Web & Marketing Analytics Interview Questions for Data Analytics

https://www.example.com/sale?utm_source=instagram&utm_medium=paid_social&utm_campaign=summer_sale_2026&utm_content=reel_video_a -- Rule 1: always lower case with underscores; 'Instagram' and 'instagram' become separate rows -- Rule 2: keep a shared UTM sheet with allowed source and medium values so every team tags the same way

SELECT s.traffic_source, COUNT(*) AS sessions, COUNT(o.order_id) AS orders, ROUND(100.0 * COUNT(o.order_id) / COUNT(*), 2) AS conversion_pct FROM sessions s LEFT JOIN orders o ON o.session_id = s.session_id GROUP BY s.traffic_source ORDER BY sessions DESC; -- assumes at most one order per session; otherwise use COUNT(DISTINCT o.session_id)

-- B = Spend, C = Conversions, D = Revenue, target ROAS in H1 E2 (ROAS): =IFERROR(D2/B2, 0) F2 (CPA): =IF(C2=0, "No conversions", B2/C2) G2 (Flag): =IF(E2<$H$1, "Below target", "OK") -- Copy down, then add conditional formatting on G to highlight 'Below target'

WITH touches AS ( SELECT t.user_id, t.channel, t.touch_ts, ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts) AS first_rank, ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts DESC) AS last_rank FROM marketing_touches t JOIN conversions c ON c.user_id = t.user_id AND t.touch_ts <= c.converted_ts ) SELECT user_id, MAX(CASE WHEN first_rank = 1 THEN channel END) AS first_touch, MAX(CASE WHEN last_rank = 1 THEN channel END) AS last_touch FROM touches GROUP BY user_id;

import pandas as pd from urllib.parse import urlparse, parse_qs def get_utms(url): params = parse_qs(urlparse(str(url)).query) return pd.Series({ 'source': params.get('utm_source', ['(direct)'])[0].lower(), 'medium': params.get('utm_medium', ['(none)'])[0].lower(), 'campaign': params.get('utm_campaign', ['(not set)'])[0].lower(), }) df[['source', 'medium', 'campaign']] = df['landing_url'].apply(get_utms)

SELECT s.campaign_id, COUNT(DISTINCT s.user_id) AS delivered, COUNT(DISTINCT o.user_id) AS opened, COUNT(DISTINCT c.user_id) AS clicked, ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT s.user_id), 1) AS open_rate, ROUND(100.0 * COUNT(DISTINCT c.user_id) / COUNT(DISTINCT s.user_id), 1) AS ctr FROM email_sends s LEFT JOIN email_opens o ON o.campaign_id = s.campaign_id AND o.user_id = s.user_id LEFT JOIN email_clicks c ON c.campaign_id = s.campaign_id AND c.user_id = s.user_id WHERE s.status = 'delivered' GROUP BY s.campaign_id;

# spend: channel, spend # customers: channel, customer_id, monthly_margin new_cust = customers.groupby('channel').agg( new_customers=('customer_id', 'nunique'), avg_margin=('monthly_margin', 'mean')) summary = spend.groupby('channel')['spend'].sum().to_frame().join(new_cust) summary['cac'] = summary['spend'] / summary['new_customers'] summary['payback_months'] = summary['cac'] / summary['avg_margin'] print(summary.round(1).sort_values('payback_months'))

WITH ordered AS ( SELECT user_id, event_ts, LAG(event_ts) OVER (PARTITION BY user_id ORDER BY event_ts) AS prev_ts FROM page_events ), flagged AS ( SELECT *, CASE WHEN prev_ts IS NULL OR event_ts - prev_ts > INTERVAL '30 minutes' THEN 1 ELSE 0 END AS new_session FROM ordered ) SELECT user_id, event_ts, SUM(new_session) OVER (PARTITION BY user_id ORDER BY event_ts) AS session_no FROM flagged;