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Description

The Platform - Snapchat dataset contains delivery, spend, engagement, video, and conversion performance metrics from Snapchat Ads. Data is available at the ad-level by date and hour.

Partition Keys

  • identifier
  • date
💡 Note: Leverage partition keys when querying the table to improve query efficiency.

Primary Key

The concatenation of the following fields can be used as a primary key:
  • identifier
  • date
  • utc_hour
  • ad_id

Field Reference

OrderFieldDescriptionType
1advertiserRockerbox account ID.str
2typeDataset type (e.g., platform_data).str
3platformAdvertising platform name (Snapchat).str
4reportDataset name (platform_performance_snapchat).str
5identifierUnique identifier of the Snapchat ad account.str
6dateDate the performance metrics occurred.date
7utc_hourUTC hour the performance metrics occurred.int
8tier_1Marketing channel categorization level 1.str
9tier_2Marketing channel categorization level 2.str
10tier_3Marketing channel categorization level 3.str
11tier_4Marketing channel categorization level 4.str
12tier_5Marketing channel categorization level 5.str
13mta_tiers_join_keyIdentifier used to join platform spend to MTA datasets. Typically ad_id.str
14campaign_nameCampaign name in Snapchat.str
15campaign_idUnique campaign ID in Snapchat.str
16ad_squad_nameAd squad name.str
17ad_squad_idUnique ad squad ID.str
18ad_nameAd name.str
19ad_idUnique ad ID.str
20ad_typeSnap ad format (e.g., SNAP_AD, LONGFORM_VIDEO).str
21spendEstimated total spend in the account’s local currency.float
22currency_codeISO currency code of the ad account (e.g., USD, EUR).str
23spend_usdEstimated total spend in USD.float
24swipesSwipe-up count.int
25impressionsTotal impressions.int
26earned_impressionsImpressions generated after being shared via Chat or Stories.int
27paid_impressionsImpressions served via paid delivery.int
28total_impressionsCombined paid and earned impressions.int
29screen_time_millisTotal time spent viewing Top Snap ads (milliseconds).float
30avg_screen_time_millisAverage Top Snap view time (milliseconds).float
31quartile_1Video views to 25%.int
32quartile_2Video views to 50%.int
33quartile_3Video views to 75%.int
34view_completionVideo views to completion.int
35video_viewsImpressions meeting qualifying video view criteria (≥2 seconds consecutive watch time or swipe-up).int
36video_views_15sImpressions meeting ≥15 seconds watched (or 97% completion if shorter) or swipe-up.int
37video_views_time_basedImpressions meeting ≥2 seconds consecutive watch time (excluding swipe-ups).int
38savesNumber of times a lens/filter was saved to Memories.int
39sharesNumber of times a lens/filter was shared via Chat or Stories.int
40attachment_impressionsImpression count from attachments.int
41attachment_quartile_1Long-form video views to 25%.int
42attachment_quartile_2Long-form video views to 50%.int
43attachment_quartile_3Long-form video views to 75%.int
44attachment_view_completionLong-form video views to completion.int
45attachment_avg_view_time_millisAverage attachment view time (milliseconds).float
46attachment_total_view_time_millisTotal attachment view time (milliseconds).int
47view_1_dayView-through conversions within a 1-day lookback window.dict
48view_7_dayView-through conversions within a 7-day lookback window.dict
49swipe_1_dayClick-through (swipe) conversions within a 1-day lookback window.dict
50swipe_7_dayClick-through (swipe) conversions within a 7-day lookback window.dict
51swipe_28_dayClick-through (swipe) conversions within a 28-day lookback window.dict
52view_1_day_valueTotal value of 1-day view-through conversions (local currency).dict
53view_7_day_valueTotal value of 7-day view-through conversions (local currency).dict
54swipe_1_day_valueTotal value of 1-day click-through conversions (local currency).dict
55swipe_7_day_valueTotal value of 7-day click-through conversions (local currency).dict
56swipe_28_day_valueTotal value of 28-day click-through conversions (local currency).dict
57view_1_day_value_usdUSD value of 1-day view-through conversions.dict
58view_7_day_value_usdUSD value of 7-day view-through conversions.dict
59swipe_1_day_value_usdUSD value of 1-day click-through conversions.dict
60swipe_7_day_value_usdUSD value of 7-day click-through conversions.dict
61swipe_28_day_value_usdUSD value of 28-day click-through conversions.dict
62rb_sync_idRockerbox-generated identifier used to sync the dataset to your warehouse.str
63updated_atTimestamp of the most recent row update.timestamp

Nested Fields

The following fields are nested JSON objects keyed by Snapchat conversion event name: view_1_day view_7_day swipe_1_day swipe_7_day swipe_28_day view_1_day_value view_7_day_value swipe_1_day_value swipe_7_day_value swipe_28_day_value view_1_day_value_usd view_7_day_value_usd swipe_1_day_value_usd swipe_7_day_value_usd swipe_28_day_value_usd

Example Stored Object

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

Snowflake - Querying Nested Fields

Extract a Single Event

select
  date,
  ad_id,
  view_1_day:"purchase"::number as purchase,
  view_1_day_value:"purchase"::float as purchase_value
from <database>.<schema>.<table_name>;

Flatten All Events Into Rows

select
  t.date,
  t.ad_id,
  f.key as conversion_event,
  f.value::number as conversions_view_1_day
from <database>.<schema>.<table_name> t,
  lateral flatten(input => t.view_1_day) f;

Redshift - Querying Nested Fields (SUPER type)

Extract a Single Event

select
  date,
  ad_id,
  view_1_day['purchase']::int as purchase,
  view_1_day_value['purchase']::decimal(18,4) as purchase_value
from <database>.<schema>.<table_name>;

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.view_1_day as kv;

BigQuery - Querying Nested Fields (JSON type)

Extract a Single Event

select
  date,
  ad_id,
  cast(json_value(view_1_day, '$.purchase') as int64) as purchase,
  cast(json_value(view_1_day_value, '$.purchase') as float64) as purchase_value
from <project_id>.<dataset>.<table_name>;

Flatten All Events Into Rows

select
  t.date,
  t.ad_id,
  k as conversion_event,
  cast(json_value(t.view_1_day, concat('$.', k)) as int64) as conversions
from <project_id>.<dataset>.<table_name> t,
unnest(json_keys(t.view_1_day)) as k;