What it tests

Ability to diagnose pipeline quality issues, identify revenue risk, and communicate findings to a sales leadership audience

Format

1Candidate receives a sanitized Salesforce export (CSV) with 200+ opportunities — stage, age, ARR, last activity date, close date, owner — and a 1-page context brief on the company's sales motion
2Task 1 (45 min): identify pipeline health issues — slipped deals, stale stages, coverage gaps, and forecast risk — and quantify the exposure
3Task 2 (30 min): draft a 1-page memo to the VP of Sales with findings, the top 3 risks, and 3 concrete process recommendations
4Task 3 (15 min): present findings live; interviewer plays a skeptical VP who pushes back on data interpretation and prioritization

What to look for

Do they segment by stage, age, and activity — or just summarize total pipeline value without depth?
Can they translate data anomalies into business language a VP actually cares about (e.g., 'We have $1.2M in deals that haven't had any activity in 30+ days in late stages')?
Does the memo lead with impact and risk, or with methodology?
Under pushback from the 'VP,' do they hold their conclusions with data or fold immediately?

Adaptation guide

Use your own CRM export — anonymized — as the dataset. For earlier-stage companies, replace Salesforce CSV with a Google Sheet. For senior candidates, add a second dataset with marketing attribution to see if they connect pipeline quality to source quality.

Full description

Format:

Candidate receives a sanitized Salesforce export (CSV) with 200+ opportunities — stage, age, ARR, last activity date, close date, owner — and a 1-page context brief on the company's sales motion
Task 1 (45 min): identify pipeline health issues — slipped deals, stale stages, coverage gaps, and forecast risk — and quantify the exposure
Task 2 (30 min): draft a 1-page memo to the VP of Sales with findings, the top 3 risks, and 3 concrete process recommendations
Task 3 (15 min): present findings live; interviewer plays a skeptical VP who pushes back on data interpretation and prioritization

Time: 90 minutes

What to look for:

Do they segment by stage, age, and activity — or just summarize total pipeline value without depth?
Can they translate data anomalies into business language a VP actually cares about (e.g., "We have $1.2M in deals that haven't had any activity in 30+ days in late stages")?
Does the memo lead with impact and risk, or with methodology?
Under pushback from the "VP," do they hold their conclusions with data or fold immediately?

Adaptation: Use your own CRM export — anonymized — as the dataset. For earlier-stage companies, replace Salesforce CSV with a Google Sheet. For senior candidates, add a second dataset with marketing attribution to see if they connect pipeline quality to source quality.