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Research & Evidence

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40 results
  1. MSC-P-001How do you turn a marketing claim into a testable question?Decision Science↗
  2. MSC-P-002Correlation or causality: what can an analysis actually support?Marketing Measurement↗
  3. MSC-P-003How should uncertainty in a marketing result be expressed?Decision Science↗
  4. MSC-P-004Statistical significance or effect size: which result should be interpreted?Decision Science↗
  5. MSC-P-005How do you measure a marketing construct that is not directly observable?Market Research↗
  6. MSC-P-006How do you design and validate a measurement scale?Market Research↗
  7. MSC-P-007Alpha or omega: how should scale reliability be assessed?Market Research↗
  8. MSC-P-009PCA, EFA or CFA: which method should you choose?Market Research↗
  9. MSC-P-010When should you run a marketing experiment?Marketing Measurement↗
  10. MSC-P-011How do you design an A/B test that actually estimates an effect?Marketing Measurement↗
  11. MSC-P-012How many observations does an experiment need?Decision Science↗
  12. MSC-P-013How do you measure campaign incrementality with a control group?Marketing Measurement↗
  13. MSC-P-017How do you detect selection, contamination and attrition in an experiment?Marketing Measurement↗
  14. MSC-P-018Predictive or causal regression: what are you trying to estimate?Marketing Models↗
  15. MSC-P-019How do you diagnose a marketing regression before interpreting it?Marketing Models↗
  16. MSC-P-022How do you estimate price elasticity and its uncertainty?Pricing Science↗
  17. MSC-P-026Logit vs Probit: how do you choose for purchase probability?Customer Science↗
  18. MSC-P-029Which customers have the highest probability of churn?Customer Science↗
  19. MSC-P-027TAM, UTAUT or UTAUT2: which framework should be used to study technology acceptance?Market Research↗
  20. MSC-H-001Measurement and causality: how can a marketing effect be established?Marketing Measurement↗
  21. MSC-H-002Marketing response models: shape, delay and saturationMarketing Models↗
  22. MSC-H-003Pricing science: connecting price, demand and contributionPricing Science↗
  23. MSC-H-004Customer and choice science: behavior, value and heterogeneityCustomer Science↗
  24. MSC-H-005Measurement science: building valid indicatorsMarket Research↗
  25. MSC-H-006Statistical decision methods: choose, quantify, validateDecision Science↗
  26. MSC-P-008How do you validate a marketing measurement scale?Market Research↗
  27. MSC-P-014How do you design a marketing geo experiment?Marketing Measurement↗
  28. MSC-P-015How do you estimate an effect with difference-in-differences?Marketing Measurement↗
  29. MSC-P-020How do you address price endogeneity?Pricing Science↗
  30. MSC-P-021Fixed or random effects: which panel model should you choose?Marketing Models↗
  31. MSC-P-023How do you estimate a demand function?Pricing Science↗
  32. MSC-P-024How do you simulate a price-volume-margin scenario?Pricing Science↗
  33. MSC-P-028How do you estimate CLV with BG/NBD and Gamma-Gamma?Customer Science↗
  34. MSC-P-030How do you analyze retention with a survival model?Customer Science↗
  35. MSC-P-031How do you build a useful customer segmentation?Customer Science↗
  36. MSC-P-032How do you test segmentation stability?Customer Science↗
  37. MSC-P-033How do you validate a marketing forecast?Decision Science↗
  38. MSC-P-034How do you build a Monte Carlo simulation for a marketing decision?Decision Science↗
  39. MSC-P-035How do you model saturation and adstock?Marketing Models↗
  40. MSC-P-039Which statistical test should you choose?Decision Science↗
← All methods
METHOD DOSSIERMSC-P-034Evidence foundations

How do you build a Monte Carlo simulation for a marketing decision?

Monte Carlo propagates input distributions through a decision model. Quality depends less on draw count than on relevant distributions, dependencies and scenarios.

Direct answer

Estimate an outcome distribution and probability of crossing a threshold.

Monte Carlo propagates input distributions through a decision model. Quality depends less on draw count than on relevant distributions, dependencies and scenarios.

Model or deliverableY(s) = f(X₁(s),…,Xk(s)); s=1…S

McKay, Beckman & Conover, 1979Saltelli et al., 2010

ESTIMAND

Estimation profile

Unit of analysis
Declared unit or comparison × horizonControlled scientific terminologyscenario × simulation draw
Exact estimand
Distribution of a decision outcome
Model or deliverable
Y(s) = f(X₁(s),…,Xk(s)); s=1…S

01—08

Verifiable analysis framework

  1. 01
    Question

    How do you build a Monte Carlo simulation for a marketing decision?

  2. 02
    Estimand

    Distribution of a decision outcome · scenario × simulation draw

  3. 03
    Data

    input_distribution · dependency · scenario · decision_threshold · random_seed

  4. 04
    Model or deliverable

    Y(s) = f(X₁(s),…,Xk(s)); s=1…S

  5. 05
    Declared calculation

    Y(s) = f(X₁(s),…,Xk(s)); s=1…S → Distribution of a decision outcome

  6. 06
    Uncertainty checks

    Distribution elicitation · Dependencies · Monte Carlo error · Convergence · Sensitivity · Reproducible seed

  7. 07
    Method-specific validation

    Check data, estimate stability and interpretation limits. Distribution elicitation · Dependencies · Monte Carlo error · Convergence · Sensitivity · Reproducible seed

  8. 08
    Limitations

    Compensate for unrealistic assumptions with more simulations.

Required variables

  • input_distribution
  • dependency
  • scenario
  • decision_threshold
  • random_seed

Checks · Controlled scientific terminology

  • Distribution elicitation
  • Dependencies
  • Monte Carlo error
  • Convergence
  • Sensitivity
  • Reproducible seed
Possible decision

Estimate an outcome distribution and probability of crossing a threshold.

Not supported

Compensate for unrealistic assumptions with more simulations.

Scientific sources

2 sources

  1. McKay, Beckman & Conover, 1979
  2. Saltelli et al., 2010

Dataset · Tool

Dataset · MSC-010Monte Carlo assumptions→Tool · MSC-T08Threshold risk→

Method connections

Parent territoryStatistical decision methods: choose, quantify, validateRequiresHow should uncertainty in a marketing result be expressed?Compare withHow do you simulate a price-volume-margin scenario?

Read next

MSC-H-006Statistical decision methods: choose, quantify, validate→MSC-P-003How should uncertainty in a marketing result be expressed?→MSC-P-024How do you simulate a price-volume-margin scenario?→MSC-P-033How do you validate a marketing forecast?→