Agent skill · marketing growth · aradotso
genpark-cross-channel-marketing-data-joiner
Join programmatic ad data with retail & inventory systems to calculate total ROAS across marketing channels
Why this skill is useful
Adds specific data joining methods and attribution models for marketing analytics that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. Requires dv360, facebook_ads, google_ads, shopify, square account access. About 4k tokens when loaded. Last updated 2026-08-05. 8 stars on the source repository.
What this skill does
genpark-cross-channel-marketing-data-joiner Skill by ara.so — Marketing Skills collection Overview The GenPark Cross-Channel Marketing Data Joiner is a Python skill that unifies programmatic advertising data with retail and inventory systems to provide comprehensive ROAS (Return on Ad Spend) analytics. It bridges the gap between digital marketing campaigns and physical/online sales outcomes, enabling marketers to understand true campaign effectiveness across channels. Installation Or install as a package: Quick Start Core Components 1. Data Source Connectors Connect to various ad platforms, retail systems, and inventory databases: 2. Data Joining Join datasets using multiple attribution models: 3. ROAS Calculation Calculate return on ad spend across channels: Configuration Configuration File Create a config.yaml file: Load configuration: Environment Variables Common Patterns Pattern 1: Weekly ROAS Report Pattern 2: Multi-Attribution Comparison Pattern 3: Real-Time ROAS Dashboard Pattern 4: Inventory-Aware Campaign Optimization Troubleshooting Connection Issues Data Matching Problems Attribution Window Issues Performance Optimization API Reference Key Methods CrossChannelJoiner.joinandcalculate() - Main method to join data and calculate ROAS DataJoiner.join() - Join datasets with specified attribution model ROASCalculator.calculatetotalroas() - Calculate overall ROAS ROASCalculator.calculatebychannel() - Get per-channel ROAS AttributionModel - Enum of available attribution models ReportGenerator.exportcsv() - Export results to CSV testconnections() - Validate all data source connections Additional Resources Homepage: https://genpark.ai Repository: https://github.com/alphaparkinc/genpark-cross-channel-marketing-data-joiner-skill Example usage: Run python exampleusage.py in the repository
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/genpark-cross-channel-marketing-data-joiner