Revenue operations consulting
We connect your sales, marketing, and CS tools so revenue data stops sitting in silos and starts helping you forecast accurately.
The Problem
Revenue teams have plenty of data and almost no signal.
CRM data is stale by the time anyone acts on it: Reps log deals inconsistently, stages drift without criteria, and by Monday's forecast call half the data is two weeks old. You're making resource decisions on a snapshot that no longer reflects reality.
Marketing and sales measure different things: Marketing reports on MQLs. Sales cares about SQLs that close. Without a shared attribution model, every QBR becomes a fight over whose numbers are right instead of a conversation about what to do next.
Lead scoring is missing or nobody trusts it: Either there's no scoring model at all and reps triage leads on intuition, or there's a legacy model nobody believes because it keeps surfacing accounts that never convert.
CS has no early warning system: Churn gets diagnosed in the offboarding call. Product usage signals, support ticket patterns, and engagement drops sit in three different tools with no one watching the correlation.
Reporting takes longer than acting on it: Weekly revenue reports get assembled manually from CRM exports, spreadsheet formulas, and Slack messages. By Friday afternoon they're already outdated.
Our Approach
We diagnose your revenue motion first, then build for it. Every engagement starts with understanding how revenue moves through your business. That rarely matches the org chart. The build follows the diagnosis.
Phase 1 — Revenue motion audit (Weeks 1–2): We interview sales, marketing, and CS leadership, then audit your CRM configuration, attribution setup, and reporting infrastructure. We map every handoff where data quality drops or decisions slow down. Deliverable: Revenue motion diagnostic: a prioritized list of friction points, data gaps, and automation opportunities ranked by pipeline impact.
Phase 2 — Architecture and instrumentation design (Weeks 3–4): Based on the audit, we design the target-state data architecture: CRM field schema, lifecycle stage definitions, lead scoring logic, attribution model, and automation rules for handoffs. We get explicit sign-off from stakeholders before any build starts. Deliverable: RevOps blueprint: technical spec covering data model, scoring criteria, attribution logic, and automation workflows.
Phase 3 — Build, integrate, and automate (Weeks 5–10): We implement the blueprint inside your existing stack (HubSpot, Salesforce, or whatever CRM you run) and wire in adjacent tools: marketing automation, product analytics, support platforms. Lead scoring models get trained on your historical conversion data. Attribution connects to actual closed-won outcomes, not first-touch form fills. Deliverable: Live system with documented automation workflows, scoring model in production, and attribution dashboard tied to revenue outcomes.
Phase 4 — Reporting layer and ongoing tuning (Ongoing retainer): We build the dashboards your team will use, set a cadence for model retraining as conversion patterns shift, and sit in on pipeline reviews to catch data quality issues before they compound. Retainer scope adjusts quarterly based on what the business needs. Deliverable: Monthly performance reports, model drift alerts, and a standing optimization backlog with impact estimates.
Deliverables
Diagnostic phase
- Revenue motion diagnostic report
- CRM health scorecard with field-level audit findings
- Handoff friction map across sales, marketing, and CS
- Prioritized opportunity backlog with effort-to-impact estimates
Build phase
- RevOps blueprint (technical spec)
- Rebuilt CRM data model with stage criteria and validation rules
- Lead scoring model trained on your historical data
- Multi-touch attribution model tied to closed-won revenue
- Automated handoff workflows between marketing, sales, and CS
- Integration layer connecting CRM to product analytics and support tools
Ongoing retainer
- Executive pipeline dashboard (real-time updates)
- CS health scoring with churn risk alerts
- Monthly revenue operations report with commentary
- Quarterly model retraining and recalibration
- Ad hoc analysis for strategic decisions (pricing, territory, headcount)
Who This Is For
Right for you if: Series A or B companies with a GTM motion that isn't scaling predictably. Revenue teams of 10–50 people where manual coordination is the bottleneck. Founders or CROs who have CRM data but don't trust it for forecasting. Companies where marketing and sales disagree on what a qualified lead is. Teams on HubSpot or Salesforce but using less than 40% of its capability. Companies with measurable churn but no early warning process.
Not right if: You have fewer than 5 people in revenue roles. A formal RevOps layer won't pay back yet.. Your CRM has less than 6 months of deal data. Not enough signal to train scoring models.. Leadership won't enforce CRM hygiene. Automation on bad inputs produces bad outputs.. You want a one-time audit with no implementation. The diagnostic alone doesn't move pipeline..
Use Cases
B2B SaaS — HR Tech: A 40-person HR tech company was closing deals but couldn't explain why. Win rates varied by 30 points across the team with no visibility into which activities or lead sources drove the difference. Marketing spent $18K/month on paid acquisition with no attribution to closed revenue. — Rebuilt the CRM stage model with explicit entry/exit criteria, implemented multi-touch attribution from ad spend to closed-won deals, and built a lead scoring model on firmographic data and behavioral signals from product trials. Dashboards connected to live CRM data, reviewed in weekly pipeline calls.. Outcome: Within 90 days the team found that two industries (logistics and retail) had a 3x higher close rate and 40% shorter sales cycle. Paid budget was reallocated. Marketing CAC dropped 28% the following quarter.
Professional Services — Legal Tech: A legal tech consultancy lost 18% of ARR annually to churn. CS was entirely reactive: customers churned before the team knew there was a problem. Product usage data existed but sat in a separate analytics tool nobody in CS could access. — Built a health scoring model that combined product engagement frequency, support ticket volume, and contract renewal proximity into a single risk score surfaced inside the CRM. Automated alerts triggered CS outreach when scores dropped below threshold. Monthly CS dashboards replaced the quarterly spreadsheet review.. Outcome: Churn dropped from 18% to 11% over two quarters. The CS team went from 80% reactive to 60% proactive outreach within the first month of go-live.
E-commerce Infrastructure: An e-commerce enablement platform had a 45-day average sales cycle but no visibility into where deals stalled. Sales leadership spent 6 hours a week pulling pipeline reports from CRM exports and reformatting them for board updates. — Standardized pipeline stage definitions, automated deal progression tracking with overdue alerts, and replaced the manual reporting process with a live board-ready dashboard. Bottleneck analysis showed deals stalled most at the security review stage, which had no internal owner.. Outcome: Average sales cycle dropped to 34 days over two quarters. Sales leadership reclaimed about 20 hours per month previously spent on manual reporting.
Results
The numbers that matter.
B2B SaaS: 28% lower CAC within one quarter of go-live. After rebuilding the attribution model and retraining lead scoring on 18 months of closed-won data, a Series B SaaS company moved $10K/month in paid spend from low-converting channels to the two segments with the highest close rates. The scoring model deprioritized MQL volume in favor of conversion probability. Marketing was uncomfortable at first. The pipeline data proved it out within 60 days.
Frequently Asked Questions
What does the retainer cost and what determines where in the range we land?
Monthly retainers range from $3K to $6K. Scope determines the price: CRM cleanup, a new scoring model, and ongoing reporting sits at the lower end. Cross-tool integration, custom attribution, and CS health scoring alongside ongoing optimization sits at the higher end. We scope precisely after the diagnostic so there are no surprises.
We're on HubSpot. Do you work with Salesforce too?
Yes. We work with both HubSpot and Salesforce and have built integrations with common adjacent tools (Outreach, Apollo, Intercom, Mixpanel, Segment, and others). The diagnostic includes an audit of your existing stack. We design the build around what you already have and only recommend a migration if one is genuinely warranted.
How long until we see results?
The revenue motion diagnostic comes at the end of week two and gives you a prioritization map whether or not you proceed to the build phase. Automation and scoring models go live between weeks six and ten depending on integration complexity. Pipeline visibility gains show up within 30 days of the system being live. CAC and win rate changes take a full quarter to show up cleanly.
Our CRM data is messy. Is that a problem?
It's the norm. The audit phase maps the state of your data before we build anything on top of it. Some engagements require a data cleanup sprint before scoring models can be trained. If so, we scope it explicitly and build it into the timeline rather than training models on data that will produce unreliable outputs.
Do we need a RevOps hire internally to make this work?
No. Most of our clients don't have a RevOps hire, which is often why they engage us. We act as the RevOps layer for the duration of the retainer. If the business reaches a scale where an internal hire makes sense, we'll advise on the role profile and run a structured handoff.
What does the engagement look like on a day-to-day basis?
During the build phase, expect one weekly sync (45-60 minutes) with key stakeholders in sales and marketing, plus async communication for approvals and feedback. During the retainer phase, cadence drops to bi-weekly unless there's active work in flight. We don't need blocked time from your team beyond those touchpoints.
What is RevOps and how is it different from just cleaning up our CRM?
CRM cleanup is one task inside a RevOps engagement. RevOps connects marketing, sales, and customer success data into a single revenue view: lead scoring, attribution, pipeline reporting, handoff automation, and health scoring. A clean CRM is table stakes. RevOps makes the data inside it useful for decisions.
Can you work alongside our existing sales and marketing teams?
Yes, and we prefer it. RevOps works best when sales and marketing teams help define what qualified means, validate scoring models, and review attribution data. We handle the technical build and ongoing optimization. Your teams provide the business context that keeps the models accurate.
