Contents
  1. 01 · Multi-source dashboards
  2. 02 · The problem it solves
  3. 03 · One view, every source
  4. 04 · Built around decisions
  5. 05 · It recommends, not just reports
  6. 06 · One template, many clients
  7. 07 · What it demonstrates
Analytics service

Multi-source Dashboards

The layer a client actually looks at. Every source a business has, their web analytics, their Google listing, their point of sale, their social, pulled into one dashboard that answers the questions an owner actually asks, in language they actually use.

Analytics
The problem

The data is everywhere. The answer is nowhere.

A business owner can already log into four dashboards. Analytics here, their Google profile there, the point of sale somewhere else, social in a fifth tab. Each one shows a slice. None of them answer the questions that actually matter. Is marketing bringing in money, which channels are pulling their weight, and what should I do next week?

A dashboard that just re-displays those slices in one place isn't worth much. The job is to combine them. Put traffic, bookings, revenue, and reach against each other so the relationships show, and shape the whole thing around decisions instead of dumping every metric a tool can export.

Analytics
The build

One view, every source

Built in Looker Studio on top of the pipeline data, the dashboard unifies four sources that normally never meet: web analytics for traffic and on-site events, the Google Business Profile for how the listing performs in search and maps, the point of sale for real revenue and service mix, and social for reach and engagement. It's organized into tabs a non-technical owner can move through. An at-a-glance overview, then acquisition, engagement, revenue, and content. Each one answers a specific question rather than showing everything at once.

Brightwell Home Services — Performance Overview
last 28 days
Sessions1,240▲ 12.4%
Booking starts86▲ 9.1%
Phone clicks31▲ 24.0%
Revenue$14.2k▲ 7.7%
GBP actions214▼ 5.2%
Sessions over time
Top channels — which convert
Organic Search
Direct
Organic Social
Referral
Sessions vs. bookings by channel
Organic Search
Direct
Organic Social
AI Assistant
↳ converted
Top search queries
hvac repair near me312
ac not cooling188
emergency furnace repair141
hvac company raleigh97
duct cleaning cost63
GBP impressions — search vs. maps, desktop vs. mobile
Mobile Maps
Mobile Search
Desktop Maps
Desktop Search
Top pages
PageViews · Eng. rate
/1,030 · 74%
/schedule410 · 88%
/services/ac-repair286 · 71%
/financing142 · 80%
Business events — 28-day totals
booking_started
phone_click
form_submit
quote_request
Booking sources
organic / schedule38
direct / schedule29
social / schedule14
referral / services5
Revenue$14,210▲ 7.7%
Jobs63▲ 4.1%
Avg ticket$225▲ 3.4%
Repeat rate28%▲ 2.0%
Revenue by day of week
Sat
Thu
Fri
Tue
Mon
Service mix — top jobs
AC repair19
Furnace tune-up12
Duct cleaning9
Install consult7
Thermostat swap5
Reach (28d)4,820▲ 18%
Engagement386▲ 11%
Followers+62▲ 3.8%
Eng. rate2.1%▲ 0.4%
⚡ Boost candidates posts worth ad spend, with a suggested budget
"Before/after: full system install"reach 612 · ER 4.1% · spend $18
"5 signs your AC is about to fail"reach 540 · ER 3.6% · spend $14
"Meet the crew — summer hours"reach 388 · ER 2.9% · spend $9
A generic example built on invented data for a made-up business. No real client. Click the tabs: every view answers a different question, and Content surfaces which posts to boost and what to spend.
Analytics
The judgment

Built around decisions, not metrics

The hard part of a dashboard isn't pulling the data. It's deciding what not to show. Every source can export hundreds of metrics, and a dashboard that includes them all is a wall an owner bounces off. So each view is built backward from a question. Acquisition doesn't just list traffic sources. It puts sessions and bookings side by side so you can see which sources actually convert, not just which send the most clicks. Revenue ties the point-of-sale numbers to the day of week and the service mix, so a slow Monday or a high-margin service is obvious at a glance.

The language matters as much as the layout. The same translation discipline from the audit work applies here. An owner sees "booking starts" and "phone clicks," not event names and parameter keys. The dashboard is for the person paying for it, not the person who built it.

Analytics
Beyond reporting

It recommends, not just reports

The piece I'm proudest of is on the content tab. Most reporting stops at "here's how your posts did." This goes a step further. It scores recent posts on reach and engagement and surfaces boost candidates, the specific posts worth putting ad money behind, with a suggested spend calculated from how they performed. That's logic I built into the dashboard, not a feature that came in the box. It turns a report the owner reads into a recommendation the owner can act on. That's the difference between a dashboard that gets opened once a month and one that drives what happens next.

Analytics
Repeatable

One template, many clients

The dashboard is a master template, not a one-off. The structure, the calculated fields, the recommendation logic, and the plain-language framing are built once; a new client is a new instance wired to their own pipeline data. That's what makes it a service rather than a favor. Onboarding a business is connecting their sources and pointing the template at them, not rebuilding from scratch.

And it sits at the end of a system I built end to end: the data pipelines pull each source on a schedule and land it in clean storage; the dashboard reads from there. Ingestion, storage, presentation. One chain, built to run.

Analytics
The synthesis

What it demonstrates

The skill to unify sources that don't naturally talk, the judgment to build around an owner's real questions instead of dumping every available metric, and the instinct to push past reporting into recommendation. A client-facing layer that makes scattered data legible, and tells the owner what to do with it.