← Boston Consulting Group Interview Insights

Boston Consulting Group·Data Scientist·Technical Phone Screen·Senior

Senior
May 2026

Summary

BCG data scientist interview with a meaty technical case around marketing measurement. One question, but it had a lot of moving parts and I felt like I was rebuilding an analytics curriculum on the spot.

Questions Asked (1)

Q1

A national retail client runs weekly SKU-level marketing campaigns and has three years of historical data. How would you design an analytical approach to measure campaign lift? Walk through your choice of target variable, predictors, model(s), validation strategy, and key caveats, and explain how the findings would shape future campaigns.

A/B Testing & ExperimentationProduct Analytics & MetricsData Modeling
Author's notes

This one sprawled fast.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Frame the problem as a causal inference task, not just prediction, and propose a quasi-experimental design like difference-in-differences or synthetic control to isolate campaign lift from confounders. Walk through the target variable (SKU-level sales or units), predictors (campaign exposure, seasonality, price, promotions, competitor activity), model choices (e.g., DiD, causal forests, or Bayesian structural time series), and validation (pre-period fit, placebo tests, holdout). Emphasize how findings translate into actionable campaign optimizations.

Pro tip: Acknowledge that randomized experiments are often infeasible in retail, so you'd leverage natural experiments and robustness checks (e.g., placebo tests, sensitivity analysis) to build credibility with the client. Also, quantify lift in business terms (ROI, incremental margin) to make the analysis actionable for executives.

1. Define the causal question and target variable

Clarify the estimand: average treatment effect on the treated (ATT) for campaign-exposed SKUs. Choose a target variable like incremental sales or units, and define treatment/control groups carefully (e.g., exposed vs. matched unexposed SKUs).

2. Select predictors and control variables

Include campaign exposure, seasonality (week, holiday), pricing, promotions, competitor actions, and SKU-level fixed effects. Consider lagged sales and external factors like weather or economic indicators if available.

3. Choose and justify the modeling approach

Propose difference-in-differences (DiD) with SKU and time fixed effects, or synthetic control for SKUs with no clean control. For heterogeneous effects, use causal forests or meta-learners. Mention Bayesian structural time series for time-series counterfactuals.

4. Design validation and robustness checks

Validate using pre-period fit, placebo tests on pre-campaign periods, and out-of-sample holdout. Check parallel trends assumption for DiD, and conduct sensitivity analysis for unobserved confounders.

5. Translate findings into business recommendations

Quantify lift and ROI per campaign, identify which SKUs/segments respond best, and recommend targeting, timing, and budget allocation changes. Discuss caveats like spillover, cannibalization, and long-term effects.

Key Points to Mention

  • Causal inference vs. prediction: emphasize the need to isolate lift, not just forecast sales.
  • Target variable: incremental sales or units, and how to define treatment/control groups.
  • Model choices: difference-in-differences, synthetic control, causal forests, and their assumptions.
  • Validation: parallel trends, placebo tests, pre-period fit, and holdout validation.
  • Caveats: confounding, spillover, cannibalization, seasonality, and data limitations.
  • Business impact: how lift estimates inform campaign targeting, budget allocation, and ROI measurement.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.