The SQL part was fine, just a join and a group by date and shop_id.
Start by writing a clear SQL query that joins the shops and page_views tables on shop_id and groups by shop_id and date to compute daily page views per shop. Then, define a visibility metric that compares the average daily page views of shops created in the last 30 days versus older shops, ensuring to account for shop age and activity. Finally, calculate the metric using SQL and interpret whether newer shops have higher visibility.
Pro tip: When comparing new vs. old shops, control for shop size or category to avoid confounding; also consider using a normalized metric like page views per day since creation to better capture visibility trends.
Identify the relevant columns: shops (shop_id, created_at) and page_views (shop_id, view_date, view_count). Write a SQL query to aggregate page views per shop per day.
Use a GROUP BY on shop_id and view_date to sum view_count, resulting in a table with shop_id, date, and daily_views.
Propose a metric such as average daily page views per shop, segmented by shop age (new: created within last 30 days; old: created earlier). Ensure the metric is comparable, e.g., by normalizing for shop age or using a fixed observation window.
Join the daily page views with shops, compute the average daily views for new and old shops, and compare them (e.g., ratio or difference). Use a CASE statement to classify shops by age.
Discuss whether the metric shows a significant difference, consider potential biases (e.g., seasonality, shop size), and suggest ways to validate or refine the metric.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.