Stop reporting on activity. Start reporting on what moved revenue.
The Outcome
Most marketing reports are a collection of numbers that describe effort: impressions, clicks, sessions, email opens, social reach. Very few of them answer the question a CEO or CFO actually asks: what did we spend on marketing, and what did it produce in terms of qualified leads, pipeline, and revenue? The gap between marketing activity metrics and business outcome metrics is where most marketing budgets get defended emotionally rather than evidentially. We build the reporting and attribution infrastructure that closes that gap — so you know which channels, campaigns, and content assets are actually moving the needle, and which ones are consuming budget without contributing to pipeline.
What We Do
We design and implement reporting systems that connect marketing activity to downstream business outcomes. That means proper analytics configuration (GA4, GTM), CRM integration that captures the full lead-to-customer journey, attribution modelling that accounts for multi-touch journeys, and dashboards that surface the metrics decision-makers actually use. We do not build dashboards that report on everything — we build ones that answer the specific questions your team asks each week, each month, and each quarter. We also build the data pipelines that bring cross-channel data into one view rather than requiring you to reconcile five different platform reports manually, and we run the ongoing analysis work: what is changing, why, and what to do about it.
AI Layer
AI makes data analysis faster and surfaces patterns that human analysts would need weeks to find manually. We use it to run anomaly detection across marketing data (flagging when something changes significantly before the end-of-month report), to build predictive models for lead quality scoring, and to generate automated narrative summaries of what the data means rather than just presenting the numbers. We also use AI to run natural-language queries against complex data sets — asking 'which channel drove the most SQLs last quarter' rather than building a pivot table. Our analysts make every strategic interpretation and recommendation. AI handles the data processing speed and pattern recognition. The strategist decides what it means and what to do.
Method
- 01
Analytics audit: review the current measurement stack — GA4 configuration, event tracking completeness, UTM discipline, CRM pipeline stages, and attribution logic.
- 02
Define the measurement framework: agree on the metrics that matter for your business at each reporting cadence — what does good look like and how will we know if things go wrong?
- 03
Implement tracking: fix or rebuild GA4 event tracking, tag management in GTM, UTM naming conventions, and CRM field mapping.
- 04
Build attribution: implement the attribution model that fits your buying cycle — last-click for short sales cycles, multi-touch linear or time-decay for complex B2B journeys.
- 05
Build dashboards: create the reporting views for marketing, sales, and leadership — each showing the right metrics for the decisions each team makes.
- 06
Run ongoing analysis: monthly performance reviews that go beyond the numbers to explain what changed, why, and what the next action should be.
Proof First
We will audit your current analytics configuration — checking for tracking gaps, attribution errors, and data quality issues — before we propose anything. Most companies have more data than they can use, but less accurate data than they think. We will show you exactly what your current setup does and does not tell you.
FAQ
We have Google Analytics. Is that not enough?
GA4 is a foundation, not a complete measurement solution. On its own, it tells you about website behaviour but does not connect to your CRM, does not show you which leads converted to customers, and does not give you cross-channel attribution across paid, organic, email, and social. The reporting infrastructure we build connects all those data sources into one coherent picture.
What attribution model should we use?
It depends on your sales cycle. For B2C with short, direct journeys, last-click is often sufficient and easy to understand. For B2B with multi-month, multi-touchpoint journeys, multi-touch attribution models (linear, position-based, or time-decay) give a more accurate picture of which channels are contributing to pipeline. We will recommend the right model for your specific journey and be honest about its limitations.
Can you connect marketing data to our CRM?
Yes. CRM integration is central to what we build. We connect the marketing data layer (GA4, paid platforms, email) to your CRM so you can see the full journey from first ad click to closed customer. We work with HubSpot, Salesforce, Pipedrive, and most mainstream CRMs. The specific integration work depends on what data your CRM captures and how your pipeline stages are defined.
Do you build in Google Data Studio / Looker Studio?
Yes. Looker Studio is our default for accessible, shareable dashboards that connect to multiple data sources. We also work in Metabase for SQL-based analysis and in HubSpot's native reporting for clients where all the data lives there. The platform choice should follow the data and the team's preferences — we are not attached to any one tool.
Want to know what your marketing is actually producing in pipeline terms? Request a free analytics audit — we will review your current measurement setup and show you the gaps.
Request a free audit