Expanding sales capacity and eliminating 3,000+ hours of manual work
Two pieces of repeat work were eating the team's day: finding employers to contact and reviewing student career-interest data. I automated both so they were done before anyone sat down.
- Apprenticeship America
- Chief Operating Officer
3,000 hrs
of manual review eliminated per year
1 hr/day
of sales prep removed
0
headcount added
180+ to 5
career options narrowed per student
Context
WIGU's sales team needed a steady flow of employers to contact, and the platform needed to turn each student's career-interest data into a short list of relevant careers. Both were being done by hand.
Problem
Finding new CNA job postings across Iowa, identifying the likely hiring manager, and writing an outreach message took roughly an hour of prep every day. Student career-interest exports sometimes listed 180 or more options per student, and reviewing them manually did not scale.
Approach
Match the automation to the shape of the work. Outreach is a daily scan-and-draft loop, so it became a morning job that ran before anyone arrived. Career matching is a judgment problem where keywords fail, so it matched on task-level themes instead.
Systems built
- Indeed outreach automation
- Scanned Indeed daily for new CNA postings across Iowa, identified the likely hiring manager, and generated ready-to-send outreach drafts each morning.
- Career-interest analyzer
- Read raw CSV career-interest data, sometimes 180+ options per student, and narrowed it to the top five relevant careers using task-level theme matching rather than keyword matching.
Results
The team got roughly an hour back every morning and stopped reviewing career data by hand, without adding anyone. Together, the two automations eliminated roughly 3,000 hours of manual work a year. Sprout, the IASourceLink chatbot, freed another 1,000 hours a year on a separate platform.
What I took from it
Keyword matching was the obvious approach and the wrong one. Matching on what a job actually involves is what made the output usable.
WIGU's sales team needed a steady flow of employers to contact, and the platform needed to turn each student's career-interest data into a short list of relevant careers. Both were being done by hand.
The team got roughly an hour back every morning and stopped reviewing career data by hand, without adding anyone. Together, the two automations eliminated roughly 3,000 hours of manual work a year. Sprout, the IASourceLink chatbot, freed another 1,000 hours a year on a separate platform.