Case Study — Team Structure

Restructuring the team around rep fit

A scaling sales team ran round-robin assignment, treating every rep as interchangeable. ML matching and prospect clusters showed they were not: a 30-point close-rate gap, and different reps winning different customer types. That turned into two structural decisions, who works what, and who to hire next.

30pp
Close-rate gap
best vs worst rep
6
Prospect segments
from intake answers
+16.8%
Routing lift
fit-routing vs round-robin (modeled)
Fit > rank
The routing rule
not 'send it to the best rep'
2,420 meetings analyzedSegment-aware routing liveReps anonymized
The problem

Round-robin treats every rep as the same rep

Even assignment feels fair, and it quietly costs the most. It spreads every kind of prospect evenly across the team, so the customers who would close best under one rep land just as often on the rep who closes them worst. The team-average win rate looked fine. Underneath, the same leads ranged from a 30.6% to a 60.9% close rate depending on who picked them up.

What the data showed

Different reps win different customers

ML matching learned each rep's pattern from past outcomes; clustering grouped prospects into five segments by their intake answers. Crossed together, no single rep was best everywhere.

The biggest segment had a best-fit rep

In the largest prospect segment, the rep who took the most of its leads closed just 24% while the best-fit rep closed 74%, and the team's top overall closer ranked only third. The full breakdown is in the segmentation case study.

Strength in one segment, not another

The rep who trailed in that segment topped the next-largest one. Fit was a property of the rep-and-segment pairing, not a number you could read off the leaderboard.

What they did

Two structural changes

Routing

Assign by fit, not by turn

When two reps were free for the same slot, the prospect was routed to whoever historically closed that prospect's segment best, falling back to round-robin only when there was no signal. Modeled against the team's own outcomes, fit-routing was worth +16.8% over round-robin.

Hiring

Hire to the gap, not to the headcount

The segment-by-rep view also pointed at the next hire: the profile that wins the highest-value segment, which the current team covered thinnest. Instead of "add another generalist," the question became "who closes the segment we're leaving on the table."

Routing and reliability together modeled to a +55% revenue lift on the same pipeline, no new ad spend required.

Find out who should be working what

Import your scheduling history and we'll surface your segment-by-rep gaps, and what closing them is worth, in minutes.