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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-014Experimentation and causalityVerified scientific dossier

How do you design a marketing geo experiment?

A geo experiment compares test and control regions before and during an intervention. The design must limit contamination, pre-period imbalance and temporal dependence.

Scientific editorial team : Marketing Science Center

Direct answer

Estimate an aggregate incremental effect in the studied regions and period.

A geo experiment compares test and control regions before and during an intervention. The design must limit contamination, pre-period imbalance and temporal dependence.

Hernán & Robins, Causal Inference: What IfVaver & Koehler, 2011

01

Operational summary

Scientific question and scope

How do you design a marketing geo experiment?

Supported

Estimate an aggregate incremental effect in the studied regions and period.

Forbidden

Attribute the effect to each individual or extrapolate unreservedly to other markets.

02

Three reading levels

  1. 01

    Decision-maker — Connect the result to a declared decision, useful threshold and error cost.

  2. 02

    Practitioner — Fix population, unit, horizon, available variables and analysis rule before calculation.

  3. 03

    Analyst — Reproduce the calculation, quantify uncertainty and document diagnostics, failures and sensitivities.

03

Concrete marketing situation

A geo experiment compares test and control regions before and during an intervention. The design must limit contamination, pre-period imbalance and temporal dependence.

04

Scientific question and scope

How do you design a marketing geo experiment?

Incremental outcome in treated geographies

Required data

geo × period

Population, unit of analysis, origin date and horizon must be declared in the deliverable. Without them, the estimand silently changes.

05

Why a simple analysis can fail

  • Attribute the effect to each individual or extrapolate unreservedly to other markets.
  • Pre-period fit
  • Spillovers
  • Power at geo level

06

Method intuition

The method does not automatically turn an association into evidence. It links a declared question to an estimand, specification, compatible data and a bounded interpretation rule.

Incremental outcome in treated geographies

07

Required data

Scientific symbol dictionary

Y_test_post
Observed aggregate outcome in test geographies during the declared intervention period. Unit: declared aggregate outcome · Type: number · Role: input
Y0_test_post
Externally supplied counterfactual aggregate outcome for those test geographies under no treatment; not fitted by this branch. Unit: same aggregate outcome · Type: number · Role: input
z
Two-sided normal critical value for the declared confidence level. Unit: dimensionless · Type: positive number · Role: parameter
supplied_SE
Externally supplied standard error for the conditional contrast; not estimated by this branch. Unit: same aggregate outcome · Type: positive number · Role: input
conditional_increment
Observed aggregate outcome minus the supplied counterfactual, conditional on both supplied inputs. Unit: aggregate outcome · Type: number · Role: output
CI
Normal interval around the conditional contrast using the supplied standard error. Unit: aggregate outcome · Type: ordered pair · Role: output

Exact sealed engine inputs

treated_geo_post_outcome
Value: 120 · Unit: index · Type: number · Data status: synthetic
counterfactual_post_outcome
Value: 110 · Unit: index · Type: number · Data status: synthetic
standard_error
Value: 4 · Unit: index · Type: number · Data status: synthetic
z_critical
Value: 1.95996398454 · Unit: dimensionless · Type: number · Data status: parameter

08

Formal model

Formal model

conditional_incremental_outcome = treated_geo_post_outcome − counterfactual_post_outcome; CI = conditional_incremental_outcome ± z_critical × standard_error

Evidence claims: MSC-P014-C01 · MSC-P014-C02 · MSC-P014-C03 · MSC-P014-C04 · MSC-P014-C05 · MSC-P014-C06

Scientific question and scope

Incremental outcome in treated geographies

09

Declared calculation

  1. 01

    Validate the treated aggregate, supplied counterfactual, supplied positive standard error and fixed intervention period.

  2. 02

    Subtract the supplied no-treatment counterfactual from the treated post-period outcome and form the normal interval.

  3. 03

    Report a conditional illustration only; a randomized geo design, counterfactual fit and geo-level uncertainty remain outside this branch.

10

Numerical example or application case

Synthetic illustration — teaching values, not observed — synthetic data or declared parameters; no real observations

Sealed inputs

  • treated_geo_post_outcome=120 [index]
  • counterfactual_post_outcome=110 [index]
  • standard_error=4 [index]
  • z_critical=1.95996398454 [dimensionless]

Reproducible results

  • conditional_increment=10
  • CI95=[2.160144; 17.839856]

Verified dossier: claims are linked to passage-level sources, the Python/R calculation is reproduced, limits are explicit, and independent scientific review is complete.

11

Validity assumptions

  • Were non-overlapping geographies assigned before intervention and analyzed by assignment?
  • Is the counterfactual construction independent of post-treatment model selection?
  • Does the uncertainty method respect the small number of geographies and temporal dependence?

12

Diagnostics and uncertainty

Diagnostics and uncertainty

Pre-period fit · Spillovers · Power at geo level · Distinguish randomized geo design from BSTS control · Placebo periods

Bounded diagnostic

Conditional illustration with supplied counterfactual and SE; not a fitted geo experiment.

Explicit uncertainty contract · quantified_conditional

Method : Deterministic reference-engine calculation on the sealed input slice.

Target : Uncertainty of the supplied-counterfactual incremental outcome.

Engine evidence : metric.ci_lower= · metric.ci_upper=

Interpretation : The interval uses a supplied standard error and counterfactual; it does not validate a fitted geo experiment.

Stop when

Attribute the effect to each individual or extrapolate unreservedly to other markets.

13

Result interpretation

  • Estimate an aggregate incremental effect in the studied regions and period.
  • The result is conditional on the declared population, horizon, specification and diagnostics. It must not be extended to another decision without new justification.

14

Supported and forbidden conclusions

Supported

Estimate an aggregate incremental effect in the studied regions and period.

Forbidden

Attribute the effect to each individual or extrapolate unreservedly to other markets.

15

Possible marketing decision

  1. 01

    Estimate an aggregate incremental effect in the studied regions and period.

  2. 02

    The result is conditional on the declared population, horizon, specification and diagnostics. It must not be extended to another decision without new justification.

16

When to use — when to stop

Use when

Estimate an aggregate incremental effect in the studied regions and period.

geo × period

Stop when

Attribute the effect to each individual or extrapolate unreservedly to other markets.

Methodological alternatives

  • Run a randomized paired-geo experiment with randomization-based inference.
  • Use a prespecified synthetic-control or BSTS design only when randomization is unavailable and label it observational.
  • Use placebo periods and leave-one-geo-out sensitivity to probe counterfactual stability.

17

Reproducibility contract

Python and R are the executable references. SPSS and SAS remain secondary syntaxes until checked on the same data, specification and diagnostics.

The engine selects only this page’s explicit slice and branch. Its hash, outputs, and diagnostics remain sealed in the atomic dossier; no generic fallback branch is allowed.

Slice fingerprint: 975b12b0153042485fcfcd6db1e894c2386ab04c6491184f6365423348c2fb46

Formal model

conditional_incremental_outcome = treated_geo_post_outcome − counterfactual_post_outcome; CI = conditional_incremental_outcome ± z_critical × standard_error

Verified dossier: claims are linked to passage-level sources, the Python/R calculation is reproduced, limits are explicit, and independent scientific review is complete.

18

Expected final deliverable

  1. 01

    How do you design a marketing geo experiment? — geo × period

  2. 02

    Y_test_post · Y0_test_post · z · supplied_SE · conditional_increment · CI

  3. 03

    conditional_incremental_outcome = treated_geo_post_outcome − counterfactual_post_outcome; CI = conditional_incremental_outcome ± z_critical × standard_error

  4. 04

    Pre-period fit · Spillovers · Power at geo level · Distinguish randomized geo design from BSTS control · Placebo periods

  5. 05

    Estimate an aggregate incremental effect in the studied regions and period. / Attribute the effect to each individual or extrapolate unreservedly to other markets.

19

Scientific sources and evidence status

Verified dossier: claims are linked to passage-level sources, the Python/R calculation is reproduced, limits are explicit, and independent scientific review is complete.

  1. MSC-P014-C01 — The causal target contrasts mean potential outcomes under two defined interventions.
  2. MSC-P014-C02 — Consistency links the observed outcome to the potential outcome under the assigned policy.
  3. MSC-P014-C03 — The causal estimand assumes one customer's assignment does not alter another customer's outcome.
  4. MSC-P014-C04 — Geo experiments randomize non-overlapping regions to treatment conditions.
  5. MSC-P014-C05 — Geo-targeting operationalizes the assigned regional intervention.
  6. MSC-P014-C06 — The design must follow an explicit process.
  1. Hernán & Robins, Causal Inference: What If
  2. Vaver & Koehler, 2011

Method connections

Parent territoryMeasurement and causality: how can a marketing effect be established?Compare withWhen should you run a marketing experiment?Compare withHow do you estimate an effect with difference-in-differences?

Read next

MSC-H-001Measurement and causality: how can a marketing effect be established?→MSC-P-010When should you run a marketing experiment?→MSC-P-015How do you estimate an effect with difference-in-differences?→MSC-P-013How do you measure campaign incrementality with a control group?→