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This is a medium difficulty Machine Learning question, commonly asked during Onsite rounds at Walmart Labs.

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This question is commonly asked for Data Scientist candidates at Walmart Labs during technical interviews.

Predict Next-Period Conversion Rate Using Historical Campaign Data

Last updated: Mar 29, 2026

Quick Overview

This question evaluates predictive modeling, target transformation, feature engineering, validation design and evaluation metrics, understanding of logistic regression loss, and PCA eigen-decomposition in the context of conversion-rate forecasting.

Walmart Labs

Aug 4, 2025, 10:55 AM

Data Scientist

Onsite

Machine Learning

Predicting Next-Period Conversion Rate from Campaign Logs

Context

You have historical campaign panel data with columns: adid, date, impressions, clicks, conversions. Define conversion_rate as the probability that a click converts in the next period (i.e., post-click conversion rate). If your team instead defines conversion rate per impression, adjust the exposure accordingly.

Questions

How would you build a model to predict next-period conversion_rate?
Do we need to transform the target conversion_rate? Why or why not?
If more data were available, what additional features would you add?
How would you evaluate model performance (including validation design and metrics)?
Explain logistic regression’s loss function.
Describe PCA’s eigenvalues, eigenvectors, and its assumptions.

Solution

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