Short essays on growth measurement, marketing operations, and how teams actually use the data.
This section supports the rest of the portfolio with written thinking: scorecards, attribution, lifecycle funnels, reporting habits, AI-assisted analytics, agents, automations, and the practical systems that make faster growth decisions possible.
The strongest posts to start with.
These are the best entry points if you want the operating logic behind the dashboards, examples, and the interview plan.
Revenue operations analytics: examples of the work.
How my past work maps to revenue modeling, scenario planning, funnel optimization, executive reporting, annual planning, trend analysis, data integration, and cross-functional collaboration.
Budget up, CAC up: how backward funnel isolation cut our CAC 60%+.
A Website Squirrel case study in diagnosing rising CAC by auditing attribution, mapping the funnel backward, and isolating the segment whose behavior actually predicted revenue.
The marketing analyst problem-solving framework.
A six-phase mental model for framing the decision, testing a prior, sourcing data cleanly, isolating the lever, and closing the loop after the recommendation ships.
How to build a growth baseline scorecard leadership will actually use.
A practical scorecard should connect spend, calls, bookings, cost per funnel goal, and revenue without turning into a dashboard archaeology project.
Practical attribution for home services.
Attribution gets useful when it is tied to calls, booked jobs, and revenue handoff quality instead of staying trapped in channel-only reporting.
The four lanes this blog will keep returning to.
Scorecards and executive reporting
How to build weekly views that stay readable, comparable, and decision-ready.
Lifecycle, attribution, and funnel leaks
Where the real operating leverage sits between first touch and repeat business.
Systems, handoffs, and governance
The stack and process work that makes metrics trustworthy enough to use.
AI, agents, automation, and fresh data
How newer workflows can reduce reporting lag and make analytics more interactive without replacing the fundamentals.
AI belongs in the analytics workflow where the next question appears.
One idea that keeps showing up in my thinking is simple: dashboards answer the first expected question, but they usually break when the follow-up question arrives. The more useful role for AI is to sit on top of live or freshly retrieved data, answer the next question in plain language, generate lightweight visuals when needed, and let the best answers graduate into durable scorecards. That same lane also makes room for automations, agents, and Python retrieval jobs that keep reporting fresher without rebuilding every dashboard by hand.
Published posts, live and linked.
The original six-month series is live, with a new revenue analytics series added for GTM and RevOps proof points.
Revenue modeling is not a spreadsheet. It is a control system.
How forecasting, pipeline coverage, quota capacity, and source-to-revenue confidence fit into one operating view.
The point of scenario planning is to make tradeoffs visible.
Sensitivity models for pricing, product mix, discounting, and attach-rate decisions should show where the business has room to move.
Revenue leakage usually hides between stages
A funnel deep-dive should isolate the handoff, stage, source, or customer segment where revenue is slowing down.
Executive reporting should end with a decision.
The useful report translates raw data into a performance narrative, confidence level, and scale, fix, test, or watch recommendation.
Annual planning needs pipeline math and operating judgment.
Territory strategy, sales capacity, and GTM goals work better when planning connects targets, coverage, conversion, and execution capacity.
Trends are only useful when they point to a lever.
Stage, vertical, product-line, and channel trends should separate durable patterns from noise and highlight where adjustment is needed.
A single source of truth is an operating agreement.
The technical work matters, but the harder work is aligning definitions, source systems, joins, and ownership across Salesforce and internal data.
Good revenue analytics is cross-functional by design.
The data team, RevOps, marketing, sales, finance, and leadership all need the same measurement layer to support different decisions.
Budget up, CAC up: how backward funnel isolation cut our CAC 60%+
A case study in diagnosing rising CAC, finding the high-intent sequence, and shifting spend toward the segment that actually converted and stayed.
The marketing analyst problem-solving framework
The six-phase scaffold that keeps analysis tied to decisions instead of drifting into reporting theater.
Why AI should handle the follow-up questions your dashboards miss
Where AI, agents, automation, and Python make reporting more interactive and fresher.
Services booked per day is the operating metric marketing leaders need
The clearest bridge between marketing activity and actual booked work.
Cost per lead vs. cost per booked customer
How to stop confusing top-of-funnel efficiency with real acquisition economics.
How to build a growth baseline scorecard leadership will actually use
The minimum useful scorecard for weekly decision-making.
Practical attribution for home services
Keep attribution close to calls, bookings, and closed-loop revenue instead of channel theater.
How a customer life cycle map makes reporting more useful
The stages and handoffs that turn reporting into an operating system.
What a canonical stack should do from first touch to referral
Think in operating lanes, not disconnected tool names.
Calls per day, book rate, and where demand actually leaks
Two measures that quickly show whether the problem is media, intake, or booking friction.
Friday growth briefs: scale, fix, or test
A short weekly brief that turns scorecards into action.
Cross-brand tracking governance without slowing marketing down
The standards that make multi-brand reporting comparable without killing speed.
ROAS only works when offline conversion handoffs are clean
Why source-to-revenue mapping is the real prerequisite for trustworthy efficiency metrics.
Python retrieval loops for fresher reporting
Using lightweight scripts to pull data on a steady rhythm so the reporting layer stays current.
What marketing ops should standardize first across multiple brands
The order of operations when you inherit a stack that is partly centralized and partly siloed.
How to turn call-center handoffs into a growth lever
The routing, response, and intake moves that change whether marketing spend becomes booked work.
Funnel conversion velocity as an executive KPI
Why speed through the funnel often matters as much as total throughput.
What clean naming and documentation do for AI-generated SQL
Why the quality of the data layer matters more than the AI hype cycle.
From disposable analysis to durable reporting assets
Let useful answers earn permanence instead of overbuilding every report on day one.
When a dashboard should become an interactive operating app
Some questions deserve more than a chart, but not everything deserves a platform build.
How to separate media problems from booking problems
The quickest way to stop blaming acquisition for downstream leakage.
Repeat, referral, and NPS as downstream growth signals
The quality metrics that deserve more space in executive reporting.
What to audit in the first 30 days of a marketing ops role
The short list that tells you where trust is breaking in the system.
Budget moves that depend on booked customers, not vanity leads
Why budget reallocation should follow the real conversion event.
Why tracking governance is a growth function
Governance is not cleanup work if it changes the speed and quality of decisions.
Measurement infrastructure as a competitive advantage
The case for treating data quality and reporting trust as strategic assets.