What types of campaigns are supported in the Yandex.Direct → Google BigQuery pipeline

The Yandex.Direct → Google BigQuery pipeline imports cost data on the following campaign types:

  • Text & Image Ads (TEXT_CAMPAIGN)
  • Smart banners (SMART_CAMPAIGN)
  • Dynamic ads (DYNAMIC_TEXT_CAMPAIGN)
  • Ads for mobile apps (MOBILE_APP_CAMPAIGN)
  • Search banner (MCBANNER_CAMPAIGN)
  • Display campaign (CPM_BANNER_CAMPAIGN)
  • Display campaign with deals (CPM_DEALS_CAMPAIGN)
  • Display campaign on the Home page (CPM_FRONTPAGE_CAMPAIGN)
  • Campaign with fixed СРМ (CPM_PRICE)
  • Campaigns created via Campaign Wizard

Read more about these campaign types in Yandex documentation.

If you use your Yandex.Direct login to promote the ads in Yandex.Zen, OWOX BI will import your total costs on this service. Costs will be reported at the level of one campaign with the type “Text & Image Ads” and the name “Zen”, without statistics on clicks and impressions.

Important

The next features are not supported due to the Yandex.Direct API limitations:

  • Retrieving the UTM tags on the "Search banner", "Display campaign with deals", "Display campaign on the Home page" campaign types, and on campaigns created via the Campaign Wizard. Yet, you can define the default values for UTM source/medium in your pipeline settings (see paragraph 7 of the instructions).
  • Retrieving the UTM tags on certain ad types of the Smart banners campaign (i.e., for the SMART_AD advertisements).
  • Retrieving the AdGroupId, AdGroupName, AdId, and CriterionId values on campaigns created via the Campaign Wizard.
  • Cost data import on the ad campaigns created via Product campaigns.

NoteThere may be discrepancies in cost data on Display campaigns (CPM_BANNER_CAMPAIGN) between your ad account and Google BigQuery due to the peculiarities of cost calculation and rounding for this campaign type in Yandex API.
The cost is calculated per impression. For example, if 1000 impressions cost RUB 0.1, then 1 impression costs RUB 0.0001, which when rounded equals RUB 0.00. Thus, the more granular grouping is in the report, the higher the probability that the rounding error occurs.

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