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

Company: Shopify

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario A three-year weekly time-series plot of Shopify shopping sessions shows a spike every November and an additional isolated spike in the third year’s June. ##### Question Explain plausible business drivers for the recurring November spikes and the one-off June spike. 2) Using the provided dataset (your choice of Python, R, or Google Sheets), explore the spikes further and propose at least two hypotheses to validate with additional data. ##### Hints Think seasonality (e.g., Black Friday/Cyber Monday), marketing campaigns, external events, and segmentation by geography or channel.

Quick Answer: 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.

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Aug 4, 2025, 10:55 AM
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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

  1. Explain plausible business drivers for the recurring November spikes and the one-off June spike.
  2. 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.

Clarifying Questions to Ask Guidance

  • 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 Guidance

  • 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 Guidance

  • 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?
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