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Case Study

How a Mid-Market Software Firm Nearly Tripled Pipeline Velocity with AI Deal Scoring

Industry B2B Software
Stage Mid-Market · $22M ARR
Timeline 12 Weeks
Team Size 28 AEs
The Situation

A 6-month sales cycle that was destroying forecast accuracy.

This B2B project management software company had built a solid product and a real customer base. At $22M ARR with 28 account executives, they weren't small, but they were stuck. A six-month average sales cycle meant deals took forever to close, pipeline forecasts were wrong more often than they were right, and leadership had no clear visibility into which deals were real and which were mirages.

Mid-funnel stalls were endemic. Deals would advance past discovery and then go quiet. Not lost, not won, just... suspended. Reps chased them without signal. RevOps tried to track them across four disconnected tools with no unified view. The revenue team was working hard but flying blind.

01
No deal-level visibility at mid-funnel Deals stalling between stage 3 and stage 5 were invisible. There were no behavioral signals to distinguish a deal drifting toward a close from one quietly dying. Reps relied on gut, and gut was wrong at least half the time.
02
Fragmented RevOps stack across 4 tools CRM, sales engagement platform, marketing automation, and a homegrown spreadsheet model each told a different story. No tool spoke to the others. Reconciling them took two RevOps hours every Monday morning and still produced a best-guess forecast.
03
28 AEs operating without consistent playbooks Each rep had developed their own follow-up cadence, their own deal qualification habits, and their own definition of what "moving" looked like. Best practices lived in the heads of two top performers and weren't being transferred.
The Approach

Four moves. Twelve weeks. No fluff.

We don't hand over slide decks and wish clients luck. We get into the systems, map the data, and build the infrastructure that makes AI insights actually usable by a 28-person sales team.

1
RevOps Stack Audit
Before building anything, we mapped every tool, integration, and data flow across the revenue org. CRM object structure, field hygiene, activity logging gaps, marketing attribution logic, engagement platform sync cadence. All of it. The audit surfaced three critical data gaps that were making any scoring model impossible to build without first fixing the underlying plumbing.
2
AI Deal Scoring Model
We built a predictive model trained on 18 months of closed-won and closed-lost deal history, identifying which deals close and which stall based on 14 behavioral signals, including email response latency, multi-threading depth, champion engagement rate, days since last meaningful activity, and stage velocity relative to cohort benchmarks. The model outputs a 0–100 score refreshed daily, with explicit reasoning surfaced to reps in plain language inside the CRM.
3
Pipeline Visibility Dashboard
We unified data from the CRM, sales engagement platform, and marketing automation tool into a single live dashboard, a single source of truth for the entire revenue team. The dashboard surfaces deal score distributions, stall risk flags, forecast roll-ups weighted by AI confidence, and a weekly variance tracker so leadership could see where the model was wrong and why. Forecast reviews went from 2 hours to 20 minutes.
4
Playbook Creation
AI scores are only useful if reps know what to do with them. We documented every key signal and trigger into rep-level playbooks: specific, actionable, and embedded directly in the CRM as task prompts. A score drop below 40 triggers a re-engagement sequence. A multi-threading gap triggers an intro request prompt. No rep needed to understand machine learning. They just needed to act on the next step, which the system told them in plain language.
The Results

2x Pipeline Velocity for a Mid-Market Software Firm

0x
Pipeline Velocity
Deals moved through the funnel twice as fast after AI scoring gave reps clear, ranked priorities and eliminated time wasted on stalled opportunities.
0%
Forecast Accuracy Improvement
The revenue team went from forecasting within a 40% variance band to within 24%, consistently. Quarterly planning became something leadership could actually rely on.
0 mo
Avg. Sales Cycle (down from 6)
Early identification of deal risk and rep-level playbooks cut the average close timeline nearly in half, without compressing deal quality or discounting to force closes.
Top 20% of deals identified with 87% accuracy by week 4 The AI scoring model surfaced the highest-probability deals within the first month of deployment, letting the team prioritize executive attention and resource allocation on the right opportunities, not just the loudest ones.
RevOps reconciliation time cut from 8 hours to under 1 hour per week With a unified pipeline dashboard replacing four manual reporting sources, the two-person RevOps team reclaimed nearly a full day every week, redirected toward analysis rather than aggregation.
Illustrative · Real data coming soon
In Their Words
"Our outbound was broken. Wrong ICPs, bad sequences, no signal. zRev rebuilt the entire motion from scratch in 8 weeks. But the deal scoring model was the real unlock. For the first time, I could walk into a forecast call and actually defend the number. Not because I believed it, but because I understood exactly which signals were driving it and which deals were at risk. First qualified pipeline in months. My reps stopped chasing ghosts and started closing deals. That's the difference between a strategy firm and one that actually gets in the trenches with you."
MARCUS T.
VP Sales · Mid-Market Software (identity anonymized per NDA)
What This Means For You

Your pipeline isn't
a forecasting problem.
It's a signal problem.

If your team is chasing deals without knowing which ones are real, or your forecast is a negotiation instead of a number, AI deal scoring can change everything, in weeks not quarters. We've done this before. We can do it for you.

Book a 30-Min Call
No pitch. Just a straight conversation about where your pipeline is leaking.