| name | periodic-sales-performance-review |
| version | 1.0.0 |
| description | Periodic sales performance review composite. Pulls rep-level and team-level sales data from any CRM or tracking system, analyzes performance across a user-defined period (weekly, monthly, quarterly), and produces both an executive summary and a detailed diagnostic. Covers quota attainment, activity metrics, deal progression, win/loss patterns, rep-level benchmarking, coaching opportunities, and forecast accuracy. Tool-agnostic — works with any CRM (Salesforce, HubSpot, Pipedrive, Close, Supabase, CSV).
|
| tags | ["research"] |
| graph | {"provides":["sales-performance-executive-summary","sales-performance-detailed-report","rep-coaching-priorities","forecast-accuracy-analysis"],"requires":["deal-data","activity-data","your-company-context"],"connects_to":[{"skill":"pipeline-review","when":"Review surfaces pipeline health concerns requiring deeper diagnostic","passes":"deal-data, pipeline-stages"},{"skill":"sales-coaching","when":"Review identifies reps needing coaching on specific skills","passes":"rep-coaching-priorities"},{"skill":"cold-email-outreach","when":"Review reveals reps with low activity or stalled pipeline needing re-engagement","passes":"stalled-leads, underperforming-segments"},{"skill":"sequence-performance","when":"Review shows outbound campaigns underperforming for specific reps","passes":"campaign-ids, rep-campaign-metrics"}],"capabilities":["data-analysis","reporting"]} |
Periodic Sales Performance Review
Pulls rep-level and team-level sales data from whatever system the user tracks performance in, analyzes it over a chosen period, and produces a report that answers the questions a sales leader actually cares about: Are we going to hit the number? Who's carrying the team? Who needs help? Where are the coaching opportunities? Are our forecasts reliable?
Two output modes:
- Executive summary: 1-page snapshot. Quota attainment, top/bottom performers, red flags, green lights. What a VP Sales reads before the Monday standup.
- Detailed diagnostic: Full rep-by-rep breakdown, activity analysis, deal progression, win/loss patterns, forecast accuracy, and coaching recommendations.
Both are always produced. The executive summary sits at the top of the report.
When to Auto-Load
Load this composite when:
- User says "sales performance review", "team performance report", "how's the team doing", "rep scorecard"
- User says "weekly sales review", "monthly performance review", "quarterly sales analysis"
- User says "who's hitting quota", "quota attainment report", "rep performance"
- An upstream workflow (Pipeline Ops, management cadence) triggers an end-of-period review
- User asks about forecast accuracy, rep productivity, or team-level trends
Step 0: Configuration (One-Time Setup)
On first run, collect and store these preferences. Skip on subsequent runs.
Data Source Config
| Question | Options | Stored As |
|---|
| Where do you track deals/pipeline? | Salesforce / HubSpot / Pipedrive / Close / Supabase / Google Sheets / CSV / Other | crm_tool |
| How do we access it? | API / Export CSV / MCP tools / Direct query | access_method |
| Where do you track activity data? (calls, emails, meetings) | Same CRM / Outreach / Salesloft / Gong / Separate tracker / Manual / Not tracked | activity_source |
Team Structure
| Question | Purpose | Stored As |
|---|
| Who are your sales reps? (names) | Rep-level analysis | rep_names |
| Is there a team/pod structure? (e.g., SMB team, Enterprise team, SDR vs AE) | Segment analysis | team_structure |
| Who manages each rep? | Manager-level rollup | managers |
Example team structure:
team_structure: {
"SDR": ["Alex", "Jordan"],
"AE - SMB": ["Sam", "Casey"],
"AE - Enterprise": ["Morgan", "Riley"]
}
managers: {
"SDR": "Taylor",
"AE - SMB": "Jamie",
"AE - Enterprise": "Jamie"
}
Quota & Targets
| Question | Purpose | Stored As |
|---|
| What is each rep's quota? (monthly or quarterly) | Quota attainment calculation | rep_quotas |
| What quota period do you use? (monthly / quarterly) | Normalize attainment | quota_period |
| What is the team-level target? | Team rollup | team_target |
| Do you have activity targets? (calls/day, emails/day, meetings/week) | Activity benchmarking | activity_targets |
Example quota config:
rep_quotas: {
"Sam": 50000,
"Casey": 50000,
"Morgan": 150000,
"Riley": 150000
}
quota_period: "monthly"
team_target: 400000
activity_targets: {
"SDR": { "calls_per_day": 50, "emails_per_day": 80, "meetings_per_week": 10 },
"AE": { "calls_per_day": 15, "emails_per_day": 20, "meetings_per_week": 8 }
}
Pipeline Stage Definitions
| Question | Purpose | Stored As |
|---|
| What are your pipeline stages in order? | Map data to funnel | pipeline_stages |
| Which stage means "qualified"? | Qualification rate | qualified_stage |
| Which stage means "closed won"? | Win rate, revenue | won_stage |
| Which stage means "closed lost"? | Loss analysis | lost_stage |
| What is your expected sales cycle length? (days) | Velocity benchmarking | expected_cycle_days |
Forecast Config (Optional)
| Question | Purpose | Stored As |
|---|
| Do you track forecasts? (commit, best case, pipeline) | Forecast accuracy analysis | tracks_forecasts |
| Where are forecasts recorded? | Pull forecast data | forecast_source |
| What forecast categories do you use? | Map to standard categories | forecast_categories |
Store config in: clients/<client-name>/config/periodic-sales-performance-review.json or equivalent.
Step 1: Pull Performance Data
Purpose: Extract deal data, activity data, and (optionally) forecast data for the specified period.
Input Contract
period: {
type: "weekly" | "monthly" | "quarterly" | "custom"
start_date: string # ISO date (auto-calculated from type, or user-specified)
end_date: string # ISO date (default: today)
comparison_period: boolean # Include prior period for trend comparison (default: true)
}
crm_tool: string # From config
access_method: string # From config
activity_source: string # From config
rep_names: string[] # From config
Process
Pull three categories of data:
A) Deal Data (per rep)
| CRM | How to Pull |
|---|
| Salesforce | SOQL query on Opportunity with Owner filter |
| HubSpot | Deals API filtered by owner |
| Pipedrive | Deals API filtered by owner |
| Close | Leads/Opportunities API by assigned user |
| Supabase | Query deals table with rep filter |
| CSV | User provides file, filter by owner column |
For each rep, pull:
- All deals closed (won + lost) in the period
- All deals created in the period
- All deals currently open (active pipeline)
- Deal amounts, stages, close dates, sources, loss reasons
B) Activity Data (per rep)
| Source | How to Pull |
|---|
| Salesforce | Tasks + Events objects by owner |
| HubSpot | Engagements API by owner |
| Outreach/Salesloft | Activity metrics API |
| Gong | Call logs and meeting data |
| Manual/CSV | User provides activity log |
For each rep, pull:
- Calls made (count, duration if available)
- Emails sent (count, reply rate if available)
- Meetings held (count, no-shows if available)
- Proposals/demos delivered
- LinkedIn touches (if tracked)
C) Forecast Data (if tracked)
Pull the forecast submitted at the start of the period for comparison to actuals:
- Commit forecast per rep
- Best case forecast per rep
- Pipeline forecast per rep
Data Standardization
Normalize all data into a standard structure:
performance_data: {
current_period: {
start_date: string
end_date: string
reps: [
{
name: string
team: string | null
manager: string | null
quota: number
revenue: {
closed_won: number
closed_lost_value: number
deals_won: integer
deals_lost: integer
avg_deal_size: number | null
largest_deal: { name: string, amount: number } | null
}
pipeline: {
deals_created: integer
pipeline_value_created: number | null
open_deals: integer
open_pipeline_value: number | null
weighted_pipeline: number | null
}
activity: {
calls: integer | null
emails_sent: integer | null
meetings_held: integer | null
meetings_booked: integer | null
proposals_sent: integer | null
no_shows: integer | null
} | null
velocity: {
avg_days_to_close: float | null
avg_days_in_stage: { stage: string, days: float }[] | null
} | null
qualification: {
meetings_to_qualified_rate: percentage | null
qualified_deals: integer | null
} | null
deals_closed: [
{
name: string
company: string
amount: number
stage: string # won or lost
close_date: string
days_to_close: integer | null
source: string | null
loss_reason: string | null
}
]
forecast: {
commit: number
best_case: number
pipeline: number
} | null
}
]
}
comparison_period: { ... } | null # Same structure for prior period
}
Human Checkpoint
## Performance Data Pulled
Source: [CRM name]
Activity source: [Activity tool name]
Current period: [start] to [end]
Comparison period: [start] to [end]
Reps found: [list of rep names]
Deals closed in period: X won, Y lost
Activity data available: [yes/partial/no]
Forecast data available: [yes/no]
Data looks correct? (Y/n)
Step 2: Analyze Performance
Purpose: Run the full analysis across six dimensions. Pure computation + LLM reasoning.
Input Contract
performance_data: { ... } # From Step 1
rep_quotas: { ... } # From config
team_target: number # From config
activity_targets: { ... } | null # From config
pipeline_stages: string[] # From config
expected_cycle_days: integer # From config
Analysis Dimensions
Run all six analyses on the current period data. Where comparison period exists, calculate period-over-period trends.
Analysis 1: Quota Attainment
Questions answered: Are we going to hit the number? Who's on track and who isn't?
| Metric | How to Calculate |
|---|
| Team revenue closed | Sum of all reps' closed won |
| Team attainment | Team revenue / team target |
| Rep attainment | Each rep's closed won / their quota |
| Attainment distribution | How many reps at >100%, 80-100%, 50-80%, <50% |
| Run rate projection | (Revenue closed / days elapsed) × days in period |
| Gap to target | Team target - closed won - weighted pipeline |
| Rep ranking | Ordered by attainment % |
| vs. Prior period | Compare attainment percentages |
Output:
quota_attainment: {
team: {
target: number
closed: number
attainment_pct: percentage
run_rate_projection: number
projected_attainment_pct: percentage
gap_to_target: number
vs_prior_period: percentage_change | null
}
by_rep: [
{
name: string
team: string | null
quota: number
closed: number
attainment_pct: percentage
run_rate_projection: number
gap_to_quota: number
rank: integer
vs_prior_period: percentage_change | null
status: "crushing" | "on_track" | "behind" | "at_risk"
}
]
distribution: {
above_100: integer
pct_80_to_100: integer
pct_50_to_80: integer
below_50: integer
}
}
Status thresholds (adjusted for time elapsed in period):
- Crushing: >110% attainment (pace-adjusted)
- On track: 90-110% attainment (pace-adjusted)
- Behind: 60-90% attainment (pace-adjusted)
- At risk: <60% attainment (pace-adjusted)
Analysis 2: Activity Metrics
Questions answered: Are reps putting in the work? Who's active and who's coasting?
| Metric | How to Calculate |
|---|
| Calls per day (by rep) | Total calls / business days in period |
| Emails per day (by rep) | Total emails / business days in period |
| Meetings per week (by rep) | Total meetings / weeks in period |
| Proposals sent (by rep) | Count in period |
| Activity-to-meeting conversion | Meetings booked / (calls + emails) |
| Meeting-to-opportunity conversion | Qualified deals / meetings held |
| No-show rate | No-shows / meetings booked |
| vs. Targets | Compare to activity_targets |
| vs. Prior period | Compare activity volumes |
| Activity efficiency | Revenue closed per activity unit (calls, emails, meetings) |
Output:
activity_analysis: {
by_rep: [
{
name: string
team: string | null
calls_per_day: float | null
emails_per_day: float | null
meetings_per_week: float | null
proposals_sent: integer | null
activity_to_meeting_rate: percentage | null
meeting_to_opp_rate: percentage | null
no_show_rate: percentage | null
revenue_per_meeting: number | null
vs_targets: {
calls: "above" | "at" | "below" | null
emails: "above" | "at" | "below" | null
meetings: "above" | "at" | "below" | null
} | null
vs_prior_period: {
calls_change: percentage | null
emails_change: percentage | null
meetings_change: percentage | null
} | null
activity_grade: "high" | "adequate" | "low" | "critical"
}
]
team_averages: {
avg_calls_per_day: float | null
avg_emails_per_day: float | null
avg_meetings_per_week: float | null
avg_revenue_per_meeting: number | null
}
efficiency_leaders: {
most_efficient_rep: string # Highest revenue per activity
most_active_rep: string # Highest total activity volume
best_conversion_rep: string # Highest meeting-to-opp rate
}
}
Activity grade thresholds (relative to targets or team average):
- High: >120% of target/average
- Adequate: 80-120%
- Low: 50-80%
- Critical: <50%
Analysis 3: Deal Progression & Velocity
Questions answered: How fast are deals moving? Are deals progressing or stalling?
| Metric | How to Calculate |
|---|
| Avg days to close (by rep) | Mean days from created to closed won |
| Avg days to close (team) | Team-level mean |
| Stage velocity by rep | Avg days in each stage per rep |
| Deals progressed this period | Deals that moved forward at least one stage |
| Deals stalled | Deals with no stage change in period |
| Pipeline creation rate | New pipeline value created / target (should be 3x+) |
| Pipeline coverage by rep | Open weighted pipeline / remaining quota |
| Fastest deal | Shortest time from created to closed won |
| vs. Expected cycle | Compare avg days to close vs. expected_cycle_days |
| vs. Prior period | Compare velocity metrics |
Output:
deal_progression: {
by_rep: [
{
name: string
avg_days_to_close: float | null
deals_progressed: integer
deals_stalled: integer
pipeline_created: number | null
pipeline_coverage: float | null
coverage_assessment: "healthy" | "adequate" | "at_risk" | "critical"
fastest_deal: { name: string, days: integer } | null
slowest_stage: { stage: string, avg_days: float } | null
}
]
team: {
avg_days_to_close: float | null
vs_expected_cycle: string
total_pipeline_created: number | null
total_pipeline_coverage: float | null
deals_progressed: integer
deals_stalled: integer
stall_rate: percentage
vs_prior_period: {
velocity_change: string | null
stall_rate_change: percentage_change | null
} | null
}
}
Pipeline coverage thresholds:
- Healthy: 3x+ remaining quota
- Adequate: 2-3x
- At risk: 1-2x
- Critical: <1x
Analysis 4: Win/Loss Patterns
Questions answered: Why are we winning? Why are we losing? Are there patterns by rep?
| Metric | How to Calculate |
|---|
| Win rate (by rep) | Won / (Won + Lost) |
| Win rate (team) | Team-level |
| Avg deal size won (by rep) | Mean amount of won deals per rep |
| Win rate by source (by rep) | Cross-reference source and outcome per rep |
| Top loss reasons (by rep) | Group loss_reason per rep |
| Top loss reasons (team) | Aggregate |
| Loss stage distribution | At which stage do deals die, per rep |
| Competitive losses | Deals lost to specific competitors |
| vs. Prior period | Compare win rates |
Output:
win_loss: {
team: {
win_rate: percentage
deals_won: integer
deals_lost: integer
avg_deal_size_won: number | null
total_revenue_lost: number | null
vs_prior_period: percentage_change | null
top_loss_reasons: [
{ reason: string, count: integer, percentage: percentage }
]
loss_by_stage: [
{ stage: string, count: integer, percentage: percentage }
]
competitive_losses: [
{ competitor: string, count: integer, deals: string[] }
] | null
}
by_rep: [
{
name: string
win_rate: percentage
deals_won: integer
deals_lost: integer
avg_deal_size_won: number | null
vs_prior_period: percentage_change | null
top_loss_reason: string | null
loss_stage: string | null # Stage where most losses occur
notable_wins: [ { deal: string, amount: number } ] | null
}
]
}
Analysis 5: Rep Benchmarking & Coaching Priorities
Questions answered: How do reps compare to each other? Where does each rep need help?
| Metric | How to Calculate |
|---|
| Composite performance score | Weighted: attainment (40%) + activity grade (20%) + win rate (20%) + pipeline coverage (20%) |
| Strength/weakness profile per rep | Best and worst metrics relative to team |
| Coaching priority ranking | Reps sorted by gap between potential and performance |
| Skill gaps | Inferred from data patterns (see below) |
Skill gap inference logic:
| Pattern | Inferred Gap | Coaching Focus |
|---|
| High activity + low meetings | Messaging/targeting issue | Refine outreach copy, review ICP targeting |
| High meetings + low qualification rate | Discovery skills gap | Coach on discovery questions, qualification framework |
| High qualification + low win rate | Closing skills gap | Coach on negotiation, objection handling, proposals |
| Low activity + decent conversion | Activity discipline | Coach on daily rhythm, accountability, pipeline generation |
| High win rate + small deal sizes | Upsell/expansion gap | Coach on multi-threading, selling to value, expansion motions |
| Long cycle times vs. team avg | Deal management gap | Coach on next-step discipline, stakeholder mapping, urgency creation |
| High loss to competitors | Competitive positioning gap | Provide battlecards, coach on differentiation and displacement tactics |
| High no-show rate | Meeting confirmation process | Implement confirmation sequences, calendar management |
Output:
rep_benchmarking: {
rankings: [
{
name: string
composite_score: float # 0-100
rank: integer
attainment_pct: percentage
activity_grade: string
win_rate: percentage
pipeline_coverage: float
trend: "improving" | "stable" | "declining" | null
}
]
coaching_priorities: [
{
rep_name: string
priority: "urgent" | "high" | "medium" | "low"
primary_gap: string # e.g., "discovery skills"
evidence: string # The data pattern that reveals this gap
recommended_coaching: string # Specific action for their manager
secondary_gaps: string[] | null
}
]
team_strengths: string[] # What the team does well
team_weaknesses: string[] # Systemic issues across multiple reps
}
Coaching priority logic:
- Urgent: Rep is <50% attainment AND activity is low — needs immediate intervention
- High: Rep is <80% attainment with identifiable skill gap — coaching can move the needle
- Medium: Rep is on track but has a specific area significantly below team average
- Low: Rep is performing well — coach to maintain and stretch
Analysis 6: Forecast Accuracy (if forecast data available)
Questions answered: How reliable are our forecasts? Who sandags? Who over-commits?
| Metric | How to Calculate |
|---|
| Team forecast accuracy | Actual closed / commit forecast |
| Rep forecast accuracy | Per-rep actual vs. commit |
| Over-commit rate | How often reps forecast more than they close |
| Sandbag rate | How often reps close significantly more than forecast |
| Best case realization | Actual / best case forecast |
| Forecast trend | Is accuracy improving or declining over time? |
Output:
forecast_accuracy: {
team: {
commit_forecast: number
best_case_forecast: number
actual_closed: number
commit_accuracy: percentage # Actual / commit
best_case_realization: percentage
accuracy_grade: "reliable" | "optimistic" | "conservative" | "unreliable"
}
by_rep: [
{
name: string
commit_forecast: number
actual_closed: number
accuracy: percentage
pattern: "accurate" | "over_commits" | "sandbags" | "volatile"
deviation: number # Actual - commit
}
]
insights: string[] # Patterns observed
} | null
Accuracy grades:
- Reliable: Team closes within 90-110% of commit
- Optimistic: Team consistently closes <90% of commit
- Conservative: Team consistently closes >110% of commit (sandbagging)
- Unreliable: Wide variance, no consistent pattern
Output Contract (Full Analysis)
analysis: {
period: { type, start_date, end_date }
quota_attainment: { ... }
activity_analysis: { ... }
deal_progression: { ... }
win_loss: { ... }
rep_benchmarking: { ... }
forecast_accuracy: { ... } | null
}
No human checkpoint after this step — the analysis feeds directly into report generation.
Step 3: Generate Report
Purpose: Transform the raw analysis into two report formats: an executive summary and a detailed diagnostic with rep-level coaching notes. Pure LLM reasoning.
Input Contract
analysis: { ... } # From Step 2
rep_quotas: { ... } # From config
team_target: number # From config
activity_targets: { ... } | null # From config
Executive Summary Format
One page. Numbers and rankings. What a VP Sales needs to see in 60 seconds.
# Sales Performance Review — [Period Type]: [Start Date] to [End Date]
## Team Snapshot
| Metric | This Period | Prior Period | Change |
|--------|------------|-------------|--------|
| Revenue closed | $X | $Y | +/-Z% |
| Team attainment | X% | Y% | +/-Z pts |
| Deals won | X | Y | +/-Z |
| Avg deal size | $X | $Y | +/-Z% |
| Win rate | X% | Y% | +/-Z pts |
| Avg days to close | X | Y | +/-Z |
| Pipeline coverage | Xx | Yx | +/-Z |
## Rep Attainment Leaderboard
| Rank | Rep | Closed | Quota | Attainment | Status |
|------|-----|--------|-------|------------|--------|
| 1 | [Name] | $X | $Y | Z% | Crushing |
| 2 | [Name] | $X | $Y | Z% | On track |
| ... | ... | ... | ... | ... | ... |
## Red Flags
- [Any rep below 50% attainment — names and specifics]
- [Activity metrics below target]
- [Pipeline coverage below 2x for any rep]
- [Win rate declining]
- [Forecast accuracy deteriorating]
## Green Lights
- [Reps exceeding quota]
- [Metrics trending up]
- [Pipeline coverage healthy]
- [Wins against key competitors]
## Top 3 Actions
1. [Most impactful thing to do this week — e.g., "Coach [rep] on discovery — 8 meetings but 0 qualified deals"]
2. [Second most impactful]
3. [Third most impactful]
Detailed Diagnostic Format
Full rep-by-rep breakdown with coaching recommendations.
# Sales Performance Diagnostic — [Period]
## 1. Quota Attainment
[Team attainment summary]
[Rep-by-rep attainment table]
[Run rate projections]
[Gap analysis — what's needed to hit the number]
[Commentary: are we going to make it? What needs to happen?]
## 2. Activity Analysis
[Team activity averages vs. targets]
[Rep-by-rep activity table]
[Efficiency metrics — revenue per meeting, conversion rates]
[Commentary: who's doing the work? Who's efficient vs. just busy?]
## 3. Deal Progression & Velocity
[Pipeline creation and coverage by rep]
[Velocity metrics — avg days to close, stage duration]
[Stalled deals by rep]
[Commentary: is the pipeline healthy? Where are bottlenecks?]
## 4. Win/Loss Analysis
[Team win rate and trends]
[Rep-by-rep win rates]
[Loss reasons — team-level and rep-level patterns]
[Competitive loss analysis]
[Commentary: why are we losing? Any rep-specific patterns?]
## 5. Rep Scorecards
[For each rep, a mini-scorecard]:
### [Rep Name] — [Status: Crushing / On Track / Behind / At Risk]
| Metric | Value | vs. Target | vs. Team Avg | Trend |
|--------|-------|-----------|-------------|-------|
| Attainment | X% | [+/-] | [+/-] | [up/down/flat] |
| Activity | [grade] | [+/-] | [+/-] | [up/down/flat] |
| Win rate | X% | — | [+/-] | [up/down/flat] |
| Avg deal size | $X | — | [+/-] | [up/down/flat] |
| Pipeline coverage | Xx | [+/-] | [+/-] | [up/down/flat] |
**Strengths:** [What this rep does well, with data]
**Gaps:** [Where they need improvement, with data]
**Coaching recommendation:** [Specific action for their manager]
## 6. Forecast Accuracy (if available)
[Team forecast accuracy]
[Rep-by-rep accuracy table]
[Patterns: who sandbags, who over-commits]
[Commentary: can we trust the forecast?]
## 7. Recommendations
[Numbered list of specific, actionable recommendations.
Each recommendation cites the data point that drives it.]
### Urgent (This Week)
1. [Action — data point — expected impact]
### High Priority (This Month)
2. [Action — data point — expected impact]
3. [Action — data point — expected impact]
### Systemic (Ongoing)
4. [Process or tooling change — data point — expected impact]
Recommendations Logic
Generate recommendations based on patterns found in the analysis:
| Pattern | Recommendation |
|---|
| >50% of reps below 80% attainment | "This is a systemic issue, not individual. Review: targets too high? Market shifted? Product gaps? Pipeline generation insufficient?" |
| One rep significantly underperforming | "[Rep] is at X% attainment with [specific gap]. Schedule a 1:1 this week to [specific coaching action]." |
| Team activity below targets | "Activity is X% below target across the team. Re-establish daily rhythm: [specific cadence]. Consider shared accountability (leaderboard, daily standups)." |
| High activity + low conversion | "Activity volume is there but conversion is low. This is a skills issue, not an effort issue. Focus coaching on [discovery/closing/qualification] — the bottleneck is at [stage]." |
| Win rate declining period-over-period | "Win rate dropped from X% to Y%. Top new loss reason: [Z]. Investigate: competitor move? Product gap? Positioning drift?" |
| Pipeline coverage <2x for multiple reps | "X reps have <2x pipeline coverage. They will not hit quota without immediate pipeline generation. Activate [outbound/referral/event] campaigns this week." |
| Forecast accuracy below 80% | "Forecast accuracy is [X%]. [Pattern: over-commit/sandbagging]. Implement: [weekly commit review / staged forecasting / deal inspection criteria]." |
| One rep crushing while others struggle | "[Rep] is at X% attainment. Study what they're doing differently: [observation from data]. Consider peer coaching or ride-alongs." |
| High competitive loss rate | "Lost X deals to [competitor] this period. Distribute updated battlecards. Run a team session on competitive positioning." |
| Long average cycle length | "Deals are taking X days to close vs. Y expected. Bottleneck is [stage]. Coach reps on [multi-threading / next-step discipline / urgency creation]." |
Output Contract
report: {
executive_summary: string # Markdown formatted
detailed_diagnostic: string # Markdown formatted
rep_scorecards: [
{
rep_name: string
status: "crushing" | "on_track" | "behind" | "at_risk"
strengths: string[]
gaps: string[]
coaching_action: string
}
]
recommendations: [
{
priority: "urgent" | "high" | "medium" | "systemic"
area: string
recommendation: string
data_point: string
expected_impact: string
}
]
}
Human Checkpoint
Present the executive summary first, then offer the detailed diagnostic:
[Executive Summary rendered]
---
Full detailed diagnostic is also available with:
- Rep-by-rep scorecards with strengths, gaps, and coaching recommendations
- Deal-level analysis (progression, stalled deals, velocity)
- Win/loss deep dive with competitive analysis
- Forecast accuracy breakdown
Coaching priorities this period:
| Rep | Priority | Gap | Recommended Action |
|-----|----------|-----|--------------------|
| ... | ... | ... | ... |
Want to see the full diagnostic? Or drill into a specific rep's scorecard?
Step 4: Export & Share (Optional)
Purpose: Save the report and optionally push it to the user's preferred location.
Process
Based on user preference:
| Destination | How |
|---|
| Markdown file | Save to clients/<client>/reports/sales-performance-review-{date}.md |
| Google Sheets | Export data tables (attainment, activity, win/loss) |
| Notion | Push to a Notion database page via Notion MCP |
| Slack | Send executive summary to a channel |
| Email | Send via agentmail |
| stdout | Just display it (default) |
Execution Summary
| Step | Tool Dependency | Human Checkpoint | Typical Time |
|---|
| 0. Config | None | First run only | 5 min (once) |
| 1. Pull Data | Configurable (CRM API, CSV, Supabase, etc.) | Verify data looks correct | 2-3 min |
| 2. Analyze | None (computation + LLM reasoning) | None — feeds directly to report | Automatic |
| 3. Generate Report | None (LLM reasoning) | Review executive summary, drill into reps | 10-15 min |
| 4. Export | Configurable (file, Sheets, Notion, etc.) | Optional | 1 min |
Total human review time: ~15-20 minutes for a full sales performance review that would normally take 1-2 hours of CRM digging and spreadsheet building.
Adapting to Data Availability
Not every team tracks every metric. The analysis degrades gracefully:
| Missing Data | What Gets Skipped | Report Still Useful? |
|---|
activity data | Activity analysis (Analysis 2), activity-based coaching | Yes — quota attainment, win/loss, pipeline analysis still run |
deal amounts | Revenue metrics, quota attainment, deal size analysis | Partially — deal count analysis and win/loss still work |
forecast data | Forecast accuracy (Analysis 6) | Yes — everything else still runs |
loss reasons | Loss reason breakdown | Yes — win/loss rate and stage analysis still work |
source data | Source-level win rate analysis | Yes — aggregate metrics still run |
comparison period | Period-over-period trends, trend arrows | Yes — single period analysis still produces full report |
activity targets | vs. target comparisons | Yes — rep-to-rep benchmarking still works |
Minimum viable data for a useful report: Rep names + deals closed (with amounts) + quota targets. Everything else enriches but isn't required.
Cadence Guide
| Review Type | Period | Audience | Focus |
|---|
| Weekly standup | Last 7 days | Sales team | Activity, deal updates, stuck deals, this week's priorities |
| Monthly review | Last 30 days | Sales leader | Full diagnostic: attainment, activity, coaching priorities |
| Quarterly review | Last 90 days | VP Sales / Founder | Trends, forecast accuracy, team composition, strategic decisions |
| Annual review | Last 12 months | Leadership | Rep performance trajectories, hiring/firing decisions, quota planning |
The report depth automatically scales with the period length. A weekly review emphasizes activity and deal movement. A quarterly review emphasizes attainment trends, coaching ROI, and forecast reliability.
Tips
- Run this on a consistent cadence. The value compounds. A monthly review reveals trends. A quarterly review reveals who's improving and who's plateauing. Without consistency, you're always flying blind.
- Don't just look at quota attainment. A rep hitting quota through one large deal is different from a rep hitting quota through consistent execution. The underlying metrics tell you who is sustainably performing.
- Activity metrics without conversion context are useless. "Jordan made 500 calls" sounds impressive until you see 0 meetings booked. Always pair activity with outcomes.
- The coaching section is the ROI. The numbers tell you WHAT is happening. The coaching priorities tell you WHAT TO DO about it. A performance review without coaching actions is just a report card.
- Watch for the "middle" reps. Top performers and bottom performers get attention. The reps at 70-90% attainment often have the highest coaching ROI — they're close enough that fixing one skill gap puts them over the line.
- Forecast accuracy is a team discipline, not individual talent. If everyone's forecasts are off, it's a process problem. Implement deal inspection criteria: "What has to be true for this deal to close this period?"
- Compare rep performance within segments, not across them. An SMB AE closing $40K/month and an Enterprise AE closing $40K/month are having very different quarters. Always normalize for role and territory.
- Use the trend data. A rep at 60% attainment who was at 40% last month is improving. A rep at 90% who was at 120% last month is declining. Trajectory matters more than a single snapshot.
