Navigating Forecasting in RevOps | Jeremey Donovan, EVP, Sales & Customer Success at Insight Partners

Measured buying behaviour combines established trends with a new focus on efficiency. Buying committees have expanded for years, while CFO involvement has intensified. Budgets once distributed among executives now often require final finance approval.

Other C-suite leaders must present stronger business cases and clearer accountability measures. Companies track EBITDA margin, free cash flow margin and CAC payback more closely, reducing spending. Slower hiring or smaller teams can lower customers’ licence needs, creating a compounding retention challenge for vendors preserving ARR.

Revenue or ARR forecasting differs from profitability forecasting, though they are becoming more connected. RevOps teams typically forecast revenue, while finance builds the profit-and-loss model. Sudden changes can make models based on the previous 12 months materially inaccurate.

At transition points, teams should shorten the historical period because older performance no longer reflects current conditions. Early COVID-19 months drove forecasts down before stimulus and sector growth pushed some models upward. Teams should combine historical trends with sales capacity and current demand, instead of relying on history alone.

Sales capacity should follow credible evidence that representatives will receive enough opportunities. Before hiring account executives, leaders should test whether inbound demand, partners or another proven source can support them. Donovan defines true inbound as hand-raisers requesting demonstrations.

He cites a smaller company considering five account executives without established demand. With annual contract value below roughly $50,000, he viewed pure outbound as unreliable. He advised investing in inbound and partner channels instead of hiring immediately. Efficient growth makes hiring ahead of demand more costly.

Hiring many sellers, assigning accounts and hoping for results no longer works. Email security and spam filtering continue to improve, but technology is only one factor. Donovan views many AI SDR products as email-focused marketing automation when they lack genuine multichannel execution.

Buyer fatigue adds to the problem. Prospects often receive simultaneous approaches from ten or twenty vendors with broadly similar products. Automation worsens matters when it generates vast email volumes without meetings, while spam filters intercept much of the activity. Forecasts should not assume more outbound activity creates proportional demand.

Donovan defines a good forecast precisely: results should fall within plus or minus 5% of the day-15 call. Timing matters because a forecast made near quarter-end offers little operational value. Two weeks lets teams resolve opportunities expected to close previously.

A disciplined B2B organisation should use an internal quarterly business review to inspect and clean opportunities. Teams remove closed-lost deals, reconsider which opportunities belong in the quarter and correct records. By day 15, this work should provide a reliable pipeline foundation for the forecast.

Forecasts need probabilities, but arbitrary stage or category percentages rarely produce reliable results. For SMB and lower-mid-market business, Donovan uses stage weights based on historical conversion. For upper-mid-market and enterprise deals, he weights categories such as pipeline, best case and commit.

During uncertainty, he shortens the historical window from 12 months to six or three. Models should distinguish opportunity types because new sales and upgrades convert differently. They also need an estimate for business created and closed within the quarter, including faster expansions from existing customers.

Every company should document definitions for sales stages and forecast categories. These definitions should prioritise required exit criteria over nominal probabilities. Representatives then gain a practical guide for deciding whether an opportunity has progressed, reducing subjective stage changes.

Donovan describes companies with sales cycles exceeding 18 months and deals around $1 million. They identify five key buying personas and define exit criteria for each persona at every stage. Teams can link requirements to MEDDIC, allow nonlinear progress and manage each opportunity as a project.

Target-setting is becoming more bottom-up. Efficient growth seeks feasible growth while improving measures such as the Rule of 40. Revenue sources need realistic contribution targets across marketing, sales, partners and product-led growth. CMOs retain discretion over portfolio spending.

Companies should match capacity to demand and invest in retaining and developing people. Representative productivity rises during the first year, improves through the second and then stabilises. Earlier departures create lost productivity plus recruitment and ramp costs. Talent retention is an underused efficient-growth tool, not only a people initiative.