Every GovTech operator eventually learns this the hard way:
A deal is not a deal until procurement says it is.
And procurement moves on its own timeline.
The biggest forecasting failures in GovTech are not caused by poor sales execution or bad product fit. They happen because teams fail to model Procurement Lag. The predictable slippage between “verbal yes” and “signed contract.”
Most operators treat the lag as random. It’s not.
It follows patterns. And those patterns can be modeled.
Here is the operating model I use.
1. The Procurement Lag Formula
Every public-sector deal has three phases:
Decision Lag
Agency says yes, but nothing moves because approvals need to stack.
Example: “We’re aligned, just need IT to review.”Procurement Lag
The decision is made, but the machine has not processed it.
Example: RFP timing, council meetings, budget routing, legal review.Administrative Lag
Everything is approved, but paperwork is slow.
Example: waiting for signature authority, PO creation, vendor setup.
Each phase has its own predictable range.
So the real model is:
Expected Close Date = Decision Date
+ Decision Lag Days
+ Procurement Lag Days
+ Administrative Lag DaysIf you only model “expected close” off of the verbal yes, you will be wrong almost every time.
2. Core Lag Inputs (These Apply Across Agencies)
These are the baseline values I use when building forecasts:
Decision Lag: 5 to 20 business days
Factors:
Leadership alignment
IT alignment
Legal questions
Interagency coordination
Procurement Lag: 30 to 120 business days
Factors:
RFP required or waived
Council meeting schedules
Fiscal year timing
Budget owner availability
Contract dollar amount thresholds
Administrative Lag: 5 to 30 business days
Factors:
PO creation
Vendor onboarding
Access/credentialing
Signature authority hierarchy
These are ranges, but in practice, they are surprisingly stable.
3. The Lag Multipliers (The Real Insight)
You can forecast a deal with far more accuracy by applying Lag Multipliers based on four characteristics:
Variable | Low Friction | Medium Friction | High Friction |
|---|---|---|---|
Dollar Amount | x0.9 | x1.3 | x1.8 |
Election Proximity | x1 | x1.5 | x2.5 |
First Time Vendor | x1 | x1.4 | x2 |
IT/Security Review | x0.8 | x1.4 | x2.2 |
Example:
You are a new vendor, 4 months before an election, $350K deal, IT review required.
Your Procurement Lag =
Base Lag (45 days)
x 1.8 (dollar amount)
x 2.5 (election)
x 2 (new vendor)
x 2.2 (IT)
= 445 daysThis sounds insane until you have lived it.
Then you nod in painful recognition.
This model explains why "aligned" deals drift across fiscal years.
4. The Practical Application
A. Forecasting
Stop using pipeline stages. They lie.
Use Lag-Based Expected Close Dates instead.
B. Board Reporting
Normalize forecast accuracy by:
Decision Lag
Procurement Lag
Administrative Lag
Boards want predictability. This gives you that language.
C. Renewal Planning
Renewals are subject to the same lag patterns.
If your renewals flow through procurement, model them the same way.
D. Sales Compensation
You cannot pay reps on dates they do not control.
Lag-informed windows reduce frustration and churn.
5. The Procurement Lag Table (Use This Starting Point)
Baseline Ranges:
Decision Lag: 10 days
Procurement Lag: 60 days
Administrative Lag: 14 daysAdjustments:
Add 45 days if the deal crosses their fiscal year
Add 30 days if the agency uses council approval
Add 15 days if the contract requires legal review
Add 20 days for new vendor onboarding
Add 10 days if any senior official is leaving or retiring
Add 30 to 90 days to any election window
6. The Core Insight
GovTech deals do not always slip.
They move exactly the way the system is designed to move.
When you understand the lag patterns, forecasting becomes calm and honest.
When you ignore them, everything feels chaotic and like everything always slips.
The Procurement Lag Model gives you a way to operationalize the reality in GovTech forecasting. Everyone knows GovTech deals drag. This model lets you measure the drag instead of guessing.
