The architecture for cross-channel digital marketing performance reports will vary depending on the size of the business and the complexity of the reporting tools used. Below is an outline of the architecture for each segment, focusing on data extraction, transformation, storage, and reporting.

1. <5 Employees (Simple Architecture)

Data Extraction:

Sources: Google Analytics, Facebook Insights, Mailchimp (for emails)

Integration:

• Google Analytics and Facebook Insights provide native export options (CSV, Excel) or API access.
• Mailchimp has built-in reporting features.
• Data extraction is done manually or via simple integrations using Google Data Studio connectors or third-party tools like Zapier.

Data Transformation:

Tools: Google Data Studio

Process:

• Basic data transformations such as combining data from different platforms and filtering it for specific metrics (e.g., click-through rates, conversion rates).
• Simple calculated fields in Data Studio (e.g., metrics like ROI, cost per click).

Data Storage:

Tools: Google Sheets (for storage) or directly in Google Data Studio

Process:

• Data is often stored temporarily in Google Sheets or directly pulled into Data Studio from sources like Google Analytics and Facebook Insights.
• No advanced storage solution is used due to the simplicity of the setup.

Reporting:

Tools: Google Data Studio

Process:

• Visual reports (dashboards) are created directly in Google Data Studio.
• Reports are shared via links or embedded in websites/PowerPoint for easy access.
• Basic KPIs are displayed in visual formats like charts, tables, and graphs.

2. 6 to 20 Employees (Intermediate Architecture)

Data Extraction:

Sources: Google Analytics, Facebook Insights, Google Ads, HubSpot or Mailchimp

Integration:

• Integration via APIs (Google Analytics API, Facebook Marketing API) to automatically extract performance data.
• Use of third-party tools like SEMrush for SEO data.
• Email marketing platforms like HubSpot or Mailchimp can be connected through their APIs to extract campaign performance.

Data Transformation:

Tools: Google Data Studio, HubSpot, SEMrush, or custom transformation scripts (Python, R)

Process:

• Data cleaning, merging from different sources (Google Analytics, Facebook Ads, SEMrush).
• Aggregating data into meaningful metrics.
• Use of Google Data Studio's calculated fields or HubSpot’s built-in reporting tools for basic transformations.

Data Storage:

Tools: Google Sheets, Cloud Storage (Google Cloud, AWS), or a simple database

Process:

• Data is stored in Google Sheets or Google Cloud Storage for ease of use and sharing.
• Can use a lightweight SQL database for more structured data storage.

Reporting:

Tools: Google Data Studio, HubSpot, SEMrush

Process:

• Reports are more sophisticated with multiple data sources combined.
• Visualizations in Data Studio or HubSpot dashboards.
• Data is stored and made available for team members to access at any time.
• Custom dashboards are created for better visualization of KPIs (like revenue, impressions, conversions).

3. 21 to 50 Employees (Advanced Architecture)

Data Extraction:

Sources: Google Analytics, Facebook Insights, Google Ads, LinkedIn Ads, Email Platforms, CRM (HubSpot, Salesforce), Social Media APIs

Integration:

• Integration with all key platforms using their respective APIs.
• Additional tools like Zapier or Stitch may be used to streamline integrations between various platforms (Google Ads, CRM, etc.).

Data Transformation:

Tools: ETL Tools (Fivetran, Stitch), Data Transformation Scripts (Python, SQL), Tableau, Power BI

Process:

• Data is extracted from platforms, cleaned, and transformed using ETL tools or custom scripts.
• Aggregated into standard metrics (e.g., CAC, LTV, ROI).
• More advanced transformations like cohort analysis, campaign performance breakdowns, etc., are handled via SQL, Power BI, or Tableau.

Data Storage:

Tools: Cloud-based data warehouse (BigQuery, AWS Redshift, or Snowflake)

Process:

• All marketing data (from multiple sources) is stored in a centralized cloud data warehouse.
• Structured storage of raw and transformed data to allow easier querying and reporting.

Reporting:

Tools: Tableau, Power BI, Google Data Studio, Custom Dashboards

Process:

• Automated data pipelines to refresh data for live dashboards in Tableau or Power BI.
• Complex reports with interactive visualizations and drill-down capabilities.
• Dashboards are shared with the team via cloud-based tools, accessible to key stakeholders.

4. 51 to 100 Employees (Enterprise-Level Architecture)

Data Extraction:

Sources: All previous sources, CRM (Salesforce, HubSpot), Ad Platforms, Social Media, Email Platforms, Customer Data Platforms (Segment, Amplitude), E-commerce Platforms (Shopify)

Integration:

• Real-time or batch data extraction from various integrated systems.
• Data is pulled via APIs, often requiring custom integration to collect data from various marketing channels (ads, social, email, CRM, website).
• Use of middleware tools like Segment or MuleSoft for seamless integrations across platforms.

Data Transformation:

Tools: ETL/ELT Tools (Fivetran, Matillion, Stitch), Python (Pandas), SQL-based tools (BigQuery, Snowflake)

Process:

• Complex transformations occur in an ETL/ELT pipeline.
• Aggregating data from multiple sources into meaningful KPIs.
• More advanced analytics, including predictive modeling, customer segmentation, and multivariate analysis.

Data Storage:

Tools: Centralized Data Warehouse (Google BigQuery, AWS Redshift, Snowflake)

Process:

• All marketing and CRM data are stored in a data warehouse.
• The warehouse is scalable, allowing for high volumes of data across multiple departments (sales, marketing, customer service).

Reporting:

Tools: Power BI, Tableau, Google Data Studio, Custom Reporting Dashboards, CRM dashboards

Process:

• Automated dashboards and reports for real-time or periodic access.
• Complex visualizations combining data from all channels for deep insights.
• Reports are shared and scheduled to be sent automatically to key stakeholders.
• Custom reporting dashboards for different departments (e.g., marketing, sales, finance).

Summary of Architecture Flow based on Company Size

This progression shows how each company size can scale its architecture from simple to more advanced setups as the need for cross-channel reporting grows and data complexity increases.