About MxM Revenue Engineering

Marius Murariu

A new revenue engineering firm built on two decades of operating work.

MxM Revenue Engineering is new. The operating record behind it is not. The work on this site comes from prior in-house roles across Microsoft, HP/HPE, and Philips where forecast pressure, market fragmentation, and revenue-data drift were already real board-level problems.

Marius Murariu

Founder, MxM Revenue Engineering

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"I don't just advise.
I build."
Bucharest, Romania / Technical precision, senior-only delivery

Pipeline exposure

$4.2B+

governed

HP/HPE, Philips, and Microsoft CEMA.

Experience

20+

years

Enterprise sales ops and revenue governance.

Market complexity

107

countries

Microsoft CEMA: CEE + MEA.

Operating span

timezones

Cross-region planning cadence.

Why MxM Revenue Engineering exists

Revenue problems are usually misdiagnosed. They look like sales team problems. They are almost always operating system problems: unclear ICP, stage definitions nobody enforces, CRM data Finance has stopped trusting, and leadership looking at different versions of the same pipeline.

I have been in the midnight forecast rebuild: CRM showing one version of the quarter, Finance working from another, leadership trying to explain variance before the board asks the obvious question. In those moments, the problem is not effort. Everyone is working hard. The problem is that the revenue system has no control layer strong enough to make the number trustworthy before the pressure arrives.

After enough cycles of firefighting, I stopped treating forecast variance as a reporting problem and started treating it as an operating-system problem. The answer was never another dashboard. It was clearer stage evidence, tighter reconciliation, better governance cadence, and a shared definition of revenue truth.

The same failure pattern appears in every environment I have worked in: at Microsoft across a multi-billion-dollar CEMA Security business, at HP/HPE across EMEA, at Philips across 19 CEE countries. MxM is that pattern diagnosis and fix, distilled for growth-stage SaaS teams.

The goal is simple: move from always firefighting to always prepared.

// The operating model

Strategic RevOps

The Why

Pipeline governance, forecast logic, board narratives. The structural reasoning behind where revenue is and where it is going.

Technical Architecture

The How

SQL, Python, Salesforce config, data lineage audits, schema design. The engineering that makes the logic enforceable and verifiable.

Automation Engineering

The Build

n8n, Power Automate, UiPath. Eliminates manual reporting layers and enforces governance without adding headcount.

No junior bench. No offshore layer. No invented case studies.

// Tools I actually use

CRM & Sales Execution

SalesforceHubSpotPipedriveMicrosoft Dynamics 365SAPAltifySeismic

Analytics & Data

GongClariPower BITableauQlik SensePython (Pandas)Advanced Excel

Automation & Orchestration

n8nMake.comPower AutomatePower AppsZapierUiPathAutomation AnywhereCustom AI Agents

Career record and methodology

These roles are the source material for MxM Revenue Engineering. They are not disguised consulting case studies. They show the kinds of operating environments that shaped the methodology now being applied to SaaS revenue systems.

HP / HPE

Sales Strategy and Planning, EMEA Enterprise Group

2006–2017Foundation

Including HP contractor role from 2006

EMEA-wide planning and forecast operations

Featured outcome

±5%

Forecast Variance

Forecast recovery, pipeline data quality initiative, Altify rollout, Solutions Selling redesign

What the work covered

Forecast variance reduced from ±28% to ±5% through structured pipeline review cadence and stage-exit discipline across the EMEA Enterprise Group.
Data quality index improved from 65% to 91% through a systematic pipeline data quality initiative covering field hygiene, validation rules, and governance controls.
Redesigned the Solutions Selling methodology into a customer-focused framework, with exit criteria tied to customer outcomes, keeping sellers aligned with buying pace rather than internal stage definitions.
Directed segmentation, quota deployment, and target-setting in close collaboration with Finance and Sales Compensation, ensuring accuracy and transparency across the EMEA Enterprise Group.
Owned reporting governance and market share analytics for the EMEA Enterprise Group, ensuring VP-level executives had reliable, consistent data for business reviews and strategic decisions.

Philips

Sales Excellence Manager, CEE

2018–2022Standardization

Central and Eastern Europe commercial operations

Featured outcome

+33%

Target Attainment Lift

Process mining, Salesforce audit trails, Lean methodology (VSM, Kaizen, Root Cause Analysis, Pareto, Daily Management)

What the work covered

Process-mined Salesforce audit trails to identify stage movement patterns and seller-level weak points.
Built individual coaching plans per seller: spider-web skill model with targeted training and best-practice references.
Created ABC player segmentation: A player identification, B-to-A coaching paths, C player exit plans, cross-referenced against quota attainment.
Designed and executed E2E market segmentation: data sourcing, provider negotiations, standardization, 19-country rollout.
Applied Lean principles (Value Stream Mapping, Kaizen, Root Cause Analysis) to commercial process design across the entire CEE region.

Microsoft

Senior Sales Operations Manager, CEMA Security

2022–2025Peak

Microsoft CEMA security environment, $3B+ business-unit exposure

Featured outcome

−50%

Revenue at Risk Eliminated

5Q forecasting model, Dynamics 365, Power Automate, Power BI, Python analytics, AI insights engine

What the work covered

Reduced Revenue at Risk by 50% within 2 quarters by detecting and addressing emerging workload usage patterns before they became churn.
Developed Power BI and Python-based diagnostics to surface pipeline anomalies, standard deviation outliers, and trend shifts, enabling earlier interventions in QBR and ROB reviews.
Automated recurring reporting and renewal workflows using Power Automate and low-code tooling, reducing manual admin and freeing sales leaders to focus on coaching.
Achieved +25% lead-to-opportunity conversion lift through structured pipeline review and stage-exit discipline.
Built an AI-driven insights engine on existing data infrastructure; sustained ~40% year-over-year growth across the CEMA Security business unit.

View full experience on LinkedIn

This page does not present MxM Revenue Engineering client case studies. The metrics above come from prior in-house operator roles. As MxM Revenue Engineering accumulates client results, those will be added separately and labeled as such.

// Operating principles

Opinion vs. Math. Choose Math.

The cadence creates predictability, not the dashboard

Dashboards are visibility. Cadence is accountability. Revenue becomes predictable when signals have owners, owners have actions, and actions have dates. The data layer is table stakes. The behavior layer is where the leverage lives.

Data quality is a behavior-design problem

CRM hygiene campaigns treat sellers as the problem. The system around them is the problem. The consequence has to be personal, the fix easy, progress visible, and accountability socially reinforced. Enforcement alone produces compliance theater.

A forecast is trusted when the number is traceable

Precision is not what creates trust. Traceability does. When every layer can follow the number back to deal-level reality and see where judgment was applied, the debate shifts from whose number is right to which assumptions to challenge.

Separate the territory problem from the behavior problem

A seller missing target is not always a seller problem. It may be account assignment, market potential, or ramp stage. Sales effectiveness starts when you can correctly diagnose the cause before prescribing the fix.

Where to go next

If your numbers do not reconcile, start with the mechanism, not the story.

The next step depends on whether you want to see the protocol, review the engagement model, or read the research behind the positioning. All three paths are linked here.

See the protocol

Understand how the Scorecard, Controls Install, and Governance sequence works.

Review pricing

Match the engagement model to your ARR stage and operating risk.

Read the research

Go deeper on forecast variance, CRM reconciliation, and AI readiness.

The board does not just want the forecast. It wants to know how it moved.

The readiness check shows where your revenue numbers are likely to break under that question. The Scorecard validates it against your actual CRM, billing, forecast, and board reporting.

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