Marketing leaders have never had more data — or less certainty. The job isn't another dashboard. It's reporting you can actually make a decision on.
Most of our reporting work comes down to two things: getting clients clean, verified data, and reading it in context — multi-channel attribution, sales-cycle lag, and shifting market conditions. We simplify complex funnels into reporting that answers business-critical questions. Not isolated ad click-through rates or surface-level conversion rates, but the questions executives actually ask: "How effective was our marketing investment last financial year?" or "Which channels drove net-new growth versus existing supporter and customer renewals?"
Start with the plumbing
Garbage in, garbage out
Your data architecture is the foundation of every report you'll ever run — how your data is captured, flows and is stored. Is it warehoused, or piped live through APIs? Does your site track client-side, server-side, or a hybrid of both? You can't trust a number until you know where it came from and where it might break.
The goal is a single source of truth. Small bits of bad or incomplete data flow downstream, and the damage compounds — skewed reports, blind spots, and eventually a team that doesn't trust its own numbers. Keep the framework as concise and robust as it can be, while staying flexible enough to adapt to each campaign.
Redundancy helps — until it doesn't. Duplicate tracking on key conversions is a safety net; track everything twice and you create bloat and cost.
- Essential redundancy: duplicate your primary conversions (purchases, donations, high-intent form fills) across client-side and server-side — Meta CAPI, GA4 Measurement Protocol — to survive ad blockers and browser privacy changes.
- Unnecessary bloat: piping every micro-interaction — hovers, scroll depths, redundant clicks — into multiple warehouses with no reporting use case.
Model for the question you'll ask
"Can we track that?"
Data modelling is too often left until after a campaign is built — then becomes a scramble to retro-fit data that was never fit for purpose. A good model is simple and focused from the start.
In Good to Great, Jim Collins describes the "Hedgehog Concept" — radical simplicity inside a complex environment. Applied to data, it means a disciplined model built strictly around the metrics that drive your core outcome. Not a "fox" model that bloats to accommodate every hypothetical edge case, but a "hedgehog" model that strips the distraction away and makes one core metric — revenue per active user, cost per acquisition, lifetime value per supporter — transparent and easy to reach. Vanity metrics like total page views and impression counts only obscure real performance.
Know each platform's bias
Every platform carries a bias — in how it attributes, or simply in what it wants you to see. Ad platforms are the obvious example: they surface the metrics that make them look good and encourage more spend. View those numbers with healthy scepticism — then make the bias work for you. Google Ads, for instance, is dogmatic about conversion attribution because conversions keep you spending; set your conversion actions up correctly and you can pull genuinely useful customer insight straight out of it.
- Google & Meta Ads — attribution bias: over-credit recent clicks and views. Neutralise: validate self-reported conversions against server-side analytics (GA4) or CRM closed-won data.
- CRM & email — engagement bias: celebrate open and click rates that don't reflect revenue. Neutralise: tie email metrics to downstream deal stages and lifetime value.
- Organic search & social — vanity bias: impressions, likes and rank over qualified demand. Neutralise: track conversion actions (form fills, sign-ups) per organic session.
Combine perspectives for the full picture
Bias aside, every platform sees a different slice of the truth. Your CRM knows how supporters and customers interact with your business and your campaigns, but little about the many touchpoints before that first purchase or gift. A digital analytics platform like GA4 sees the journey to your site. Join the two and you get both: how someone arrived, every touch before they converted, matched to their profile in your CRM.
The takeaway for leaders: build one unified view that links upper-funnel web behaviour directly to CRM pipeline. When click-stream data sits next to real deal stages, leadership judges performance on bottom-line impact — not isolated platform reports. It's the same discipline behind the cross-system attribution we engineered for Standards Australia: reconnecting the ad click to the enrolment, across separate systems, so the numbers finally told one story.
Reporting is a decision tool, not a scoreboard
Whether you measure pipeline or lifetime donor value, the test is the same: can your leadership team make a confident call from it?
Report for decisions, not decoration.
The platforms will keep multiplying and the metrics will keep shouting. Clean architecture, a disciplined data model and a clear eye on each platform's bias are what turn that noise into numbers you can trust.
At BROAD REFORM, we build the data architecture, models and reporting that help purpose-driven and commercial teams see clearly — and decide with confidence.