How do I calculate ROI on RevOps technology?
Model hard savings (time), revenue lift (conversion & velocity), and risk reduction. Subtract total cost of ownership, then track payback and ROI% over 12–24 months.
Inputs and formulas
| Component | What to capture | Formula | Notes |
|---|---|---|---|
| Time saved | Hours saved/month × roles | Hours × Fully-Loaded Hourly Rate | Use conservative baselines and validate with time studies |
| Conversion lift | % lift at a funnel stage | ΔConversion × Volume × ASP × Gross Margin | Apply to one or two stages you can attribute to the tech |
| Cycle time (velocity) | Days faster to close | (Revenue × Days Saved ÷ 365) × Discount Factor | Approximates cash-flow acceleration value |
| Risk reduction | Incidents avoided × avg impact | Events Avoided × Cost/Impact per Event | E.g., bad routing, compliance hits, data corruption |
| Total cost of ownership | Licenses, implementation, data, admin | Licenses + Services + Data + FTE Admin | Annualize one-time costs or track payback separately |
| Core outputs | ROI%, Payback, NPV-lite | ROI% = (Benefits − Costs)/Costs; Payback = Investment/Net Benefit; NPV ≈ Net Benefit × Months × (1−d) | Use 5–10% discount (d) for NPV-lite if you want time value |
Worked example (plug your numbers)
Assume: 3 RevOps/FMOps roles save 25 hrs/mo each at $70/hr fully-loaded; +2% MQL→SQL lift on 2,000 MQLs at $1,500 ASP / 60% GM; 5 days faster cycle time on $3M pipeline; avoid 2 routing incidents/yr at $7,500 each. Annual costs: $68k licenses + $45k services + 0.25 FTE admin ($30k).
- Time saved: 3×25×$70×12 = $63,000
- Conversion lift: 0.02×2,000×$1,500×0.60 = $36,000
- Velocity (proxy): $3,000,000×(5/365)×0.5 ≈ $20,548
- Risk reduction: 2×$7,500 = $15,000
- Total benefits: ≈ $134,548
- Total annual costs: $68,000 + $45,000 + $30,000 = $143,000
- Net benefit: −$8,452 (year 1, because of services); year-2 benefits usually rise while services fall.
- ROI% (Yr1): (134,548 − 143,000) ÷ 143,000 ≈ −5.9%
This is common when implementation costs are front-loaded. Re-run with year-2 costs (remove services) and any adoption-driven lift to see the steady-state ROI and payback.
Make the model auditable
- Baseline first: 4–8 weeks of pre-change metrics (time studies, conversion, SLAs).
- Tag experiments: promotion gates, feature flags, and holdouts where possible.
- Trace outcomes: dashboards for adoption, pass rates, MTTR, and cost per successful action.
- Review cadence: monthly ROI checkpoint; quarterly threshold tuning.
Frequently asked questions
Use operational proxies you can trust: time saved (validated with time studies), SLA adherence, error/incident rate, and cycle time. Convert those to dollars with fully-loaded rates and conservative velocity assumptions.
For most RevOps platforms, 9–18 months is typical. Projects focused on data quality or routing hygiene can pay back faster if incidents are costly.
Include admin time in costs (fractional FTE to run the tool). Include time saved as a benefit only if you redeploy that time to value-creating work or remove external spend.
Use low/most-likely/high ranges and report the most-likely case. Cap the share of revenue credited to the tool, and require documented evidence for each uplift.