# Rosalyn Santa Elena

Rosalyn Santa Elena shares practical guidance for revenue operations teams on forecasting, CRM data quality, and connecting operational work to business goals.

- Profile: https://b2binfluencer.marketing/creators/rosalyn-santa-elena/
- LinkedIn: https://www.linkedin.com/in/rosalyn-santa-elena
- LinkedIn followers: 41,361
- Topics: artificial-intelligence, sales, revenue-operations, sales-forecasting
- Based on 12 dated posts, latest 2026-10-07

## Summary

Santa Elena offers practical RevOps guidance on forecasting, CRM data quality, and linking operations to business goals.

- Best for: B2B SaaS companies seeking RevOps workshops or advisory on forecasting and data governance.
- Not for: Sponsored video demos with guarantees, targeting verification, or defined deliverables.
- Check first: Confirm pricing, deliverables, audience specifications, sponsorship terms, and video examples before contacting.

### Takeaways

- Forecasting should use clear categories, stage criteria, historical conversion rates, and warning signs.
- Clean CRM data needs validation, clear responsibilities, and regular review, not one-time cleanup.
- A Salesforce license request should reflect actual use, planned growth, user needs, and team conversations.

## Main ideas

- **Strategic is a mindset, not a title you wait for**: Santa Elena argues that RevOps practitioners hold back on strategic work because they believe it requires a VP title, a seat in the leadership meeting, or someone else's permission. Strategy, they argue, is a set of behaviors anyone can adopt now: ask what business outcome a task enables before starting it, tie every deliverable to a goal such as pipeline, retention, or efficiency, bring a recommendation rather than a status update, describe work in terms of outcomes rather than process, and anticipate leadership's next question. Tactical work does not disappear or get outgrown; the point is to approach it with a strategic lens.
- **Clean CRM data is a practice, not a project**: Santa Elena argues against the standard response to dirty data — a large one-time cleanup — because it feels good, then decays, leaving data dirty again in roughly 90 days. The costs are concrete: forecasts built on incomplete pipeline, reps working duplicate records, marketing sending to the wrong contacts, leadership making decisions based on numbers they do not trust, and RevOps spending hours re-cleaning. The alternative is a set of standing controls: a data dictionary; a written data strategy covering what data is collected and why; validation rules that stop bad entries at the point of entry; integration to reduce manual input; clear roles and responsibilities; guardrails and governance; repeated training; a policy for governing changes; and a standing review cadence. The thesis: clean data is a practice, not an end state.
- **Forecasting should be engineered, not felt**: Forecasting is part art and part science, Santa Elena argues, and organizations should minimize the art. In this account, forecasting feels like guesswork when it rests on data reps enter inconsistently, stage definitions no one understands, gut feel, close dates that keep slipping or sit in the past, commit categories that mean different things to different managers, thin deal inspection, and no historical visibility. The proposed corrections are specific: define each forecast category in writing; align stage definitions and simple yes/no exit criteria across the organization (five or six stages, not two or three); use required fields to gate stage movement; run the same cadence on the same day every week; track accuracy by rep, manager, and segment; pressure-test numbers against historical conversion rates by stage; build an early-warning system for deals showing lost engagement or repeated pushes; and coach managers to challenge deals with "why" questions.
- **AI is speeding RevOps up before teams agree on direction**: Santa Elena identifies a tension rather than recommending a tool: AI is everywhere in revenue operations, yet practitioners feel more confused than helped. The post outlines four versions of the problem: AI speeds up work without confirming the direction is right; surfaces data gaps without fixing them; enables builds without a shared standard for doing them correctly; and creates pressure to appear aligned without evidence that alignment exists. The response is a live discussion about what RevOps needs to fix before AI can bring alignment to a go-to-market team, framed partly as peer support for people in the role.

## Work to read

- [Forecasting is a bit of art and a bit of science — but the science should win](https://www.linkedin.com/posts/rosalyn-santa-elena_revops-opstherapy-activity-7510371580938678272-tGfO)
- [Clean data is not a project, it is a practice](https://www.linkedin.com/posts/rosalyn-santa-elena_revops-opstherapy-activity-7508556130000752640-OBci)
- [Three versions of the same Salesforce license request](https://www.linkedin.com/posts/rosalyn-santa-elena_revops-opstherapy-activity-7513632163712438273-Ce5z)
- [Stop waiting to be strategic](https://www.linkedin.com/posts/rosalyn-santa-elena_revops-opstherapy-coaching-activity-7512908299755962368-45qk)
- [Live Q&A on what RevOps must fix before AI can align go-to-market teams](https://www.linkedin.com/posts/rosalyn-santa-elena_opstherapy-backstorypartner-activity-7513264352347557888-Gw9k)

## Brand relationships

- **Backstory** (disclosed_partnership)

## Partnerships

Rosalyn Santa Elena's posts show one public collaboration: a free, 45-minute live Q&A-style discussion co-hosted with Chad O'Connor, the CRO at Backstory, about what RevOps teams need to sort out before AI can align go-to-market work. A LinkedIn post promoted the event with a free registration call to action and a partner hashtag. The posts don't establish payment or sponsorship terms, so ask directly about those, along with pricing, deliverables, audience specifications, and how to inquire about a partnership.

## Alternatives

- Jennifer Hines: For a company seeking to improve revenue operations and forecast accuracy through a workshop or advisory engagement, Jennifer Hines may be a better choice when the central problem is qualifying pipeline and coaching sales managers. One post walks through testing late-stage opportunities against buyer engagement, documented decision criteria, measurable outcomes, and mapped decision-makers. It describes a mid-market example in which a reported $14 million pipeline became $4 million after that test. The post offers a concrete deal-inspection exercise and a sales-process focus.
- Leore Spira: Consider Leore Spira when the buyer’s immediate need is revenue governance around commercial approvals and an auditable deal history, or when deciding what a first RevOps hire should own before reporting. The available posts outline a governance sequence—definitions, architecture, process, measurement, then automation—and describe preserving quote versions, approvals, and contract changes in a deal trail.
- Keith Fleming: Keith Fleming may be the better choice if the buyer has not yet established whether its revenue problem is actually a RevOps or forecasting problem. A client story traces a lead-handling issue through response time, ownership, process, management inspection, and CRM friction, making the work useful for a diagnostic engagement across sales, marketing, and operations.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/