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):

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.

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