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 Onsite rounds at Shopify.

What role is this question designed for?

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

Analyze Causes of November and June Shopify Traffic Spikes

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Analyze Causes of November and June Shopify Traffic Spikes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Analyze Causes of November and June Shopify Traffic Spikes

Analyzing Recurring and One-off Spikes in Weekly Shopify Sessions

Scenario

You have a three-year weekly time-series of Shopify shopping sessions. The plot shows:

A pronounced spike every November.
An additional isolated spike in June of the third year.

Assume you have access to a dataset of weekly sessions with a date field, and optionally common marketing dimensions (e.g., country/region, channel/source, device, campaign/UTM). If the dataset is daily, you can aggregate to weekly.

Tasks

Explain plausible business drivers for the recurring November spikes and the one-off June spike.
Using the dataset (choose Python, R, or Google Sheets), outline how you would explore the spikes and propose at least two concrete hypotheses to validate with additional data.

Hints

Consider: seasonality (e.g., Black Friday/Cyber Monday), marketing campaigns, external events, segmentation by geography or channel, product or tracking changes, and data quality checks.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.

What a Strong Answer Covers

A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions

What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?
What decision would you make if metrics disagree?

Quick Overview

Scenario

You have a three-year weekly time-series of Shopify shopping sessions. The plot shows:

A pronounced spike every November.

An additional isolated spike in June of the third year.

Hints

Consider: seasonality (e.g., Black Friday/Cyber Monday), marketing campaigns, external events, segmentation by geography or channel, product or tracking changes, and data quality checks.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.

State assumptions about instrumentation, randomization, sample size, and data quality.

Separate descriptive analysis from causal claims.

What a Strong Answer Covers

A metric framework with primary, guardrail, and diagnostic metrics.

A credible analysis or experiment design with clear assumptions and bias checks.

SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.

An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions

What sanity checks would you run before trusting the result?

How would you handle novelty effects, seasonality, or selection bias?

What decision would you make if metrics disagree?

Analyze Causes of November and June Shopify Traffic Spikes

Quick Overview

Analyze Causes of November and June Shopify Traffic Spikes

Analyze Causes of November and June Shopify Traffic Spikes

Analyzing Recurring and One-off Spikes in Weekly Shopify Sessions

Scenario

Tasks

Hints

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

Analyze Causes of November and June Shopify Traffic Spikes

Quick Overview

Analyze Causes of November and June Shopify Traffic Spikes

Analyze Causes of November and June Shopify Traffic Spikes

Analyzing Recurring and One-off Spikes in Weekly Shopify Sessions

Scenario

Tasks

Hints

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer