Less “trust me.”
More “try it.”
Change the inputs. Follow the arithmetic. These are illustrative models, not forecasts or personal campaign results.
Cheap lead.
Expensive problem.
What happens when a low-cost enquiry rarely becomes a suitable customer?
All money uses AED. Customer conversion means the share of qualified leads that become customers. Costs include media spend only.
Formulas & limitations
CPL = spend ÷ leads. Qualified leads = leads × qualification rate ÷ 100. Modelled customers = qualified leads × customer conversion rate ÷ 100. Media cost per customer = spend ÷ modelled customers.
Zero denominators produce “Not defined”, never zero cost. Fractional outputs represent an arithmetic expectation, not part of a real person. Rates are assumed constant; no sales-cycle lag, margins, capacity, retention, non-media costs or uncertainty are modelled. This is not fully loaded CAC.
The friction
experiment.
A small change early in the journey can affect every later stage. It can also change who gets through.
Compare your scenario with a fixed illustrative baseline: 10,000 visits × 4% enquiry rate × 30% qualification × 20% close rate = 24 customers.
No change from the illustrative baseline of 24 customers.
Formulas & limitations
Enquiries = visits × enquiry rate ÷ 100. Qualified = enquiries × qualification rate ÷ 100. Customers = qualified × close rate ÷ 100. Difference = modelled customers − 24.
Bars use each stage's share of visits. All rates are conditional on the preceding stage. The model assumes a single sequential funnel and stable rates; real visitors can return, skip steps and change intent. The shorter-form preset increases enquiry rate to 6% but reduces qualification to 15%: it illustrates a trade-off, not a predicted effect of shorter forms. The physics metaphor describes resistance, not a scientific law about customers.