Analyze Rep Performance by Lead Source

You are a sales manager doing a fair, data-grounded diagnosis of a rep's performance at a digital marketing agency. You isolate the effect of lead source from rep skill — you do not coach or reassign in this output.
Agency: [AGENCY_NAME] — [SERVICES] | Rep: [REP_NAME] | Period: [PERIOD]
Lead sources active: [LEAD_SOURCES]
Performance by source for this rep:
[REP_PERFORMANCE_BY_SOURCE]
Team baseline by source (close rate, ACV):
[TEAM_BASELINE_BY_SOURCE]
For each lead source, compare [REP_NAME]'s close rate and ACV to the team baseline. Determine whether overall underperformance is explained by lead-mix (more low-converting sources) or by skill (lower conversion within the same source).

- Compute close rate AND ACV per source — both matter.
- Distinguish mix effect (rep got more of source X which always converts low) from skill effect (rep converts source X below team avg).
- Flag sources with fewer than 10 leads as low-confidence.
- Stay diagnostic. Do not recommend lead reassignment yet.
- If the rep beats the team on a specific source, call it out.

Output:
1. Per-source comparison table (Source | Rep close rate | Team avg | Rep ACV | Team ACV | Confidence)
2. Decomposition: how much of the rep's overall gap is mix vs skill (rough %)
3. Sources where the rep over-performs
4. Sources to monitor for skill gap

• [AGENCY_NAME] — Agency name
• [SERVICES] — Services sold
• [REP_NAME] — Rep being analyzed
• [PERIOD] — Reporting period
• [LEAD_SOURCES] — Active sources: e.g., Paid Search, Referral, SEO, Outbound, Partner
• [REP_PERFORMANCE_BY_SOURCE] — Rep's leads, opps, won, ACV broken out by source
• [TEAM_BASELINE_BY_SOURCE] — Team-average close rate and ACV per source

Agency: Northwind | Rep: Marco | Period: Q1 2026
Sources: Paid Search, Referral, Outbound.
Marco: Paid 40 leads / 12% close / $3.2k ACV. Referral 8 leads / 50% close / $4.8k ACV. Outbound 60 leads / 5% close / $2.9k ACV.
Team baseline: Paid 18% close $3.5k. Referral 55% close $5k. Outbound 10% close $3k.

Per-source comparison:
| Source | Marco close | Team | Marco ACV | Team ACV | Confidence |
| Paid | 12% | 18% | $3.2k | $3.5k | High (40) |
| Referral | 50% | 55% | $4.8k | $5.0k | Low (8) |
| Outbound | 5% | 10% | $2.9k | $3.0k | High (60) |

Decomposition: ~60% of Marco's gap is a skill effect (he converts Paid and Outbound below team rates inside the same source). ~40% is a mix effect — his lead pool is 56% Outbound (the lowest-converting source) vs the team average of ~35%. Both matter.

Over-performs: None at statistical confidence; Referral close rate is in-range but n=8 is too small to judge.

Monitor for skill: Paid (-6pp) and Outbound (-5pp). Both are real gaps even after adjusting for mix.

• Always run this before launching a PIP — a 'bad' rep often just got a bad lead mix.
• Pair with a lead-routing audit; the mix issue may be a routing rule, not a rep choice.
• Track ACV per source not just close rate — closing cheap deals fast can hide a real problem.