End-to-end measurement infrastructure. Not just another tag implementation.

Measurement Foundation

Consent management, server-side Google Tag Manager, and event delivery to your ad platforms. Better data quality, longer cookie life, improved match rates, privacy compliance. This is where every engagement starts.

Data Infrastructure

Your marketing data in BigQuery, transformed and modeled by Dataform, with AI-native enrichment built into every layer. A single source of truth that replaces the six-platform CSV export your team does every Monday morning.

Business Intelligence

Dashboards your team can actually use, AI-powered data exploration, and automated weekly reporting that reads like an analyst wrote it. Built on the infrastructure from the previous phases, not bolted on top of raw data.

Attribution & Advanced Analytics

Multi-touch attribution, media mix modeling, and incrementality testing. The only way to honestly answer "what’s actually working." Available exclusively to clients whose measurement foundation I’ve built, because the models are only as good as the data feeding them.

Four tiers. Each one delivers standalone value. Each one makes the next one possible.

I structure engagements as discrete tiers with decision gates between them. You buy what you need, see the results, and decide whether to go further. The first two tiers have non-negotiable components. These are the things that must be done properly or not at all. Everything else is modular and scoped to your specific situation.

Measurement Foundation

Get the data right at the source.

Server-side Google Tag Manager is the backbone of modern measurement. It moves your tracking from the browser to a server you control, which means longer cookie life, higher ad platform match rates, immunity to ad blockers, and proper consent enforcement. Most marketing teams know they should migrate to server-side. Very few have the expertise to do it properly.

What's included (non-negotiable):

Tag Audit & Data Layer Specification: Before building anything new, I audit your existing client-side GTM container to identify what to keep, what to fix, and what to remove. The output is a documented data layer specification that becomes the blueprint for your server-side implementation. This step is non-negotiable because implementing sGTM on top of a messy client-side container just moves the mess server-side.
Server-Side GTM Deployment: Full sGTM container setup with custom domain configuration and same-origin setup for extended cookie life. Hosted on Stape by default, with options for EU-hosted infrastructure or self-hosted on Google Cloud for enterprise requirements.
Warehouse Event Sink: Your sGTM event stream flows directly into a data warehouse from day one. This is the bridge to everything downstream. Without it, Tier 1 is a dead end and you should hire a GTM specialist, not me.

Additional components (scoped per client):

Consent Management Implementation: CMP deployment and integration with GTM consent mode, ensuring consent state is enforced server-side. Cookiebot by default, with OneTrust or Didomi for enterprise-scale or multi-brand requirements. Not required if you already have a working CMP.
Event Delivery Configuration: Server-side event delivery to your ad platforms. GA4, Meta Conversions API, and Google Ads Enhanced Conversions are the standard package. TikTok Events API, LinkedIn, Pinterest, and Snap are scoped individually based on your channel mix.
Real-Time Event Architecture: Your sGTM implementation is designed with awareness of future real-time event streaming capabilities (Pub/Sub, webhooks) even if they're not activated on day one. No architectural decisions that close doors.

What you get at the end of Tier 1: Server-side tracking that's properly consented, delivering higher-quality data to your ad platforms and streaming raw events into your warehouse. Better match rates, better attribution signals, privacy compliance, and a foundation for everything that follows.

Data Infrastructure

Turn raw events into a source of truth.

Raw event data in a warehouse is a start, not a finish. Tier 2 transforms that data into a structured, documented, AI-enriched analytics layer that your team and your tools can actually work with. This is where the real value starts compounding.

What's included (non-negotiable):

Warehouse Configuration: BigQuery project setup with proper dataset structure (raw, staging, marts), IAM permissions, and cost controls. The organizational foundation that prevents your warehouse from becoming an ungoverned data swamp.
Dataform Transformation Models: A standardized transformation pipeline following a medallion architecture: raw events are cleaned and flattened in staging, then assembled into business-ready mart tables: campaign performance, channel attribution, customer lifetime value, session events. These models are the intellectual property of the engagement and the single most valuable deliverable. Clients with existing dbt implementations stay on dbt, with a migration conversation available.
Automated Data Quality Framework: Dataform assertions that continuously validate your data: schema checks, null rate monitoring, volume anomaly detection, and source freshness verification. This is a standard line item under every quality Dataform implementation, not an optional add-on. Silent data failures are how teams make decisions on broken numbers for weeks before anyone notices.
Data Dictionary & Schema Documentation: AI-generated, human-reviewed documentation of every model, every column, every business logic definition, and every upstream dependency. This ships as a deliverable and lives in the Dataform repository. It's what makes your data self-describing, both for your team and for any AI system that needs to work with it.
AI Access Layer: A scheduled BigQuery export to Cloud Storage in standard formats (parquet or JSON), providing a clean handoff point for any AI tool (Claude, ChatGPT, Gemini, or your own internal systems) without requiring direct warehouse access. For clients who want direct database connections, a read-only BigQuery service account scoped to mart datasets. LLM-friendly naming conventions are enforced across all models so that any AI system can query your data effectively.

Additional components (scoped per client):

Data Pipeline Deployment: Ingestion of ad platform spend data, CRM data, and e-commerce platform data into BigQuery. BigQuery Data Transfer Service is the default for supported sources (Google Ads, Shopify, YouTube, Mailchimp). Airbyte for sources not covered by native transfers (Meta Ads, TikTok, Klaviyo, HubSpot) or for clients needing broader connector coverage. Clients with existing ELT tools keep what they have.
Automated Campaign Taxonomy: Dataform models using BigQuery's native AI functions to automatically standardize your campaign names, UTM parameters, and ad group naming into a clean, consistent taxonomy. Covers channel classification, campaign type, funnel stage, and product categorization. Includes a validation layer for review and override. Runs continuously as new campaign data flows in, eliminating the eternal spreadsheet-mapping exercise that no one wants to maintain.

What you get at the end of Tier 2: A single source of truth for your marketing data. Clean, documented, AI-enriched, and accessible to any downstream tool or AI system. No more Monday morning CSV exports from six platforms. No more "wait, which campaign is that?" in budget meetings.

Business Intelligence

Answers, not dashboards.

Dashboards are a means to an end. The end is your team being able to answer questions about marketing performance without filing a ticket, without waiting three days, and without second-guessing the numbers. Tier 3 is entirely modular. You take what you need.

Components (all modular, scoped per client):

Dashboard Design & Build: Executive summary, channel performance, and campaign drill-down dashboards built on the Tier 2 mart layer. Looker Studio for straightforward reporting needs, or Metabase for embedded dashboards with role-based access and client portal use cases. This is a decision gate based on your security and embedding requirements, not an upgrade path.
Natural Language Data Exploration: Gemini-powered querying is available natively in the BigQuery console for technical users. For most marketing teams, the AI Access Layer from Tier 2 enables data exploration through whatever AI tool your team already uses: upload the export to ChatGPT, connect Claude, use Gemini directly. Custom AI workflow implementations for more sophisticated setups are scoped as a separate engagement.
Automated Narrative Reporting: A RAG pipeline built natively in BigQuery that queries your mart tables, retrieves semantically relevant data, and generates written performance summaries delivered to Slack or email on a schedule you define. Replaces the weekly reporting meeting where someone reads a dashboard aloud. No external AI infrastructure required. The entire pipeline runs inside BigQuery using native AI functions.

What you get at the end of Tier 3: Your marketing team self-serves answers instead of asking the data person. Your executives get a written summary every Monday morning that tells them what changed, why, and what to pay attention to. Your data infrastructure works for the business, not the other way around.

Attribution & Advanced Analytics

Finally answering "what’s actually working."

This tier is only available to clients who have completed Tiers 1 and 2 with Patterson Consulting. Not because of a commercial requirement, but because the models are only as good as the data feeding them, and I need to know the data is right.

Components (scoped independently based on readiness):

Multi-Touch Attribution Modeling: Shapley value or position-based attribution models built in Dataform on your event data in BigQuery. Replaces platform-reported attribution with logic you own, can inspect, and can trust. Privacy-compliant by design, built on first-party data collected through your Tier 1 infrastructure.
Geo-Lift Incrementality Testing: The most rigorous, privacy-friendly method for measuring whether a channel is driving incremental revenue or just capturing existing demand. Uses synthetic control methodology (the same statistical framework used in econometrics and public policy research) to compare test and control markets. Doesn't depend on user-level tracking, cookies, or device IDs. The only method that directly measures causation rather than inferring it from correlation.
Media Mix Modeling: Bayesian media mix modeling using Google's open-source Meridian framework, running on BigQuery. Requires 2+ years of historical spend and outcome data. Answers "how should I allocate my budget across channels" with statistical rigor, scenario planning, and an AI-assisted interpretation layer that makes the outputs accessible to non-technical stakeholders.
Measurement Strategy & Testing Roadmap: Ongoing advisory on what to test next, which channels to validate, and how to build a culture of measurement within your marketing team. This is the retainer component: not maintaining infrastructure, but guiding the questions you ask of it.

What you get at the end of Tier 4: An honest answer to the hardest question in marketing. Not "what did the platform report" but "what actually happened when we spent this money." Attribution you own, methodology you can defend to your CFO, and a testing roadmap that gets smarter over time.