Campaign Naming Convention Playbook for Automated Reporting

Most marketing teams are drowning in bad campaign names:

“Test1”
“Summer2023_FINAL”
“New Audience v3”

Then they wonder why:

• Dashboards don’t work

• Reporting eats all of Friday

• AI insights are not meaningful

Here’s the truth: this isn’t rocket science.

Naming conventions, data modeling, and reporting automation are easier than they’ve ever been — especially now that LLMs can do half the heavy lifting.

Let’s fix that.

Step 1: Campaign Naming (The Foundation)

Every campaign name should answer who, what, where, why, when.

Structure:
Client_Geo_Channel_Platform_Objective_Targeting_Type_Date_Version

Example:
YourBRAND_US_Search_GoogleAds_Prospecting_Lookalike_Promo_202510_v1

Step 2: Ad Group / Ad Set Naming

Break down audience + placement + device.

Example:
Lookalike1%_Feed_Mobile_v2

Step 3: Creative Naming

Creatives should spell out format + message + size.

Example:
Video_Discount_9x16_v1

Step 4: Audience Naming

Source + window + segment.

Example:
CRM_Purchasers_90d_HighValue

Step 5: UTM Parameters

UTMs connect your ad platforms to GA4/Looker.

• utm_source = google/meta/linkedin

• utm_medium = search/social/display

• utm_campaign = campaign name

• utm_content = creative details

• utm_term = keyword/audience

Example: ?utm_source=google&utm_medium=search&utm_campaign=YourBRAND_US_Search_Prospecting_202510&utm_content=Video_Discount_9x16_v1

Step 6: Build It Yourself (Free + Easy)

1. Google Form → capture metadata (Client, Geo, Channel, etc.).

2. Google Sheets → responses flow in automatically.

3. Concatenation Formula → outputs clean names.

=JOIN("_", B2, C2, D2, E2, F2, G2, H2, I2)

Boom. Free naming convention generator.

Step 7: Bulk Upload + AI Parsing

“But what about my thousands of messy campaigns across platforms?”

Easy:

1. Export campaign names from Google Ads, Meta, LinkedIn, etc.

2. Upload into Google Sheets.

3. Drop them into ChatGPT or Gemini with this prompt:

Split these campaign names into Client, Geo, Channel, Platform, Objective, Targeting, CampaignType, FlightDate, Version.

Congratulations: you just turned chaos into a clean dataset.

Step 8: Data Modeling & Automation

This is where the gatekeepers really overcomplicate things.

• Fact tables = numbers (spend, clicks, conversions).

• Dimension tables = labels (campaigns, ad groups, creatives, audiences).

• Split your campaign names → boom, dimensions appear.

Plug into Looker Studio or Power BI → automated reporting at scale.

Step 9: The AI Layer

Clean naming isn’t just about dashboards — it’s about being AI-ready.

You can’t bolt AI onto chaos.

If your campaigns are called “Test1”, no model in the world can answer: “How did prospecting perform in EMEA across Google + Meta last quarter?”

But with structured names, that’s a one-line query.

Final Word

This isn’t rocket science.
It’s not “advanced ops.”

• Clean names → clean data.

• Clean data → automated dashboards.

• Automated dashboards → AI insights that actually matter.

The future of marketing ops is open, simple, scalable. Start with naming conventions.