Supermetrics – Marketing Data Integration and Reporting

Overview

Supermetrics functions as a connector layer between marketing data sources and reporting destinations. Its primary purpose is to streamline the process of collecting and consolidating marketing performance data from various platforms, such as Google Ads, Facebook Ads, Google Analytics, and other ad networks and social media platforms. By automating this data extraction, Supermetrics aims to reduce the manual effort involved in data collection, allowing marketing teams and analysts to focus on data analysis and strategic decision-making rather than repetitive data preparation tasks.

The platform is designed to cater to a range of users, from solo marketers managing their own reports to agencies and large enterprises requiring extensive data warehousing solutions. Its core offerings include connectors for popular spreadsheet applications like Google Sheets and Microsoft Excel, as well as integrations with business intelligence (BI) tools such as Google Looker Studio (formerly Google Data Studio) and Microsoft Power BI. For organizations requiring more robust data infrastructure, Supermetrics also provides direct integrations with cloud data warehouses like Google BigQuery and Snowflake. This multi-destination approach allows users to select the environment best suited for their analytical needs and existing technology stack.

Supermetrics aims to address a common challenge in marketing: the fragmentation of data across numerous platforms, each with its own API and reporting interface. By abstracting these complexities, it offers a unified mechanism for data retrieval. This consolidation facilitates cross-channel analysis, enabling users to compare performance metrics across different campaigns and platforms in a consistent format. The platform's capabilities extend to scheduled refreshes, ensuring that reports and dashboards are updated automatically with the latest data, which is critical for real-time monitoring and agile marketing adjustments. The developer experience is also considered, with an Supermetrics API available for custom integrations and more programmatic data extraction workflows.

Key features

• Automated Data Connectors: Provides pre-built connections to over 100 marketing data sources, including advertising platforms, social media, and analytics tools, automating data extraction.
• Multiple Data Destinations: Supports data output to various platforms, including Google Sheets, Excel, Google Looker Studio, Power BI, Google BigQuery, Snowflake, and Amazon S3.
• Data Warehousing Integrations: Offers direct pipelines to cloud data warehouses like BigQuery and Snowflake, enabling scalable data storage and advanced analytics.
• Custom Query Building: Allows users to select specific metrics, dimensions, and date ranges from connected sources, tailoring data pulls to reporting requirements.
• Scheduled Data Refreshes: Configurable schedules for automatic data updates, ensuring reports and dashboards display current performance metrics.
• Supermetrics API: Provides programmatic access to extracted marketing data for developers to build custom applications, automations, or integrate with proprietary systems.
• Template Gallery: Offers pre-built report templates for various marketing channels in Google Sheets and Google Looker Studio to accelerate reporting setup.
• Historical Data Backfills: Capable of retrieving historical data from connected sources, useful for trend analysis and year-over-year comparisons.

Pricing

Supermetrics offers tiered pricing based on the number of data sources, destinations, and features required. All plans are typically billed annually, with monthly billing often available at a higher effective cost. A 14-day free trial is available to test the platform's capabilities.

For detailed and up-to-date pricing information, including specific data source and destination inclusions per plan, refer to the Supermetrics pricing page.

Common integrations

Supermetrics provides connectors for a variety of marketing platforms and data destinations. Examples of key integrations include:

• Google Looker Studio: Connects directly to Google Looker Studio to build interactive marketing dashboards and reports. More information on Supermetrics for Looker Studio documentation.
• Google Sheets: Extracts data directly into Google Sheets for custom reporting, analysis, and data manipulation. See Supermetrics for Google Sheets documentation.
• Microsoft Excel: Provides an add-on to pull marketing data into Excel spreadsheets for local analysis and reporting.
• Google BigQuery: Automates the loading of marketing data into Google BigQuery for scalable storage and advanced SQL-based analysis.
• Snowflake: Connects to Snowflake data warehouses to centralize marketing data alongside other business data.
• Power BI: Integrates with Microsoft Power BI for creating business intelligence dashboards and reports from consolidated marketing data.
• Google Ads: Pulls campaign performance data, keywords, ad groups, and more from Google Ads.
• Facebook Ads: Extracts campaign, ad set, and ad-level performance metrics from Facebook Ads Manager.
• Google Analytics: Provides access to website traffic, user behavior, and conversion data from Google Analytics.
• LinkedIn Ads: Fetches campaign statistics, impressions, clicks, and costs from LinkedIn advertising campaigns.

Alternatives

When considering marketing data integration and reporting tools, several alternatives offer similar or complementary functionalities. These typically focus on ETL processes for marketing data, often with varying specialties in data warehousing, BI tool integration, or overall platform flexibility.

• Fivetran: A cloud-based ETL service that automates data integration from a wide array of sources (including marketing platforms) into data warehouses without requiring coding.
• Funnel.io: Specializes in marketing data integration, transformation, and activation, offering a platform tailored for unifying marketing data for analysis and reporting.
• Stitch Data: An ETL platform that connects to numerous data sources and replicates data into data warehouses, supporting a range of marketing and business data connectors.

Getting started

To demonstrate a basic programmatic interaction with Supermetrics, the following Python example illustrates how to make a request to the Supermetrics API. This example assumes you have an API key and are looking to fetch data from a specific data source, such as Google Analytics, into a JSON format. This approach is typically used for integrating Supermetrics data into custom applications or workflows outside of its pre-built connectors.

Before running this code, ensure you have a Supermetrics user account with API access enabled and have generated an API key. You would also need to specify the correct dataSourceId and accountIds corresponding to your Google Analytics property.

import requests
import json

# Replace with your actual Supermetrics API Key
API_KEY = "YOUR_SUPERMETRICS_API_KEY"

# Define the API endpoint for fetching data
API_ENDPOINT = "https://api.supermetrics.com/query"

# Define query parameters
# This example fetches sessions and users from Google Analytics for the last 7 days.
# Replace 'YOUR_DATA_SOURCE_ID' (e.g., 'GA') and 'YOUR_ACCOUNT_ID' (e.g., 'ga:123456789')
# For detailed data source IDs and metrics, refer to Supermetrics API documentation.
query_params = {
    "apiKey": API_KEY,
    "dataSourceId": "YOUR_DATA_SOURCE_ID", # e.g., "GA"
    "accountIds": "YOUR_ACCOUNT_ID",      # e.g., "ga:123456789"
    "startDate": "LAST_7_DAYS",
    "endDate": "TODAY",
    "metrics": "sessions,users",
    "fields": "date,sessions,users",
    "format": "json"
}

try:
    response = requests.get(API_ENDPOINT, params=query_params)
    response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)

    data = response.json()

    # Pretty print the JSON response
    print(json.dumps(data, indent=2))

    # Example of accessing retrieved data
    if 'data' in data and data['data']:
        print("\nFirst row of data:")
        print(data['data'][0])

except requests.exceptions.RequestException as e:
    print(f"An error occurred: {e}")
except json.JSONDecodeError:
    print("Failed to decode JSON response.")
    print("Raw response:", response.text)

This script initiates a GET request to the Supermetrics API with specified parameters. The dataSourceId and accountIds are critical for targeting the correct data source and specific account within that source (e.g., a particular Google Analytics view or Google Ads account). The metrics and fields parameters define what data points to retrieve. The response is parsed as JSON, and the script then prints the formatted data. Developers can adapt this pattern to fetch data for various sources, apply filters, and integrate the results into databases, custom dashboards, or automated reporting scripts.