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Case Study: ElevenLabs lifted SQLs 50% and 4x'd demand-gen SQLs with Clay

ElevenLabs Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
ElevenLabs
Industry
AI Voice Technology
Challenge
One data layer across multiple GTM motions
Headline result
ElevenLabs delivered +50% incremental SQLs and a 4x increase in demand-gen SQLs with Clay

Key results

+50%
Incremental sales-qualified leads
4x
Increase in SQLs from demand gen
<5 min
Speed-to-lead, form to first touch

The challenge

ElevenLabs wanted a single data layer to qualify, score, and route leads across several GTM motions without building bespoke data pipelines for every change.

The solution

ElevenLabs used Clay to pre-qualify and score every lead on unique signals and route it automatically, cutting speed-to-lead and reclassifying existing accounts at scale.

“Every lead is pre-qualified, scored on unique signals, and routed automatically through Clay.”

RK
Raman Khanna
Growth, ElevenLabs

The results, in context

A shift from static scoring to Clay's multi-signal model delivered +50% incremental sales-qualified leads and a 4x increase in SQLs from demand-gen channels. Response time from form submission to first touch dropped to under five minutes.

Products used

Clay Clay