Analyze Causes of November and June Shopify Traffic Spikes
Overview
This question evaluates a data scientist's skills in time-series analysis, web analytics, segmentation, causal inference, and data-quality investigation applied to traffic anomalies on an e-commerce site.
Shopify
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
0
0
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.
Solution
Login required
Show