Services
Marketing
MQL-to-SQL Handoff Diagnosis
Analyze the handoff between Marketing and Sales to find where qualified leads get lost, ignored, or misrouted.
Performance
Before this engagement, the conversation between marketing and sales has usually been running for months. Marketing is generating leads that meet the defined criteria. Sales is correct that many of those leads don't convert. Both are right, which is what makes it hard to resolve without data. The handoff diagnosis traces every MQL through the full process to show exactly where the breakdown happens, at what stage, for which lead types, and under which conditions.
The MQL-to-SQL handoff is where marketing and sales misalignment becomes a revenue problem. Marketing defines MQL based on engagement or firmographic criteria. Sales defines "qualified" based on whether the lead picked up the phone and had budget. The two definitions rarely match, and the gap between them is where leads disappear.
What makes this hard to fix without data: both teams are usually right about their part of the picture. Marketing is generating leads that meet the criteria. Sales is correct that many of those leads don't convert. The breakdown is somewhere in the middle: routing logic that delays first contact, criteria that don't reflect actual sales readiness, or a handoff process that works on paper but fails in practice.
What we do
We pull every MQL from the past 12 months and trace it forward: when was it routed, when was it first touched by sales, what happened after that, and what was the eventual outcome. Then we segment those outcomes by source, score, rep, and time-to-first-touch to find where the pattern breaks.
We also audit the MQL criteria themselves against closed-won data to see whether the signals you're using to define readiness actually correlate with deals that close.
For context on what this type of analysis typically surfaces, read how MQL-to-SQL friction drives invisible revenue loss.
Deliverable
An MQL outcome analysis with routing time distributions, acceptance and rejection rates by segment, a criteria validation report showing which MQL signals predict SQL conversion, and specific recommendations for criteria changes, SLA definitions, and process fixes.
Outcome
Higher MQL acceptance rates. Faster first-touch times. A shared definition of qualified that marketing and sales both own. Fewer conversations that start with "sales isn't following up" and "marketing sends us garbage."
How Levara Doubled MQL-to-SQL Conversion and Increased Revenue by 28% — tripled lead response speed, doubled MQL-to-SQL, and increased conversions 28%.
See how it worked in practice: Nova Lending built a lead scoring model that sales actually used.
Best Fit
If your MQL-to-SQL conversion rate is below 30%, or if leads routinely sit untouched for more than 48 hours after handoff, or if your last conversation about lead quality devolved into a blame exchange between marketing and sales, this is the engagement to run first.