Analyze · Rep Performance Diagnostics
Diagnose Why a Rep is Missing Quota
Pinpoint the most likely reason a sales rep at your agency is missing quota, ranked by evidence.
When to use
Run this when a rep has missed quota for at least one full period and you want a fair, data-grounded diagnosis before any coaching conversation. It separates skill, pipeline, and effort issues so you don't misdiagnose. Use the output to brief the rep in a 1:1 or to scope a targeted improvement plan.
The prompt
You are a sales manager doing a fair, data-grounded diagnosis of a rep's performance at a digital marketing agency. You diagnose causes — you do not coach or prescribe fixes in this step. Agency: [AGENCY_NAME] — [SERVICES] | Rep: [REP_NAME] | Tenure: [TENURE_MONTHS] months | Quota: [QUOTA] | Attainment: [ATTAINMENT_PCT] | Period: [PERIOD] Data: [REP_PIPELINE] [ACTIVITY_DATA] [BENCHMARK_TEAM_AVERAGES] Diagnose the single most likely reason [REP_NAME] is missing quota this period, plus 2 secondary contributing factors, each backed by specific numbers from the data. - Cite specific data points (counts, conversion rates, dollar amounts) — never opinion. - Distinguish clearly between a skill gap, a pipeline gap (top-of-funnel volume), and an effort gap (activity levels). - Compare to [BENCHMARK_TEAM_AVERAGES] where possible to avoid blaming the rep for market conditions. - Tone is constructive and clinical. No character judgements like "lazy" or "not coachable." - If data is missing to confirm a hypothesis, say so explicitly. Output: 1. Headline diagnosis (1 sentence, name the primary gap: skill / pipeline / effort / mix) 2. Ranked root-cause hypotheses (Primary + 2 Secondary), each with: hypothesis, supporting evidence (numbers), confidence (High/Med/Low) 3. Data gaps to fill before final judgement
Variables
[AGENCY_NAME]Your agency's name[SERVICES]Core services sold (e.g., SEO, paid social retainers)[REP_NAME]First name of the rep being diagnosed[TENURE_MONTHS]How long the rep has been in seat[QUOTA]Period quota in MRR or new business $[ATTAINMENT_PCT]Their actual % of quota hit[PERIOD]The period being analyzed (e.g., Q1 2026)[REP_PIPELINE]Pipeline export: stage, value, age, last activity[ACTIVITY_DATA]Calls, emails, meetings booked, proposals sent[BENCHMARK_TEAM_AVERAGES]Team-average activity + conversion rates for comparison
Example input
Agency: Northwind Digital — SEO + Google Ads retainers | Rep: Marco | Tenure: 9 months | Quota: $25k new MRR | Attainment: 48% | Period: Q1 2026 Pipeline: 14 open deals, total $38k MRR, avg age 47 days, 6 deals with no activity in 14+ days. Activity: 112 calls (team avg 180), 38 discoveries booked (team avg 42), 9 proposals sent (team avg 14), discovery→proposal 24% (team 33%).
Example output
Headline diagnosis: Primary gap is EFFORT at the top of funnel, with a secondary SKILL gap converting discoveries into proposals. Ranked hypotheses: 1. Primary — Effort gap (Confidence: High). Marco made 112 calls vs team avg 180 (-38%). Discoveries booked are close to average (38 vs 42), so connect rate is fine — raw dial volume is the constraint. 2. Secondary — Skill gap at discovery→proposal (Confidence: Medium). 24% conversion vs team 33%. Suggests qualification or value-framing weakness, but sample size (38) is moderate. 3. Secondary — Pipeline hygiene (Confidence: Medium). 6 of 14 open deals have no activity in 14+ days. Inflates pipeline value but may mask true coverage. Data gaps: Need call recordings from discoveries that did NOT convert, and lead-source breakdown to rule out a lead-quality issue before confirming the skill gap.
Pro tips
- Always paste the team averages alongside the rep's numbers — without a benchmark Claude will over-index on absolute figures.
- Run this BEFORE the 1:1, not during. The diagnosis should shape your questions, not be read aloud.
- If attainment has been low for 2+ periods, also paste prior-period data so Claude can spot a trend vs a one-off.
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