##### Scenario Instacart’s weekly revenue fell 4 % versus the prior week and you only have the historical weekly-revenue time series. ##### Question With only weekly revenue data, how would you investigate the 4 % decline? If you later gained access to richer data (orders, geography, AOV, etc.), what additional analyses would you perform? ##### Hints Think seasonality, holiday effects, trend breaks, changepoint tests, segmentation and drill-downs.

Instacart analytics prompt on investigating a 4% weekly revenue decline, covering time-series anomaly detection, seasonality, changepoints, prediction intervals, and richer-data decomposition by orders, AOV, geography, cohorts, and categories.

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

Investigate an Instacart Weekly Revenue Decline

Instacart's weekly revenue fell 4% versus the prior week. Initially, you only have the historical weekly revenue time series. Later, you may gain access to richer data such as orders, geography, AOV, cohorts, categories, and retailers.

Constraints & Assumptions

In Part A, use only the weekly revenue time series.
Decide whether the 4% week-over-week decline is expected seasonality or an anomaly.
Quantify uncertainty rather than relying on a visual impression.
In Part B, explain how richer data would support root-cause attribution.

Clarifying Questions to Ask

Is the week complete and aligned to the same business calendar?
Has the revenue definition changed?
Are there holidays, weather events, promotions, outages, or fee changes?
How much historical data is available?
Which revenue components are included?

What a Strong Answer Covers

Time-series hygiene: missing weeks, duplicates, partial weeks, calendar alignment, 53-week years, and metric-definition changes.
Baseline expectations using YoY comparisons, seasonal indexes, rolling averages, STL decomposition, ARIMA/ETS/Prophet-style forecasting, or anomaly detection.
Quantify expected range with prediction intervals and residual comparisons.
Assess trend breaks or changepoints and compare the 4% drop to historical week-over-week volatility.
Communicate the result as likely normal, ambiguous, or anomalous with confidence and caveats.
With richer data, decompose revenue into orders, AOV, take rate, fees, promotions, refunds, active users, conversion, retention, geography, retailer, category, cohort, and acquisition channel.
Use contribution analysis, segmentation, causal checks, and drill-downs to identify root causes and recommend next actions.

Follow-up Questions

What if the same week last year had a similar decline?
How would you handle a holiday that moves week to week?
What richer dataset would you ask for first?
How would you separate demand decline from AOV decline?

Investigate an Instacart Weekly Revenue Decline

Constraints & Assumptions

In Part A, use only the weekly revenue time series.

Decide whether the 4% week-over-week decline is expected seasonality or an anomaly.

Quantify uncertainty rather than relying on a visual impression.

In Part B, explain how richer data would support root-cause attribution.

Clarifying Questions to Ask

Is the week complete and aligned to the same business calendar?

Has the revenue definition changed?

Are there holidays, weather events, promotions, outages, or fee changes?

How much historical data is available?

Which revenue components are included?

What a Strong Answer Covers

Time-series hygiene: missing weeks, duplicates, partial weeks, calendar alignment, 53-week years, and metric-definition changes.

Baseline expectations using YoY comparisons, seasonal indexes, rolling averages, STL decomposition, ARIMA/ETS/Prophet-style forecasting, or anomaly detection.

Quantify expected range with prediction intervals and residual comparisons.

Assess trend breaks or changepoints and compare the 4% drop to historical week-over-week volatility.

Communicate the result as likely normal, ambiguous, or anomalous with confidence and caveats.

With richer data, decompose revenue into orders, AOV, take rate, fees, promotions, refunds, active users, conversion, retention, geography, retailer, category, cohort, and acquisition channel.

Use contribution analysis, segmentation, causal checks, and drill-downs to identify root causes and recommend next actions.

Follow-up Questions

What if the same week last year had a similar decline?

How would you handle a holiday that moves week to week?

What richer dataset would you ask for first?

How would you separate demand decline from AOV decline?

Investigate Instacart Revenue Decline Using Weekly Data

Quick Overview

Investigate Instacart Revenue Decline Using Weekly Data

Investigate an Instacart Weekly Revenue Decline

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

Investigate Instacart Revenue Decline Using Weekly Data

Quick Overview

Investigate Instacart Revenue Decline Using Weekly Data

Investigate an Instacart Weekly Revenue Decline

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer