CRM architecture, routing, attribution and reporting, wired together so sales, marketing and finance stop debating whose spreadsheet is right. Ten years of it, across B2B and fintech GTM systems.
Ask two teams for lead volume and you get two answers, both defended with screenshots. When marketing, sales and finance each pull from their own export, every pipeline review starts with reconciliation before anyone can talk about what to do. That is the problem I get hired to fix.
I architect the CRM across Salesforce, HubSpot or Pipedrive, then build the measurement spine on top: attribution frameworks, UTM strategy, tracking plans, data integrity rules and cross-system sync, finished with dashboards for funnel health, velocity, conversion and revenue. At PingPong I owned that stack end to end, Salesforce, HubSpot, Webflow, 6sense, Zapier, ZoomInfo, Salesloft, GA4 and Google Tag Manager, one architecture instead of nine islands.
I owned marketing on the cross-border payments line end to end, with sales and finance running their own sides, and it scaled from $2M to $76M in year one and past $250M+ by year two without my ops layer ever becoming the bottleneck.
They answer different doubts, so they are grouped by kind rather than by date. The case studies show the stack running under a real revenue line. The systems show what I build when an off-the-shelf tool will not fit. The writing shows the diagnosis, including the times the obvious cause turned out to be wrong.
Lifecycle stages, qualification logic, routing rules and handoff points, defined once and written down before anything gets automated. Most “attribution problems” I've been handed were definition problems: two teams counting different things under the same name. I settle the language first, because no tool fixes a disagreement about words.
Settle the words before buying the toolSource-of-truth definitions, CRM structure, field governance and sync logic. Hygiene runs as a standing rule on write. This is the layer I build so one cleaned dataset can feed eight reports that agree by construction, and the layer I refuse to skip no matter how loudly someone wants a dashboard first.
No dashboard before the definitions holdLead assignment, enrichment, follow-up and lifecycle triggers move from human queues to workflows that inherit the shared definitions. That's how the 200+ workflows I built stayed coherent instead of becoming a pile of one-off zaps, and how the lead handoff I automated collapsed from ~12 hours to 1–2 minutes, end to end.
Workflows inherit the definitions, or they driftDashboards for funnel health, velocity, conversion and revenue, all projections of the same dataset. I treat reporting as a product of the architecture rather than a monthly chore, which is why my monthly report compiles in under five minutes and the daily snapshots I stood up exist at all.
Reporting is a product of the architectureBefore I rebuilt it, a form-fill sat for ~12 hours while a human noticed it, qualified it by eye and forwarded it to a rep. I rebuilt the path as system behavior: capture normalized at the form, enrichment and intent scored on arrival, routing rules that put the lead in front of the right rep with context attached, and a cadence triggered the moment the record lands. Handoff now takes 1–2 minutes, automated end to end.
The 200+ workflows I grew from this layer share one architecture, so every new workflow inherits the same definitions instead of inventing its own. The count scaled; the chaos didn't.
Lead handoff ~12 hours → 1–2 minutes · 200+ workflows in productionI collapsed reporting into a single cleaned dataset that generates eight different reports, channel, lifecycle, pipeline and campaign views that feed themselves from the same numbers. Monthly compile time fell from one-to-two hours to under five minutes, and I added daily snapshots because refreshing suddenly cost nothing.
To keep the inputs honest I built the Marketing Report Assistant myself, an AI tool that turns scattered CSV and channel exports into clean centralized data, normalizing messy column names and duplicates so attribution isn't quietly broken by dirty data before anyone reads a chart.
Monthly report 1–2 hours → under 5 minutes · daily snapshots on demand