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One of the main reasons to implement a strict and consistent naming convention is to enable granular, scalable analytics. With clean campaign naming, you can extract structured fields like campaign type, country, or focus area — and then use them to filter, group, or visualize data however you need.

In this guide, you’ll learn how to apply your Naming Convention setup directly inside an Improvado transformation, so that your enriched naming data (from the Naming Convention product) becomes available inside your raw Google Ads data and powers deeper reporting.

Apply Naming Convention with AI Agent

The fastest way to apply your Naming Convention to a transformation recipe is through the AI Agent.

Step 1. Open the AI Agent from the sidebar.

Step 2. On the home screen, find the Naming Convention use-case card and click it.

Step 3. The AI Agent walks you through the setup: it finds the right taxonomy table for your recipe, recommends which dimension to standardize (e.g., campaign type, region, objective), asks whether to keep original values alongside the corrected ones or replace them, and configures the join in your recipe automatically.

No need to locate table names, set up keys, or select fields manually — the Agent handles it for you.

{%docs-informer info%} This works best when your Naming Convention is already set up and at least partially validated. If you're starting from scratch, complete Step 1 of the manual setup below first. {%docs-informer-end%}

If you prefer full control over field selection and join configuration, follow the manual steps below.

Step 1: Create and Confirm Your Naming Convention

Before you apply naming data inside a transformation, make sure your Naming Convention setup is complete, consistent, and verified.

• Go to the Naming Convention Section.
• Clean your naming convention data.
• Review the table and confirm that all campaigns are 100% compliant (no broken values, empty fields, or skipped rows).

📄 Read the guide: How to Set Up Your Naming Convention

Once this is done, you’re ready to bring this clean structure into your data pipeline.

Step 2: Open or Create a New Transformation Recipe

We’ll now add this structured naming data into your Google Ads transformation.

• Go to Transformations
• Create or open a recipe.

Step 3: Add the Raw Google Ads Table

Start by pulling the necessary Google Ads fields into your recipe.

• Add the table with your raw campaign data.
• Select required fields (e.g. campaign_id, spend, clicks, impressions, revenue, etc.)

Step 4: Join the Naming Convention Table

Now that your base Google Ads data is ready, the next step is to enrich it with taxonomy fields from your Naming Convention.

1. Go to the Naming Convention.
2. Locate the exact name of the output table for the platform you’re working with.

• Example: ```google_campaign_name_data```.

3. Copy the table name or just find it directly in the join UI.
4. In your recipe, add this table in the join step.

5. Use the correct join key:

• Typically, this will be campaign_id or another dimension ID that both tables share.

6. Select the fields you want from both sides — usually:

• From your Google Ads table: performance metrics.
• From the Naming Convention table: new_campaign_name, campaign_type, focus_area, country, etc.

Step 5: Run and Save

Once everything is connected:

• Click Run & Save.
• You’ll now have a merged table that includes:
• ~All your Google Ads metrics
• ~Structured naming fields (campaign_type, focus_area, country)
• ~Clean new_campaign_name

Step 6: Use in Reporting

You can now use this enriched data in any visualization tool:

• Load the table into Looker Studio, Tableau, or any BI platform
• Use the parsed fields from the naming convention to:
• ~Group campaigns by type or region
• ~Filter dashboards by focus area
• ~Run performance comparisons at any taxonomy level

Conclusions

By joining the Naming Convention table directly into your transformation recipe, you can stop hardcoding extraction logic and let the structured taxonomy do the work. This makes your dashboards faster to build, easier to maintain, and far more consistent across teams.

No manual parsing. No regex. Just clean, ready-to-use campaign fields — every time.