Funnel.io

Overview

Funnel.io is a marketing data platform that centralizes data from various advertising, analytics, and CRM sources. Its primary function is to automate the extraction, transformation, and loading (ETL) of marketing performance data, addressing the challenge of fragmented data across numerous platforms. The platform is designed for marketing teams, data analysts, and business intelligence professionals who require a consolidated view of their marketing efforts without extensive manual data manipulation or custom engineering.

The core value proposition of Funnel.io lies in its ability to connect to a broad spectrum of marketing platforms, including Google Ads, Facebook Ads, LinkedIn Ads, Google Analytics, and various DSPs and ad servers. Once connected, it standardizes the data schema, allowing for consistent reporting and analysis across disparate sources. This standardization is crucial for accurate cross-channel performance measurement and attribution modeling, which can be complex when data formats differ significantly between platforms, as discussed by industry publications like Search Engine Land on cross-channel attribution.

Funnel.io's architecture supports both direct reporting within its interface and export capabilities to external business intelligence (BI) tools, data warehouses (e.g., Google BigQuery, Amazon Redshift, Snowflake), and even Google Sheets or Excel. This flexibility allows organizations to integrate marketing data into their existing data ecosystems, facilitating broader business analysis and decision-making. The platform aims to reduce the operational overhead associated with data collection and preparation, freeing up resources for strategic analysis and optimization.

For developers and technical buyers, Funnel.io offers a low-code/no-code approach to data integration. While it provides robust connectors and transformation rules, direct API usage for data ingestion or advanced custom manipulation by end-users is not its primary focus. Instead, it emphasizes pre-built connectors and a user-friendly interface for configuration. This design choice aims to democratize access to marketing data for non-developers, while still providing the necessary data pipelines for technical teams to integrate into their data infrastructure. Compliance with standards such as SOC 2 Type II, GDPR, and CCPA indicates a focus on data security and privacy, which is a critical consideration for enterprises handling sensitive marketing data.

Key features

• Automated Data Collection: Connects to hundreds of marketing and advertising platforms to automatically collect performance data, eliminating manual exports and imports.
• Data Transformation & Mapping: Provides tools to clean, normalize, and transform raw data into a consistent format, enabling cross-channel comparisons and custom metrics.
• Data Warehousing & Storage: Offers a managed data warehouse to store consolidated marketing data, ensuring historical data availability and query performance.
• Data Export & Integration: Exports processed data to various destinations including BI tools (e.g., Tableau, Power BI), data warehouses (e.g., Google BigQuery, Amazon Redshift), Google Sheets, and custom APIs.
• Custom Metrics & Dimensions: Allows users to define custom metrics and dimensions based on their specific business rules and reporting needs.
• Reporting & Visualization: Includes built-in reporting dashboards and visualization tools to monitor marketing performance directly within the platform.
• Data Governance & Compliance: Adheres to data privacy regulations such as GDPR and CCPA, and maintains SOC 2 Type II compliance for data security.

Pricing

Funnel.io's pricing structure is tiered, starting with a base plan and offering custom solutions for larger enterprises. Detailed pricing information is available on their official Funnel.io pricing page.

Common integrations

Funnel.io offers a wide range of pre-built connectors for various marketing and advertising platforms. A comprehensive list and documentation can be found on their Funnel.io data sources documentation.

• Advertising Platforms: Google Ads, Facebook Ads, Instagram Ads, LinkedIn Ads, TikTok Ads, Microsoft Advertising, Amazon Ads, Pinterest Ads, Criteo, X Ads.
• Analytics Platforms: Google Analytics (Universal Analytics & GA4), Adobe Analytics.
• CRM Systems: Salesforce, HubSpot.
• Data Warehouses: Google BigQuery, Amazon Redshift, Snowflake, Azure Synapse Analytics.
• Business Intelligence Tools: Tableau, Power BI, Looker Studio (formerly Google Data Studio), Qlik Sense, Domo.
• Spreadsheets & Storage: Google Sheets, Amazon S3, SFTP.

Alternatives

• Supermetrics: Offers data connectors to pull marketing data into spreadsheets, data warehouses, and BI tools, often favored for its direct integration with Google Sheets and Excel.
• Improvado: A marketing data aggregation platform providing ETL services and custom dashboards, focusing on enterprise-level data integration and visualization.
• Adverity: An intelligent data platform that automates the extraction, harmonization, and transformation of marketing, sales, and e-commerce data.

Getting started

While Funnel.io primarily operates through a web-based UI for data source configuration and export setup, direct programmatic interaction for data retrieval typically involves connecting a BI tool or data warehouse to Funnel.io's output. Below is an example of connecting to a Google BigQuery dataset that Funnel.io has populated, using Python with the google-cloud-bigquery client library. This assumes Funnel.io has been configured to export data to a specified BigQuery dataset.

from google.cloud import bigquery

# Initialize a BigQuery client
# Ensure your GOOGLE_APPLICATION_CREDENTIALS environment variable is set
# or pass explicit credentials.
client = bigquery.Client()

# Define your project ID, dataset ID, and table ID where Funnel.io exports data
project_id = "your-gcp-project-id"
dataset_id = "funnel_exported_data"
table_id = "google_ads_performance"

# Construct a fully qualified table ID
table_ref = f"{project_id}.{dataset_id}.{table_id}"

# Define a simple SQL query to retrieve data
query = f"""
SELECT
    date,
    campaign_name,
    impressions,
    clicks,
    cost
FROM
    `{table_ref}`
WHERE
    date >= '2026-04-01'
ORDER BY
    date DESC
LIMIT 100
"""

print(f"Executing query:\n{query}")

# Execute the query
query_job = client.query(query)  # API request

# Fetch the results
rows = query_job.result()  # Waits for the query to finish

print("Query Results:")
print("--------------------------------------------------")
for row in rows:
    print(f"Date: {row.date}, Campaign: {row.campaign_name}, Impressions: {row.impressions}, Clicks: {row.clicks}, Cost: {row.cost:.2f}")
print("--------------------------------------------------")

# Example of accessing schema information (optional)
table = client.get_table(table_ref)
print(f"\nTable schema for {table_ref}:")
for field in table.schema:
    print(f"  - {field.name} ({field.field_type})")

This Python snippet demonstrates how a developer would typically interact with data that Funnel.io has prepared and exported to a data warehouse. The process involves setting up credentials for Google Cloud, specifying the target table, and executing standard SQL queries to retrieve the desired marketing performance metrics. For detailed instructions on setting up BigQuery exports within Funnel.io, refer to the official Funnel.io documentation on BigQuery exports.