Data-driven D2C brands grow 2.3x faster than those making decisions from gut feel. Scale D2C builds the analytics infrastructure — data warehouses, BI dashboards, and predictive models — that gives your team complete visibility into what's actually driving growth and what's destroying margin.
Most analytics projects deliver dashboards nobody uses. Scale D2C builds analytics infrastructure around the specific questions your D2C leadership team needs answered — then makes the data accessible and trustworthy.
BigQuery or Snowflake data warehouse connecting Shopify, ad platforms, ESP, CRM, and ERP into a single analytical data model.
Looker, Power BI, or Metabase dashboards giving your CMO, media buyer, ops, and finance teams their own real-time views.
Data-driven multi-touch attribution models accurately crediting every channel — eliminating the last-click bias costing you millions.
Customer cohort analysis and LTV modelling giving your leadership team visibility into the long-term value of each acquisition channel.
dbt transformation pipelines, Fivetran/Airbyte connectors, and event tracking infrastructure as the foundation of reliable analytics.
Natural language BI layer letting your team ask 'What drove the drop in ROAS last week?' and get a data-backed answer.
Full-stack analytics engineering for D2C brands — from raw event collection through to executive dashboards and predictive models.
BigQuery, Snowflake, or Redshift warehouse architecture with semantic layer and access controls.
Fivetran/Airbyte connectors, dbt transformations, and orchestration (Airflow/Dagster) for reliable data pipelines.
Looker, Power BI, or Superset dashboards tailored to D2C KPIs — acquisition, retention, margins, and operations.
Cross-channel attribution modelling with incrementality testing to accurately measure channel contribution.
Customer cohort dashboards, LTV models, and payback period calculations for smarter acquisition decisions.
ML models for churn prediction, demand forecasting, and LTV projection built on your data warehouse.
We audit your current data, identify high-impact AI use cases, and prioritise by revenue potential and implementation complexity.
We build a working POC in 2–4 weeks to validate the AI approach before committing to full development.
Full production-grade AI system development with testing, safety evaluation, and integration to your D2C stack.
Continuous model monitoring, performance tracking, and retraining to keep your AI system accurate as your business evolves.
We connect all major D2C data sources including Shopify/Adobe Commerce (orders, products, customers), Meta Ads, Google Ads, TikTok Ads, Klaviyo, Triple Whale, Northbeam, Gorgias, Recharge, and any custom data sources via API or CSV import.
A data warehouse (BigQuery, Snowflake) stores and organises all your data in a clean, queryable format. A BI tool (Looker, Power BI) connects to the warehouse and presents the data as visualisations and dashboards. You need both: the warehouse for reliable data, the BI tool for accessible insights.
A basic data warehouse with core dashboards takes 6–10 weeks. A full analytics platform including attribution modelling, LTV predictions, and advanced segmentation takes 14–20 weeks. We always start with the most business-critical reporting and build incrementally.
Stop making marketing decisions based on platform-reported ROAS. Let Scale D2C build the analytics infrastructure that shows you what's actually working.