow patterns and sources – we begin to see the role played by location – Place1, as a supporting element of the commercial activity in the informal economy. While farmers are least likely to have much control over the location of the land they may inherit, their risk mitigation strategies to minimize volatility of their income streams and maximize their ability to plan for the future and manage emergencies will be discussed in depth in the section2 on rural household financial management. These practices are the foundation of business development strategies commonly observed in the informal economy in developing countries which tend to be less urbanized, and as is often the case, more dependent on agriculture as a component of national GDP.

 

Appendix
1 People, Pesa, Place: A Multidisciplinary Lens on Innovating in Emerging Markets
2 Rural Household Financial Behaviour on Irregular Income Streams at the Base of the Pyramid

Work in Progress: An Introduction to the Informal Economy’s Commercial Environment


This topic is being shared in the form of a collection of essays on the following themes, each becoming hyperlinked on completion. Do bookmark this page for regular updates.


Introduction to Background and Context, some caveats apply
Fundamental Elements of Informal Sector Commercial Activity
Rural household financial management as a foundation
Linkages and Networks span Urban and Rural Markets
Underlying Principles for Financial and Social Contracts in the Informal Economy
Informal Sector Business Development Strategies and Objectives
Why A Blanket Approach to Formalization is not a Panacea
Disaggregating and Segmenting the Informal Sectors
The Journey to Formalization Cannot be Leapfrogged

 


Appendix:
Creating Economic Value by Design (John Heskett, IJD 2009)
Financial Behaviour Patterns Observed Among Households in Rural Informal Economy (IDRC, 2009)
More or Less: The Fundamental Principle of Flexibility” Slides (Informal Economy Symposium, 2012)
A Comprehensive Analysis of the Literature on Informal Cross Border Trade in East Africa (TMEA, 2016)

Financial Behaviour Patterns Observed Among Households in Rural Informal Economy in Asia

This is the original working paper of the research conducted on rural household financial management, in developing country conditions, pioneering the use of methods from human centered design for discovery, during Nov 2008 to March 2009, aka the Prepaid Economy Project. It was peer reviewed by Brett Hudson Matthews, and I have incorporated his comments into the PDF.

This research study was carried out with the aid of a grant from the iBoP Asia Project (http://www.ibop-asia.net), a partnership between the Ateneo School of Government and Canada’s International Development Research Centre (www.idrc.ca)

The abstract:


The challenge faced by Bottom of the Pyramid (BoP) ventures has been the lack of knowledge about their intended target audience from the point of view of business development whereas decades of consumer research and insights are available for conventional markets. What little is known about the BoP’s consumer behaviour, purchasing patterns and decision making tends to assume that there are no primary differences between mainstream consumers and the BoP except for the amount of their income – pegged most often between $2 to $5 a day.

In practice, the great majority at the BoP manage on incomes earned from a variety of sources rather than a predictable salary from a regular job and have little or no access to conventional financial tools such as credit cards, bank accounts, loans, mortgages. This is one of the biggest differentiators in the challenge of value creation faced by BoP ventures, particularly among rural populations (over 60% of the global BoP population lives in rural areas).

Exploratory research was conducted in the field among rural Indian and rural Filipino populations in order to understand how those on irregular incomes managed their household expenses. Empirical data collected by observations, interviews and extended immersion led us to identify patterns of behaviour among the rural BoP in their management of income and expenditure, ‘cash flow’ and ‘working capital’ and the significance of social capital and community networks as financial tools. Practices documented include ‘conversion to goods’, ‘stored wealth’, ‘cashless transactions’, and reliance on multiple sources of income that mature over different times.

This paper will share our observations from the field; identify some challenges these behaviours create for business and also explore some opportunities for value creation by seeking to articulate the elements that BoP ventures must address if they are to do business profitably with the rural ‘poor’ based on their own existing patterns of financial habits and norms.


The Conclusion:

In sum, it can be concluded that the challenges for value creation can be quite different for BoP ventures interested in addressing the rural markets. From the observations made in the field, we can highlight three key implications for business development. These are:

  • Seasonality – with the exception of the salaried, everyone else in the sample pool was able to identify times of abundance and scarcity over the course of natural year in their earnings. Identification of a particular region or market’s local pattern of seasonality would benefit the design of payment schedules, timing of entry or new product and service launch, for example.
  • Relative lack of liquidity – The majority of the rural households observed tended to ‘store wealth’ in the form of goods, livestock or natural resources, relying on a variety of cashless transactions within the community for a number of needs. Conventional business development strategies need to be reformulated to take this into account as these patterns of behaviour may reflect the household’s purchasing power or income level inaccurately.
  • Increasing the customer’s span of control over the timing, frequency and amount of cash required – Since the availability and amount of cash cannot be predicted on calendar time, this implication is best reflected by the success of the prepaid mobile phone subscriptions in these same markets. When some cash is available, it can be used to purchase airtime minutes for text or voice calls, when there is no money, the phone can still receive incoming calls. Models which impose an external schedule of periodicity, frequency and amount of cash required may not always be successful in matching the volatile cash flow particular to each household’s sources of income.

A theoretical approach to Value for Money in aid & development: Optimizing research and design for ‘best fit’ iterative programming

Last year, I briefly touched upon this concept as an approach to cost effective programme design that was still flexible enough to provide room for iteration for best fit.

Today, I want to explore the concept further to evaluate its potential as a framework for incorporating the concurrent shift in development thinking towards Value for Money (DFID) principles, in addition to designing for best fit.

Value for Money as a Process Driver

Value for Money (VfM) is not the same as traditional monitoring and evaluation which seeks to measure impact of a project, and occurs usually after the fact. In many large scale projects, this may not happen until years after inception.

Instead, VfM is defined by the UK’s National Audit Office as ‘the optimal use of resources to achieve intended outcomes’, which in turn, the DFID document contextualizes for their aid programming investments as “We maximise the impact of each pound spent to improve poor people’s lives.”

If this applies to all investments in aid related programme development, then it follows that it must also apply to earliest stage of discovery and exploration that leads to problem framing i.e. the necessary groundwork to write a comprehensive and inclusive design brief for future programming.

Thus, the conceptual approach that I introduced at the beginning of this post, which is taken from the discipline of Operations Research, and seeks to solve the challenge framed so – what is the optimal solution that minimizes resources (inputs) for maximum outputs (value creation) – fits as a potential framework that can theoretically apply from the earliest stages of implementing development strategy, even before inception of any related projects, including early stage research and feasibility studies. After all, the function of Linear Programming is optimization.

Note: Here I will only consider the theoretical aspects from the point of view of programme design research and development, and not the mathematics. That will have to wait until I have gathered enough data for validation.

Design Research for Programme Design Purposes

In this context, the primary function of such an exploratory project is to identify the opportunity spaces for interventions that would together form an integrated programme designed to effect some sort of positive change in the ecosystem within which it would be implemented, and offer a wider (more inclusive) range of cross-cutting benefits.

In the language of product development, we are attempting to build a working prototype. We cannot build and test first prototypes to see if they work, directly, because our room for failure is much less spacious for experimenting with aid related programming, ethically speaking. This is not a laboratory environment but the real world with enough challenges and adversity already existent.

Programmes are not the same as consumer products, nor are they meant to be designed and tested in isolation before being launched for pilot testing in the market. Their very nature is such that innocent people are involved from the start, often with a history of skepticism regarding any number of well meant donor funded projects aimed at improving their lives. This changes the stringency of the early stage requirements for design planning.

At the same time, the nature of the task is such that no first prototype can be expected to be the final design. So, from the very beginning, what we must do is set the objective of the outcome as a Minimal Working Prototype (MWP) that meets all the criteria for an optimal solution, and NOT a Minimal Viable Product (which may or may not work wholly as intended until tested in the field for iteration.)

That is, the first implementation of the iterative programme design must fall within the bounds of the solution space – that which is represented by the shaded area in the diagram above.

The Optimal Solution is the Iterative Programme Design

Thus, what we must be able to do at the end of the discovery phase of research necessary to write the design brief, is tightly constrain the boundary conditions for the solution space within which the MWP can then be iterated. This minimizes the risk of utter failure, and maximizes the chances of discovering the best fit, and all of this within the definitions of Value for Money and it’s guidelines.

There are numerous ways to set the goals for optimization – one can minimize resources and maximize constraints, or minimize risk and maximize return on resources invested. These will guide our testing of this framework in field conditions to validate the robustness of this theoretical approach.

In this way, we can constrain our efforts to discover best fit within predefined limits of tolerance, while retaining the flexibility to adapt to changing real world circumstances and progressive transformation of operating conditions.

Best fit, then, becomes less a matter of experimentation without boundary conditions and more a discovery of which of the many right answers – if we take the entire shaded area as containing “right answers” to the problem at hand – help us meet the goals of intervention in the complex adaptive system in an optimal manner.

The point to note from this conceptual framework is that there is never any ONE right answer, so much as the answer will be that which we discover to the question “What is needed right now for us to meet our goals, given these changes since we last looked at the system?”

It is this aspect that loads the burden of a successful outcome on the front end of the entire research and development process, given that framing the problem correctly at the outset is what drives the research planning and steers the discovery process in the direction of relevant criteria, conditions, constraints, and user needs that will not only form the bounds of our solution space, but also act as waymarkers for monitoring change and evaluating its progression.

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.

Frame Insights: Going back to first principles in the Innovation Planning Process

After conducting research, we need to bring structure to what has been found and learned. We sort, cluster, and organize the data gathered and begin to find important patterns. We analyze contextual data and view patterns that point to untapped market opportunities or niches. Finding insights and patterns that repeatedly emerge from multiple analyses of data is beneficial. ~ Vijay Kumar, 101 Design Methods

“It’s what happens after the research that’s important” is something I found myself saying three times to three different people in three different contexts over the past couple of days. Anyone can go out and interview users and beneficiaries. What’s important is what happens during the Analysis phase.

To ponder this in detail, I wanted to go back to first principles, and drill down into the post research stage where we are expected to frame our insights.

Vijay’s slide pops out 5 key outcomes from this phase, and these are critical for solution development in the subsequent phase. These 5 outcomes from analysis of the data collected during the research phase are:

  1. Looking for patterns
  2. Exploring systems
  3. Identifying opportunities
  4. Developing guiding principles
  5. Constructing overviews

It is this stage that distinguishes the quality of the outcome. Now, in the case of our work in the informal economy operating environment, we have built up an overview of the landscape over the past several years, primarily through immersion and thick data collection using design ethnography methods.

Starting from the purchasing patterns and buyer behaviour of low income consumers, back in early 2008, all the way through to the development of guiding principles such as flexibility, we have explored and mapped the ecosystem from numerous vantage points.

Today, our synthesis of user research does not happen in isolation from the body of work – intellectual property – that has been developed over time, through experiential and practical knowledge.

This, then, is what underlay my conviction when I spoke about the importance of the quality of interpretation of the data, and the transmutation of these interpretations into implemented insights in the form of new product features, service design elements, or nuances of the payment plan in the business model.

Increasingly, the Frame Insights phase of our work has led to the evolution of our understanding of the commercial landscape in rural and informal markets where incomes tend to be irregular and volatile, and infrastructure is inadequate or missing. It is this that I’ve been attempting to capture under the category of Biashara Economics.

It’s not Africa specific. The patterns hold, give or take ~30% margin for historical/cultural/social differences, across continents. That is because these patterns are the natural response to the common characteristics of seasonality, volatility, uncertainty, and unpredictability. And this is why one can see the success of the prepaid business model around the world.

It strikes me here that this in fact validates the methodology and approach to exploration and discovery in unknown contexts, something I had framed as the starting point for the very first such project almost a decade ago. Over time, I discovered how much the methods, as delineated by Vijay in Chicago, had to be adapted for the context but that is a topic for another time.