Revenue Operations leader who diagnoses why a scaling company’s revenue system is lagging and builds the operating model that fixes it.

Most people in RevOps will tell you the problem is the process. I’ll tell you I’m good at reading rooms.

I’ve sat in board prep sessions where the numbers told a harder story than leadership wanted to present — and I’ve held the line. I’ve inherited CRM environments where sellers had stopped logging activity because no one acted on the data anyway — and rebuilt the trust that made it worth their time. I’ve been the first dedicated RevOps hire at organizations that had been running on spreadsheets and optimism.

I am Catherine Jones — and after 15 years, what I’ve learned is that revenue problems are almost never about the tool. They’re about what the organization decided to tolerate before I got there.

Three case studies below show exactly how that plays out.

Catherine Jones — Revenue Operations Leader
Austin, TX  •  Hybrid or Remote
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Catherine Jones

Systems thinker.
Revenue architect.

Most RevOps leaders are operators. I’m a diagnostician.

I’ve spent years learning how revenue organizations actually break — not how they look on a slide deck, but where the real fault lines are between data, incentives, process, and people. That’s the work. Everything else is implementation.

15+ Years in RevOps
5 Years as an IC Seller
20+ Years Leading People
28 Largest Team Led

Core Expertise

Where strategy meets execution

Fifteen years in, I’ve learned to hold both. These are the domains that make up a functioning revenue operating model.

Revenue Operations

End-to-end RevOps design: pipeline governance, forecasting models, ARR and NRR reporting, renewal operations, expansion revenue tracking, and the operating cadences that give leadership real-time clarity on where revenue is growing, renewing, and at risk.

GTM Strategy & Marketing Ops

Go-to-market planning that connects strategy to execution: territory design, segmentation, sales motion alignment, lead routing and scoring, and marketing-to-sales handoff design. I have built and trained sales teams on the handoff structures that make the full revenue motion more effective — from first touch to closed won.

CRM Governance

CRM architecture and governance that creates clean, reliable data. I rebuild the processes and structures that ensure your system of record actually reflects reality — and stays that way.

Forecasting & Reporting

Forecasting methodologies and executive dashboards that create a shared, trusted view of pipeline health, attainment, and risk — designed for both operational leaders and the board.

Compensation Design

Incentive compensation plans that align rep behavior with company goals — clear, fair, and administratively clean. Including OTE modeling, accelerators, SPIFFs, and quota methodology.

Revenue Cadence & Executive Alignment

The operating rhythms that connect daily execution to boardroom decisions: MBR and QBR architecture, pipeline review design, ExCo reporting cadences, and the cross-functional accountability structures that keep leadership and the revenue team looking at the same numbers.

Where I Create Impact

The revenue challenges that bring organizations to a standstill. And where I start.

Pipeline data that doesn’t match what Finance sees. I rebuild the governance layer that creates one trusted number across every function.

Reps who can’t state their own attainment. I build the seller analytics that give each rep and leader clear, real-time performance visibility.

Scaling headcount into revenue architecture that hasn’t kept pace. I design the handoffs, CRM governance, and comp structures that grow with the business.

A CRM that’s a system of record in name only. I rebuild the governance, stage signal definitions, and process discipline that restore trust in the data.

Leadership making resourcing calls without reliable data. I build the reporting and forecasting infrastructure that closes the visibility gap.

NRR is lagging but no one can say why. Expansion revenue is invisible, renewal risk surfaces too late, and CS has no shared data model with Sales. I build the reporting infrastructure that makes revenue retention as visible as revenue acquisition.

My Approach

How I lead a RevOps function

I don’t arrive with a template. I diagnose before I design, build with the team rather than for them, and the infrastructure I leave behind keeps running when I’m not in the room.

01

Diagnose Before Designing

Most revenue problems aren’t what they appear to be on the surface. A forecasting problem is usually a data quality problem. A CRM adoption problem is usually a process design problem. Before any architecture decision gets made, I want a clear map of where trust has broken down — between Sales and Finance, between the system of record and what leadership actually believes.

02

Design for the Org, Not the Ideal

The best system is the one the organization can actually adopt. I build toward the end state in phases the org can absorb — because a perfect system that never gets used is just a PowerPoint. Solutions have to fit the team’s current capacity, the technology already in place, and the political reality of how decisions actually get made.

03

Set the Standard. Hold the Team to It.

My job is not to be the last person touching a system. It’s to set the architecture standard, make sure the team has what it needs to execute against it, and be accountable for the outcome. CRM governance, comp plan design, executive reporting infrastructure — these get built to a standard that holds after I stop checking in on them.

04

Make the Right Path the Default

Leadership has the visibility to make confident decisions. Revenue teams have the systems to perform with accountability. The infrastructure holds after I stop touching it — that’s the only standard that matters at this level. If the org reverts to the old behavior the moment I leave the room, the design wasn’t finished.

Tools & Platforms

I open these on day one

Hands-on experience across the full RevOps tech stack — CRM, compensation, analytics, pipeline intelligence, and GTM tooling.

CRM

Compensation

Xactly

AI-Native Tools

Salesforce Einstein
Gong AI
Clari AI
Claude
Perplexity

What I Bring

What a functioning revenue operating model looks like when I’m in the seat

A RevOps leader who can diagnose, design, and build is a different hire from one who can only do one of those things. Here is what I bring across all three from day one.

Leadership

RevOps Function Leadership

I’ve built and led RevOps functions where none previously existed — from single-person contributor to team of 2–5. I own the full function: systems, process, people, and reporting architecture.

  • Team Building
  • Cross-functional Alignment
  • Executive Reporting
  • Board-level Visibility
Systems

Revenue Architecture & CRM

CRM governance, stage discipline, data architecture, and the ARR and NRR reporting infrastructure that gives leadership a real-time view of revenue health across new business, renewal, and expansion. I’ve built the CS reporting layer from scratch — bridging the transparency gap between CS, Finance, and Operations so that retention and expansion revenue is as legible as new revenue.

  • CRM Governance
  • ARR & NRR Reporting
  • CS Ops & Retention Infrastructure
  • AI Integration
GTM

GTM Strategy & Planning

Annual planning from model to execution: I’ve built the bottoms-up revenue model, owned the territory architecture and quota design, and presented the outputs directly to the CEO and CSO. The plan I hand off is one I can defend — assumption by assumption.

  • Annual Planning & Revenue Modeling
  • Territory & Quota Design
  • GTM Alignment
  • Board-Level Planning Output
Compensation

Incentive Compensation Design

Sales compensation plans that drive the right behaviors — clear structure, competitive benchmarks, and administratively clean execution. OTE modeling, accelerators, SPIFFs, and quota setting through rollout.

  • Plan Design
  • OTE Modeling
  • Accelerators & SPIFFs
  • Quota Methodology
Marketing Ops

Marketing Operations & Lead Routing

Lead routing logic, MQL scoring, and marketing-to-sales handoff design in HubSpot and Salesforce. I have built the routing architecture and trained sales teams on the handoff model — so leads move through the system with speed and accountability, not manual triage.

  • Lead Routing (HubSpot & Salesforce)
  • MQL Scoring & Handoff Design
  • Sales Training on Handoff
  • Marketing & Sales Alignment

How I Think

How I diagnose revenue systems

A Series B with 20 sellers and a €1B global org with 140 present differently. But underneath the scale difference, I’ve found the same four structural failure modes, in different proportions. I map them before I redesign anything.

01

Data Integrity & Trust

Do the people making decisions trust the numbers in front of them? If not — at what layer does the data actually break? A forecast call where leadership argues about the inputs is not a forecasting problem. It’s a data architecture problem.

02

Governance & Accountability

When something breaks or underperforms, is it clear who owns it? I look for the governance voids — territories without owners, processes without DRIs, handoffs that exist in someone’s memory rather than a system. Ambiguity at this level is expensive and compounds quarterly.

03

Incentive & Performance Alignment

Are the people doing the work measured in a way that actually incentivizes what the business needs? Comp disputes, behavioral drift, and missed ramp targets are almost always symptoms of a misalignment between the performance model and the operational model — not a people problem.

04

Execution Infrastructure & Cadence

Do teams have the systems and rhythms to execute consistently at the volume and speed the business requires? Strategy without an operating model is a slide deck. I build the cadences, stage signal architecture, and workflow infrastructure that make the plan executable — and keep it that way.

The organizations I’ve worked with weren’t unique. The dysfunction was structural — and structural problems have structural solutions. The case studies below show what that looks like in practice.

Proof of Work

Results that speak for themselves

Three organizations at different stages and scales. The architecture decisions made, the structural bets taken, and what came out the other side.

Series B • SaaS-type • Sub-$20M ARR • 2021–2024

From Revenue Opacity to Board-Ready Infrastructure — Before the Next Raise

Senior Director, Revenue Operations & Sales Enablement

Data Integrity Governance Incentive Alignment Execution Infrastructure

The Situation

This organization brought me in to solve a contained problem: pricing and contract governance lived in spreadsheets and verbal approvals. What I found beneath it was structurally more serious. The CRM carried a 45% error and duplication rate. Stage definitions were rep-interpreted. Territory ownership was ungoverned. There was no CS reporting layer — NRR, renewal performance, and customer health were invisible to Finance and leadership alike.

For a Series B company preparing for its next raise, that level of revenue opacity is a data room problem. Investors stress-test ARR cohorts, pipeline coverage ratios, and stage-weighted forecasts. None existed in a form leadership could stand behind. The annual plan had no model underneath it — and no one owned the process of building one.

Process Technology People

Data quality and manual workflows were the constraint — not the tools. Before any automation could work, the governance layer underneath it had to be rebuilt.

The Architecture

  • Rebuilt the revenue data model underpinning the entire pipeline — enforced stage signal entry and exit criteria, eliminated the 45% data error rate, and transformed every pipeline conversation from a negotiation into a decision
  • Established governed territory and account ownership logic, giving quota planning, renewal tracking, and compensation design a reliable foundation for the first time
  • Replaced informal intake with a SLA-governed Salesforce queue — compressing deal-support turnaround from 7 days to 2 and removing a chronic source of rep friction that was slowing contract close
  • Designed incentive compensation architecture in partnership with Finance, anchored directly to the governed quota and territory data — ending the commission disputes that were eroding trust between Sales and leadership
  • Delivered the company’s first integrated reporting layer — a single source of truth feeding MBR, QBR, ExCo, and board decks, replacing rep-by-rep spreadsheet submissions no investor could stand behind
  • Built the RevOps function from zero — lean team, full functional scope — owning the cross-functional operating model the organization needed to scale safely
  • Built the CS reporting infrastructure from scratch — working directly with the Head of CS, Finance, and Operations to create shared visibility into NRR, renewal performance, NPS, and customer health for the first time
  • Owned the annual planning cycle: built the bottoms-up revenue model, designed territory and quota logic, and presented the outputs directly to the CEO and CSO

Impact

45% CRM data error rate eliminated — closing the gap between what the system said and what was true, and making the pipeline defensible to investors for the first time
7→2 Days deal-support cycle compressed, removing a structural brake on revenue velocity
30% Faster contract close through governed pricing workflow — improving cash conversion
Board-ready ARR reporting, NRR, pipeline coverage ratios, and stage-weighted forecast built from zero — the financial infrastructure the company needed to enter a Series B data room with confidence
20% ARR growth over the engagement period — a direct result of the governed revenue infrastructure, clean pipeline data, and annual planning model built from zero
7 days Reduction in average sales cycle length — freed from the process friction and CRM ambiguity that had been slowing contract close

“At Series B, the question investors ask isn’t whether the product works. It’s whether the revenue system is real. Is the ARR clean? Does the pipeline coverage hold up? Is the forecast methodology something leadership can defend in a data room? I built the infrastructure that made those answers yes — and ARR grew 20% while the average sales cycle shortened by seven days.”

€1B Global Sales Org • Multi-Region • 140 Sellers • 2017–2019

Rebuilding the Revenue Engine of a €1B Global Sales Org — Seller by Seller, Market by Market

Global Senior Manager, Sales Force Effectiveness — promoted from Head of Sales Operations, Americas

Data Integrity Governance Performance Alignment Execution Infrastructure

The Situation

A €1B professional services firm with 140 sellers across multiple global markets — and no shared version of the truth. Each region produced its own reports. Leadership received conflicting numbers from conflicting systems. No standardized CRM governance, no single forecasting methodology, no operating model connecting Sales performance to ExCo decision-making.

Commission disputes corroded rep trust. Forecasts sat unused. A global sales organization at the scale to drive significant growth had no infrastructure to know whether it was. At that size, fragmented revenue visibility isn’t a reporting problem. It’s a strategy execution problem.

Process Technology People

The technology existed. The workflows were misaligned across regions and the change management to standardize them had never happened. Standardizing governance required behavioral change at the rep and manager level — not just a new dashboard.

The Architecture

  • Standardized CRM governance and stage signal definitions across every global market — creating the single forecasting model that gave ExCo a consolidated view of pipeline health they could act on
  • Built individual seller analytics dashboards that had never existed at this org: close rate by stage signal, service mix, proposal-to-close conversion, and a precise diagnosis of where each rep was losing — turning anecdotal performance conversations into data-driven coaching decisions
  • Designed and owned the MBR and ExCo operating cadence, translating raw pipeline data into boardroom-ready strategic recommendations — connecting sales performance to business outcomes
  • Restructured new seller onboarding around a 90/180-day milestone framework with SME integration and stakeholder mapping — reducing ramp time 18% and compressing the period between hire and revenue contribution
  • Promoted to global leadership of Sales Force Effectiveness — overseeing four regional heads across all markets — after the Americas operating model proved its value at scale

Impact

25% YoY revenue growth enabled by infrastructure that made the commercial engine legible at scale
140 Sellers given individual performance data for the first time — changing behavior where revenue is actually made
18% Faster ramp to productivity — compressing CAC payback on every hire and accelerating the point at which headcount starts generating return
One truth A single global forecasting model replaced conflicting regional reports — giving ExCo the clarity to make consequential decisions with confidence
Global Promoted to lead Sales Force Effectiveness across all markets, overseeing four regional heads — a direct result of the operating model’s performance

“When a €1B sales org can’t consolidate its own forecast, the operating model is the problem. I built the model — CRM governance across every market, a single forecasting methodology, individual seller analytics, and the ExCo cadence that connected commercial performance to strategic decisions. And when the data revealed a margin decline in one market that leadership wanted to soften in the board deck, I held the line. The number that went into the room was the real one. Twenty-five percent YoY revenue growth, with a commercial infrastructure you could actually explain to a board. That’s what the model was built for.”

Global Enterprise • 28-Person Offshore Team • 2021

Operational Architecture for a Global Enterprise Sales Support Function

Sales Enablement SME — Global Technology Client

Governance Execution Infrastructure

The Situation

This global technology organization runs one of the most complex compensation and enablement support functions in enterprise sales. I inherited the seller Helpdesk — the frontline support layer for compensation inquiries across a worldwide seller base — with 28 offshore agents operating against constantly-changing workflows and chronically missed SLAs.

The cost wasn’t measured in tickets. It was measured in seller productivity. Every unresolved comp inquiry kept a rep off the floor. Workflows had grown organically across five stakeholder groups — IT, HR, Technology, Sales, and Compensation — and no one had owned the architecture that connected them. The governance problem was upstream. The seller productivity problem was the consequence.

Process People

The agents were capable. The workflows were broken and ungoverned. This was a change management and process design problem — not a staffing or technology problem. Fixing it required redesigning the system, then getting five stakeholder groups to adopt the new model.

The Architecture

  • Simplified cross-functional workflows across five stakeholder teams — reducing complexity without losing coverage, and giving agents a single governed path for routing and escalation
  • Built execution infrastructure for 28 offshore agents: escalation protocols, SLA governance, and performance visibility — replacing ad hoc decision-making with structured process
  • Designed dashboards surfacing SLA trends and process gaps for leadership in real time, replacing reactive firefighting with proactive visibility
  • Managed stakeholder alignment across IT, HR, Technology, Sales, and Compensation — the integrations that determined whether the redesigned system would actually be adopted

Impact

22 hrs Average SLA resolution time reduced through workflow simplification and governed escalation paths
28 Offshore agents operating with new execution discipline, structured escalation, and real-time visibility
5 Cross-functional stakeholder teams aligned into a single governed workflow model
Global Seller organization served — less time waiting on comp answers, more time selling

“At global scale, execution failures are rarely talent failures. They are architecture failures. The infrastructure governing how 28 agents across time zones routed, escalated, and resolved tickets was the problem. I redesigned it. Resolution time dropped 22 hours. Agents stopped routing on instinct and started routing on process — and sellers spent less time waiting on comp answers and more time selling.”

20% ARR growth at a Series B organization, built on a governed revenue infrastructure that had not previously existed
7 days Reduction in average sales cycle length after eliminating CRM ambiguity and process friction from the close motion
45% CRM data error rate eliminated — turning the pipeline into an investable asset and a defensible board-ready number
25% YoY revenue growth at a €1B global sales org, enabled by unified forecasting infrastructure across every market
18% Faster new-hire ramp to revenue contribution through redesigned global onboarding and milestone framework
22 hrs SLA resolution time reduced for a 28-person global offshore support team through workflow redesign and governed escalation

Three organizations, different scales, different industries. The same four structural gaps appeared in each one — data people couldn’t trust, accountability without clear ownership, incentives misaligned to outcomes, and execution rhythms that had never been formalized. Revenue systems that outgrow their operating model look different on the surface. Underneath, they fail the same way.

Catherine doesn’t just manage revenue operations — she transforms how an organization understands itself. She built systems and reporting we’d never had before, and for the first time leadership had a single number they could take to the board without a footnote. That kind of infrastructure is invisible when it works, and she made it work.
Former Executive Stakeholder
Series B SaaS-type Organization

AI & Revenue Architecture

Most AI investments in RevOps stall at the data layer. That’s where I start.

87% of enterprises missed their 2025 revenue targets despite record AI investment. The revenue operating model beneath the tools was the constraint — and in most cases, no one had built it. You cannot build intelligent systems on top of data you do not trust. The same four structural failure modes that break revenue systems also determine whether AI investments compound or stall. The foundation has to exist before AI can be layered on top of it. That foundation is what I build.

The Problem Most Companies Face

48% of enterprises admit their revenue data isn’t AI-ready. 91% of CRM data is incomplete. Companies are buying AI tooling and skipping the data architecture it requires — then wondering why the outputs are unreliable. The tools aren’t the constraint. The foundation is.

Data Governance CRM Architecture AI Readiness

AI-Ready Data Infrastructure

AI models are only as good as the data they’re trained on. CRM governance, field-level controls, and data architecture are the prerequisites — not the afterthought. The organizations that get reliable AI outputs are the ones that invested in the data layer first. That is the infrastructure I have spent my career building.

Salesforce HubSpot Clari

Giving Sellers Their Time Back

Reps spend 70% of their week on non-selling work. AI tools like Gong and Einstein can compress that — but only when CRM fields, stage signal logic, and process design are clean enough for automation to run without generating new exceptions. The process layer underneath the AI tool is the deciding variable. Without it, automation creates noise instead of removing it.

Gong AI Salesforce Einstein Clay

Forecasting & Pipeline Intelligence

AI-driven forecasting tools like Clari surface deal risk and call revenue with precision — but only when the underlying pipeline data is governed and stage signal definitions are enforced. A Clari number is only as credible as the CRM data feeding it. Cleaning that foundation is where forecasting accuracy is actually won or lost.

Clari AI Gong Salesforce

ICP Scoring & Lead Intelligence

Clay, ZoomInfo, and AI enrichment tools surface the right accounts — but ICP scoring only works when historical win/loss data is clean and segmentation logic is governed. The organizations getting value from AI lead intelligence are the ones that already did the data governance work. That sequencing matters.

Clay ZoomInfo HubSpot AI

Customer Health & Retention Intelligence

AI health scoring in tools like Gainsight surfaces churn risk before it’s visible to the CSM — but only when onboarding milestones, product usage signals, and account data are structured and governed. The CS reporting infrastructure I have built — NRR, renewal performance, customer health visibility — is the same foundation that makes retention AI worth running.

Gainsight Clari Salesforce

Let’s have that conversation.

I’m looking for a revenue operations leadership role or fractional engagement at a growth-stage or scaling organization. One where building the infrastructure right is the mandate, not an afterthought.

If that’s where you are, I’d like to have that conversation.

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Direct: ccj.0777@proton.me