Commercial site selection: from catchment evidence to a defensible 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 – 00:00:46.344]
Choose a viable site, then justify the trade-offs
ON SCREEN
Hypothetical case: two service outlets
Evidence → preferences → cash viability
Deliverable: conditional investment memo

TEACHING COMMENTARY
Imagine a service business comparing two urban locations. One has stronger apparent demand; the other offers easier access and lower operating costs. The practical question is not which location looks busiest. It is which location can support the intended service, at an acceptable risk, under an explicit set of priorities. In this IDER in-house clinic, we will build that decision from observable evidence. All locations and figures are hypothetical teaching examples. Keep a spreadsheet nearby. You will calculate a ranking, discover when that ranking reverses, and test whether the preferred option actually covers its operating costs. The final product is a recommendation another analyst can reproduce and a manager can challenge.

[00:00:46.344 – 00:01:32.413]
Separate eligibility gates from scored preferences
ON SCREEN
Gates: permissions, access, minimum utilities
Preferences: demand, convenience, cost
Record who can approve exceptions

TEACHING COMMENTARY
Start with conditions that a location must satisfy before you compare its attractiveness. If the service requires reliable power and a site cannot supply it at a feasible cost, an attractive demand score should not compensate for that failure. Write each gate as a test, with an evidence source and an accountable reviewer. Then identify genuine trade-offs among eligible sites. A slightly longer journey may be acceptable if customers obtain better service or lower prices. This separation prevents an impressive weighted average from hiding a fatal constraint. Record exceptions explicitly. An unresolved permission issue belongs in a conditional decision, not inside a quietly reduced score that executives might never notice.

[00:01:32.413 – 00:02:19.325]
Model real journeys and competing destinations
ON SCREEN
Travel time by customer mode and hour
Origins, barriers and alternative providers
Separate people, visits and purchases

TEACHING COMMENTARY
A circular radius on a map is convenient, but it rarely describes the journey customers actually make. Build catchments using the transport modes and hours relevant to your service. A road crossing, transfer or flooded route can change effective access without changing physical distance. Sample customer origins and ask which alternative destination they would use. This reveals overlapping catchments and substitution. Also define your unit carefully. The same commuter passing twice is two observed visits, not necessarily two people or two purchase opportunities. For a practical study, maintain separate fields for eligible visits, identifiable customers and completed purchases. That distinction will become essential when we turn observed movement into a contribution forecast.

[00:02:19.325 – 00:03:04.245]
Stratify observations before expanding totals
ON SCREEN
Weekday/weekend × morning/evening
Equal definitions and observer protocols
Retain weather, closures and event flags

TEACHING COMMENTARY
Suppose your team counts Site A on a payday evening and Site B on a quiet weekday morning. Precise counting will not repair that comparison. Create a schedule crossing the time periods and day types that matter, then observe both sites within comparable strata. Expand each stratum using the number of similar operating periods it represents, rather than multiplying one convenient hour across the month. Record unusual weather, promotions and closures so that analysts can investigate anomalies. Repeated observations within one day may share the same shock, so do not treat every minute as independent evidence. Before leaving the field, reconcile observer definitions using a short joint counting exercise and review disagreements.

[00:03:04.245 – 00:03:54.617]
Anchor scores to explicit performance ranges
ON SCREEN
Demand / access / cost: 0–100 scales
A: 80 / 60 / 40
B: 65 / 85 / 75

TEACHING COMMENTARY
Our illustration assigns each site a score for demand, access and cost attractiveness. Higher always means better, including for cost, where a cheaper comparable operating arrangement receives a higher score. The numbers are useful only after you define their anchors. For example, specify what zero and one hundred mean and why intermediate performance deserves its position. Otherwise two assessors may attach different meanings to eighty. Avoid scoring the same benefit twice through correlated criteria such as footfall and forecast sales unless the model explicitly separates their contributions. Keep the raw measurements alongside the converted scores. When someone disputes the ranking, you can then distinguish a disagreement about observed performance from a disagreement about priorities.

[00:03:54.617 – 00:04:43.132]
The baseline priorities favour Site B
ON SCREEN
Weights: demand 50%, access 30%, cost 20%
A = 66 points; B = 73 points
Weights value defined performance swings
A = 0.50×80 + 0.30×60 + 0.20×40 = 66; B = 73
TEACHING COMMENTARY
We now apply demand, access and cost weights of fifty, thirty and twenty percent. Site A receives forty points from demand, eighteen from access and eight from cost, giving sixty-six. Site B receives thirty-two point five, twenty-five point five and fifteen, giving seventy-three. These are preference scores, not probabilities of commercial success. Elicit weights against the defined improvement from the worst to the best performance on each scale. Asking which criterion sounds most important without showing that range can create misleading weights. Record whose preferences the weights represent and obtain their confirmation before revealing the final ranking. This reduces the temptation to adjust the model until it endorses a favourite site.

[00:04:43.132 – 00:05:28.261]
Would stronger demand priorities reverse the choice?
ON SCREEN
Raise demand weight to 70%
Split remaining 30% as access:cost = 3:2
Calculate both scores and explain the change

TEACHING COMMENTARY
Pause the video and challenge the baseline. A decision maker now proposes a seventy percent demand weight, while keeping the relationship between access and cost weights at three to two. First calculate the new access and cost weights. Then recompute both site scores without changing their underlying performance. Finally, write one sentence explaining whether a changed ranking would indicate better evidence or different preferences. That final distinction matters in practice: a model can change its recommendation even when no new observation has been collected. Show your intermediate contributions rather than only the totals. If your weights do not add to one hundred percent, correct the model before interpreting any resulting ranking.

[00:05:28.261 – 00:06:18.784]
Site A wins under the revised preferences
ON SCREEN
Access 18%; cost 12%
A = 71.6; B = 69.8
The evidence is unchanged; priorities differ
A = 0.70×80 + 0.18×60 + 0.12×40 = 71.6; B = 69.8
TEACHING COMMENTARY
The remaining thirty percent divides into eighteen percent for access and twelve percent for cost. Site A now scores seventy-one point six, while Site B scores sixty-nine point eight. The ranking reverses because Site A's demand advantage receives more influence. Nothing in this calculation proves that the original observations were wrong. You have changed the decision maker's valuation of performance differences. In a committee discussion, show both scenarios and ask whether the higher demand priority reflects the actual business strategy. Also check whether all participants understood the same performance ranges. A useful sensitivity analysis reveals a consequential disagreement; it does not average that disagreement away and present an artificial consensus as objective measurement.

[00:06:18.784 – 00:07:04.280]
Quantify how much preference change matters
ON SCREEN
Let demand weight be w
Remaining access:cost stays 3:2
Switch at w = 29/44 ≈ 65.91%
52 + 28w = 81 − 16w → w = 29/44 = 65.91%
TEACHING COMMENTARY
Instead of trying isolated weights, solve for the exact point where the two scores are equal. Let the demand weight be w. Access receives three fifths of the remainder and cost two fifths. Site A's score becomes fifty-two plus twenty-eight times w. Site B's becomes eighty-one minus sixteen times w. Equating them gives forty-four times w equal to twenty-nine, so the switching point is approximately sixty-five point nine one percent. Below that value, B ranks higher; above it, A does. This threshold is useful because decision makers can assess a meaningful proposition: do we really value the defined demand improvement enough to allocate roughly two thirds of the total weight to it?

[00:07:04.280 – 00:07:55.341]
Preferences and measurements need different tests
ON SCREEN
Weights express choices
Demand estimates contain sampling error
Test jointly plausible combinations

TEACHING COMMENTARY
Weight sensitivity addresses preferences; it does not capture uncertainty in measured demand. Treat those as separate layers. Revisit footfall estimates, conversion assumptions and operating costs using defensible ranges linked to actual evidence quality. Then examine combinations that can occur together. A road disruption may reduce visits and raise delivery costs simultaneously, so independent one-variable changes could understate risk. Conversely, combining every imaginable worst case without a coherent mechanism can exaggerate it. For repeated field observations, consider resampling at the day or other relevant cluster level rather than individual counts. Report how often rankings change under the stated scenarios, but do not call that a probability of success unless you have justified a probability model.

[00:07:55.341 – 00:08:45.470]
A high score does not guarantee an operating surplus
ON SCREEN
1,200 visits/day × 4% conversion × 26 days
GHS 35 contribution/purchase; GHS 42,000 fixed cost
Monthly contribution GHS 43,680; surplus GHS 1,680
1,200 × 0.04 × 26 × GHS 35 − GHS 42,000 = GHS 1,680
TEACHING COMMENTARY
Now move from relative attractiveness to absolute viability. Assume twelve hundred eligible visits per day, a four percent purchase conversion and twenty-six operating days. That gives twelve hundred and forty-eight monthly purchases. At thirty-five Ghana cedis contribution per purchase, monthly contribution is forty-three thousand six hundred and eighty. Subtract fixed operating costs of forty-two thousand and the surplus is only sixteen hundred and eighty. Contribution means selling price less the costs that vary with the purchase; it is not sales revenue. This small surplus should immediately focus your evidence collection on conversion and visit quality. It also excludes investment recovery, financing and tax, so a positive result alone does not establish an acceptable investment return.

[00:08:45.470 – 00:09:34.685]
Test the assumption that could change the decision
ON SCREEN
Match the service and customer journey
Predefine conversion and cost measures
Set a stop, adapt or proceed rule

TEACHING COMMENTARY
A pilot is most valuable when it resolves a decision-sensitive uncertainty. If the economic case depends on four percent conversion, simply collecting more general footfall data may add little. Test the actual offer, opening hours and customer journey at a comparable location or through a permitted temporary operation. Record the eligible denominator before observing purchases and track repeat customers separately. Specify the duration and decision rule before seeing results, including what would trigger redesign or further evidence. A short successful trial can still overstate normal performance if novelty, discounts or exceptional staffing drove the outcome. Explain how the full operation would differ and adjust the forecast using documented mechanisms, not a convenient unexplained haircut.

[00:09:34.685 – 00:10:22.840]
Can the site survive a plausible combined downside?
ON SCREEN
Eligible visits fall 10%
Conversion falls from 4% to 3.5%
Keep 26 days, GHS 35 contribution and GHS 42,000 fixed cost

TEACHING COMMENTARY
Pause and calculate a combined downside. Daily eligible visits fall by ten percent, and conversion falls to three point five percent. The operating calendar, contribution per purchase and fixed costs stay unchanged. Calculate monthly contribution and the resulting surplus or deficit. Then explain why subtracting ten percent from the original operating surplus would give the wrong answer. The revenue-generating activity changes before fixed costs are deducted, and conversion has changed as well. In your written recommendation, distinguish a ranking conclusion from a viability conclusion. A site can remain the best of the compared locations while all available options fail the business's minimum economic requirement. Deciding to defer is a legitimate output of professional research.

[00:10:22.840 – 00:11:09.439]
Small demand changes can eliminate a thin surplus
ON SCREEN
1,080 × 3.5% × 26 × GHS 35 = GHS 34,398
Operating result = −GHS 7,602
Reconsider terms, service design or timing
1,080 × 0.035 × 26 × 35 − 42,000 = −GHS 7,602
TEACHING COMMENTARY
Ten percent fewer visits gives one thousand and eighty per day. Multiplying by three point five percent conversion, twenty-six days and thirty-five cedis contribution yields thirty-four thousand three hundred and ninety-eight. After fixed costs, the site loses seven thousand six hundred and two cedis monthly in this scenario. Fixed commitments explain why the operating result deteriorates much faster than the visit count. You should now investigate whether rent terms, staffing patterns or the service offer can change without undermining demand. Recalculate the whole mechanism after any proposed adjustment. A lower price may improve conversion while reducing contribution per sale; treating that change as a pure demand benefit would repeat the same forecasting error.

[00:11:09.439 – 00:11:59.243]
Make the recommendation inspectable
ON SCREEN
Eligibility evidence and catchment methods
Scores, weights and switching threshold
Cash scenarios, conditions and accountable owners

TEACHING COMMENTARY
Your decision memo should let a reviewer reconstruct the recommendation without rebuilding the entire study. Show which sites passed the eligibility gates, how the catchments were defined and when observations were collected. Present raw measures alongside scores, identify the agreed weights and display the switching threshold. Then attach the operating model with baseline and coherent downside cases. State a conditional recommendation in operational language: proceed only if a specified uncertainty is resolved, an acceptable lease condition is obtained, or a pilot meets the predefined requirement. Assign an owner and deadline to every condition. Preserve the data version and calculation file so that the next update changes the evidence transparently instead of silently rewriting the original decision.

[00:11:59.243 – 00:12:47.938]
Use the method on your next location decision
ON SCREEN
Audit one denominator and one scoring anchor
Calculate one ranking and one viability threshold
Complete the assessment and applied tasks

TEACHING COMMENTARY
Before using this method in your next assignment, audit one denominator and one scoring anchor. Ask whether your visits represent genuine purchase opportunities and whether a ten-point score improvement means the same thing to every decision maker. Then calculate both a ranking threshold and an economic threshold. The first tells you which option is preferred under stated values; the second tells you whether an option is worth operating at all. Those are complementary decisions. Complete the assessment below, then prepare the two applied tasks using either the hypothetical case or a suitably anonymised project. Evaluate your work by traceability, coherent assumptions and the clarity of the conditions attached to your recommendation, not by how confidently you name a winner.

METHOD REFERENCES
UK Government — Multi-criteria analysis: a manual
https://www.gov.uk/government/publications/multi-criteria-analysis-manual-for-making-government-policy
Option appraisal, explicit criteria, preference weights and sensitivity analysis; all numerical cases are original hypothetical teaching examples.

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