Meta · Statistics & Data Analysis

Evaluate brand ads effectiveness on social media causally

TrueInterview

October 7, 2026 · 1 min read

Hypothesis: "Social media (such as Facebook) is less effective for brand advertising than other channels." You have access to historical multi-channel data and can run experiments. Put together a causal measurement plan to test that claim. Be specific:

Identify the primary brand outcomes and how each will be measured (for example, aided/unaided awareness surveys, ad recall, branded search lift, direct type-in traffic, share-of-voice). Give exact definitions and attribution windows.
Suggest at least two experiment designs—a within-platform holdout cell and a geo or matched-market lift test—and pick one. Specify the randomization unit, eligibility criteria, cooldown/lag windows for awareness effects, frequency caps, and how you will handle contamination/spillover across friends or markets.
Power/MDE: lay out the inputs (baseline awareness, expected lift, intraclass correlation for geo tests), calculate the required sample size and duration, and note which assumptions you would validate.
Analysis: pre-register a model (for example, difference-in-differences with a pre-period, or synthetic control). List covariates such as seasonality, prior brand equity, competitor spend, creative quality, and device mix. Define the estimand (ATE on exposed), robust standard errors/clustering, and how you will handle staggered rollout.
Budget confounding: advertisers tend to spend less on social brand campaigns—explain how you will adjust for spend levels and diminishing returns (for example, log response curves, adstock/lag, saturation modeling) so that budget differences are not mistaken for effectiveness.
Guardrails and constraints: CPA/ROAS on direct response, site performance, user complaints; define failure stops.
Heterogeneity: plan pre-specified subgroup tests (age, market maturity, frequency buckets) and include multiple-testing control.
Interference diagnostics: proximity/overlap checks, geographic buffers, and placebo markets.
Decision rule: exact thresholds (for example, lift $\ge X$ percentage points with a 95% CI that does not cross 0) and how the results will change next-quarter channel mix.

Overview: This question assesses skill in causal inference and experimental design for marketing measurement, including defining primary brand outcomes, designing randomized and geo/matched-market lift tests, power and MDE calculations, adjustment for spend and saturation confounding, and pre-specified heterogeneity and interference diagnostics.

Meta · Statistics & Data Analysis

Evaluate brand ads effectiveness on social media causally

TrueInterview

October 7, 2026 · 1 min read

Identify the primary brand outcomes and how each will be measured (for example, aided/unaided awareness surveys, ad recall, branded search lift, direct type-in traffic, share-of-voice). Give exact definitions and attribution windows.
Suggest at least two experiment designs—a within-platform holdout cell and a geo or matched-market lift test—and pick one. Specify the randomization unit, eligibility criteria, cooldown/lag windows for awareness effects, frequency caps, and how you will handle contamination/spillover across friends or markets.
Power/MDE: lay out the inputs (baseline awareness, expected lift, intraclass correlation for geo tests), calculate the required sample size and duration, and note which assumptions you would validate.
Analysis: pre-register a model (for example, difference-in-differences with a pre-period, or synthetic control). List covariates such as seasonality, prior brand equity, competitor spend, creative quality, and device mix. Define the estimand (ATE on exposed), robust standard errors/clustering, and how you will handle staggered rollout.
Budget confounding: advertisers tend to spend less on social brand campaigns—explain how you will adjust for spend levels and diminishing returns (for example, log response curves, adstock/lag, saturation modeling) so that budget differences are not mistaken for effectiveness.
Guardrails and constraints: CPA/ROAS on direct response, site performance, user complaints; define failure stops.
Heterogeneity: plan pre-specified subgroup tests (age, market maturity, frequency buckets) and include multiple-testing control.
Interference diagnostics: proximity/overlap checks, geographic buffers, and placebo markets.
Decision rule: exact thresholds (for example, lift $\ge X$ percentage points with a 95% CI that does not cross 0) and how the results will change next-quarter channel mix.