Vertex CRM is a mid-market CRM software company with a 14-person revenue team. Before implementing TavMind, they had a solid outbound infrastructure — Salesforce, Outreach, a competent SDR team — but they were generating meetings at a rate that felt disconnected from their investment. Their SDRs were working hard, but their qualified opportunity rate (meetings that converted to Stage 2 opportunities) was 18%, well below the 30–35% their comp plans assumed.
The Problem: Infrastructure Without Signal
When we conducted the initial diagnostic with Vertex's Head of Revenue, the root cause became clear within the first hour of conversation. Their SDRs were building account lists based on industry lists, peer recommendations, and manual research — a process that was slow, inconsistent across reps, and completely disconnected from actual buyer behavior in the market.
"We had eight SDRs all defining their territories differently," their Head of Revenue explained. "There was no shared framework for deciding which accounts were worth calling this week versus next month. And we had no way of knowing if a company was in the middle of a vendor evaluation or not even thinking about changing their CRM."
The result was meetings with companies that had no genuine buying urgency — which explained the low qualified opportunity rate. The meetings were happening; they just were not with the right companies at the right time.
The Implementation
TavMind was implemented over a 10-day period, integrating with Vertex's Salesforce instance and their Outreach sequencing environment. The initial setup process involved:
- Importing Vertex's historical closed-won and closed-lost deal data to train the initial scoring model
- Defining firmographic filters based on Vertex's ICP (mid-market companies, 50–500 employees, specific industry verticals)
- Configuring intent signal sources relevant to CRM software evaluation cycles
- Setting up Salesforce field mapping so TavMind scores appeared as native CRM fields
- Training the SDR team on the new account prioritization workflow (two 30-minute sessions)
The workflow change was deliberately minimal: each SDR now starts their day by reviewing their TavMind-prioritized account list in Salesforce, filtering to accounts scored 70+ by the AI model. All research, sequencing, and outreach proceeded through their existing tools — TavMind just changed which accounts they spent that effort on.
Results: 90 Days Later
After 90 days, the results were measurable across every key metric:
Qualified opportunity rate: 18% → 29% (+61% relative improvement)
Pipeline generated (qualified only): +40% quarter-over-quarter
Average meeting-to-opportunity cycle time: 18 days → 12 days (-33%)
SDR outreach volume: Decreased by 22% (reps working fewer, higher-quality accounts)
SDR job satisfaction score: Increased (reps reported less wasted effort on unresponsive accounts)
The pipeline increase was particularly notable because it came with a decrease in outreach volume. Vertex's SDRs were sending fewer emails and making fewer calls — but the accounts they were targeting were substantially more receptive. The time saved from low-quality prospecting was reinvested into deeper research and more personalized outreach on high-scoring accounts.
What Vertex's Revenue Leader Said
"The change in our reps' morale was almost immediate. When you stop sending cold emails into a void and start reaching out to companies who are actively evaluating, the conversations are different. The objections are different. The conversion rates are different. TavMind gave us a signal layer that changed everything about how our team thinks about their day."
— Head of Revenue, Vertex CRM
Key Lessons
The Vertex case reinforces several principles that we see consistently across our customer base:
- Minimal workflow disruption is important for adoption. Vertex's SDRs did not learn a new tool — they just received a better prioritized account list in Salesforce each morning.
- The impact of signal intelligence is not just on pipeline quantity — it significantly improves pipeline quality, as measured by stage conversion rates and sales cycle length.
- The ROI of signal intelligence compounds over time as the model learns from each closed deal. Vertex's Q4 performance was even better than Q3, as the model incorporated their Q3 outcome data.