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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-023Pricing scienceMethod note · validation in progress

How do you estimate a demand function?

A demand function connects quantity, price and context over an observed domain. Shape, heterogeneity, competition and endogeneity determine what can be simulated.

Direct answer

Compare scenarios near observed data with intervals and diagnostics.

A demand function connects quantity, price and context over an observed domain. Shape, heterogeneity, competition and endogeneity determine what can be simulated.

Model or deliverableAssociational specification: ln(Q) = α + βln(P) + γX + ε; do not relabel β as causal without identification

Tellis, 1988Bijmolt, Van Heerde & Pieters, 2005

ESTIMAND

Estimation profile

Unit of analysis
Market × product × period or scenarioMarket × product × period
Exact estimand
Associational target by default: E[ln(Q)|ln(P),X]. Structural target only under a separately justified identification design: counterfactual demand Q(p)
Model or deliverable
Associational specification: ln(Q) = α + βln(P) + γX + ε; do not relabel β as causal without identification

01—08

Verifiable analysis framework

  1. 01
    Question

    How do you estimate a demand function?

  2. 02
    Estimand

    Associational target by default: E[ln(Q)|ln(P),X]. Structural target only under a separately justified identification design: counterfactual demand Q(p) · Market × product × period

  3. 03
    Data

    quantity · price · promotion · competitor_price · seasonality · identification_status

  4. 04
    Model or deliverable

    Associational specification: ln(Q) = α + βln(P) + γX + ε; do not relabel β as causal without identification

  5. 05
    Declared calculation

    Associational specification: ln(Q) = α + βln(P) + γX + ε; do not relabel β as causal without identification → Associational target by default: E[ln(Q)|ln(P),X]. Structural target only under a separately justified identification design: counterfactual demand Q(p)

  6. 06
    Uncertainty checks

    Declare associational or structural target · Functional form · Price endogeneity · Residual structure · Observed support for scenarios

  7. 07
    Method-specific validation

    Check data, estimate stability and interpretation limits. Declare associational or structural target · Functional form · Price endogeneity · Residual structure · Observed support for scenarios

  8. 08
    Limitations

    Extrapolate stable demand through an unobserved structural change.

Required variables

  • quantity
  • price
  • promotion
  • competitor_price
  • seasonality
  • identification_status

Checks

  • Declare associational or structural target
  • Functional form
  • Price endogeneity
  • Residual structure
  • Observed support for scenarios
Possible decision

Compare scenarios near observed data with intervals and diagnostics.

Not supported

Extrapolate stable demand through an unobserved structural change.

Scientific sources

3 sources

  1. Tellis, 1988
  2. Bijmolt, Van Heerde & Pieters, 2005
  3. Villas-Boas & Winer, 1999

Method connections

Parent territoryPricing science: connecting price, demand and contributionRequiresHow do you address price endogeneity?EstimatesHow do you estimate price elasticity and its uncertainty?

Read next

MSC-H-003Pricing science: connecting price, demand and contribution→MSC-P-020How do you address price endogeneity?→MSC-P-022How do you estimate price elasticity and its uncertainty?→MSC-P-024How do you simulate a price-volume-margin scenario?→