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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at CVS Health.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at CVS Health during technical interviews.

Launch and measure a TV campaign | CVS Health Interview Question

Quick Overview

This question evaluates a data scientist's competency in experimental design and causal inference, including geo-level matching, KPI definition, media measurement (GRP/TRP, reach/frequency, adstock and saturation modeling), power analysis, and operational risk management for measuring incremental vaccinations.

6-Week Linear TV Experiment to Increase Flu Vaccinations

Design a 6-week linear TV campaign and its measurement plan to causally estimate incremental flu vaccinations. Assume access to DMA-level verified vaccinations, media delivery (GRP/TRP), and basic operational data (inventory, staffing).

Scope

Select 12 test DMAs from the 210 U.S. DMAs and assign matched controls (1:1).
Define the KPI, causal identification strategy, media plan (TRPs, dayparts, reach/frequency), and modeling choices (adstock, saturation).
Address execution risks (spillover, concurrent media, shocks, supply/ops constraints) and outline power analysis and triangulation.

Requirements

DMA Selection and Matching
- Choose 12 test DMAs and 12 matched controls.
- Describe matching criteria and method (e.g., distance metric, pair matching/stratification), pre-period length used for matching, and exclusion rules.
- Explain how you will avoid or control for news/sports/holiday shocks in market selection and scheduling.
KPI and Causal Design
- Primary KPI: incremental verified vaccinations per DMA over the 6-week post period (and per-capita normalization).
- Choose and justify a causal design: geo-randomized experiment with matched pairs (preferred), difference-in-differences, and/or synthetic controls for sensitivity.
- Specify how you will handle market spillovers, unequal TRP delivery, and concurrent media.
Media Plan Parameters
- GRP/TRP targets by demo, weekly distribution, and total.
- Daypart mix and content exclusions to mitigate shocks.
- Reach-frequency goals and how you will estimate/verify them.
- Modeling of adstock/decay and saturation (include formulas/assumptions).
Measurement and Analysis
- Pre-period length and cadence; checks for parallel trends.
- Estimation approach (e.g., DiD regression), weighting, and covariates.
- Power and sample size: show how you’d compute Minimum Detectable Effect (MDE) using market-level variance; include a worked numeric example.
- Guardrails (e.g., call-center load, pharmacy stockouts) and pause criteria.
- Triangulation with MMM and pharmacy footfall data; how to reconcile findings.

Quick Overview

Scope

Select 12 test DMAs from the 210 U.S. DMAs and assign matched controls (1:1).

Define the KPI, causal identification strategy, media plan (TRPs, dayparts, reach/frequency), and modeling choices (adstock, saturation).

Address execution risks (spillover, concurrent media, shocks, supply/ops constraints) and outline power analysis and triangulation.

Requirements

DMA Selection and Matching

Choose 12 test DMAs and 12 matched controls.
Describe matching criteria and method (e.g., distance metric, pair matching/stratification), pre-period length used for matching, and exclusion rules.
Explain how you will avoid or control for news/sports/holiday shocks in market selection and scheduling.

KPI and Causal Design

Primary KPI: incremental verified vaccinations per DMA over the 6-week post period (and per-capita normalization).
Choose and justify a causal design: geo-randomized experiment with matched pairs (preferred), difference-in-differences, and/or synthetic controls for sensitivity.
Specify how you will handle market spillovers, unequal TRP delivery, and concurrent media.

Media Plan Parameters

GRP/TRP targets by demo, weekly distribution, and total.
Daypart mix and content exclusions to mitigate shocks.
Reach-frequency goals and how you will estimate/verify them.
Modeling of adstock/decay and saturation (include formulas/assumptions).

Measurement and Analysis

Pre-period length and cadence; checks for parallel trends.
Estimation approach (e.g., DiD regression), weighting, and covariates.
Power and sample size: show how you’d compute Minimum Detectable Effect (MDE) using market-level variance; include a worked numeric example.
Guardrails (e.g., call-center load, pharmacy stockouts) and pause criteria.
Triangulation with MMM and pharmacy footfall data; how to reconcile findings.

Launch and measure a TV campaign

Quick Overview

6-Week Linear TV Experiment to Increase Flu Vaccinations

Scope

Requirements

Solution

Comments (0)

Launch and measure a TV campaign

Quick Overview

6-Week Linear TV Experiment to Increase Flu Vaccinations

Scope

Requirements

Solution

Comments (0)