Data Export Formats

Example:

Email,Phone,Conversion Name,Conversion Time,Conversion Value,Conversion Currency
ed8e83c2f4cb7b9f43cdc75c148b0b09,628923b3c489bda7dd9ebba89cb5b46c,paid_subscription,2025-11-03T00:00:00Z,2388.0,USD
1bffb899c9248b28e37cda02dbd59444,0dd03c9dc463ab5efcd15f3343035ffa,paid_subscription,2025-10-31T00:00:00Z,2189.0,USD

Google Ads upload path: Tools & Settings → Conversions → Upload conversions → Import

Customer Match CSV (Retargeting Audiences)

For uploading audience lists to Google Ads.

Minimal columns (email only):

Email

Extended columns (better match rate):

Email,Phone,First Name,Last Name,Country,Zip

Format requirements:

• Email: SHA256 hash (32 hex chars, lowercase) OR plaintext (Google hashes it)
• Phone: SHA256 hash OR E.164 format (+14155551234)
• First Name / Last Name: Plaintext, lowercase, trimmed
• Country: 2-letter ISO code (US, GB, etc.)
• Zip: 5-digit US or local format

Example (hashed):

Email,Phone
ed8e83c2f4cb7b9f43cdc75c148b0b09,628923b3c489bda7dd9ebba89cb5b46c
1bffb899c9248b28e37cda02dbd59444,0dd03c9dc463ab5efcd15f3343035ffa

Google Ads upload path: Tools & Settings → Audience Manager → Customer Match → Email list

Meta Custom Audiences CSV

For uploading to Meta Ads Manager.

Columns:

email,phone,fn,ln,country,zip

Format requirements:

• email: SHA256 hash (lowercase hex) OR plaintext lowercase
• phone: Digits only, no formatting (14155551234)
• fn / ln: Lowercase, trimmed
• country: 2-letter ISO lowercase (us)
• zip: 5-digit

Example:

email,phone,fn,ln,country,zip
ed8e83c2f4cb7b9f43cdc75c148b0b09,14155551234,john,doe,us,94105

Project Data Schemas

users.csv (~5,000 records)

User master file with acquisition data.

events.csv (~57,000 records)

Event stream with funnel progression.

Funnel stages (event_name values):

1. page_view - Landing page visit
2. pricing_view - Viewed pricing page
3. checkout_start - Started checkout
4. form_submit - Submitted form
5. conversion - Completed purchase

daily_metrics.csv (~60 records)

Daily aggregated metrics with engineered anomalies.

Engineered anomalies:

• Nov 15: -63% sessions (signup flow bug)
• Nov 21: +99% conversions (onboarding improvement)
• Nov 28-30: -72% conversion rate (activation issue)

trial_users.csv (~500 records)

Trial user conversion data (Demo 4).

feature_usage.csv (~2,500 records)

Feature adoption events (Demo 4).

Key features:

• create_form_onboarding - Created first form
• publish_form - Published a form
• embed_form - Embedded form on site
• configure_integration - Set up integration (aha moment)
• view_analytics - Viewed form analytics

Aha moment pattern: Users who configure_integration within 3 days convert at 70% vs 19% baseline (3.7x lift).

utm_data.csv (~12 records)

Campaign UTM data with intentional inconsistencies (Demo 5).

Engineered issues:

• Source fragmentation: linkedin vs LinkedIn vs LINKEDIN
• Typos: product_upd_dec instead of product_update_dec

Common Export Patterns

High-Value Converters (Enhanced Conversions)

SELECT
  u.email AS Email,
  u.phone AS Phone,
  'paid_subscription' AS "Conversion Name",
  e.timestamp AS "Conversion Time",
  e.conversion_value AS "Conversion Value",
  'USD' AS "Conversion Currency"
FROM users u
JOIN events e ON u.user_id = e.user_id
WHERE e.event_name = 'conversion'
  AND e.conversion_value > 100
ORDER BY e.conversion_value DESC

Retargeting Audience (Customer Match)

WITH pricing_views AS (
  SELECT user_id, COUNT(*) as view_count
  FROM events
  WHERE event_name = 'pricing_view'
  GROUP BY user_id
  HAVING COUNT(*) >= 2
),
checkout_starters AS (
  SELECT DISTINCT user_id FROM events WHERE event_name = 'checkout_start'
),
converters AS (
  SELECT DISTINCT user_id FROM events WHERE event_name = 'conversion'
)
SELECT u.email AS Email, u.phone AS Phone
FROM users u
JOIN pricing_views pv ON u.user_id = pv.user_id
JOIN checkout_starters cs ON u.user_id = cs.user_id
LEFT JOIN converters c ON u.user_id = c.user_id
WHERE c.user_id IS NULL

File Locations

BigQuery Location

Project: agents-webinar-2025 Dataset: webinar_demos Tables: users, events, daily_metrics, trial_users, feature_usage