FORECASTING & ANALYTICS DISCIPLINE

Every dollar, traced to bookings.

At Globalization Partners, SmartLinx, Brightcove, and Rapid7, I built budget-to-revenue forecasting models and the operating cadence to run them, so marketing could tell Finance and the C-suite what a dollar of spend would produce, and then prove it.

What the discipline delivered

  • +21% bookings YoY while reducing program investment 10% (Rapid7)

  • 5.5:1 highest marketing budget efficiency in six quarters, vs. 3.9:1 (Rapid7)

  • 8:1 → 33:1 pipeline efficiency (Brightcove)

  • 15:1 ARR:CAC (SmartLinx)

Two engines, one model

Inputs marketing controls. Conversion rates agreed with Finance. Outputs the CEO sees.

The inbound volume engine and the account-based engine run side by side on one model. Spend flows through each funnel stage by region, channel, and month, using conversion rates and cycle times calculated from actuals.

Spend → MQLs / MQAs → SQLs → Opportunities → Pipeline → Bookings

Built for a CFO to trust

Every number traces to an input

No hard-coded values in the calculations. Rates come from trailing actuals, and any override carries a documented reason.

Targets come from Finance

Bookings targets come from RevOps; pipeline targets are derived through a coverage ratio, so marketing and sales plan against the same number.

Three scenarios, one view

Conservative, base, and aggressive cases run at once, flexing cost, conversion, deal size, and cycle time against the same spend.

Cohort and in-year views

Targets are measured on the pipeline each quarter creates, with a separate in-year landed view for Finance's timing.

Timing, not just ratios

Cycle times are built in. With a six-to-twelve-month enterprise cycle, this year's number is largely decided by pipeline that exists now, and the model shows it.

Two lenses on attribution

Single-source attribution stays the system of record. ABM gets a second lens: influenced pipeline, velocity, and win rate, stated in dollars and never double counted.

Built-in integrity checks

A checks tab confirms the model ties out: budgets reconcile, rates stay in bounds, and planning risks get flagged.

What the C-suite sees

Illustrative output from a demo model built for a fictional company. The model itself is proprietary, and real employer data stays confidential.

  • $7.0M planned spend

  • $91.4M pipeline created

  • 13.1× pipeline ROAS

  • 87% of pipeline target, flagged early

Scenario comparison, pipeline vs. a $104.8M target: Conservative $65.1M · Base $91.4M · Aggressive $124.5M. The base plan shows a gap to close before the year starts, not after.

A forecast is only as good as the rhythm around it

  • Weekly: pacing against the model by channel and segment

  • Monthly: variance review with FP&A: which assumption broke, and what moves

  • Pre-agreed: reallocation triggers when a channel drifts past threshold

  • Quarterly: re-base conversion rates on actuals

  • Agencies: commitments written against model lines, not activity

Built with FP&A

Budget-to-revenue models from cost per acquisition to closed bookings.

QBRs and pipeline reviews

Ran the monthly review, QBR, and GTM pipeline review cadence at four companies.

OKRs that tie to revenue

Created and owned marketing OKRs at Brightcove and Rapid7.

Board-ready narratives

Prepared board narratives and slides with the CMO.