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