Scenario contribution anatomyBridge height is currency; the final diamond is risk-adjusted contribution
Marketing & Advertising
Shopping Campaign Scenario Calculator
Compare three executable shopping strategies rather than a generic low, base, and high case. Feed repair spends on catalog quality to improve response, auction scale commits more cash to traffic under greater cost exposure, and margin curation prioritizes higher-contribution products. The calculator keeps inventory pressure and execution discounts visible before choosing a preferred path.
Input evidence: freeze one sellable catalog, auction window, approval definition, and contribution convention before comparing paths. Available cash must include the operating work assigned to each strategy; inventory means sellable units after reservations.
Preferred operating path—
Highest risk-adjusted contribution—
Highest approved orders—
Strongest contribution ROAS—
Inventory pressure—
Cash left uncommitted—
Three-strategy contribution bridges
Compare feed repair, auction scale, and margin curation as different shopping decisions
Each bridge begins with its own media and operating cost, then adds order value and subtracts fulfillment before execution risk is applied.
| Strategy | Media + operating cost | Effective CPC | Approved orders | Revenue | Net contribution | Execution risk | Risk-adjusted contribution |
|---|
How to compare shopping strategies
Choose coherent operating paths before ranking outcomes
- Enter the cash ceiling, base auction cost, and backend-approved funnel rates.
- Reconcile order value, margin, fulfillment, and available inventory.
- Review what each strategy changes: feed repair changes response and operating cost; auction scale changes media exposure and price pressure; margin curation changes assortment economics.
- Compare approved orders and contribution before applying the execution-risk discount.
- Use the bridge and table to identify whether cost, demand, margin, inventory, or execution is driving the preference.
- Run a separately governed scenario if the real plan combines assumptions from multiple paths.
Shopping scenario fundamentals
Strategy changes several linked levers together
Feed repair
Catalog work intended to improve eligibility and conversion.
Auction scale
Higher media allocation with greater exposure to price pressure.
Margin curation
Prioritization of products with stronger contribution economics.
Approval
Backend fraction of modeled orders that retain value.
Inventory ceiling
Maximum approved orders sellable in the period.
Execution discount
Planning haircut for strategy-specific delivery risk.
Result interpretation
Choose an executable path, not the largest isolated output
Preferred path
The strategy with the strongest risk-adjusted contribution under all entered constraints.
Adjusted contribution
Net contribution after the explicit execution haircut; it is not a confidence interval.
Approved orders
Backend-valid demand after the inventory ceiling is applied.
Contribution ROAS
Contribution returned per media and operating dollar, not revenue ROAS.
Inventory pressure
Shows whether modeled approved demand is being truncated by sellable stock.
Cash uncommitted
Available funds not used by the preferred strategy; it may be reserve, not waste.
Calculation method
Recalculate the entire commercial path for every strategy
Each scenario allocates cash to media and operating work, modifies CPC, conversion, or margin coherently, caps approved demand at inventory, and calculates contribution before and after an explicit risk discount.
Scenario integrity
Do not create a fictional best-of-everything hybrid
The feed-first path pays more operating cost to improve response. Auction scale buys more traffic but carries more price and execution exposure. Margin curation limits assortment while improving value per order.
Constraint reading
The preferred path can change when inventory binds
Once approved demand exceeds sellable stock, additional clicks cannot create additional modeled orders. A high-demand strategy can then lose to a lower-volume path with stronger contribution.
Risk interpretation
Risk-adjusted contribution is a decision haircut, not probability
The discount supports comparison under entered assumptions. It is not a confidence interval or an expected value unless the risk rate is backed by a probability model.
How to read the visualization
Compare bridge composition before accepting the winning diamond
- Meaning and axes
- Each horizontal strategy bridge adds commercial value and subtracts media, operations, fulfillment, and risk; bridge height is currency.
- Inputs that move it
- Cash and CPC change traffic, conversion and approval change demand, inventory caps orders, and margin or fulfillment change value per approved order.
- Decision pattern
- The final diamond ranks paths, but the colored steps explain whether the advantage comes from demand, unit economics, cost, or a smaller risk haircut.
- Misleading boundary
- The paths are only comparable when their multiplier sets are operationally coherent and evidenced. Combining the best assumption from every path creates a scenario that cannot be executed.
Detailed calculation process
Carry each strategy from cash allocation to risk-adjusted contribution
1. Strategy media and operating costMₛ = min(C × pₛ, C − Hₛ)Tₛ = Mₛ + Hₛ
2. Clicks and approved demandKₛ = Mₛ ÷ (CPC × qₛ)Dₛ = Kₛ × c × uₛ × a
3. Inventory-constrained ordersOₛ = min(Dₛ, I)
4. Contribution and risk adjustmentPₛ = Oₛ × [A × m × vₛ − f] − TₛP′ₛ = Pₛ × (1 − rₛ)
In plain language: fund each executable path, translate media into approved demand, stop demand at available inventory, value the fulfilled orders, subtract the full path cost, and only then apply the documented execution haircut.
Rates are divided by 100; cash and cost values use currency; CPC is currency/click; orders, clicks, and inventory are counts.
- C
- available campaign cash; currency
- pₛ
- strategy media-allocation share; decimal
- Hₛ
- strategy operating cost; currency
- qₛ
- CPC multiplier; dimensionless
- uₛ
- conversion multiplier; dimensionless
- vₛ
- margin multiplier; dimensionless
- c
- base conversion rate; decimal
- a
- approval rate; decimal
- I
- sellable inventory; orders
- rₛ
- execution-risk discount; decimal
- P′ₛ
- risk-adjusted contribution; currency
Default substitution
Base conversion c = 3.2% ÷ 100 = 0.032; approval a = 92% ÷ 100 = 0.92; margin m = 56% ÷ 100 = 0.56.
For feed repair, M = min($75,000 × 0.58, $75,000 − $12,500) = $43,500. Effective CPC = $1.18 × 0.96 = $1.13, and conversion = 0.032 × 1.22.
Clicks, approved demand, inventory-capped orders, gross order contribution, fulfillment, and total strategy cost are calculated in that order. The 8% feed-repair execution discount is applied only after net contribution.
Reconciliation: for every row, risk-adjusted contribution equals displayed net contribution × (1 − displayed risk). Media plus operating cost equals total cost and cannot exceed available cash.
Evidence discipline
Attach each scenario multiplier to an operational hypothesis
- Use feed-test evidence for conversion response.
- Use auction experiments for CPC response at scale.
- Use SKU-level contribution for margin curation.
- Use sellable stock after reservations and known replenishment.
Model limitations
Three deterministic paths do not describe the full outcome distribution
The model excludes stochastic auctions, SKU-level conversion and margin, replenishment, cross-channel effects, organic cannibalization, causal incrementality, returns beyond the entered fulfillment economics, customer lifetime value, and correlations among risks.
Key terminology
Shopping scenario glossary
- Coherent scenario
- A linked set of assumptions representing one executable strategy.
- Feed response
- Conversion change attributed to catalog improvements.
- Auction scale
- Additional paid delivery purchased under a different cost profile.
- Margin curation
- Assortment choice based on contribution quality.
- Inventory pressure
- Demand exceeding sellable units.
- Execution discount
- Entered reduction applied to net contribution.
- Preference frontier
- Boundary where one strategy overtakes another.
Practical decision cases
Strategy preference changes with the binding constraint
Catalog eligibility bottleneck
Feed repair wins because additional eligible products improve approved demand without exhausting stock. The operating expense is accepted only with a measurable diagnostic and conversion hypothesis.
Abundant stock, short selling window
Auction scale produces the most approved orders and the calendar can absorb them. The decision still requires a CPC response test because the scale multiplier is the largest source of downside.
Inventory-constrained promotion
More traffic cannot exceed sellable units, so margin curation creates the strongest contribution from fewer orders. The team chooses assortment quality over headline volume.
Important note
Before relying on this result
The scenarios exclude stochastic auctions, replenishment, SKU-level response, returns, cross-channel effects, organic cannibalization, customer lifetime value, and correlations among execution risks.
Additional Shopping Campaign Scenario Calculator questions
Why do several assumptions change in each scenario?
A real operating strategy changes linked costs, response, assortment, and risk together; one-at-a-time sensitivity is a different analysis.
What happens when modeled demand exceeds inventory?
Approved orders are capped at sellable inventory, so additional clicks add cost without impossible revenue.
Is the execution discount a probability?
No. It is a deterministic planning haircut unless supported by a separate probability model.
Can assumptions be mixed across the three paths?
Create and govern a new coherent scenario; choosing only the best assumption from each path creates an infeasible hybrid.