Launching an offer: passing five gates before amplifying.

SUPPORTED DECISIONCommit the budget in steps and pass each gate only if the pre-registered economic, behavioural and operational thresholds are reached.

Evidence status

What was reproduced, and what it does not prove.

The CSV and the Python run verify the margin, the returns correction, the cannibalisation and the payback threshold in the illustrative scenario. They do not demonstrate a net market demand.

  1. Technical specification of the offer launch scenario, specification in French (opens in a new tab)

    v1, 31 August 2026 · CC BY 4.0 · specification in French · Method document written by marketing-mix.net. The figures are illustrative: they are not market measurements.

Key distinctions

Three conditions before calculating.

01

Net demand

Distinguish new buyers, reactivation, purchase brought forward and transfer from the portfolio. Pre-orders concentrated among loyal customers do not prove a wider market.

02

Unit economics

Net price minus variable cost, service, returns, commissions and acquisition. The volume threshold must be computed on the contribution genuinely available to absorb the launch.

03

Capacity before amplification

Availability, quality, support and delivery time can turn a good acquisition signal into a lasting bad experience.

Method

Orchestrating the launch as a series of options

  1. 01Validate the problem

    Observe current behaviour, the cost of the problem and the alternatives; interest in an idea does not replace the will to change.

  2. 02Test price and commitment

    Ask for a costly action or a payment on a controlled perimeter, with explicit conditions and refund.

  3. 03Measure the full economics

    Track contribution, acquisition, returns, service, cannibalisation and retention on cohorts.

  4. 04Amplify in steps

    Increase areas, channels or budget only if the thresholds stay true once the population is widened.

ORIGINAL ASSET

The five gates of the launch

Each gate authorises an additional commitment. A favourable signal does not offset the failure of an indispensable condition.

01

Problem

Observed behaviour

✓ cleared
02

Commitment

Payment or real effort

✓ cleared
03

Economics

Positive net contribution

✓ cleared
04

Deliverability

Quality and capacity

✓ cleared
05

Repeatability

Broader population

to be proven
NET CONTRIBUTION€34.22 / unit
PAYBACK THRESHOLD7,014 sales
DECISIONExtend the pilot
MarkdownDownload the launch dossier↓
ILLUSTRATIVE EXAMPLE · SIMULATED DATA

An encouraging pilot that does not yet justify the national launch

01 · SITUATION

Price €79, variable cost €31, launch investment €240,000. The pilot sells 900 units in six weeks, with 25% cannibalisation and 11% returns.

02 · CALCULATION

Theoretical contribution per unit: €48. After returns, €42.72. The margin displaced by cannibalisation is €8.50 per sale, hence a net contribution of €34.22. The launch payback threshold is 240,000 ÷ 34.22 = 7,014 sales.

03 · DECISION

The annualised pace exceeds the threshold, but the pilot population is 68% existing customers. Extend to two cold areas before committing the national budget.

Acceptance conditions

What must be true to act.

  1. 01

    The problem, the target and the current alternative are observed before the concept.

  2. 02

    Contribution includes returns, service, acquisition and displaced margin.

  3. 03

    Each step uses a wider or more representative population.

  4. 04

    The stopping conditions and the maximum budget at risk are set before the test.

Limits

What this analysis does not prove.

  • Early adopters often overestimate demand in the main market.
  • A small pilot underestimates the costs of complexity and support at scale.
  • Seasonality and novelty can make the first pace unsustainable.

References

Works cited.

  1. Thomke, S. (2020), Experimentation Works.
  2. Kohavi, R., Tang, D. & Xu, Y. (2020), Trustworthy Online Controlled Experiments.
  3. Moorthy, K. S. (1984), “Market Segmentation, Self-Selection, and Product Line Design”, Marketing Science.