Google Ads: Tips and best practices

Google Ads: Tips and best practices#

Reporting on Performance Max campaigns#

Performance Max campaigns are included in reports that use the Campaign or Asset Group resource level.

Reports using ad group or ad level dimensions will not include Performance Max campaigns, as these campaigns use asset groups instead of traditional ad groups or ads.

For more detailed analysis, select the Asset Group, Asset Group Asset, or Asset Group Product Group View resource levels to access additional attributes such as ad strength and performance labels, or to report on listing groups in retail campaigns.

Selecting the date range for a fetch#

The selected date segment determines the date range you can choose for a data fetch. Because of the requirements of the Google Ads API, the date range needs to start and end on the first day of the selected date segment. Adverity automatically changes the date range of the fetch to be compatible with this requirement. For example, if you select a custom date range of 15 March - 15 April with a monthly date segment, Adverity modifies the time range to 1 March - 1 April, and collects data for the months of March and April. To collect data for the month of March only, select a date range of 1 March - 1 March.

Previewing an image#

To be able to preview an image, you need to collect Image ad preview image URL dimension.

Using Adverity datastream templates#

Use Adverity Datastream Templates to save you time editing all the options in this connector.

Performance optimization#

Enrichment required to calculate the aggregate metric potential impressions.

Fetch field Advertising Channel to view campaign type in the UI.

Clients may be confused by the Google Ads Keyword Planner connector. This is used for researching search terms, not reporting on own paid performance.

For dashboards, if there are multiple Google Ads datastreams make sure widgets are filtered by datastream to avoid double counting metrics.

Understanding Performance Max campaigns#

Performance Max automates ad serving. (It started around Sept 2022). You’ll see it reported as Network = Mixed because the actual networks are not yet reported. Use Advertising Channel to report the type of campaign. Performance Max is reported at campaign level, they do not have an ad group.

Recommended datastream configurations#

Google Ads | Campaign Report | Campaign Level#

This is your standard datastream with basic metrics.

Report Type = Campaign Basic Performance
Resource Level = Campaign
Segments = Day, Network

Google Ads | Keyword Report#

Select ‘Custom’ report type and resource level ‘Keyword View’ or ‘Search Term View’. Segments may include Day, Network, Device, Keyword, Match type.

Google Ads | Custom Report | Gender#

If you require gender breakdown, select custom report type and resource level ‘Gender View’

Google Ads | Custom Report | Age Range#

If you require age breakdown, select custom report type and resource level ‘Age Range View’

Understanding campaign types#

The API calls this Advertising Channel you can add it to attributes in your datastream settings.

Reach reporting limitations#

Unfortunately reach is not supported by the Google Ads API. Google Ads AverageFrequency and ImpressionReach have been deprecated.

Understanding “Mixed” network reporting#

This is how Google Ads currently reports on its automated campaigns, this could be Performance Max, Discovery Ads and Dynamic search ads. Use the Advertising Channel to denote between those types.

Organic search data sources#

Google Ads only reports on paid search. The most common data sources our clients are using for organic search are:

Google Analytics: traffic by keywords
SEMRush: Competitors in Organic Search

Performance Max campaign access#

Performance Max-related data is only accessible in the ‘Campaign Basic Performance’ report type and ‘Campaign’ resource level. If you’re unable to view PMAX campaigns you’ve likely selected the wrong resource level.

Revenue field selection#

API field corresponding to revenue is “Conversion value (current model)” - the first part returns the sum of conversion values for all conversions, the second stands for the current attribution model selected in the Google Ads UI.

Available granularity levels#

Account
Campaign
Ad Group (recommended level if not specified)
Ad

Connector limitations#

There are many different report combinations. You may require numerous datastreams to pull all the fields you require.
Search Impressions Share comes through as a percentage and must be enriched to calculate the aggregate metric potential impressions
Keywords report doesn’t allow ad group
Reach not currently supported by the API
Custom columns are not supported by the Google Ads API

Setup best practices#

Visualization considerations#

If there are multiple Google Ads datastreams make sure widgets are filtered by datastream to avoid double counting metrics.

Authentication setup#

All customers in one authorisation. You must have ‘Read only’ or higher permissions.

Data retention recommendations#

Local data retention: unique by day using Day
Overwrite: Date range is Day

Using filters with Google Ads#

When configuring a Google Ads datastream, you can use the Custom Filter field in the Advanced section to filter your data before fetching results. This allows you to apply specific conditions to your queries, such as retrieving only certain campaign types or filtering by performance metrics.

When configuring the Custom Filter field, you are defining conditions for the WHERE clause described in the Google Ads documentation.

The filter expressions support a number of operators, including:

Equality operators: =, !=
Comparison operators: >, >=, <, <=
String matching: LIKE, NOT LIKE, CONTAINS ANY, CONTAINS ALL, CONTAINS NONE
Set operators: IN, NOT IN
Null checks: IS NULL, IS NOT NULL
Date ranges: DURING, BETWEEN
Regular expressions: REGEXP_MATCH, NOT REGEXP_MATCH

Example filter expressions:

metrics.clicks > 7
campaign.id IN (918735746)
campaign.name LIKE '%Brand%'
metrics.impressions > 0 AND metrics.clicks > 10
campaign.advertising_channel_type = 'PERFORMANCE_MAX'

You can combine multiple conditions using AND operators. If your filter syntax is incorrect, you’ll see a validation error.

For more complex queries, refer to the full query grammar reference in the Google Ads documentation.

Calculating impression share and related metrics#

The Google Ads connector returns some metrics as percentages (including impression share). Percentages cannot be aggregated or combined. However, data returned as percentages can be further processed to make them ready for aggregation. For more information, see the Google documentation.

This section enables you to calculate potential impressions from the impression share metric. You can then send potential impressions to Explore & Present or load them into destinations and use this metric to calculate impression share percentages for any combination of dimensions.

Prerequisites#

Create a Google Ads datastream.
Apply the correct Data Mapping to your datastream.
Make sure that your data extract contains the following fields:
- Impressions
- Search impression share
- Search lost impression share (budget)
- Search lost impression share (rank)

Procedure#

To calculate potential impressions from the impression share metric, apply the following Custom Script transformation:

[
    [
        "extendmissing",
        {
            "subtable": null,
            "fieldnames": [
                "Search lost impression share (rank)",
                "Search lost impression share (budget)"
            ],
            "missing": "0.0",
            "_comment": null
        }
    ],
    [
        "convertnumbers",
        {
            "fields": [
                "Impressions",
                "Search impression share",
                "Search lost impression share (rank)",
                "Search lost impression share (budget)"
            ],
            "default": 0,
            "strict": false,
            "subtable": null,
            "locale": "en",
            "_comment": null
        }
    ],
    [
        "addfieldx",
        {
            "index": null,
            "subtable": null,
            "field": "Potential impressions",
            "expression": "round({Impressions} / {Search impression share}) if {Search impression share} > 0 else '0'",
            "_comment": null
        }
    ],
    [
        "addfieldx",
        {
            "index": null,
            "subtable": null,
            "field": "Lost impressions (budget)",
            "expression": "round({Potential impressions}*{Search lost impression share (budget)}) if {Search lost impression share (budget)} > 0 else '0'",
            "_comment": null
        }
    ],
    [
        "addfieldx",
        {
            "index": null,
            "subtable": null,
            "field": "Lost impressions (rank)",
            "expression": "round({Potential impressions}*{Search lost impression share (rank)}) if {Search lost impression share (rank)} > 0 else '0'",
            "_comment": null
        }
    ]
]

After applying the transformation, map the new fields in Data Mapping as follows:

Potential impressions → potential_impressions (Type: Float, Measure: Sum)
Lost impressions (rank) → your target field (Type: Float, Measure: Sum)
Lost impressions (budget) → your target field (Type: Float, Measure: Sum)