What exactly does a RevOps engagement include?

A diagnostic of your stack and funnel, a rebuilt CRM object model, automated routing and enrichment, forecast models tuned on your win/loss data, executive dashboards, and documented operating rituals.

Do we have to replace our CRM?

Almost never. In most cases the CRM is fine and the object model, process, and data hygiene are the problem. We re-architect in place and migrate only when the platform genuinely blocks the motion.

How do you improve forecast accuracy?

By making stage definitions objective and evidence-based, scoring deals on real signals rather than rep optimism, and back-testing the model against historical win/loss data before it drives commitments.

Who owns the system afterwards?

You do. Everything is documented, versioned, and handed over with enablement. We stay on for optimisation only if you want a compounding partner.

How fast are results visible?

Data hygiene and routing improvements land in weeks. Forecast accuracy and cycle-time gains typically show within one to two full sales cycles.

/ Revenue Operations

The operating system for growth.

We architect your CRM, connect your stack, and turn revenue data into predictable pipeline.

3.2×

forecast accuracy

40%

shorter sales cycle

2.1×

MQL → SQL rate

60d

typical rebuild

/ Services

Every layer of your revenue stack, unified.

Modern RevOps is a systems-engineering problem, not a spreadsheet exercise. We treat your funnel like production infrastructure — versioned, tested, observable.

CRM Architecture

Salesforce, HubSpot, and Attio re-architected around the true shape of your revenue motion.

Automation

Cross-system workflows that eliminate rep swivel-chair work and reclaim the sales hour.

Pipeline & Forecasting

AI-assisted forecast models tuned on your win/loss data — with rep-level accountability.

Dashboards & Reporting

Executive scorecards, segment cohorts, and revenue attribution built to survive a board meeting.

Sales Process Design

MEDDPICC/SPICED playbooks encoded directly into stage-gates, tasks, and AI coaching.

Marketing Automation

Lifecycle programs, lead scoring, and nurture flows that compound MQL quality quarter over quarter.

Customer Journey

Unified data across acquisition, onboarding, expansion, and retention — one identity, one timeline.

Lifecycle Analytics

Cohort retention, NRR/GRR, LTV, and payback modelled correctly the first time.

/ Engagement

From audit to autonomy in 90 days.

— Week 0–2

Diagnostic

Full stack audit, funnel decomposition, data model review, and revenue-motion mapping.

— Week 2–6

Architecture

Rebuild the object model, deduplicate pipelines, wire attribution, and codify stage-gates.

— Week 6–10

Automation

Replace manual workflows with AI-driven routing, enrichment, and forecast intelligence.

— Ongoing

Optimization

Weekly ops rituals, dashboard reviews, and forecast retros — compounding gains every sprint.

/ Stack we architect

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/ Diagnostic

Six symptoms of a broken revenue engine.

If three or more of these sound familiar, the problem is architectural — no amount of rep coaching will fix it.

— 01

Forecast is a negotiation

Commit numbers move because reps sandbag or dream, not because deals change. Stage definitions are subjective and unenforced.

— 02

Nobody trusts the dashboard

Two reports, two answers. Attribution is broken, duplicates are everywhere, and finance keeps a parallel spreadsheet.

— 03

Reps live in six tabs

Manual enrichment, copy-paste between tools, and CRM hygiene tasks consume the selling hour.

— 04

Leads decay in the queue

Routing is manual or round-robin, response times run in hours, and high-intent signals are invisible.

— 05

Marketing and sales disagree on reality

Different definitions of MQL, no shared lifecycle model, and no closed-loop reporting on pipeline sourced.

— 06

Every new motion breaks the system

PLG, partners, or expansion get bolted onto a schema designed for a single, older motion.

/ Data model

Four layers everything else depends on.

Dashboards, forecasting, and automation are downstream of the schema. Fix the schema and most reporting problems disappear on their own.

— Layer 1

Identity

One canonical account and person record with deterministic dedupe, hierarchy for parents and subsidiaries, and a single source of truth across marketing, sales, and finance.

— Layer 2

Lifecycle

Explicit stages with entry and exit criteria for both person and account, so MQL, SQL, opportunity, and expansion mean the same thing in every report.

— Layer 3

Activity

Every touch — email, call, meeting, product event — normalised onto a timeline that feeds scoring, attribution, and coaching.

— Layer 4

Revenue

Opportunities, products, bookings, renewals, and churn modelled so ARR, NRR, and payback are computed, not hand-assembled.

/ Benchmarks

What moves after a rebuild.

Median change across recent mid-market engagements, measured against the baseline captured in week one.

Metric

Before

After

Forecast variance

±32%

±9%

Lead response time

4h 20m

under 3 min

CRM data completeness

54%

96%

Rep admin hours / week

11.5

3.2

Pipeline sourced attribution

unclear

fully closed-loop

/ Deliverables

Exactly what you receive.

Stack and funnel audit with prioritised findings
Rebuilt CRM object model and field governance
Deduplication and data-hygiene automation
Lead scoring, routing, and SLA enforcement
Multi-touch attribution wired end to end
AI-assisted forecast model with back-testing
Executive scorecard and segment cohort reporting
MEDDPICC/SPICED stage-gates encoded in the CRM
Lifecycle nurture and re-engagement programs
Runbooks, enablement, and admin handover

Turn your funnel into infrastructure.

A 30-minute RevOps diagnostic. Leave with three concrete architectural fixes.

Book a diagnostic →