Marketing Operations & Martech
The data layer, automation layer, and tooling layer that makes everything else work. Attribution, tracking, CRM, automation, dashboards, and the integrations between them.
Marketing operations martech that maps spend to revenue
Most marketing teams own a stack they cannot trust. Twelve tools, three of them duplicates, and a dashboard that disagrees with the bank account. I fix that. I treat marketing operations martech as one connected system, not a pile of subscriptions. The job is plumbing first, reporting second: get the data clean at the source, route it through tools that actually talk to each other, and tie every dollar of spend to a stage in the pipeline you can defend in a board meeting.
I work as a Fractional Head of Growth, and the first thing I audit is the gap between what your tools say and what your revenue says. That gap is almost always tracking debt. Pixels fire twice. Events drop on mobile. UTMs get stripped before they reach your CRM. Strong marketing operations martech closes that gap so the number you report and the number you book are the same number. When I took Elementor to 100x ARR, none of it held together without an operations layer that could survive that growth without breaking.
The build follows a fixed order. First, the data layer: one source of truth for events, consistent naming, and server-side capture so ad blockers and browser changes stop eating your signal. Second, the tool layer: a CRM, an analytics platform, an ad-side conversion API, and a lifecycle engine, each wired so a lead created in one place updates everywhere within minutes. Third, the attribution layer: first-touch and last-touch side by side, plus a view that shows which channel actually moved a deal forward. This is where marketing operations martech earns its keep, because it kills the channels you only thought were working.
I am deliberate about what goes into the stack. Every tool has to pull its weight against a clear job: capture, route, measure, or activate. If two tools do the same thing, one goes. If a tool needs three weeks of setup to answer a question a spreadsheet answers today, it waits. The goal is a lean operations layer that a two-person team can run without a full-time admin, not a sprawling system that needs a specialist to read it. For the standards I hold tracking to, I lean on Google's own analytics documentation, including the GA4 Measurement Protocol guidance for server-side events.
Attribution is the part most teams get wrong, so I spend real time there. A click-only model rewards the last touch and starves the channels that create demand earlier. A model that credits everything tells you nothing. I set up reporting that separates branded search from genuine demand, flags assisted conversions, and shows cost per qualified pipeline by source. That is what let me drive Riverside to +337% MRR: I could see which spend produced revenue and which spend produced noise, then move budget accordingly. Marketing operations martech without honest attribution is just an expensive habit.
This work pays back fast because it stops waste before it stops growth. When the data is clean and the tools are connected, you cut the campaigns that never converted, you find the lifecycle gaps where signups go cold, and you give finance a number they trust without a meeting. I have managed $100M+ in budgets, and the accounts that scaled were always the ones with an operations layer that told the truth under pressure. If your stack is fighting you instead of feeding you, that is the work I do. Strong marketing operations martech is not a tool decision, it is a revenue decision, and I build it to be measured.
Related
Frequently asked questions
What does a marketing operations martech engagement actually deliver?
A connected stack and an attribution model you can defend. I audit your tools, fix the tracking at the source with server-side events, wire your CRM, analytics, and ad conversion APIs together, then build reporting that maps spend to pipeline by source. You leave with one source of truth and a clear view of which channels produce revenue versus noise.
How is marketing operations martech different from just hiring an analytics person?
An analyst reads the data. I fix why the data is wrong first. Most reporting problems are plumbing problems: pixels firing twice, events dropping on mobile, UTMs stripped before the CRM. I rebuild the data and tool layers so the numbers are trustworthy, then set up attribution on top. Without that foundation, an analyst is reporting on broken inputs.
How long before the martech stack starts paying back?
The waste cuts come first, usually within weeks. Once tracking is clean and tools are connected, you can see which campaigns never converted and kill them immediately. Attribution and lifecycle improvements compound over the following months. I sequence the work so the fastest savings land early and fund the deeper build.
Do I need to rip out my current tools to do this?
Usually not. I start by auditing what you have against four jobs: capture, route, measure, activate. Most stacks have duplicates and gaps, not a need for a full rebuild. I remove tools that overlap, fix the wiring on the ones that earn their place, and only add a tool when an existing one cannot do the job. The goal is lean, not new.
Will this work if I only have a small marketing team?
That is exactly who it is built for. The point of good marketing operations martech is a lean layer a two-person team can run without a full-time admin. I favor consolidation, clear naming, and automation that updates every system from one event. Small teams gain the most because they cannot afford to manually reconcile tools or chase reporting that disagrees with revenue.
Marketing ops is invisible when it works and catastrophic when it doesn't. Yaniv builds the infrastructure: analytics implementation, server-side tracking, CRM configuration, automation workflows, attribution models, and the dashboards that make data trustworthy. If your marketing team can't answer 'how much revenue did organic generate last month,' this is where we start.
What's Included
- Analytics implementation (GA4, Mixpanel, Amplitude)
- Server-side tracking and conversion APIs
- CRM setup and lifecycle automation
- Marketing automation (n8n, HubSpot, Braze)
- Attribution modeling and revenue reporting
- Dashboard building and executive reporting
- Tool evaluation, migration, and integration
- Data hygiene and governance
Track Record
Elementor: $200K to $20M Riverside: 337% MRR cnvrg.io: Intel Acquisition
Frequently Asked Questions
We already have GA4. Do we need more?GA4 is a starting point. It doesn't connect to your payment system, doesn't score leads, doesn't trigger automation, and doesn't report revenue per channel accurately without server-side integration. Marketing ops fills those gaps.
Can you work with our existing tools?Yes. I audit what you have, fix what's broken, and only recommend new tools when the existing stack can't do the job. No rip-and-replace for the sake of it.
Ready to Talk?
15 minutes. No decks. Just a conversation about your growth bottleneck.
Book a 15-Min CallLet's turn this into measurable revenue
Book a 15-min call. I will tell you whether this is your next move, or whether your money is better spent elsewhere.