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Research & Evidence

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41 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-043What is a subscriber worth with only six months of retention data?Customer Science↗
  36. MSC-P-031How do you build a useful customer segmentation?Customer Science↗
  37. MSC-P-032How do you test segmentation stability?Customer Science↗
  38. MSC-P-033How do you validate a marketing forecast?Decision Science↗
  39. MSC-P-034How do you build a Monte Carlo simulation for a marketing decision?Decision Science↗
  40. MSC-P-035How do you model saturation and adstock?Marketing Models↗
  41. MSC-P-039Which statistical test should you choose?Decision Science↗
← All methods
MSC-H-004Customer scienceScientific territory

Customer and choice science: behavior, value and heterogeneity

This territory models choice, retention, future value and segments without confusing prediction, causality and description. Observation time and heterogeneity are explicit.

Scientific editorial team: Marketing Science Center

Direct answer

Estimate a probability, expected value or segmentation with out-of-sample validation.

This territory models choice, retention, future value and segments without confusing prediction, causality and description. Observation time and heterogeneity are explicit.

Model or deliverableQuestion → design → estimand → uncertainty → decision boundary

Fader, Hardie & Lee, 2005McFadden, 1974

ESTIMAND

Estimation profile

Unit of analysis
Decision × population × horizon
Exact estimand
A defensible chain from question to evidence
Model or deliverable
Question → design → estimand → uncertainty → decision boundary

01—07

Territory pathway

01 · MSC-P-026Logit vs Probit: how do you choose for purchase probability?→02 · MSC-P-028How do you estimate CLV with BG/NBD and Gamma-Gamma?→03 · MSC-P-029Which customers have the highest probability of churn?→04 · MSC-P-030How do you analyze retention with a survival model?→05 · MSC-P-031How do you build a useful customer segmentation?→06 · MSC-P-032How do you test segmentation stability?→07 · MSC-P-043What is a subscriber worth with only six months of retention data?→

01—08

Verifiable analysis framework

  1. 01
    Question

    Customer and choice science: behavior, value and heterogeneity

  2. 02
    Estimand

    A defensible chain from question to evidence · Decision × population × horizon

  3. 03
    Data

    population · unit_of_analysis · decision_horizon · outcome · assumptions

  4. 04
    Model or deliverable

    Question → design → estimand → uncertainty → decision boundary

  5. 05
    Declared calculation

    Question → design → estimand → uncertainty → decision boundary → A defensible chain from question to evidence

  6. 06
    Uncertainty checks

    Unique decision intent · Explicit identification assumptions · Reproducible evidence trail

  7. 07
    Method-specific validation

    Check data, estimate stability and interpretation limits. Unique decision intent · Explicit identification assumptions · Reproducible evidence trail

  8. 08
    Limitations

    Present a predictive score as proof that an action will change behavior.

Required variables

  • population
  • unit_of_analysis
  • decision_horizon
  • outcome
  • assumptions

Checks

  • Unique decision intent
  • Explicit identification assumptions
  • Reproducible evidence trail
Possible decision

Estimate a probability, expected value or segmentation with out-of-sample validation.

Unsupported decision

Present a predictive score as proof that an action will change behavior.

Scientific sources

2 sources

  1. Fader, Hardie & Lee, 2005
  2. McFadden, 1974

Read next

MSC-P-026Logit vs Probit: how do you choose for purchase probability?→MSC-P-028How do you estimate CLV with BG/NBD and Gamma-Gamma?→MSC-P-029Which customers have the highest probability of churn?→MSC-P-030How do you analyze retention with a survival model?→MSC-P-031How do you build a useful customer segmentation?→MSC-P-032How do you test segmentation stability?→