Tag Archives: disapproved

“Disapproved Ads Auditor” tool

Posted by Nir Kalush, Dvir Kalev, Chen Yoveg, Elad Ben-David

Ads Review Tool

This tool flags (and optionally deletes) policy violating ads across your accounts. Advertisers can learn from the output to ensure their ads are compliant with Google Ads Policies at all times.

Business Challenge:

Advertisers operating at scale need a solution to holistically review policy violating ads across their accounts so they can ensure compliance with Google’s Ad Policies. As Google introduces more policies and enforcement mechanisms, advertisers need to continue checking their accounts to ensure they comply with Google’s Ads Policies.

Solution Overview:

“Disapproved Ads Auditor” is a tool that enables advertisers to review at scale all disapproved ads across their Google Ads accounts. This view allows advertisers to proactively audit their accounts , analyze the ad disapprovals holistically and identify learnings to minimize and reduce submission of potentially policy violating ads.

The tool is based on a Python script, which can be run in either of the following modes:

  • “Audit Mode”- export an output of disapproved ads across your accounts
  • “Remove Mode” - delete currently disapproved ads and log details.

There are a few output files (see here) which are saved locally under the “output” folder and there is an optional feature to export on BigQuery for further data analysis (“Disapproved Ads Auditor” dataset).

Skills Required:

Google Products Used:

  • Google Ads
  • BigQuery

Estimated time to implement the solution: ~2h

Implementation instructions: View on github

Impact:

“Disapproved Ads Auditor” tool automates auditing ads across your accounts to provide you insights into non compliant ads. You can take the learnings from the output to ensure ads are compliant with Google Ads Policies and avoid creating non compliant ads. Moreover, you can optionally remove disapproved ads.

“Bowling” automatic disapproved ads remover

Posted by Elad Ben-David, Nir Kalush, Dvir Kalev, Chen Yogev, Tzahi Zilberstein, Eliran Drucker

Image that shows three bowling pins, a bowling ball, and text that reads 3 Strikes Bowling

Tagline:

In light of the new policy that might cause accounts suspension, Bowling is a mitigation tool allowing clients to act and remove disapproved ads before risking account suspension.
The tool audits (and offers the option to delete) disapproved ads that may lead eventually to account suspension in perpetuity.

Business Challenge:

Starting Oct 2021 Google is introducing a new strike-based system to enforce against advertisers who repeatedly violate Google Ads policies (read more about the change here).

An advertiser’s first policy violation will result in a warning. If we detect continued violation of our policies advertisers will receive a notice they got a strike on the account, with a maximum of three strikes. The penalties applied with each strike will progressively rise. Temporary account holds will be applied for the first and second strikes, while the third strike will cause an account suspension.

Advertisers with hundreds of accounts and billions of search keywords lack the bandwidth to monitor each violation, thus might receive repeated strikes and get suspended.

Solution Overview:

“3 Strike Bowling” is an automated solution which identifies and gathers all relevant disapproved apps and includes the option to remove violating ads, in order to ensure compliance and avoid account suspension.
*The user can define an exclusion list of policy topics to ignore and not remove.

It’s a simple Python script, which can be run in either of the following modes:

  • “Audit Mode”- export all the disapproved ads without deleting them;
  • “Remove Mode” - delete all the disapproved ads and log the disapproved ads’ details.

There are a few output files (see here) which are saved locally under the “output” folder and optionally on BigQuery as well ( “google_3_strikes” dataset).

Skills Required:

  • Install technical prerequisites.

Google Products Used:

  • Google Ads API
  • BigQuery API (optional)

Estimated time to implement the solution: ~2h

Implementation instructions: View on github