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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-035Regression and econometrics

How do you model saturation and adstock?

Adstock represents persistence over time; saturation represents declining marginal return. Their parameters can be confounded with trend, seasonality and budget choices.

Direct answer

Compare response forms and produce conditional curves within observed support.

Adstock represents persistence over time; saturation represents declining marginal return. Their parameters can be confounded with trend, seasonality and budget choices.

Model or deliverableAₜ=xₜ+λAₜ₋₁, 0≤λ<1; Rₜ=αAₜᵝ/(γᵝ+Aₜᵝ), α,β,γ>0

Vakratsas & Ambler, 1999Jin et al., Bayesian methods for media mix modeling

ESTIMAND

Estimation profile

Unit of analysis
Observation, entity or series × periodControlled scientific terminologychannel × period
Exact estimand
Carryover and response-shape parameters
Model or deliverable
Aₜ=xₜ+λAₜ₋₁, 0≤λ<1; Rₜ=αAₜᵝ/(γᵝ+Aₜᵝ), α,β,γ>0

01—08

Verifiable analysis framework

  1. 01
    Question

    How do you model saturation and adstock?

  2. 02
    Estimand

    Carryover and response-shape parameters · channel × period

  3. 03
    Data

    date · channel_spend · outcome · seasonality · controls · adstock_initialization

  4. 04
    Model or deliverable

    Aₜ=xₜ+λAₜ₋₁, 0≤λ<1; Rₜ=αAₜᵝ/(γᵝ+Aₜᵝ), α,β,γ>0

  5. 05
    Declared calculation

    Aₜ=xₜ+λAₜ₋₁, 0≤λ<1; Rₜ=αAₜᵝ/(γᵝ+Aₜᵝ), α,β,γ>0 → Carryover and response-shape parameters

  6. 06
    Uncertainty checks

    Parameter identifiability · Declare A₀ and transform order · Lag support · Shape sensitivity · Holdout or experiment calibration

  7. 07
    Method-specific validation

    Check data, estimate stability and interpretation limits. Parameter identifiability · Declare A₀ and transform order · Lag support · Shape sensitivity · Holdout or experiment calibration

  8. 08
    Limitations

    Infer a reliable causal reallocation from an unidentified time-series fit.

Required variables

  • date
  • channel_spend
  • outcome
  • seasonality
  • controls
  • adstock_initialization

Checks · Controlled scientific terminology

  • Parameter identifiability
  • Declare A₀ and transform order
  • Lag support
  • Shape sensitivity
  • Holdout or experiment calibration
Possible decision

Compare response forms and produce conditional curves within observed support.

Not supported

Infer a reliable causal reallocation from an unidentified time-series fit.

Scientific sources

2 sources

  1. Vakratsas & Ambler, 1999
  2. Jin et al., Bayesian methods for media mix modeling

Dataset · Tool

Dataset · MSC-006Non-identified media series→

Method connections

Parent territoryMarketing response models: shape, delay and saturationRequiresHow do you validate a marketing forecast?

Read next

MSC-H-002Marketing response models: shape, delay and saturation→MSC-P-018Predictive or causal regression: what are you trying to estimate?→MSC-P-014How do you design a marketing geo experiment?→MSC-P-033How do you validate a marketing forecast?→