Research & Evidence

Search for a method

Search titles, questions, territories and MSC identifiers.

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

Research & Evidence · Calculation

Reproducible tools

Each calculator exposes its inputs, result and the boundary that prevents a simulation from becoming a recommendation.

MSC-T01 — MSC-T08

Five calculations, five interpretation boundaries.

Each tool exposes the calculation and states what it does not prove.

MSC-T01

A/B sample size

3,841 per arm

Two-sided normal approximation, 1:1 allocation, 80% power, α = 5%, without attrition or design effect.

MSC-T02

Difference-in-differences

Difference-in-differences: +7.0 index points

Descriptive 2×2 contrast; causal only when the design supports parallel trends.

MSC-T04

Constant elasticity

-6.6%

Conditional scenario, not a price recommendation.

MSC-T05

Price-volume-margin

P₀ 100 € · VC 55 € · Q₀ 10 000
Contribution change: +3.8%

Conditional scenario, not a price recommendation.

MSC-P-024 →
MSC-T08

Monte Carlo

Probability of positive contribution: ≈ 99.9% (MC SE0.1 pp · Wilson 95% CI99.6–100.0%)

2,000 fixed-seed draws; only elasticity follows an unbounded normal, without MSC-010 dependencies.

MSC-P-034 →