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What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Boston Consulting Group.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Boston Consulting Group during technical interviews.

Evaluate Campaign Lift with Predictive Analytics and Validation Strategy | Boston Consulting Group Interview Question

Quick Overview

This question evaluates a data scientist's competency in causal inference and uplift estimation, time-series feature engineering (seasonality, adstock/carryover), KPI definition for incremental units/revenue/margin, and validation and diagnostic strategies for measuring marketing campaign lift.

Evaluate Marketing Campaign Lift (Weekly SKU-Level, 3 Years)

Context

You have 3 years of panel data at weekly SKU (and optionally region/store) granularity for a national retailer. The client runs weekly SKU-level marketing campaigns (e.g., spend, impressions, channels, creative) and wants to estimate causal lift from these campaigns.

Task

Design an analytical approach to quantify campaign lift and translate findings into actionable guidance for future campaigns. Clearly specify:

Target variable (Y) and KPI(s)
Predictors (X) and key feature engineering (including seasonality)
Chosen causal/modeling approach(es)
Validation and diagnostic strategy
Key caveats/assumptions
How the insights will inform future campaign planning and targeting

Consider

Causal inference options: difference-in-differences (including staggered adoption), uplift modeling (treatment effect heterogeneity), regression with controls / doubly robust learners
KPI definition (incremental units, revenue, margin, ROI/pROAS)
Seasonality, trends, holidays, and adstock/carryover
Data pitfalls: selection bias, overlap, stockouts, cannibalization, interference

Quick Overview

Task

Design an analytical approach to quantify campaign lift and translate findings into actionable guidance for future campaigns. Clearly specify:

Target variable (Y) and KPI(s)

Predictors (X) and key feature engineering (including seasonality)

Chosen causal/modeling approach(es)

Validation and diagnostic strategy

Key caveats/assumptions

How the insights will inform future campaign planning and targeting

Consider

Causal inference options: difference-in-differences (including staggered adoption), uplift modeling (treatment effect heterogeneity), regression with controls / doubly robust learners

KPI definition (incremental units, revenue, margin, ROI/pROAS)

Seasonality, trends, holidays, and adstock/carryover

Data pitfalls: selection bias, overlap, stockouts, cannibalization, interference

Evaluate Campaign Lift with Predictive Analytics and Validation Strategy

Quick Overview

Evaluate Marketing Campaign Lift (Weekly SKU-Level, 3 Years)

Context

Task

Consider

Solution

Comments (0)

Evaluate Campaign Lift with Predictive Analytics and Validation Strategy

Quick Overview

Evaluate Marketing Campaign Lift (Weekly SKU-Level, 3 Years)

Context

Task

Consider

Solution

Comments (0)