Why does the model use daily snapshots?

Snapshots preserve point-in-time state to enable historical analysis, trend detection, and accurate reporting despite the limited data retention periods of platforms like Meta.

Which data sources are compatible with this scoring model?

The architecture is designed to integrate data from Google Analytics 4 (GA4), Google Ads, Meta (Facebook) Ads, and raw product feeds.

Can I customize the scoring rules?

Yes, the skill provides a framework for defining custom exclusion and inclusion rules based on specific thresholds for ROAS, revenue, view velocity, and availability rates.

How does this skill handle variant-level data?

It uses an aggregation pattern where metrics are collected at the variant level but scoring is calculated at the product (item_group_id) level, while maintaining stock overrides for specific variants.

What is the primary purpose of the Expanly Scoring Model?

The model categorizes e-commerce products into three priority tiers (High, Standard, Low) based on performance metrics to optimize bidding strategies and reduce wasted ad spend.

Expanly E-commerce Scoring Model

Name: Expanly E-commerce Scoring Model
Author: Expanly

byExpanly

0•

마케팅 자동화

Implements and manages multi-tier product scoring models to optimize e-commerce advertising spend and performance.

This skill provides a comprehensive framework for developers and data engineers to implement the Expanly scoring model architecture. It facilitates the creation of a three-tier priority system (Low, Standard, High) that segments products based on real-time performance signals from GA4, Google Ads, and Meta. By leveraging SQLX-based data modeling and daily snapshots, it helps automate product prioritization, reduces wasted ad spend on 'zombie' products, and ensures that dynamic remarketing campaigns focus on high-ROAS, in-stock inventory.

주요 기능

01Rule-based evaluation for product exclusions, inclusions, and default classifications

02Three-tier priority scoring system (0, 1, 2) for automated campaign allocation

03Multi-layered SQLX architecture covering raw sources, snapshots, and reporting

040 GitHub stars

05Product-level aggregation logic for variant-heavy e-commerce catalogs

06Automated custom label generation for Google PMAX and Meta Advantage+ campaigns

사용 사례

01Identifying and scaling 'Trending' or 'Top Seller' items across multiple ad platforms

02Reducing wasted ad spend by automatically down-weighting out-of-stock or low-ROAS products

03Building historical performance dashboards using point-in-time product state snapshots

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add expanly/expanly-claude-code-agents expanly-scoring-model

For use in Claude.ai and ChatGPT

주요 기능

01Rule-based evaluation for product exclusions, inclusions, and default classifications

02Three-tier priority scoring system (0, 1, 2) for automated campaign allocation

03Multi-layered SQLX architecture covering raw sources, snapshots, and reporting

040 GitHub stars

05Product-level aggregation logic for variant-heavy e-commerce catalogs

06Automated custom label generation for Google PMAX and Meta Advantage+ campaigns

사용 사례

01Identifying and scaling 'Trending' or 'Top Seller' items across multiple ad platforms

02Reducing wasted ad spend by automatically down-weighting out-of-stock or low-ROAS products

03Building historical performance dashboards using point-in-time product state snapshots

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add expanly/expanly-claude-code-agents expanly-scoring-model

For use in Claude.ai and ChatGPT