Market entry: from attainable demand to a staged decision
Produced: 2026-09-20
Language: en
IDER in-house training materials: practical cases, illustrations and guided exercises.
All cases and figures are hypothetical teaching examples, not current market data.

[00:00:00.000 – 00:00:45.589]
Define the decision before estimating demand
ON SCREEN
Hypothetical cold-chain service
Launch, pilot or defer
Evidence must change an action

TEACHING COMMENTARY
Imagine you advise a cold-chain operator considering a new city. Management wants a market size, but a large market does not tell us whether this operator should enter. We need a decision with alternatives, a time horizon, and a loss the organisation can tolerate. Throughout this clinic, every number describes a hypothetical training case, not current market statistics. Our alternatives are immediate launch, a limited pilot, and deferral. Notice that the research question changes when a pilot is available: we ask which uncertainty could reverse the decision and how cheaply we can reduce it. Keep a calculation sheet beside you. By the end, you will build a defensible recommendation rather than a persuasive market story.

[00:00:45.589 – 00:01:35.803]
Write the decision contract
ON SCREEN
Decision owner and deadline
Financial and operational gates
Evidence required for each gate

TEACHING COMMENTARY
Start a decision contract with the person who can approve entry. Specify the city, customer segment, service standard, and first operating year. Then agree gates before anyone sees favourable results. For example, paid orders must cover monthly operating costs, available capacity must meet the promised service, and cash must fund the collection cycle. A gate is useful only when its definition is testable. Saying demand must be strong leaves room for interpretation; specifying a contribution threshold establishes a measurable requirement. Record which evidence could support, weaken, or overturn each gate. This prevents the familiar problem of changing success criteria after the fieldwork. The contract should also name who resolves conflicting commercial and operational evidence.

[00:01:35.803 – 00:02:23.486]
Build a conditional demand funnel
ON SCREEN
800 eligible firms × 60% reachable
25% adoption × 4 monthly orders
480 potential orders per month
800 × 0.60 × 0.25 × 4 = 480
TEACHING COMMENTARY
Take eight hundred firms that genuinely need the service. Sixty percent are reachable through the proposed sales route, leaving four hundred and eighty. If one quarter of those firms adopt and each places four monthly orders, potential demand is four hundred and eighty orders. These percentages are conditional: adoption applies to reachable firms, not to every firm in the city. That distinction prevents double counting and unjustified optimism. Give each assumption a source, observation date, and plausible range. A company registry may establish eligibility only after checking activity and product fit. Interview interest cannot establish paid adoption. Trace the funnel backwards when a manager challenges the result; you should be able to identify the specific assumption that drives disagreement.

[00:02:23.486 – 00:03:15.971]
Measure behaviour behind each assumption
ON SCREEN
Eligibility: verified business frame
Reach: channel access and capacity
Adoption: paid behaviour and repeat use

TEACHING COMMENTARY
Design evidence collection around the funnel rather than one general questionnaire. Verify eligibility against actual temperature-sensitive shipments. For reach, test whether sales staff can contact decision makers at the proposed acquisition cost. For adoption, ask customers to make a real commitment under clearly explained service and price terms. Separate first purchase from repeat use, because a launch promotion can produce trial without durable demand. Sample across firm size, route and current supplier, including businesses outside management's existing network. A convenient sample of enthusiastic contacts usually overstates accessible demand. Keep refusal and nonresponse records so the reader can assess selection. Interview explanations help reveal mechanisms, but do not convert a compelling quotation into a numerical population estimate.

[00:03:15.971 – 00:04:06.916]
Translate orders into contribution
ON SCREEN
Price GHS300 − variable cost GHS180
Contribution GHS120 per order
Fixed costs GHS48,000 → 400 orders
48,000 ÷ (300 − 180) = 400
TEACHING COMMENTARY
Now turn market evidence into operating economics. Our hypothetical price is three hundred cedis and the incremental cost per order is one hundred and eighty, so each order contributes one hundred and twenty toward fixed costs. Divide monthly fixed costs of forty-eight thousand by that contribution: operating break-even is four hundred orders. The denominator must include costs that actually change with an extra order, such as route labour, fuel and packaging. If overtime begins above a capacity threshold, one constant variable cost will mislead. This calculation also excludes initial investment, financing and tax. A positive operating surplus therefore does not establish an acceptable investment return. State that boundary explicitly before using break-even as an entry gate.

[00:04:06.916 – 00:04:57.130]
Apply capacity before recognising revenue
ON SCREEN
Potential demand: 480 orders
Reliable capacity: 420 orders
Billed revenue: GHS126,000 monthly

TEACHING COMMENTARY
Demand is not delivered volume. Suppose the operation can reliably complete only four hundred and twenty monthly orders at the promised service standard. Revenue is then four hundred and twenty times three hundred, or one hundred and twenty-six thousand cedis, rather than the unconstrained demand forecast. Calculate capacity at the bottleneck: refrigerated vehicle hours, loading slots, or storage throughput. Average capacity can conceal peak-day failure, so test the order pattern as well as the monthly total. Rejecting sixty orders may also affect customer retention and future sales. Expansion might remove the bottleneck, but it introduces capital and staffing costs. Put those costs into a separate option instead of quietly assuming capacity grows for free when demand increases.

[00:04:57.130 – 00:05:45.591]
What if adoption is only 20%?
ON SCREEN
Keep 800 firms, 60% reach, 4 orders
Contribution GHS120; fixed GHS48,000
Calculate orders, surplus and the binding constraint

TEACHING COMMENTARY
Pause the video and change adoption from twenty-five percent to twenty percent. Keep every other assumption unchanged: eight hundred eligible firms, sixty percent reach, and four orders per adopter each month. First calculate potential orders. Next compare that result with the capacity limit of four hundred and twenty, because revenue uses the lower number. Finally calculate contribution less fixed costs. Write one sentence identifying whether demand or capacity now constrains performance. Do not simply reduce the earlier surplus by five percent; the adoption change is five percentage points, and the relationship passes through both the demand funnel and a capacity ceiling. Show the intermediate calculations so another analyst could reproduce your result without asking which figures you changed.

[00:05:45.591 – 00:06:36.129]
A small adoption change reverses the result
ON SCREEN
800 × 60% × 20% × 4 = 384 orders
Contribution GHS46,080
Operating loss GHS1,920; demand binds
384 × 120 − 48,000 = −1,920
TEACHING COMMENTARY
The revised funnel produces three hundred and eighty-four orders. That is below capacity, so all forecast demand can be served. Multiply by one hundred and twenty cedis contribution and you obtain forty-six thousand and eighty. After forty-eight thousand fixed costs, the operation loses one thousand nine hundred and twenty cedis per month. The important lesson is not the arithmetic alone. A five percentage point change in adoption moves the business from capacity constrained to demand constrained and crosses operating break-even. That makes paid adoption a priority research variable. It does not mean every other uncertainty can be ignored. If delivery reliability affects repeat adoption, demand and operational performance are linked, and the pilot must observe both together.

[00:06:36.129 – 00:07:23.735]
Profit and cash answer different questions
ON SCREEN
420 orders × GHS300 = GHS126,000
30 additional collection days
Approximately GHS126,000 more receivables

TEACHING COMMENTARY
Consider the capacity-limited base case with monthly billed revenue of one hundred and twenty-six thousand cedis. Under a simplified steady billing assumption, an additional thirty days before collection ties up approximately one extra month of revenue in receivables. That is another one hundred and twenty-six thousand cedis of funding, even if reported operating profit does not change. Build a monthly cash schedule including customer deposits, supplier terms, payroll, taxes and opening inventory. The receivables shortcut is a screening calculation, not a complete liquidity forecast. Seasonal sales and uneven payment dates can create a much larger peak requirement. Entry should be conditional on funding that peak, with room for delayed collections, rather than on an annual profit estimate alone.

[00:07:23.735 – 00:08:15.357]
Find the assumptions that change the decision
ON SCREEN
Thresholds before attractive averages
Joint shocks when assumptions interact
Separate evidence gaps from unavoidable risk

TEACHING COMMENTARY
A useful sensitivity analysis locates switching points. Holding reach and order frequency constant, break-even adoption is four hundred divided by eight hundred times sixty percent times four: approximately twenty point eight three percent. This tells the research team what precision matters. Distinguishing twenty-one from twenty-five percent may matter more than measuring the total city population perfectly. Then test coherent joint scenarios: weaker adoption could coincide with higher acquisition costs or slower collections. Changing one assumption at a time misses these interactions. Avoid assigning independent random distributions simply because a spreadsheet can simulate them. Use observed relationships or clearly labelled scenarios. Report which uncertainties can be reduced with evidence and which remain commercial risks even after careful research.

[00:08:15.357 – 00:09:09.967]
Design the smallest decision-changing test
ON SCREEN
Representative customers and actual prices
Repeat orders and delivery reliability
Pre-agreed stop, revise and expand rules

TEACHING COMMENTARY
A pilot should test the mechanism most capable of changing the entry decision. Recruit across the intended customer segments, offer the planned commercial terms, and track both first orders and repeat orders. Record acquisition effort, cancellations, delivery failures and collections using linked customer identifiers. Set the observation period long enough to reveal repeat behaviour, not just launch excitement. Before starting, define expansion, revision and stopping rules using contribution and service performance. A small pilot may use unusually attentive staff or spare vehicles; those conditions can make results difficult to scale. Document resources per order and recreate the proposed operating model where feasible. The pilot is valuable because it resolves a decision, not because activity alone demonstrates market interest.

[00:09:09.967 – 00:10:02.085]
Put an upper bound on research spending
ON SCREEN
Launch: 60% chance of +GHS200,000
40% chance of −GHS100,000
Defer: GHS0 in either state

TEACHING COMMENTARY
We can formalise whether more research is worth buying. Consider a separate simplified decision with two possible states. Launch earns two hundred thousand cedis in the favourable state, with probability sixty percent, and loses one hundred thousand otherwise. Deferral pays zero. Without further information, the expected payoff from launch is eighty thousand: one hundred and twenty thousand of probability-weighted gain less forty thousand of probability-weighted loss. This is a risk-neutral calculation using hypothetical probabilities and comparable payoffs. A cash-constrained organisation may rationally reject that choice because the loss threatens survival. Keep the decision rule explicit. Information value also depends on whether management can actually act on the finding before contracts, leases or other commitments become irreversible.

[00:10:02.085 – 00:10:47.005]
What is perfect information worth?
ON SCREEN
Choose launch or defer after learning the state
Compare with today's best expected payoff
Exclude research cost and delay for now

TEACHING COMMENTARY
Pause here and imagine a test that reveals the true state perfectly before management decides. In the favourable state, management can launch and earn two hundred thousand cedis. In the unfavourable state, it can defer and avoid the hundred thousand loss. Calculate the expected payoff when the decision can depend on that information. Then subtract the best expected payoff available without the test, which you calculated on the previous slide. The difference is the expected value of perfect information. Explain why that figure cannot automatically become the budget for a real customer survey. Real studies make mistakes, take time, and may not address every uncertainty. Write your explanation alongside the numerical answer before moving to the solution.

[00:10:47.005 – 00:11:33.995]
Research value depends on decision improvement
ON SCREEN
With perfect information: GHS120,000
Without information: GHS80,000
Upper bound: GHS40,000 before delay and cost
0.60 × 200,000 − 80,000 = 40,000
TEACHING COMMENTARY
With perfect information, launch occurs only in the favourable state. Sixty percent times two hundred thousand gives an expected payoff of one hundred and twenty thousand cedis. Subtract the current best expected payoff of eighty thousand and perfect information is worth forty thousand. A real study will usually be worth less because its signal is imperfect, and waiting can reduce opportunity value. This is an upper bound under the stated probabilities, payoffs and risk-neutral rule, not a suggested invoice. To assess a real pilot, estimate how often each result occurs, how it updates beliefs, and which action follows. Research that produces interesting detail without changing the action may have little decision value despite excellent technical execution.

[00:11:33.995 – 00:12:23.907]
Make the recommendation auditable
ON SCREEN
Recommendation with explicit conditions
Model, evidence register and uncertainty
Decision owner, review date and trigger

TEACHING COMMENTARY
Your final memorandum should fit the decision rather than reproduce every research activity. State the recommended option and the conditions that would change it. Show the demand funnel, constrained volume, contribution, cash requirement and critical switching points. Link each important assumption to its evidence, date and limitation. Distinguish measured facts, modelling choices and management judgements so a reviewer knows where disagreement belongs. Include a short adverse case and explain the operational response, not merely the financial result. Assign an owner to every unresolved gate and a date for review. Avoid writing that the market is attractive when the evidence supports only a limited pilot. The recommendation should be no stronger than the weakest essential condition supporting it.

[00:12:23.907 – 00:13:13.009]
Turn your next market study into a decision tool
ON SCREEN
Rebuild one existing demand forecast
Identify its critical switching point
Propose an evidence-linked entry gate

TEACHING COMMENTARY
Apply this method to one market study you already use. Replace the headline market size with a customer funnel whose units and conditional rates are explicit. Recalculate attainable volume after capacity, then test contribution and peak cash needs. Identify the assumption nearest its switching point and propose a feasible observation that could improve it. For a junior practitioner, the immediate discipline is reproducible calculation and a clear evidence trail. For a senior practitioner, the harder judgement is deciding which uncertainty deserves resources and which must be accepted or transferred. Complete the questions and applied tasks below the video. A strong answer explains the decision consequence of each calculation and acknowledges the limits of the evidence, rather than reporting numbers alone.

METHOD REFERENCES
HM Treasury — The Green Book (2026)
https://www.gov.uk/government/publications/the-green-book-appraisal-and-evaluation-in-central-government
General framework for comparing options, costs, benefits and risks. IDER original hypothetical commercial calculations; no government discount rate is imported. / Cadre général de comparaison des options ; calculs commerciaux hypothétiques originaux IDER.

Audio credits
https://iderafrica.com/downloads/briefings/audio-credits.txt