Marketing mix and marketing plan: turning the mix into fundable assumptions.
Key distinctions
Three conditions before calculating.
Economic objective
An objective of awareness, traffic or leads remains intermediate. The plan must explain how it influences volume, net price, retention, cost to serve or risk. The expected contribution is computed over the same horizon as the budget, with a low scenario and an opportunity cost.
Falsifiable assumption
"Increase visibility" cannot be invalidated. "An additional coverage of 12 points raises prompted consideration by 3 points and produces at least 1,800 incremental sales" exposes a mechanism, an amplitude and a period that can be tested.
Option to revise
The budget is not irrevocable. An initiative comprises an initial tranche, a reading date, a continuation threshold and a reallocation option. The value of the plan comes as much from its ability to interrupt a bad assumption as from its central scenario.
Method
Turning initiatives into a portfolio of decisions
- 01Start from the economic account
Break the objective down into customers, frequency, net price, variable margin and incremental costs; check that the sum of the expected contributions of the initiatives can explain the gap to be closed without double counting the same sales.
- 02Write the causal chain
Connect lever, exposure, change of perception or behaviour, commercial result and contribution; identify the first observable break in that chain, so as not to wait for the end of the year to notice the failure.
- 03Value the risk
For each initiative compute the expected value: probability of success × contribution if successful + probability of failure × contribution if unsuccessful − cost; add the maximum exposure if the assumptions are correlated.
- 04Orchestrate the reviews
Set before launch the measurement window, the owner, the admissible data and the possible decisions — extend, correct, maintain to learn or stop. A review with no predefined decision becomes a performance comment.
The traceability chain of the plan
Four initiatives are connected to a behaviour, an expected value, a cost, a probability and a verifiable decision rule.
A €620k plan creates €354k of expected value
A company must generate €900k of additional contribution. It examines an offer overhaul, a price test, a video campaign and a CRM programme. The contributions if successful are respectively €520k, €260k, €430k and €180k, with probabilities of 55%, 70%, 45% and 80%. The costs are €180k, €70k, €250k and €120k.
Net expected value: offer 0.55 × 520 − 180 = €106k; price 0.70 × 260 − 70 = €112k; video 0.45 × 430 − 250 = −€56.5k; CRM 0.80 × 180 − 120 = €24k. The initial plan without sequencing is worth €185.5k. By turning the video into a €60k pilot before a €190k extension, the initial loss is capped and the value of the information is estimated at €168.5k. Total value of the sequenced plan: €354k.
The plan does not mechanically promise the €900k. It funds offer, price and CRM first, then makes the video conditional on intermediate evidence. The remaining gap becomes an explicit decision on capacity or ambition, not an assumption hidden in the budget.
Acceptance conditions
What must be true to act.
- 01
Every initiative has an economic objective without double counting.
- 02
The expected value, the maximum exposure and the opportunity cost are published.
- 03
An initial tranche and a stopping rule precede any material commitment.
- 04
The review leads to a named decision, carried by an owner.
Limits
What this analysis does not prove.
- Probabilities of success can be politically inflated without a reference base.
- Interactions between initiatives make the sum of expected values optimistic.
- Brand and capability effects sometimes require a longer horizon than the annual plan.
References
Works cited.
- Little, J. D. C. (1970), “Models and Managers: The Concept of a Decision Calculus”, Management Science, 16(8), B466–B485.
- McDonald, M. & Wilson, H. (2016), Marketing Plans: How to Prepare Them, How to Profit from Them.
- Davenport, T. H. (2009), “How to Design Smart Business Experiments”, Harvard Business Review, 87(2), 68–76.