AMA: Cisco Senior Director, Global Virtual Sales Strategy and Operations, Ignacio Castroverde on Building a Revenue Ops Team

Start with business outcomes (revenue, pipeline, retention, CAC/LTV) and get explicit CEO/CFO alignment. Triage requests with an Impact-vs-Effort grid and the “so what” test - if it won’t move a core KPI, it waits. Land quick wins to earn trust, then fix foundations: CRM hygiene, data dictionary/definitions, routing, and SLAs. Publish a one-page 90-day roadmap and a weekly scorecard with progress, risks, and 2–3 decisions. You can also use a user steering committee for intake/validation; time-bo ...Read More

Here’s the simple, no-drama setup I use: Head of RevOps (single throat to choke) + three core pillars, with a fourth if you’re lucky. Run the Business (RTB)Owns day-to-day: CRM hygiene/admin, territory & quotas, routing/SLAs, pricing/deal desk/APD, forecast cadence, change calendar, enablement ops. Goal: reliable pipeline & forecast, zero surprises. Data & InsightsBI and governance: metrics, dashboards, pipeline quality, attribution, forecasting models, experiment readouts, data dict ...Read More

We keep comms simple—less is more—and audience-specific. Each week we publish a one-page scorecard showing progress on every project, what changed, blockers, and 2–3 actions with owners and dates. We run biweekly GTM syncs focused on decisions, plus a short monthly update with the “so what” and what’s next. Everything lives in a self-serve hub (live dashboards, data dictionary, playbooks, roadmap), and changes ship with a short Looms, quick guides, and office hours. A user steering committee and ...Read More

Here’s the playbook I use as the first RevOps hire—simple, outcome-led, and built for speed: Align up-front: Get explicit CEO/CFO buy-in on 3–5 business outcomes (revenue, retention, CAC/LTV). Map reality fast: Current-state of funnel, tooling, data, and pain points; quantify impact. Land quick wins: Fix obvious leaks (routing, SLAs, basic automation) to earn trust. Lay foundations: CRM hygiene, data dictionary/definitions, forecast cadence, single-source dashboards. Trash in, trash out. Priorit ...Read More

Short answer: you don’t need a full-time data engineer to start—but you do need the skills. Start lean:A strong analytics/generalist engineer + RevOps can stand up the basics: SaaS ELT (e.g., Salesforce -> warehouse), a managed warehouse (BigQuery/Snowflake/Redshift), dbt for modeling, light orchestration (Prefect/Airflow later), data quality tests, and a BI layer. Core skills: SQL, dimensional modeling, ELT, version control/CI, governance/PII, and dashboarding. A data scientist isn’t the rig ...Read More