Sales outreach · Production

Elsevier Clinical Solutions generates $1M+ pipeline with Outreach sales automation and machine learning

The problem

Elsevier's Clinical Solutions team was spending excessive time on manual, non-selling tasks — mail merges, Excel, and creating custom templates — leaving too little time for the creative, customer-focused work that drives new business. Post-pandemic email volume made it even harder for reps to reach customers effectively.

First attempt

The existing approach was entirely fragmented and manual: each rep independently developed their own pipeline with no shared system, relying on mail merges and Excel, with no smart way to prioritize contacts or adapt to the post-pandemic digital-first environment.

Workflow diagram · grounded in source
1
Daily prospecting queue
trigger
“our reps know exactly what they need to do each day to stay on top of their prospecting activities”
2
ML-guided content decisions
ai_action
“Sellers, their managers, and sales leadership are using Outreach's machine learning capabilities to make decisions about content and messaging that increase pipeline coverage”
3
Prioritized outreach execution
output
“they can identify the highest-priority contacts and send them customized emails in 15 minutes or less - something that previously took at least an hour or more”
4
Sequence performance review
feedback_loop
“Tim hosts monthly content committee meetings, pulling up Outreach-native reports that help stakeholders investigate areas for improvement and coaching. They examine how sequences perform, perform A/B testing on subject lines”
5
Pipeline generation reported
output
“We've seen over a million dollars of pipeline created directly from Outreach sequences so far this year”
Reported outcome

Elsevier's Clinical Solutions Division generated over a million dollars of pipeline directly from Outreach sequences and closed about a quarter of that.
Reps now send customized emails to highest-priority contacts in 15 minutes or less, and seller adoption remained strong eighteen months after initial deployment.

Reported metrics
pipeline created from Outreach sequencesover a million dollars
pipeline closed from Outreach sequencesabout a quarter of that
Time to send customized emails to priority contacts15 minutes or less
Previous time to send prioritized customized emailsat least an hour or more
Show all 7 reported metrics
pipeline created from Outreach sequencesover a million dollars
pipeline closed from Outreach sequencesabout a quarter of that
time to send customized emails to priority contacts15 minutes or less
previous time to send prioritized customized emailsat least an hour or more
rep productivity gainsmeaningful productivity gains
new rep onboarding timeaccelerated the onboarding time
seller time on custom template creationhalf their day
Reported stack
Outreach
Source
https://www.outreach.io/resources/stories/elsevier-customer-story
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Elsevier's Clinical Solutions Division generated over a million dollars of pipeline directly from Outreach sequences and closed about a quarter of that.

What tools did this team use?

Outreach.

What results were reported?

pipeline created from Outreach sequences: over a million dollars; pipeline closed from Outreach sequences: about a quarter of that; Time to send customized emails to priority contacts: 15 minutes or less; Previous time to send prioritized customized emails: at least an hour or more (source-reported, not independently verified).

What failed first in this deployment?

The existing approach was entirely fragmented and manual: each rep independently developed their own pipeline with no shared system, relying on mail merges and Excel, with no smart way to prioritize contacts or adapt…

How is this sales outreach AI workflow structured?

Daily prospecting queue → ML-guided content decisions → Prioritized outreach execution → Sequence performance review → Pipeline generation reported.

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