How to Load Google Ads Data into Snowflake with OWOX Data Marts

Modern performance teams don't just need Google Ads data in Snowflake – they need it modeled, governed, and ready to answer questions in seconds. This tutorial walks you through that full journey: connecting Snowflake, ingesting Google Ads data, transforming it into standardized, reusable data marts, enriching it with other channels, and delivering insights to stakeholders and AI agents without SQL bottlenecks.

We'll focus on how to move from raw export tables and fragile spreadsheets to a reliable analytics layer that supports marketing performance reporting, budget optimization and forecasting, attribution and incrementality analysis, and AI-assisted analytics and decision support.

What You'll Build in This Guide

By the end of this guide, you will have:

• Automated Google Ads → Snowflake ingestion – A robust, scheduled pipeline that keeps your ad performance data continuously up to date.
• Standardized Google Ads data marts – Modeled tables with consistent metrics, dimensions, and business logic, reusable across dashboards and tools.
• Cross-channel and revenue-joined views – Combined datasets that align Google Ads data with other platforms and backend systems for full-funnel analysis.
• A governed reporting layer ready for BI and AI – Clean, documented tables that downstream users – analysts, marketers, or AI copilots – can query safely.

Throughout the tutorial, we'll highlight where OWOX Data Marts helps you automate modeling and enforce governance. If you want to follow along in your own environment, you can start by creating a workspace.

Why Move Google Ads Data into Snowflake?

Google Ads reporting works for campaign-level decisions, but it has limitations when analytics becomes cross-functional or long-term.

Native tools and direct exports struggle with:

• Long-term historical storage
• Cross-channel analysis
• Revenue-level modeling
• Consistent business logic
• Advanced forecasting and experimentation

By moving Google Ads data into Snowflake, teams gain:

• Centralized storage
• Flexible SQL-based transformations
• Integration with BI tools and downstream systems
• A foundation for AI-assisted analytics

This shifts reporting from platform dashboards to warehouse-driven modeling.

Why a Data Mart–First Approach Beats DIY ETL

Raw Google Ads tables mirror API structures and require repeated transformation logic across dashboards. Without a standardized modeling layer, every analyst recreates joins and metric calculations.

A data mart–first approach solves this by:

• Defining standardized metrics such as CTR, CPC, CPA, and ROAS
• Centralizing business logic for attribution and breakdown rules
• Creating reusable reporting-ready tables
• Reducing maintenance overhead when API schemas change

OWOX Data Marts is built around this philosophy: ingestion is the first step, but the objective is governed, analytics-ready tables that teams can safely reuse.

Architecture Overview: Google Ads to Snowflake

The Google Ads → Snowflake pipeline follows a structured, warehouse-centric architecture that separates ingestion from modeling and reuse.

Extraction

OWOX connects to the Google Ads API and retrieves campaign, ad group, keyword, and conversion data on a scheduled basis.

Loading

The extracted data lands in Snowflake raw tables that closely mirror the API schema. This layer preserves full granularity and supports validation or edge-case queries.

Re-using

On top of raw tables, OWOX builds data marts that:

• Normalize cost and currency values
• Standardize derived metrics
• Join related entities
• Prepare datasets for reporting and AI workflows

This layered architecture separates ingestion from business logic.

What You Need Before You Start

Before configuring the pipeline, ensure you have proper access to both Snowflake and Google Ads. The requirements below cover what you need to connect the warehouse, authorize the source, and start loading data reliably.

Snowflake Access

You need a Snowflake role or user that can:

• Create or use a database and schema for marketing data
• Create and modify tables and views
• Run SELECT and INSERT operations
• Use a warehouse for loading and transformation

Many teams create a dedicated database (e.g., MARKETING) and schema (e.g., GOOGLE_ADS) to keep assets organized.

Google Ads (or MCC) Access

You need a Google Ads account with:

• Access to the required ad accounts
• Permissions to read campaigns, ad groups, keywords, and conversions
• API-level access enabled

If using an MCC account, ensure visibility across all managed accounts.

What Data Lands in Snowflake

After the Google Ads connector runs, data lands in Snowflake in two distinct layers: a raw ingestion layer that mirrors the API structure, and a modeled data mart layer designed for reporting and analysis.

Understanding this separation is critical – raw tables preserve granularity and traceability, while data marts standardize metrics and prepare the data for reuse across dashboards, SQL queries, and AI workflows.

Raw Tables

These tables mirror the Google Ads API structure and typically include:

• Campaign-level statistics
• Ad group performance data
• Keyword statistics
• Conversion metrics
• Account-level attributes

They preserve full granularity and are primarily used for validation, reconciliation, and advanced edge-case analysis.

Data Marts (Analytics Layer)

On top of raw tables, OWOX builds reporting-ready data marts with:

• Daily reporting grain
• Standardized metrics (impressions, clicks, cost, conversions, conversion_value)
• Derived metrics (CTR, CPC, CPA, ROAS)
• Structured joins across campaigns, ad groups, and keywords

This layer becomes the stable foundation for dashboards, cross-channel analysis, and AI-driven insights.

Step 1: Connect Snowflake as Your Storage in OWOX Data Marts

The first step in your Google Ads → Snowflake pipeline is to configure Snowflake as your primary storage inside OWOX Data Marts. This connection is created once and reused across all data marts, including the one that will ingest Google Ads data.

Creating a Snowflake connection in OWOX Data Marts

First, you’ll tell OWOX how to reach your Snowflake account and which credentials to use.

1. Log in to OWOX Data Marts

• Go to https://www.owox.com/app-signup
• Create a workspace if you don’t already have one.
  
  

2. Go to the data storages

• In the OWOX Data Marts interface, open the Storages section
• Then click “+ new storage”.

3. Select Snowflake as the storage type

• From the list of available destinations, choose Snowflake.
• This opens the Snowflake configuration form, where you’ll enter connection parameters.
  
  

4. Enter Snowflake connection details

Provide the required connection parameters:

• Account Locator and Account Identifier (for example, mycompany-xy12345.eu-central-1)
• Warehouse Name (for example, WH_MARKETING_ANALYTICS)

These define where Google Ads raw data and marts will be created.

5. Choose an authentication method

OWOX supports two authentication approaches:

• Password-based (username + password)
• Key-based (requires admin permissions)

Enter:

• A Snowflake username (a dedicated technical user is recommended)
• The corresponding password or private key credentials
• Click ‘Save’

Using a dedicated technical user ensures controlled permissions and easier auditing.

Step 2: Create Google Ads Insights Data Mart

With Snowflake connected, the next step is to plug in Google Ads so OWOX can start pulling data and writing it to your warehouse. This is where you define what gets ingested (accounts, objects, and fields) and how often the data mart should refresh.

Authorizing Google Ads and choosing ad accounts

OWOX uses Google’s OAuth flow to connect securely to your Google Ads account. You’ll need a Google user with access to the relevant Google Ads account (or MCC), with permissions to read reporting data.

Click Create a New Data Mart

• In the OWOX UI, go to '+ New Data Mart'.
• Give the data mart a clear title, for example, 'Google Ads Ad Group Stats'.
• Select Snowflake as the Data Storage.
• Click 'Create Data Mart'.

Configure Data Mart

• Select 'Connector' as an 'Input Source'
• Choose 'Google Ads' as the 'Connector'

• Authorize using OAuth (you'll be redirected to Google's login and permissions screen)
• Log in with a user that:

1. Has access to all Google Ads accounts you want to ingest.
2. Has read access to campaign and performance data.

• Add your Google Ads Account ID(s) and click Next.

• Select the performance API endpoint with the campaign, ad group, keyword, or ad-level data
• Click 'Next'

• Select the fields you want to collect – metrics such as impressions, clicks, cost, conversions, and conversion value.
• Click 'Next'

• Select your storage details like Database, Schema, and Table.
• You can keep the defaults – the table will be created automatically.
• Click 'Save'
• Then click 'Publish the Data Mart’

Common pitfalls & tips:

• Make sure the Google user has at least Standard or Admin access to all accounts you plan to export
• If you manage multiple MCC levels, choose the level that has visibility into all required child accounts
• If access changes (e.g., user removed, permissions downgraded), you'll need to re-authenticate in OWOX

Run Your Connector

It’s time to do the first pull to confirm that data is flowing correctly. OWOX connects to the Google Ads API and lets you control what gets loaded into Snowflake.

• Click on the 'Manual Run' button
• You'll see two options: Incremental or Backfill

Historical backfill

You decide how far back to load data. Use Backfill if this is your first run, and select the historical window you need.

You can choose 3 days, 7 days, 365 days, or multiple years.

Recommendations:

• For an initial setup, 90 days is often a practical balance between historical context and load time.
• If migrating from another system, consider a full fiscal year if you rely on year-over-year comparisons.
• Be mindful of API rate limits and data volume, especially for large or MCC-managed accounts.

Incremental load

To handle late conversions and attribution updates, OWOX supports a rolling lookback window.

Example configuration:

• On every run:

1. Pull data for today + last 2 days.
2. Overwrite those days in Snowflake.

This ensures:

• Recent performance data remains accurate.
• You avoid reprocessing the entire historical dataset each time.

Scheduling

Google Ads data updates daily – and sometimes retroactively as conversions are attributed or adjusted. You’ll want to configure a schedule for ongoing updates and monitoring.

Go to the Triggers tab:

• Select Connector Run as a Trigger Type.
• Then configure Frequency:
  
  1. Daily (e.g., every night at 02:00) for most reporting needs.
  2. Intra-day (e.g., every 4 hours or every 15 minutes) if you need near-real-time visibility.
• Align the time zone with your business reporting timezone (for example, the Google Ads account timezone).
• Click ‘Create Trigger’ to activate the schedule.

Connector Meta Data

With OWOX Data Marts, you can document your Google Ads data mart to keep things organized and easier to manage over time.

Go to the Overview tab and add a clear Description that explains:

• What the data mart contains (e.g., campaign + keyword performance)
• The reporting grain (daily, ad group level, keyword level)
• Who owns or maintains it
• Any important modeling notes (attribution logic, naming conventions, etc.)

This step is optional – but documenting your marts improves collaboration and makes long-term maintenance significantly easier.

Control

Go to the 'Run History'  tab to monitor every execution of your Google Ads data mart, covering both connectivity and data enablement.

You can review:

• Run status (success, running, failed)
• Start and end time
• Duration
• Number of rows processed
• Error details (e.g., expired credentials, permission issues, API limits)

If a run fails, open the logs to identify the issue and re-run the connector after resolving it.

Data Marts Best Practices

When setting up a new Google Ads data mart, follow these simple rules:

• Start with a smaller subset (for example, one account or campaign) and a limited set of fields to validate structure and completeness.
• Add all active Google Ads accounts only after you confirm the configuration works as expected.
• Expand fields gradually using '+Fields' – your Snowflake schema will update automatically.
• Add a clear description once setup is complete to document ownership and reporting scope.

Step 3: Re-Use Google Ads Data Mart for Reporting

With a governed Google Ads data mart in Snowflake, you can move beyond exports and one-off dashboards. The data mart becomes a stable, queryable layer for any BI tool, spreadsheet, or AI workflow.

Now, you can:

• Connect Google Sheets and Looker Studio directly to this Google Ads Data Mart.
• Use the blended data for analyses: cross-channel funnels, attribution, and ROAS by campaign or audience, and LTV-driven profitability insights.

The goal is to make the Google Ads data mart the default source of truth for performance reporting and decision-making.

Connecting Google Sheets and Looker Studio to Your Data Mart

You don’t have to change your reporting tools to benefit from OWOX Data Marts. As long as they can connect to Snowflake, they can query the Google Ads data mart in real time. The same Google Ads data mart.

Google Ads Report in Google Sheets

Go to the Destinations tab and connect Google Sheets to use it for reporting.

Then, create a new report, and schedule reports by entering the sheet URL & choosing the refresh time. 

This gives marketers access to record-level live data in spreadsheets updated on your schedule without manual CSV exports.

Best practices:

• Use read-only data tabs.
• Lock the sheet structure to prevent sync issues.
• Document the SQL query or data mart powering each sheet.

Connecting Looker Studio

Connecting Looker Studio to OWOX Data Marts is pretty simple. In the OWOX Data Marts UI, navigate to Destinations in the main navigation pane, then click + New Destination.

• Select Looker Studio from the Destination Type dropdown.
• Provide a Title – a unique name for this Destination (e.g., “Looker Studio Access (Marketing Team)”).
• Click Save.
• Then open it again and follow the instructions from Looker Studio.

Example Analytics Use Cases for Google Ads Data

Once your Google Ads data mart is in place, the real power of marketing analytics in Snowflake comes from blending it with other sources – web analytics, additional ad platforms, CRM events, and product data.

Because everything lives inside Snowflake, you can:

• Build cross-channel reporting without duplicating data.
• Apply consistent business logic across platforms.
• Tie ad spend directly to revenue, LTV, and retention.

Below are three practical analysis patterns you can implement using the Google Ads data mart built with OWOX Data Marts.

1. Cross-channel funnel and performance reporting

Most marketing teams need a unified view comparing Google Ads with Meta, LinkedIn, organic, and email. Snowflake becomes the central hub by joining standardized data marts from each platform with web analytics exports.

Ingest channel data into Snowflake

• Google Ads via OWOX Data Marts.
• Other ad platforms use similar pipelines.
• GA4 or other web analytics exports.

Normalize channel and campaign taxonomy

• Map platforms to canonical channel, source, and medium definitions.
• Standardize campaign naming conventions using a dedicated mapping table.

Build a cross-channel performance mart

• Combine channel-specific marts into a shared schema with common dimensions and metrics.
• Use unified fields such as report_date, channel, campaign_name, impressions, clicks, conversions, and cost.

Connect BI and AI tools

• Point dashboards to the cross-channel mart.
• Allow AI assistants to query standardized performance data across platforms

2. Attribution and ROAS analysis by campaign, audience, and region

With unified data in Snowflake, you can move beyond basic last-click metrics and evaluate performance through consistent attribution models and segmentation logic.

Define attribution windows and rules centrally

• Implement rules-based attribution in Snowflake SQL using Google Ads and web analytics data.
• Maintain multiple attribution models as separate calculated fields or views.

Enrich with audience and region dimensions

• Join the Google Ads data mart with audience segment definitions.
• Add geo dimensions such as country, region, or custom market groupings.

Compute segmented ROAS and CPA

• Calculate ROAS = attributed_revenue / cost.
• Calculate CPA = cost / attributed_conversions.
• Break down results by campaign, audience segment, and region.

Create optimization-ready views

• Build Snowflake views summarizing performance by campaign and attribution model.
• Create segmented views for audience and geo performance analysis.

3. LTV and profitability analysis from CRM or product data

The most strategic use case is tying Google Ads spend to real business outcomes such as revenue, margin, and lifetime value.

Ingest CRM and product data into Snowflake

• CRM events, including leads, deals, and recurring revenue.
• Product events such as signups and activations.
• Billing or transactional tables for orders and net revenue.

Create a user or customer identity map

• Build an identity mapping table using email, user ID, or customer ID.
• Connect CRM identifiers with web analytics and Google Ads click identifiers.

Link ad interactions to downstream revenue

• Connect sessions or click identifiers to customers.
• Attribute first-order revenue and long-term LTV to campaigns.

Build an ROI or LTV by campaign mart

• Join Google Ads spend with customer-level revenue and identity tables.
• Calculate paid_customer_count, ltv_per_customer, and ltv_to_cac_ratio.

Use for strategic budgeting

• Identify campaigns driving the highest LTV/CAC ratio.
• Compare platform-reported conversions with revenue-validated metrics.
• Reallocate budget based on real profitability.

Step 4: Turn Google Ads Data into Proactive AI Insights

Once your Google Ads data is modeled in Snowflake, the next step is delivering insights into the tools your stakeholders actually use – corporate messengers.

Your goal here is to push AI-powered alerts and summaries into Slack or Microsoft Teams, & Maintain self-service access to insights instead of exploring dashboards.

OWOX Data Marts provides a governed AI-insights layer (that never hallucinates!).

Configuring AI Insights on Top of Your Google Ads Data Mart

AI Insights operates on the modeled tables inside your Google Ads data mart. Because your schema, joins, and metrics are already standardized, AI can query clean, trusted definitions instead of raw, fragmented tables.

To configure insights:

• Navigate to the Insights tab.
• Use an OWOX-generated prompt template or create your own.
• Click ‘Create insight’ and then ’Run’ to preview results.
• Add a destination such as Google Chat, Microsoft Teams, Slack, or Email.
• Schedule delivery so stakeholders receive updates automatically.

Once AI is linked to the data mart, there is no need to embed complex SQL logic into prompts. The governed schema provides all the necessary context.

Designing Prompts and Business Context for Marketing Teams

The quality of AI-generated insights depends on the clarity of your instructions. Well-designed prompts ensure AI reflects your performance goals and business thresholds.

1. Frame the AI’s Role

Define what the assistant is analyzing. For example:

“You are a performance marketing analyst reviewing Google Ads results for the last 7 days compared to the previous 7 days.”

Clear framing reduces generic outputs and keeps insights focused.

2. Specify Focus Metrics and Thresholds

Tell the AI what matters most:

• ROAS, CPA, revenue, spend, impressions, clicks, CTR
• Acceptable performance ranges
• Alert thresholds (e.g., ROAS drop greater than 20% week-over-week)
• Spend anomalies compared to historical averages

You can embed these rules in prompts or configure them directly in the interface.

3. Provide Business Context

Context makes insights actionable.

Include:

• Target ROAS or CPA benchmarks
• Priority markets or product lines
• Campaign naming conventions
• Seasonality considerations

Example snippet:

“Focus on non-brand Google Ads campaigns in EMEA. Flag campaigns where CPA exceeds target by 25% and conversions declined week-over-week.”

4. Make Outputs Marketing-Friendly

AI outputs should be usable by stakeholders without technical translation.

Ask the AI to:

• Use clear, non-technical language
• Structure responses with:
  
  • Key highlights
  • Risks or anomalies
  • Recommended actions

This transforms raw metrics into short performance briefings your team can act on immediately.

Delivering Scheduled Insights into Slack, Teams, Google Chat, and Email

Once your prompts are configured, the final step is pushing insights to where decisions happen.

1. Configure Delivery Destinations

Connect your Google Ads AI Insights to:

• Slack channels
• Microsoft Teams groups
• Google chats
• Email distribution lists

Choose the right audience for each insight type.

2. Tailor Frequency and Stakeholders

Avoid alert fatigue by aligning cadence with use case:

• Daily – high-level performance summary and anomaly detection
• Weekly – trend analysis and optimization recommendations
• Monthly – strategic summaries across campaigns and markets

You can configure multiple insights for the same Google Ads data mart, each serving different stakeholders:

• Channel owners – tactical campaign alerts
• Marketing leadership – KPI trends and risk indicators
• Analytics teams – data validation or anomaly checks

3. Continuous Improvement and Expansion

As your strategy evolves:

• Refine prompts and thresholds
• Add new KPIs
• Expand to cross-channel insights, combining Google Ads with other platforms

Because everything runs on your Snowflake data mart layer, changes are centralized and consistent across tools.

By combining Snowflake, a governed Google Ads data mart, and AI-powered delivery, you move from reactive reporting to proactive performance management.

If you’d like to activate this workflow in your own environment, you can start using OWOX Data Marts today.

With this setup, marketers gain fast access to trusted Google Ads insights in the tools they already use, while data teams retain control over definitions, security, and data quality. If you’d like to try this end-to-end workflow with your own Google Ads data, you can start using OWOX Data Marts today.

Start Centralizing Your Google Ads Data in Snowflake Today

You now have a clear blueprint to replace manual exports and fragmented reporting with a governed, scalable workflow:

• Google Ads data continuously lands in Snowflake.
• A standardized, reusable data mart layer in OWOX that owns joins, metrics, and naming.
• Cross-channel, revenue-linked analytics and AI-powered insights delivered into the tools your teams already use.

This approach eliminates inconsistent numbers, fragile spreadsheet logic, and slow time-to-insight. Instead of rebuilding SQL queries or recreating dashboards every quarter, you invest once in a durable Google Ads → Snowflake → OWOX Data Marts architecture and reuse it everywhere.

And it doesn’t require a long transformation project. With the prerequisites in place, you can:

• Connect Snowflake as your warehouse.
• Link Google Ads as a source.
• Publish your first governed data mart.
• Start powering Sheets, dashboards, and AI summaries – often in days, not weeks.

If you’re ready to centralize your Google Ads data and enable trusted self-service analytics, you can start using OWOX Data Marts today.

Let your teams focus on optimization, experimentation, and growth – not exports, broken formulas, and conflicting metrics.

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