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Research & Evidence

Search for a method

Search titles, questions, territories and MSC identifiers.

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↗

Research & Evidence · Learn by doing.

Tools to analyze,
understand and make marketing decisions

For marketing leaders and enterprise data and insights teams.

A public laboratory for the methods, data and evidence that inform marketing decisions.

Explore the methods ↘View the datasets →
01 Explicit assumptions02 Downloadable data03 Visible limitations
Three journeys 01 / 04

Choose your point of entry.

The laboratory separates learning a method, reproducing it and using its results in a decision.

01

Understand

Start from a question, compare methods and identify their assumptions.

Explore the territories →02

Reproduce

Inspect the data, rerun the calculation and compare the reference result.

Access the datasets →03

Decide

Separate what the result supports from what it cannot establish.

View verified dossiers →
Our evidence contract

From question to decision

A method has value only when it can be understood, reproduced and challenged.

The laboratory uses one consistent protocol for its reference dossiers. It shows what the data can establish — and what they cannot support.

View the featured dossiers ↓
  1. 01Question
  2. 02Assumptions
  3. 03Data
  4. 04Model
  5. 05Calculation
  6. 06Uncertainty
  7. 07Limitations
  8. 08Decision
Scientific territories 02 / 04

The scientific and methodological hub

Six disciplines.
One standard.

Six fields for measuring effects, making models explicit and defining what the data can support in a decision.

01Δ

Marketing Measurement

Measure marketing effects while separating attribution, incrementality and causality.

  • MMM
  • Incrementality
  • Experimentation
02ε

Pricing Science

Connect price, demand and perceived value with explicit assumptions and uncertainty.

  • Elasticity
  • Willingness to pay
  • Conjoint
03C

Customer Science

Analyze customer value, retention and behavior over time without hiding data limitations.

  • CLV
  • Churn
  • Cohorts
04μ

Market Research

Design reliable measures for observing markets, perceptions and preferences.

  • Sampling
  • Scales
  • Bias
05ƒ

Marketing Models

Formalize response, diffusion and demand to compare mechanisms rather than narrate a curve.

  • Saturation
  • Carry-over
  • Demand functions
06P

Decision Science

Compare scenarios and quantify uncertainty before preparing a human judgment.

  • Monte Carlo
  • Sensitivity
  • Bayes
Method library 35 / 41

41 contents · 35 verified dossiers · 0 notes in validation · 6 territories

From a question to a challengeable method.

Every content item shows its maturity. Only verified dossiers have passed the complete scientific and reproducibility protocol.

01MSC-P-001

Evidence foundations

How do you turn a marketing claim into a testable question?

Verified scientific dossier
Open content↗
02MSC-P-002

Evidence foundations

Correlation or causality: what can an analysis actually support?

Verified scientific dossier
Open content↗
03MSC-P-003

Evidence foundations

How should uncertainty in a marketing result be expressed?

Verified scientific dossier
Open content↗
04MSC-P-004

Evidence foundations

Statistical significance or effect size: which result should be interpreted?

Verified scientific dossier
Open content↗
05MSC-P-005

Measurement science

How do you measure a marketing construct that is not directly observable?

Verified scientific dossier
Open content↗
06MSC-P-006

Measurement science

How do you design and validate a measurement scale?

Verified scientific dossier
Open content↗
07MSC-P-007

Measurement science

Alpha or omega: how should scale reliability be assessed?

Verified scientific dossier
Open content↗
08MSC-P-009

Measurement science

PCA, EFA or CFA: which method should you choose?

Verified scientific dossier
Open content↗
09MSC-P-010

Experimentation and causality

When should you run a marketing experiment?

Verified scientific dossier
Open content↗
10MSC-P-011

Experimentation and causality

How do you design an A/B test that actually estimates an effect?

Verified scientific dossier
Open content↗
11MSC-P-012

Experimentation and causality

How many observations does an experiment need?

Verified scientific dossier
Open content↗
12MSC-P-013

Experimentation and causality

How do you measure campaign incrementality with a control group?

Verified scientific dossier
Open content↗
13MSC-P-017

Experimentation and causality

How do you detect selection, contamination and attrition in an experiment?

Verified scientific dossier
Open content↗
14MSC-P-018

Regression and econometrics

Predictive or causal regression: what are you trying to estimate?

Verified scientific dossier
Open content↗
15MSC-P-019

Regression and econometrics

How do you diagnose a marketing regression before interpreting it?

Verified scientific dossier
Open content↗
16MSC-P-022

Pricing science

How do you estimate price elasticity and its uncertainty?

Verified scientific dossier
Open content↗
17MSC-P-026

Choice models

Logit vs Probit: how do you choose for purchase probability?

Verified scientific dossier
Open content↗
18MSC-P-029

Customer science

Which customers have the highest probability of churn?

Verified scientific dossier
Open content↗
19MSC-P-027

Measurement science

TAM, UTAUT or UTAUT2: which framework should be used to study technology acceptance?

Verified scientific dossier
Open content↗
20MSC-P-008

Measurement science

How do you validate a marketing measurement scale?

Verified scientific dossier
Open content↗
21MSC-P-014

Experimentation and causality

How do you design a marketing geo experiment?

Verified scientific dossier
Open content↗
22MSC-P-015

Experimentation and causality

How do you estimate an effect with difference-in-differences?

Verified scientific dossier
Open content↗
23MSC-P-020

Pricing science

How do you address price endogeneity?

Verified scientific dossier
Open content↗
24MSC-P-021

Regression and econometrics

Fixed or random effects: which panel model should you choose?

Verified scientific dossier
Open content↗
25MSC-P-023

Pricing science

How do you estimate a demand function?

Verified scientific dossier
Open content↗
26MSC-P-024

Pricing science

How do you simulate a price-volume-margin scenario?

Verified scientific dossier
Open content↗
27MSC-P-028

Customer science

How do you estimate CLV with BG/NBD and Gamma-Gamma?

Verified scientific dossier
Open content↗
28MSC-P-030

Customer science

How do you analyze retention with a survival model?

Verified scientific dossier
Open content↗
29MSC-P-043

Customer science

What is a subscriber worth with only six months of retention data?

Verified scientific dossier
Open content↗
30MSC-P-031

Customer science

How do you build a useful customer segmentation?

Verified scientific dossier
Open content↗
31MSC-P-032

Customer science

How do you test segmentation stability?

Verified scientific dossier
Open content↗
32MSC-P-033

Regression and econometrics

How do you validate a marketing forecast?

Verified scientific dossier
Open content↗
33MSC-P-034

Evidence foundations

How do you build a Monte Carlo simulation for a marketing decision?

Verified scientific dossier
Open content↗
34MSC-P-035

Regression and econometrics

How do you model saturation and adstock?

Verified scientific dossier
Open content↗
35MSC-P-039

Evidence foundations

Which statistical test should you choose?

Verified scientific dossier
Open content↗
Open data laboratory 04 / 04

Reproducible datasets

Learn from data you can inspect.

Each example dataset is synthetic, documented and intentionally compact. It is designed to reproduce a method, not to generalize a result to a market.

FORMAT CSV UTF‑8 LICENSE CC0 1.0
Variables, methods and limitations →
MSC-001

Price Elasticity

Price · volume · seasonality · 24 observations

CSV ↓
MSC-002

Advertising Response

Spend · exposure · response · 24 observations

CSV ↓
MSC-005

Promotion Incrementality

Test · control · sales · 24 observations

CSV ↓
MSC-004

Customer Lifetime Value

Recency · frequency · value · 30 customers

CSV ↓
MSC-006

Marketing Mix Modeling

Channels · sales · controls · 36 periods

CSV ↓
MSC-007

Measurement Validity

Respondents · items · groups · 30 respondents

CSV ↓
MSC-010

Monte Carlo Inputs

Scenarios · distributions · dependencies · 24 assumptions

CSV ↓
Reproducible tools 05 / 05

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 SE ≈ 0.1 pp · Wilson 95% CI ≈ 99.6–100.0%)

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

MSC-P-034 →

From evidence to decision

The method bounds the evidence. Arbitration comes next.

Marketing Science Center establishes what the data support. When several scenarios must then be compared, documented and arbitrated, Innovatio takes over.