E-commerce Data Infrastructure and Analytics Stack
An e-commerce analytics stack collects, stores, and analyses data from all touchpoints to enable evidence-based decisions. The data team's customers — marketing, commercial, product, and operations — need reliable, timely data to optimise their functions. Building and maintaining a capable analytics infrastructure is an investment that pays back in better decisions at every level of the business.
Data Collection Layer
- Web analytics: GA4 (Google Analytics 4), Segment, Rudderstack — session data, page views, events, conversion funnels
- Server-side events: Order data, customer data, inventory events from backend systems — more reliable and GDPR-friendly than client-side pixel tracking
- Tag management: Google Tag Manager, Tealium — manage marketing pixels and analytics tags without code deployments
Data Storage and Processing
- Data warehouse: BigQuery, Snowflake, or Redshift — central analytical store. All operational data flows here for analysis.
- ETL/ELT: Fivetran, Airbyte, Stitch — automated connectors from Shopify, Klaviyo, Google Ads, Facebook Ads, and more into the warehouse
- dbt (data build tool): Transform and model raw data into clean analytical tables — version-controlled SQL transformations
Analytics and BI Layer
Looker, Mode, Metabase, or Power BI for dashboards and self-service analytics. Standardised KPI definitions in the data model ensure consistency — one definition of "repeat customer" across the organisation. Marketing attribution modelling connects spend to revenue across channels.