How do I approach Analytics & Experimentation interview questions?

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 medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Instacart.

What role is this question designed for?

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

Investigate Instacart Revenue Decline Using Weekly Data

Quick Overview

This question evaluates a Data Scientist's skills in time-series analysis, anomaly detection, changepoint testing, decomposition and attribution, and segmentation within the Analytics & Experimentation domain, emphasizing work with constrained weekly data and with richer order-level signals.

Scenario

You are the on-call Data Scientist for Instacart. This week’s total revenue is down 4% versus the prior week. Initially, you only have access to the historical weekly revenue time series (no sub-weekly, no segmentation).

Task

Part A: With only weekly revenue data, outline how you would investigate the 4% week-over-week decline. Be explicit about how you would assess whether this is expected (seasonality/holidays) vs. anomalous (trend break/changepoint), and how you’d quantify the expected range.
Part B: If you later gain access to richer data (orders, AOV, geography, cohorts, etc.), describe the additional drill-downs and attribution analyses you would run to identify root causes.

Constraints and Hints

Data constraint (Part A): weekly revenue time series only.
Use appropriate time-series tools: seasonality and holiday effects, YoY/seasonal comparisons, changepoint tests, anomaly detection against forecasts, and uncertainty intervals.
With richer data (Part B): perform decomposition (e.g., Orders × AOV × Take Rate), segmentation (geography, retailer, category, cohort), and drill-downs.
State minimal assumptions when needed and describe decision criteria for whether −4% is noteworthy.

Quick Overview

Task

Part A: With only weekly revenue data, outline how you would investigate the 4% week-over-week decline. Be explicit about how you would assess whether this is expected (seasonality/holidays) vs. anomalous (trend break/changepoint), and how you’d quantify the expected range.

Part B: If you later gain access to richer data (orders, AOV, geography, cohorts, etc.), describe the additional drill-downs and attribution analyses you would run to identify root causes.

Constraints and Hints

Data constraint (Part A): weekly revenue time series only.

Use appropriate time-series tools: seasonality and holiday effects, YoY/seasonal comparisons, changepoint tests, anomaly detection against forecasts, and uncertainty intervals.

With richer data (Part B): perform decomposition (e.g., Orders × AOV × Take Rate), segmentation (geography, retailer, category, cohort), and drill-downs.

State minimal assumptions when needed and describe decision criteria for whether −4% is noteworthy.

Investigate Instacart Revenue Decline Using Weekly Data

Quick Overview

Scenario

Task

Constraints and Hints

Solution

Comments (0)

Investigate Instacart Revenue Decline Using Weekly Data

Quick Overview

Scenario

Task

Constraints and Hints

Solution

Comments (0)