The Problem with Borrowed Templates
Most feasibility study templates in circulation were designed for markets with deep secondary data — industry databases, reliable government statistics, active trade associations, and audited competitor filings. Applying these templates in African markets without adjustment produces studies that look rigorous but are built on data that is two years old, nationally aggregated when you need city-level, or simply unavailable.
The result is investor confidence built on assumption stacks rather than evidence. When those assumptions fail — as they frequently do at the market entry stage — the feasibility study becomes a liability rather than an asset, and the consultant who produced it loses credibility.
The answer is not to lower the analytical standard. It is to change the sequence of the work.
The Framework: Field First, Model Second
Phase 1: Define the Decision
A feasibility study should begin with a precise articulation of the decision it needs to support. This sounds obvious, but most briefs received in practice say something like "assess the feasibility of entering the Nigerian market" — a question so broad it admits almost any answer.
Before any data collection begins, MMK Consult works with clients to define: what specific product or service proposition is being assessed; what constitutes a viable investment case (minimum scale, minimum return, maximum payback period); and what would constitute a no-go signal. Without these parameters, the study cannot be structured efficiently and the client cannot use the output to make a binary decision.
Phase 2: Primary Data Collection
In African markets, primary research is not a supplement to secondary data — it is the primary evidence base. The research design varies by sector and market, but typically includes structured interviews with target customers and channel partners, observation-based data collection at relevant market locations, and competitor intelligence gathered through direct engagement rather than desk research.
Quality control at this stage is critical. Common errors include over-sampling formal sector respondents (easier to access, less representative), leading questions that confirm the client's hypothesis, and relying on intermediaries who filter information before it reaches the analyst. MMK Consult's field protocols address each of these failure modes explicitly.
Phase 3: Market Sizing
Bottom-up market sizing — building from unit economics and addressable customer counts rather than top-down from national GDP figures — produces more useful numbers in African markets. The discipline of bottom-up sizing also forces early confrontation with the actual unit economics of the business, which frequently reveals assumptions that look reasonable in aggregate but are implausible at the unit level.
Phase 4: Scenario-Based Financial Modelling
Single-point projections are not useful for investment decisions. Every assumption in a financial model has a range, and the range matters more than the central estimate when the underlying data is thin. MMK Consult structures feasibility models around three explicit scenarios — base, downside, and stress — with each assumption clearly labelled as either observed (from primary or reliable secondary data), estimated (from analogous markets or expert judgement), or assumed (arbitrary but internally consistent).
This labelling discipline serves two purposes. It forces the analyst to be honest about the evidence quality underlying each projection. And it allows the client to run their own sensitivity analysis against the assumptions they are least confident in, without needing to reverse-engineer the model.
Phase 5: Risk Tiering
Feasibility studies that present a binary "feasible / not feasible" conclusion rarely reflect the actual decision landscape. Most investment decisions in African markets involve accepting some risks while mitigating others and monitoring a third category. A risk-tiering matrix — classifying risks by likelihood, impact, and mitigability — allows the client to make a more nuanced go/no-go decision that accounts for their specific risk tolerance and existing operational capabilities.
Common Failure Modes to Avoid
The competitive landscape section is frequently the weakest part of African market feasibility studies. The instinct to list only registered formal competitors misses the informal competition that typically takes the largest share of the addressable market in most African consumer segments. Any credible feasibility study must account for informal competition explicitly, even if the data is harder to collect.
Regulatory risk is systematically underweighted. Analysts comfortable with regulatory environments in their home markets tend to apply similar assumptions — stable rules, predictable enforcement, low political risk — to African markets where these assumptions frequently fail. Regulatory risk should be assessed independently of market risk, with explicit engagement with regulators or regulatory counsel during the primary research phase.
Exchange rate and currency risk is almost always undermodelled for cross-border investments. A feasibility study that projects USD or GBP returns without explicit modelling of currency depreciation scenarios is not fit for purpose in most African markets.
Conclusion
Feasibility studies in African markets can be as rigorous as anywhere else — they just require different sequencing, different data sources, and different output structures than studies designed for markets with deep secondary data. The discipline of field-first evidence collection, explicit assumption documentation, scenario-based modelling, and systematic risk tiering produces outputs that clients can use to make defensible investment decisions even in high-uncertainty environments.
The alternative — a study that looks comprehensive but is built on unreliable foundations — serves no one. Not the client, not the analyst, and not the investors or partners who rely on the output.