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Overview

The Platform Performance – Google dataset contains aggregated performance metrics from Google Ads (formerly AdWords), including spend, impressions, clicks, and conversion data. Primary Key The concatenation of the following fields can be used as a primary key:
  • identifier
  • date
  • utc_hour
  • campaign_id
  • ad_group_id
  • ad_network_type

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Schema

OrderNameDescription
1advertiserThe name of the Rockerbox account associated with the data export.
2typeThe dataset type. For this table, the value will be platform_data.
3platformThe advertising platform name. For this dataset, the value is Adwords (Google Ads).
4reportThe name of the dataset. For this table, the value is platform_performance_ad.
5identifierThe unique identifier of the advertiser account in Google Ads.
6dateThe calendar date associated with the reported performance metrics.
7utc_hourThe UTC hour during which the activity occurred.
8tier_1Marketing channel categorization level 1, as defined in your Rockerbox configuration.
9tier_2Marketing channel categorization level 2, as defined in your Rockerbox configuration.
10tier_3Marketing channel categorization level 3, as defined in your Rockerbox configuration.
11tier_4Marketing channel categorization level 4, as defined in your Rockerbox configuration.
12tier_5Marketing channel categorization level 5, as defined in your Rockerbox configuration.
13mta_tiers_join_keyThe unique identifier used to join spend data from Google Ads to Rockerbox MTA datasets. This is typically the ad_id, but may vary depending on your account setup.
14ad_network_typeThe Google Ads network where the ad was served (e.g., SEARCH, SEARCH_PARTNERS, MIXED, YOUTUBE_WATCH).
15advertising_channel_typeThe primary advertising channel type for the campaign (e.g., SEARCH, DISPLAY, SHOPPING).
16campaign_nameThe name of the campaign in Google Ads. A campaign may contain one or more ad groups.
17campaign_idThe unique identifier of the campaign in Google Ads.
18ad_group_typeThe type of ad group (e.g., SEARCH_STANDARD, DISPLAY_STANDARD).
19ad_group_nameThe name of the ad group. An ad group contains one or more ads.
20ad_group_idThe unique identifier of the ad group in Google Ads.
21spendThe total estimated spend in the account’s local currency for the given date and dimension breakdown.
22currency_codeThe local currency of the Google Ads account (e.g., USD, EUR).
23spend_usdThe total estimated spend converted to USD.
24clicksThe number of clicks recorded on the ads.
25impressionsThe number of times ads were served across Google properties or partner networks.
26cost_microsAd spend in micro units of the account’s local currency. For example, $1.23 is represented as 1,230,000 (1.23 × 1,000,000).
27all_conversions_by_conversion_dateThe total number of conversions attributed based on the actual conversion date.
28all_conversionsThe total number of conversions attributed based on the date of the associated advertising interaction (impression or click).
29view_through_conversionsThe number of conversions attributed to impressions without a click interaction.
30all_conversions_value_by_conversion_dateThe total value of conversions attributed based on the actual conversion date.
31all_conversions_valueThe total value of conversions attributed based on the date of the associated advertising interaction (impression or click).
32view_through_lookback_window_daysThe maximum number of days between an impression and a conversion for the conversion to be attributed without an interaction.
33click_through_lookback_window_daysThe maximum number of days between a click and a conversion for the conversion to be attributed.
34rb_sync_idInternal identifier used by Rockerbox to sync this dataset to your data warehouse.
35updated_atTimestamp indicating when the record was last updated in Rockerbox.

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Notes

  • All spend metrics reflect values reported by Google Ads.
  • Currency fields reflect the account-level configuration in Google Ads.
  • Conversion metrics may vary depending on attribution settings configured within Google Ads.
  • For reconciliation, cost_micros can be divided by 1,000,000 to match the local currency spend.

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Nested Fields

The following fields are nested JSON objects keyed by Facebook conversion event name:
  • all_conversions_by_conversion_date
  • all_conversions
  • view_through_conversions
  • all_conversions_value_by_conversion_date
  • all_conversions_value
  • view_through_lookback_window_days
  • click_through_lookback_window_days

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Example Stored Object

{
  "purchase": 12,
  "add_to_cart": 41
}

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Snowflake - Querying Nested Fields

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Extract a Single Event

select
  date,
  ad_id,
  all_conversions_by_conversion_date:"purchase"::number as purchase,
  all_conversions_value_by_conversion_date:"purchase"::float as purchase_value
from <database>.<schema>.<table_name>;

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Flatten All Events Into Rows

select
  t.date,
  t.ad_id,
  f.key as conversion_event,
  f.value::number as all_conversions
from <database>.<schema>.<table_name> t,
  lateral flatten(input => t.all_conversions_by_conversion_date) f;

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Redshift - Querying Nested Fields (SUPER type)

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Extract a Single Event

select
  date,
  ad_id,
  all_conversions_by_conversion_date['purchase']::int as purchase,
  all_conversions_value_by_conversion_date['purchase']::decimal(18,4) as purchase_value
from <database>.<schema>.<table_name>;

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Flatten All Events Into Rows

select
select
  t.date,
  t.ad_id,
  kv.key as conversion_event,
  kv.value::int as conversions
from <database>.<schema>.<table_name> t,
  t.all_conversions_by_conversion_date as kv;

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BigQuery - Querying Nested Fields (JSON type)

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Extract a Single Event

select
  date,
  ad_id,
  cast(json_value(all_conversions_by_conversion_date, '$.purchase') as int64) as purchase,
  cast(json_value(all_conversions_value_by_conversion_date, '$.purchase') as float64) as purchase_value
from <project_id>.<dataset>.<table_name>;

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Flatten All Events Into Rows

select
  t.date,
  t.ad_id,
  k as conversion_event,
  cast(json_value(t.all_conversions_by_conversion_date, concat('$.', k)) as int64) as conversions
from <project_id>.<dataset>.<table_name> t,
unnest(json_keys(t.all_conversions_by_conversion_date)) as k;