What I've learned
Most reporting fails at the same place: it answers "what happened" and stops there. The number exists. The chart is there. But the decision, the thing the stakeholder actually needs, isn't in it.
I build dashboards backward. I start with the decision that needs to be made, then figure out which numbers inform it, then figure out how to make those numbers readable to someone who isn't staring at them every week.
The visual design part matters too. A report that looks like a spreadsheet gets skimmed. A report that's organized, labeled clearly, and respects the reader's time gets used.
What I build with
The primary tool for client-facing reporting. Pulls from GA4, Google Ads, Search Console, and third-party data via connectors.
Event tracking, conversion paths, traffic attribution. I set up custom events and build views that surface the signals that matter for B2B buyer journeys.
Organic visibility, keyword ranking trends, and AIO performance. I build reports that connect search position to traffic to pipeline, not just rankings in a vacuum.
Paid search, LinkedIn, programmatic. Spend, CPA, conversion rate, and how it all connects to actual pipeline.
Examples
Examples coming. Waiting on client clearance for public use. What to expect here: campaign dashboards that tell the story of a quarter, organic search reports that explain an algorithm shift, and GA4 setups that actually track what matters for a long B2B sales cycle. Reach out if you'd like to see samples before they're posted.
A note on AI Overview reporting
Most clients have no idea whether their content is appearing in ChatGPT, Perplexity, or Google's AI Overviews — and most reporting tools don't track it. I've been building the frameworks to measure it, and this past year produced a clean case study in why it matters.
A client in the custom sports equipment space went from zero AI citations to consistently cited across ChatGPT, Perplexity, and Google AI Overviews within ten months of structured SEO work. By May 2026 they ranked #1 in Google's AI Overview for their core product query. That visibility translated into ten confirmed AI-referred leads in HubSpot — real people who found the brand through an AI recommendation and clicked through. Seven of those ten came through a single product page.
One thing worth documenting: overall organic traffic declined during this period — an industry-wide pattern as AI search absorbs clicks before users reach a website. The reporting I built focused on leads, not traffic. 579 inbound leads captured since HubSpot was installed, 37% from organic search, with AI-referred contacts arriving consistently since September 2025. Intentionally excluding raw traffic numbers was the right call — because a period that looks bad in traffic can be genuinely good for pipeline. That's the shift in AIO reporting. You have to measure the right thing, or you'll read a working strategy as a failing one.