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Forecasting SaaS Renewal Timing with ChatGPT + Excel

March board meeting.
You open the renewal pipeline slide.

On paper, the numbers look solid — high logo renewal rates, expansion potential, optimistic AE notes. But deep down, you know the danger: timing.

Half your top contracts renew in Q3, yet the plan smears them evenly across the year. The CFO wants predictability. The CEO wants certainty. The board wants answers. But your forecast doesn’t respect time.

Here’s the paradox: SaaS renewal math is simple. SaaS renewal timing is chaos.

And that chaos is the silent killer of forecasts.

Fallout (Ripple Effects of Getting It Wrong)

When renewal timing isn’t modeled correctly:

  • Cash planning collapses. A Q2 shortfall wipes out hiring plans.
  • Investor trust erodes. Churn “surprises” feel like negligence.
  • Credibility breaks. The board stops believing your forecasts.
  • Ops gets blindsided. Support teams face a flood of churn saves in the wrong month.

Miss timing once, and everyone thinks your whole model is broken.

Walkthrough (Step-by-Step, with Formulas)

Step 1. Build a Contract-Level Renewal Calendar

Extract renewal dates from your CRM. In Excel:

=EDATE([Start_Date], [Contract_Length_Months])

Example: A 12-month contract starting 4/15/24 renews on 4/15/25.

Step 2. Flag Renewal Window

Not every contract renews on the exact date. Build a 90-day window.

=IF(AND(TODAY()>=EDATE([Start_Date],[Length])-90, TODAY()<=EDATE([Start_Date],[Length])),1,0)

This lets you see which contracts are “at risk” in any given quarter.

Step 3. Layer in Probability Weights (ChatGPT)

Instead of flat churn assumptions, prompt ChatGPT with deal notes:

“Based on this renewal note [paste AE text], estimate renewal probability (0–100%) and main risk factor.”

Export that back into Excel.

Step 4. Forecast Monthly Renewal Revenue

Multiply ACV × Probability × Month Flag.

=ACV * Probability * Month_Flag

Aggregate by month. Now your renewal forecast respects timing and deal risk.

The Framework: Renewal Timing OS

I package this as a 3-Layer Operating System:

  1. Calendar Layer → exact renewal dates.
  2. Probability Layer → AE intel + ChatGPT weights.
  3. Aggregation Layer → monthly rollups for FP&A reporting.

This transforms renewal math from blunt averages into timing-sensitive, risk-adjusted cash visibility.

Schlott Co. – Proof Layer

At The Schlott Company, we’ve seen SaaS finance teams trip on this repeatedly. The pattern is always the same:

  • Renewal forecasts averaged across quarters.
  • Finance “fixing” sales optimism with a flat churn discount.
  • Timing ignored until cash misses trigger panic.

Our Renewal Timing OS flips the script: contract-by-contract, month-by-month, probability-adjusted. It rebuilds board trust in the forecast, because it shows when risk hits, not just how much.

Close – Surprising Reframe

Here’s the twist: renewal forecasting isn’t about percentages at all.

It’s about calendars.

Get the timing right, and the math finally tells the truth.

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