They wait early, for value nobody proved and a signer nobody met. They wait late, for approvals and paper built for some other deal. I find where yours wait, using your own reports and the people who worked them, then fix what costs the most.
The Baseline: four weeks, $15K fixed for one segment.
Glenn Vander Laan. Business value selling at Quest Software, a founding member of US deal strategy at Splunk, then go-to-market systems at Okta and Klaviyo. More on the background
For software and AI companies whose deals slowed down when the business changed.
A new deal desk or RevOps team, enterprise buyers arriving early, and no playbook yet for how sales, finance and legal work a deal together.
Pricing moving to usage, a push upmarket, or two companies coming together with two price books and two sets of contract paper.
Approval layers that rarely change the outcome, steps added after one bad deal and never taken out, and cost to close rising faster than bookings.
Your numbers first. It starts from the pipeline and deal reports you already run: time in each stage, slipped deals, deals lost to no decision, and late discounting. Those become the before.
I then walk a sample of won and lost deals with the sellers, managers and deal desk who ran them. Was value proven, a champion found, the economic buyer confirmed, the buying process mapped? Then approvals, deal structures and the handoff to billing.
What you get is a ranked list: the gaps that cost the most, sized in days and dollars against your own numbers, each tied to the measure a fix should move. One segment, in about four weeks.
The readout shows where the process slows deals, not who. Where a deal desk lead is in place, the work is built with them. And the readout is yours to act on, with or without me.
Mid-market segment · 60 won, lost and slipped deals over four quarters · median 120 days from first meeting to booking
How it is sized: days compare deals with and without the gap. Dollars are the bookings in the affected deals, a measure of exposure, not proof the gap alone caused the loss. Figures here are made up for a fictional company, to show the format.
Built on the tools and process the team already runs, and handed to the team when it is done. What the work delivers:
Exit criteria for each stage, so a deal does not advance until the value and the buyer are confirmed.
Discount bands and an approval matrix agreed with finance, with routine approvals routed automatically and in parallel.
Order forms, pre-approved fallback clauses, standard structures for commits and ramps, and bookings billing can use without rework.
AI where it removes a manual step, such as drafting quotes, routing approvals or flagging risky clauses. Each one measured before and after.
For finance: revenue recognition questions flagged to your team before signature, and approval controls documented with an audit trail. Faster approvals come from clearer rules, not looser controls.
The fixes, delivered and measured. Scope and fee are agreed from the readout. The same measures keep running and are reviewed with you each quarter, adjusted for deal mix, so you can see what moved.
Deal strategy and a regular review of the largest deals, run against MEDDPICC, so problems surface before a deal reaches the desk. One to three days a week, more around quarter end. It supports a deal desk lead already in place, or fills the seat until you hire for it.
Larger build work, such as systems implementation, runs through specialists named before the work starts.
A founding member of US deal strategy at Splunk, on deals from $500K to $20M in total contract value, while annual revenue grew from about $450M to $1.8B.
Built the program behind a move from perpetual to term licenses. In year one, the share of bookings on the new model ran twelve points above plan, a year ahead of target.
Approvals ran in single file, each approver waiting on the one before. I redesigned them to run in parallel, a system change that took two months to put in place, measured in the CRM.
From in-house roles at Splunk, Okta and Klaviyo.
The front of the deal. Fifteen years at Quest Software, as a technical specialist, in business value selling and running professional services sales. Then deal strategy at Splunk: working inside each strategic deal's process, finding the gaps, and writing the customer's specific needs into the contract so the deal could close.
The back of the deal. At Splunk, the program behind the move from perpetual to term licensing. At Okta, business process automation across go-to-market, including one of the company's first production AI uses. At Klaviyo, go-to-market systems. Along the way: booking policies, deal structure guidebooks, CPQ and contract lifecycle implementations, and territory assignment automated across 400-plus reps.
More in the newsletter AI in GTM Operations.
I lead every engagement. Senior operators join when an engagement needs more depth, and I name them before the work starts.
With captions.
Analytics partners build them first: a quick point-in-time read for the Baseline, or ongoing revenue intelligence if you want it to keep running. Their work is scoped and priced separately, and using them is your choice.
More than one segment, or a larger sample of deals. The fee is fixed before the work starts.
The reports you already run, a few hours of interviews with sellers, managers and the deal desk, and an executive sponsor who reviews the readout.
Work is performed under NDA and a data-handling agreement. Call recordings are optional, and notes from the deal walkthroughs work too.
No. The work is built with the deal desk lead and handed to them, and a seat on the team supports a lead already in place.
How long did each one take, and where did it wait? No deal desk yet? Then tell me who approves a non-standard deal today.
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