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Analytics & Data Infrastructure

The analytics layer DTC operators need after they stop trusting the platform dashboards. GA4 migration without data loss, BigQuery on a budget, warehouse-first attribution, cohort reports from Shopify raw data, and the event schemas that survive the next platform pivot.

13 postsFor: DTC analytics owners and fractional data leads rebuilding measurement

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BigQuery cost optimization for DTC: under $100 setup

ANALYTICS·MAY 9·15 MIN

BigQuery cost optimization for DTC: under $100 setup

BigQuery setup for $2-10M DTC stores under $100 a month. Partitioning, clustering, query habits that control cost, and when to migrate off the warehouse.

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A DTC data warehouse on $1,000 a month: the real budget

ANALYTICS·APR 17·9 MIN

A DTC data warehouse on $1,000 a month: the real budget

Field notes on a mid-market DTC data warehouse running at $1,000 per month. Every line item, every vendor, where the spend actually goes.

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GA4 migration playbook for DTC: what actually breaks

ANALYTICS·APR 2·10 MIN

GA4 migration playbook for DTC: what actually breaks

A tutorial walkthrough on the GA4 migration for DTC brands. The specific events, props, and joins that fail during the move and the fixes that hold up.

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Cohort LTV from Shopify raw data: SQL patterns that hold up

ANALYTICS·MAR 24·9 MIN

Cohort LTV from Shopify raw data: SQL patterns that hold up

Pattern library for cohort LTV in BigQuery against Shopify raw data. By acquisition month, by channel, by product, with retention curves and a revenue mart.

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Reconciling Shopify, GA4, and Meta: the forensic workflow

ANALYTICS·MAR 18·10 MIN

Reconciling Shopify, GA4, and Meta: the forensic workflow

Field notes on the forensic workflow for reconciling Shopify, GA4, and Meta. Diff windows, join keys, the five bugs that hide in plain sight.

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Attribution modeling in BigQuery: the SQL I keep copying

ANALYTICS·MAR 6·10 MIN

Attribution modeling in BigQuery: the SQL I keep copying

Tutorial walkthrough on attribution modeling in BigQuery. Last-click, first-click, linear, time-decay, and position-based SQL that runs against your warehouse.

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Event schema design for DTC: naming that survives replatform

ANALYTICS·FEB 12·9 MIN

Event schema design for DTC: naming that survives replatform

Pattern library for DTC event schema design. Canonical event names, typed payloads, versioning, and naming rules that survive a replatform.

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Dashboard design for operators: what founders actually read

ANALYTICS·FEB 12·9 MIN

Dashboard design for operators: what founders actually read

Contrarian essay on dashboard design for DTC operators. Why the 12-tile exec overview is a trap and what founders actually read in 90 seconds.

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Looker Studio DTC templates worth building versus buying

ANALYTICS·FEB 11·8 MIN

Looker Studio DTC templates worth building versus buying

A decision log on Looker Studio for DTC brands. Which templates to build yourself, which to buy, and when to graduate to Metabase or Hex instead.

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Server-side GA4 via Measurement Protocol: the real setup

ANALYTICS·FEB 5·10 MIN

Server-side GA4 via Measurement Protocol: the real setup

Tutorial walkthrough for server-side GA4 using the Measurement Protocol. Endpoint, payload shape, api_secret rotation, and the consent-aware event filter.

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Klaviyo to warehouse ETL: three ways that work in 2026

ANALYTICS·FEB 2·10 MIN

Klaviyo to warehouse ETL: three ways that work in 2026

Pattern library for Klaviyo to BigQuery ETL. Native connector, Fivetran, and DIY API. Latency, cost, and schema for each, with the dbt models that sit on top.

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BigQuery for Shopify data: the schema that does not regret you

ANALYTICS·JAN 29·10 MIN

BigQuery for Shopify data: the schema that does not regret you

Pattern library for landing Shopify raw data in BigQuery. Partitioning, clustering, dbt staging models, and cost math that keeps warehouse cheap.

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More on this cluster

Why this matters.

Most DTC analytics stacks fail in the same way. Shopify, GA4, and Meta each report a different conversion count, the operator picks the one that flatters the channel, and decisions get made on a number nobody trusts. The fix is a warehouse-first reporting layer where Shopify raw orders are the source of truth, GA4 and Meta are session and ad-platform diagnostics, and a small dbt project (or even a SQL view set) reconciles them on a daily cadence. BigQuery is overkill at $2M and indispensable at $10M; the migration in between is most of the work.

This cluster covers the analytics architecture that holds. GA4 migration without losing year-over-year comparability. BigQuery setup on a budget. Cohort retention reports built directly off Shopify orders rather than a platform's prebuilt cards. Event schemas that survive a tracking rebuild. Klaviyo-to-warehouse ETL so lifecycle revenue ties cleanly to the P&L.

If your dashboards disagree by more than a few percent and the team is running on vibes, start with the hub piece on warehouse-first reporting and walk the GA4 and BigQuery posts before the next quarterly review.

Put this to work

GA4, BigQuery, and the warehouse-first analytics rebuild.

> Get the DTC Stack Audit

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