Contact us

Case Study: Doubling MQL:SQL Conversion for pipline growth

Company: CompuCom
Project: Lead Lifecycle & Nurture Redesign

Marketing was hitting MQL targets, but SDRs were missing demo and SQL goals. Sales blamed lead quality; Marketing benchmarks said MQL performance was fine. Pipeline slowed, and trust between teams was breaking down.

Task  Figure out why MQLs weren’t turning into SQLs and fix the process, without inflating volume or damaging sales confidence.

"I had the pleasure of working with Michelle on a variety of project and business initiatives. What stood out to me most about Michelle was her focus, care, inclusive nature, and attention to detail and quality of the mission. "
Ross Feldman

Action:

Diagnosed the real conversion problem

  • Analyzed MQL-to-SQL conversion by channel, industry, and job title

  • Compared hand raisers vs. behavioral MQLs

  • Identified follow-up and routing gaps instead of assuming a volume issue

Fixed low-quality MQLs at the source

  • Found content syndication leads converted to SQL at just 3% (vs. a 10% benchmark) while driving a large share of MQL volume

  • Removed content syndication leads from MQL qualification

  • Built a dedicated nurture + AI SDR motion to educate and qualify before sales involvement

Prioritized high-intent buyers

  • Separated hand raisers from behavioral MQLs

  • Partnered with Sales Ops to redesign Salesforce routing

  • Triggered instant HubSpot alerts for SDRs on form fills

  • Created a clearly labeled “Hand Raiser” Salesforce campaign to ensure fast follow-up

  • Set SDR follow-up SLAs for time-to-first-touch and number of touches.

Results:

The changes materially improved conversion, alignment, and predictability:

  • Overall MQL-to-SQL conversion doubled from 9% to 18%.  Hand Raiser MQL-to-SQL conversion increased from 26% to 50%+

  • Content syndication leads converted at 2% with AI SDR (vs. 3% direct to sales) &  32% once re-qualified through nurture 

  • SDRs hit SQL goals every month after launch. Total MQL volume decreased, but pipeline quality improved.

  • Sales agreed to reset the MQL KPI to a 15% MQL-to-SQL benchmark, aligning success to pipeline, not raw lead volume.

Identifying Pipeline Risk Early with Monthly Scorecard Reporting

I implemented monthly scorecard reporting to surface challenges early—before they became pipeline problems. By tracking lead lifecycle and conversion trends, the team was able to quickly identify gaps, optimize handoffs, and improve overall pipeline performance.

Learn How

Driving Product-Led Growth with PQLs

Product-led growth only works when you know which behaviors actually lead to upgrades. This case study shows how I defined Atlassian’s PQL framework, identifying the product usage signals that matter and using targeted messaging to drive conversion and expansion.

Learn How