Data Export Formats

Use this skill when creating CSV exports for ad platforms or when you need to understand the project's data schemas.

Google Ads Export Formats

Enhanced Conversions CSV

For uploading offline conversion data to improve Smart Bidding.

Required columns:

Email,Phone,Conversion Name,Conversion Time,Conversion Value,Conversion Currency

Format requirements:

• Email: SHA256 hash (32 hex chars, lowercase)

• Phone: SHA256 hash (32 hex chars, lowercase)

• Conversion Name: String matching your Google Ads conversion action

• Conversion Time: ISO 8601 UTC format (2024-11-15T14:32:00Z )

• Conversion Value: Numeric, no currency symbol

• Conversion Currency: 3-letter code (USD , EUR , etc.)

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.

Field Type Description Example

user_id UUID Unique identifier 208763df-9843-4c82-b4f1-bc6382a44acf

email String SHA256 hash (32 chars) 0f9f75d98cacdbd135ccbf18f1aa2e54

phone String SHA256 hash (32 chars) eb18808fce984c7887799fe9e45f3d66

signup_date Date Registration date 2024-11-15

traffic_source String Acquisition channel organic , paid_search , paid_social , direct , referral

utm_source String UTM source google , facebook , linkedin

utm_medium String UTM medium cpc , organic , social , referral

utm_campaign String Campaign ID google_ads_q4 , fb_retargeting

events.csv (~57,000 records)

Event stream with funnel progression.

Field Type Description Example

event_id UUID Unique event ID 22794184-df27-4329-86b1-acccd60b79b2

user_id UUID FK to users a0505dd5-4327-4f86-82cc-bf39ba62c92e

event_name String Event type page_view , pricing_view , checkout_start , form_submit , conversion

page_url String Page path / , /pricing , /checkout , /success

timestamp DateTime ISO 8601 UTC 2024-11-15T14:32:00Z

session_id UUID Groups session events 80ea1bd9-d628-4ef6-bf26-64d928490205

conversion_value Numeric USD (conversions only) 150.00 or empty

Funnel stages (event_name values):

• page_view

• Landing page visit

• pricing_view

• Viewed pricing page

• checkout_start

• Started checkout

• form_submit

• Submitted form

• conversion

• Completed purchase

daily_metrics.csv (~60 records)

Daily aggregated metrics with engineered anomalies.

Field Type Description

date Date Metric date

sessions Integer Daily sessions

users Integer Unique users

conversions Integer Daily conversions

revenue Numeric Daily revenue (USD)

conversion_rate Numeric Conversions / users

avg_order_value Numeric Revenue / conversions

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).

Field Type Description

user_id UUID Unique identifier

signup_date Date Trial start date

plan_type String Always free_trial

converted Boolean Whether converted to paid

conversion_date Date When converted (nullable)

days_to_convert Integer Days from signup to conversion

feature_usage.csv (~2,500 records)

Feature adoption events (Demo 4).

Field Type Description

user_id UUID FK to trial_users

feature_name String Feature used

first_used_date Date First usage date

usage_count Integer Total uses

days_since_signup_first_use Integer Days from signup to first use

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).

Field Type Description

url String Landing page URL

utm_source String Source parameter

utm_medium String Medium parameter

utm_campaign String Campaign parameter

session_count Integer Sessions with this UTM

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

File Location Records

users.csv data/users.csv

~5,000

events.csv data/events.csv

~57,000

daily_metrics.csv data/daily_metrics.csv

~60

trial_users.csv data/trial_users.csv

~500

feature_usage.csv data/feature_usage.csv

~2,500

utm_data.csv data/utm_data.csv

~12

BigQuery Location

Project: agents-webinar-2025

Dataset: webinar_demos

Tables: users , events , daily_metrics , trial_users , feature_usage