Overview

A large global FinTech enterprise ran high volume events across multiple regions (AU/NZ, US, EU, LATAM, APAC, UK). While event activity was strong, the organization wasn’t seeing meaningful downstream movement where event-sourced leads were taking months to qualify, and conversion visibility was poor.

ConsonantOne was engaged to diagnose the operating gaps behind this leakage and build a RevOps aligned hiring system that could scale across regions.

The Challenge

The client’s go to market engine was generating leads through global events and pre-event marketing, but the post-event motion was inconsistent and slow.

Key issues observed:

• MQL bottleneck: Leads stalled at the very first stage (MQL qualification), and because downstream stages follow sequentially, delays cascaded through the funnel.
• Fragmented qualification workflows: Each region had its own version of “qualification,” resulting in inconsistent follow ups, duplicated effort, and unclear ownership.
• Role ambiguity in Revenue Operations: Sales Ops and RevOps responsibilities were vaguely defined, especially across teams with overlapping process ownership.
• Ecosystem complexity: Parts of the organization operated within an SFMC ecosystem, but lead handling and definitions were fragmented across teams and regions.

The outcome was predictable: lead aging, slow movement, and revenue leakage not because events didn’t work, but because the operating system behind lead conversion wasn’t built for scale.

Our Approach

1) Revenue System Diagnosis

We started by observing how event leads entered the system and where they stalled. Instead of jumping directly into hiring, we mapped:

• How leads were captured and routed
• Where qualification decisions slowed down
• Which teams owned which steps (and where ownership overlapped)
• How regional differences were creating inconsistent execution

We also aligned with the functional head on the core intent behind the function:
Was the goal maximum lead volume, or building a highly niche pipeline with higher quality leads?

This decision shaped the operating model and role design.

2) Workflow Mapping and Stakeholder Alignment

Because this RevOps/Sales Ops function served multiple regions from India, the same process touchpoints were shared across teams and overlaps were a major source of friction.

To address this, we built a structured Hiring Canvas that created clarity on:

• Shared definitions and success criteria
• Ownership boundaries across overlapping processes
• What “good” looks like across cross-functional stakeholders
• Hiring priorities by region and workflow dependency

This alignment step reduced ambiguity before hiring even began.

3) Role Architecture Built for the Real Workflow

We then designed roles around workflow ownership, not generic titles.

Roles hired for included:

• Sales Ops Specialists
• SFDC Analysts
• Process Coordinators / Process Analysts
• Program Managers
• Team Leads

All roles were based in India, supporting regional teams across AU/NZ, US, EU, LATAM, APAC and the UK.

4) A “3D JD” and Structured Hiring System

A standard JD doesn’t work in a RevOps environment where success depends on continuous cross functional execution. So we converted each role into a 3 Dimensional version of the JD detailing:

• Skills (tools, process execution, analytical capability)
• Behaviors (ambiguity handling, problem-solving, stakeholder management)
• Cross functional operating ability (working cleanly across overlaps and handoffs)

From there, we built the full hiring system:

• Interview rubrics and scorecards for consistent evaluation
• Work simulations/assignments aligned to real RevOps workflows
• Structured touchpoints to reduce candidate dropouts and improve experience

Outcomes (Early Indicators)

The RevOps team is approximately two weeks old, so it is too early to publish lead conversion or revenue metrics post hire. However, the engagement delivered immediate operational gains:

• Stakeholder calibration improved through a legitimate hiring canvas in a highly overlapped RevOps environment
• Search precision increased, reducing interview load and accelerating closure
• Hiring efficiency improved: Instead of interviewing ~20 candidates, the client typically closed roles after ~7 interviews due to tighter searches and better upfront calibration

What Made This Work

This engagement succeeded because it treated RevOps as what it is: a revenue system and not a set of job titles.

We didn’t just “fill roles.” We helped our client:

• Hire operators using structured, workflow-based evaluation
• Diagnose where revenue movement was breaking
• Align stakeholders on workflows and ownership