Win-Loss Analysis From Real Signals

When someone types /winloss [segment or time period] in Slack, analyze
every won and lost deal in that scope using the Clearskies Context Graph.
Surface the patterns: which competitors we lose to, the stage where deals
most often stall, the objections that recur in losses, and what reps do
differently in the wins. Post the readout in the same channel where the
command was run.

Win-Loss Readout — Q1, Mid-Market
Generated for @nate · 4:22 PM · 38 closed deals (22 won, 16 lost)

Why we win
Wins consistently have an engaged economic buyer by the second call.
In 19 of 22 wins the EB was named and active early. Winning reps
anchor on consolidation, not features.

Why we lose
• Champion silence. 7 of 16 losses show the champion going quiet for
  14+ days with no escalation to a second contact.
• Lost to Northwind on price in 5 deals — all scoped narrowly to
  attribution, where our consolidation story doesn't land.
• Stalled in security review twice — data residency questions with
  no fast answer.

Where deals stall
Most lost deals died between Proposal and Negotiation, not at the top
of funnel. The pipeline looked healthy until it didn't.

What winning reps do differently
Multi-thread early — wins average 3.2 engaged contacts, losses 1.6.
The single-threaded deal is the at-risk deal.

The one thing
Champion silence is our most common and most preventable loss. A
"second contact by call two" rule would have flagged 7 of 16 losses
while they were still live.

Using the Clearskies Context Graph, run a win-loss analysis on our Q1
mid-market deals.

Cover:
- Why we win — the patterns common to closed-won deals
- Why we lose — the patterns common to closed-lost deals
- Which competitors we lose to, and in what kind of deal
- Where in the cycle deals most often stall
- What winning reps do differently
- The one change that would move the most deals

Ground every pattern in the actual deals. Don't rely on disposition codes alone — read what happened.