sales-performance-review（销售业绩回顾）

Sales Performance Review

The all-up periodic report. Every other analytics composite looks at one slice — a single campaign's emails, or the pipeline's current state. This one answers the question a founder or sales leader asks in every team meeting: "What did we do this period, and did it move the needle?"

It inventories every sales initiative, measures each one's contribution to pipeline and revenue, compares them against each other, and produces a presentation-ready report that the team can review together.

What makes this different from pipeline-review and sequence-performance:

This composite can call sequence-performance for deeper campaign-level diagnosis and pipeline-review for pipeline state — but its primary job is the cross-initiative view.

When to Auto-Load

Load this composite when:

• User says "sales review", "periodic review", "what did we do this month", "team performance report", "sales standup report"
• User says "prepare for the weekly/monthly/quarterly sales meeting"
• User asks "what's working", "where should we focus", "are our initiatives paying off"
• End of period (weekly, biweekly, monthly, quarterly) review is due

Step 0: Configuration (One-Time Setup)

On first run, collect and store these preferences. Skip on subsequent runs.

Data Sources

Common active_channels:

active_channels: [
  "outbound_email",        # Cold email campaigns
  "outbound_linkedin",     # LinkedIn outreach
  "inbound_content",       # Blog, SEO, content marketing
  "inbound_website",       # Website conversions, demo requests
  "events",                # Conferences, webinars, meetups
  "referrals",             # Partner/customer referrals
  "paid_ads",              # Google Ads, LinkedIn Ads, Meta Ads
  "social_organic",        # LinkedIn posts, Twitter, community
  "partnerships",          # Co-selling, integrations, channel
  "product_led",           # Free trial, freemium, PLG
  "warm_outbound"          # Re-engagement, nurture, expansion
]

Team & Goals

Targets structure:

targets: {
  meetings_booked: integer | null
  qualified_opportunities: integer | null
  pipeline_created: number | null       # Dollar value
  deals_closed: integer | null
  revenue_closed: number | null
  other: [                              # Custom KPIs
    { name: string, target: number }
  ]
}

Report audience options:

• "team" — Sales team standup. Tactical, action-oriented. "What do we do next week?"
• "leadership" — VP/Founder review. Strategic, trend-focused. "Are we on track? Where to invest?"
• "board" — Board/investor update. High-level, metric-heavy. "Is the GTM motion working?"

Initiative Categories

Define the categories of initiatives the team runs. This creates the taxonomy for the report.

initiative_categories: [
  {
    name: "Outbound Email Campaigns"
    data_source: "smartlead"           # or "instantly", "outreach", etc.
    metrics_available: ["sent", "opens", "replies", "meetings"]
  },
  {
    name: "LinkedIn Outreach"
    data_source: "manual_tracking"     # or "dripify", "expandi"
    metrics_available: ["connections_sent", "accepted", "replies", "meetings"]
  },
  {
    name: "Events & Webinars"
    data_source: "spreadsheet"
    metrics_available: ["attended", "leads_captured", "meetings", "pipeline"]
  },
  {
    name: "Inbound / Content"
    data_source: "hubspot"             # or "website_analytics"
    metrics_available: ["visitors", "leads", "mqls", "meetings"]
  },
  {
    name: "Referrals & Partnerships"
    data_source: "crm"
    metrics_available: ["intros", "meetings", "pipeline", "closed"]
  }
]

Store config in: clients/<client-name>/config/sales-performance-review.json or equivalent.

Step 1: Inventory All Initiatives

Purpose: Build a complete inventory of every sales initiative executed during the review period. Nothing should be missing from the report.

Input Contract

period: {
  type: "weekly" | "fortnightly" | "monthly" | "quarterly" | "custom"
  start_date: string
  end_date: string
  comparison_period: boolean          # Default: true
}
initiative_categories: [...]          # From config
data_sources: { ... }                 # From config

Process

For each initiative category, pull the list of specific initiatives run during the period:

A) Outbound Email Campaigns

Pull from configured outreach tool:

For each campaign, capture:

• Campaign name
• Launch date
• Status (active / paused / completed)
• Target audience description
• Total leads loaded
• Current metrics snapshot (sent, opens, replies — detailed analysis comes in Step 3)

B) LinkedIn Outreach

Pull from LinkedIn automation tool or manual tracking:

• Campaign/sequence name
• Connection requests sent
• Messages sent
• Period of activity

C) Events & Webinars

Pull from tracking spreadsheet, CRM, or user provides:

• Event name
• Date
• Type (conference, webinar, meetup, hosted event)
• Attendees/leads captured
• Follow-up status

D) Inbound / Content

Pull from website analytics, CRM, or marketing tool:

• Content published (blog posts, case studies, whitepapers)
• Website traffic / lead form submissions
• Demo requests
• MQL count

E) Referrals & Partnerships

Pull from CRM or manual tracking:

• Referral sources (who referred)
• Intro count
• Meeting count

F) Any Other Initiatives

Ask the user: "Were there any other sales initiatives this period not captured above?"

• Ad campaigns launched
• Product-led growth experiments
• New channel tests
• One-off outreach efforts (conference follow-up blast, etc.)

Output Contract

initiative_inventory: [
  {
    id: string                         # Generated identifier
    name: string                       # Initiative name
    category: string                   # Which category it belongs to
    channel: string                    # "email", "linkedin", "event", "inbound", "referral", etc.
    launch_date: string
    status: "active" | "paused" | "completed"
    description: string                # 1-2 sentence summary of what this initiative was
    target_audience: string            # Who it was aimed at
    data_source: string                # Where the data came from
    raw_data_available: boolean        # Can we pull detailed metrics?
  }
]

Human Checkpoint

## Initiative Inventory — [Period]

Found X initiatives across Y categories:

| # | Initiative | Category | Channel | Launched | Status |
|---|-----------|----------|---------|----------|--------|
| 1 | Q1 Series A Outbound | Outbound Email | Email | Jan 15 | Active |
| 2 | LinkedIn ABM Campaign | LinkedIn Outreach | LinkedIn | Jan 20 | Active |
| 3 | SaaStr Conference | Event | In-person | Feb 5 | Completed |
| 4 | Blog: "State of X" report | Inbound Content | Content | Feb 10 | Active |
| 5 | Partner intro program w/ Acme | Referral | Partner | Jan 1 | Active |

Missing anything? Any initiatives not captured here?

Step 2: Pull Initiative-Level Metrics

Purpose: For each initiative in the inventory, pull the detailed performance metrics. This is the data collection step — no analysis yet.

Input Contract

initiative_inventory: [...]           # From Step 1
period: { ... }
outreach_tools: [...]                 # From config
crm_tool: string                     # From config

Process

For each initiative, pull metrics appropriate to its channel:

Outbound Email Metrics

LinkedIn Outreach Metrics

Event Metrics

Inbound / Content Metrics

Referral / Partnership Metrics

Pipeline Attribution

Critical step: Connect initiatives to pipeline and revenue. For each deal in the CRM created during the period:

• What initiative sourced it? (first touch attribution)
• What initiative influenced it? (multi-touch, if trackable)
• Current stage
• Dollar value

This creates the bridge between "what we did" and "what it produced."

Attribution methods (use the best available):

Output Contract

initiative_metrics: [
  {
    id: string                         # Matches inventory
    name: string
    category: string
    channel: string
    metrics: {
      # Activity metrics (what we did)
      activity_volume: integer          # Emails sent, connections sent, leads captured, etc.
      activity_label: string            # "emails sent", "connections sent", etc.

      # Response metrics (what happened)
      responses: integer                # Replies, accepts, form fills, etc.
      response_rate: percentage
      response_label: string            # "replies", "accepted", "submitted", etc.

      # Meeting metrics (pipeline entry)
      meetings_booked: integer
      meeting_rate: percentage           # Meetings / activity_volume
      meetings_qualified: integer | null
      qualification_rate: percentage | null

      # Pipeline metrics (business impact)
      pipeline_created: number | null    # Dollar value
      deals_created: integer | null
      avg_deal_size: number | null

      # Revenue metrics (if deals closed)
      deals_won: integer | null
      revenue_closed: number | null

      # Efficiency metrics
      cost: number | null                # If cost data available
      cost_per_meeting: number | null
      cost_per_pipeline_dollar: number | null
      roi: number | null                 # Revenue / cost

      # Comparison period
      vs_prior: {
        activity_change: percentage
        response_rate_change: percentage
        meetings_change: percentage
        pipeline_change: percentage
      } | null
    }
  }
]

Human Checkpoint

## Metrics Pulled

| Initiative | Activity | Responses | Meetings | Pipeline | Revenue |
|-----------|----------|-----------|----------|----------|---------|
| Q1 Series A Outbound | 2,500 sent | 125 replies (5%) | 18 | $240K | $0 |
| LinkedIn ABM | 300 requests | 180 accepted (60%) | 8 | $120K | $45K |
| SaaStr Conference | 150 leads | 45 follow-ups | 12 | $180K | $0 |
| Blog content | 5 posts | 28 leads | 4 | $50K | $0 |
| Partner intros | 15 intros | 12 meetings | 12 | $200K | $80K |

Pipeline attribution notes:
- X deals could not be attributed to a specific initiative
- Y deals have multi-touch attribution (counted in primary source)

Data look accurate? Any corrections?

Step 3: Analyze Initiative Performance

Purpose: Score each initiative, compare them against each other, and identify what's working, what's not, and where the gaps are. Pure LLM reasoning + computation.

Input Contract

initiative_metrics: [...]             # From Step 2
targets: { ... }                      # From config
team_members: [...]                   # From config
period: { ... }

Analysis Sections

A) Initiative Scorecards

For each initiative, produce a scorecard:

Initiative: [Name]
Category: [Category] | Channel: [Channel]
Status: [Active/Paused/Completed]

Activity:    [X sent/captured/etc.]    [+/-Y% vs prior]
Response:    [X replies/accepts]       [Z% rate] [above/at/below benchmark]
Meetings:    [X booked]               [Z% conversion]
Pipeline:    $[X]                     [+/-Y% vs prior]
Revenue:     $[X]                     [if applicable]
Efficiency:  $[X] cost per meeting    [if cost data available]

Grade: [A/B/C/D/F]
Verdict: [One sentence — "Strong performer, scale up" / "Underperforming, diagnose copy" / etc.]

Grading criteria:

B) Cross-Initiative Comparison

Rank all initiatives on consistent metrics:

C) Funnel Analysis (Full Funnel View)

Map the complete funnel across ALL initiatives combined:

Total activity → Total responses → Total meetings → Qualified → Pipeline → Won
[X emails+      [Y replies+       [Z meetings]    [W qual]    [$P]      [$R]
 connections+    accepts+
 leads+etc.]     leads]

Conversion rates at each stage:
Activity → Response: X%
Response → Meeting: X%
Meeting → Qualified: X%
Qualified → Pipeline: X%
Pipeline → Won: X%

D) Gap Analysis

Compare actuals to targets:

If behind on any target:

• Which initiatives need to increase output?
• How much incremental activity is needed to close the gap?
• At current conversion rates, how many more [emails/events/intros] to hit the target?

E) Effort vs. Impact Matrix

Classify each initiative into a 2x2:

                    HIGH IMPACT
                        │
    SCALE UP            │           OPTIMIZE
    (High impact,       │       (High impact,
     low effort)        │        high effort)
                        │
────────────────────────┼─────────────────────
                        │
    MAINTAIN            │           QUESTION
    (Low impact,        │       (Low impact,
     low effort)        │        high effort)
                        │
                    LOW IMPACT

• Scale Up: High pipeline/meetings relative to effort. Pour fuel on this fire.
• Optimize: Producing results but resource-intensive. Find ways to make it more efficient.
• Maintain: Low effort, modest results. Keep running, don't invest more.
• Question: High effort, low results. Diagnose why or shut it down.

F) Trend Analysis (If Comparison Period Available)

Output Contract

analysis: {
  initiative_scorecards: [
    {
      id: string
      name: string
      category: string
      grade: "A" | "B" | "C" | "D" | "F"
      verdict: string
      key_metrics_summary: string
    }
  ]

  cross_initiative_ranking: {
    by_meetings_per_dollar: [ { name: string, value: number } ] | null
    by_meeting_conversion: [ { name: string, value: percentage } ]
    by_pipeline_per_meeting: [ { name: string, value: number } ] | null
    by_win_rate: [ { name: string, value: percentage } ] | null
    overall_best_performer: string
    overall_worst_performer: string
  }

  funnel: {
    total_activity: integer
    total_responses: integer
    total_meetings: integer
    total_qualified: integer | null
    total_pipeline: number | null
    total_won: number | null
    stage_conversions: [ { from: string, to: string, rate: percentage } ]
    biggest_drop_off: string          # Which stage loses the most
  }

  gap_analysis: {
    targets_summary: [ { metric: string, target: number, actual: number, gap: number, status: string } ]
    behind_on: string[]               # Which targets are behind
    incremental_needed: string        # "Need X more meetings from Y channel to close the gap"
  } | null

  effort_impact_matrix: {
    scale_up: [ { name: string, reason: string } ]
    optimize: [ { name: string, reason: string } ]
    maintain: [ { name: string, reason: string } ]
    question: [ { name: string, reason: string } ]
  }

  trends: {
    overall_direction: "improving" | "stable" | "declining"
    notable_shifts: string[]
  } | null
}

Step 4: Generate Insights & Recommendations

Purpose: Distill the analysis into actionable insights — what to double down on, what to fix, what to kill, and what to start. Pure LLM reasoning.

Process

A) What's Working (Double Down)

Identify initiatives or patterns that are clearly producing results:

B) What's Not Working (Fix or Kill)

C) What's Missing (Start)

D) Resource Allocation Recommendation

Based on the effort-impact analysis, recommend how to allocate the team's time next period:

Recommended time allocation (next period):
- [Channel A]: Increase from X% → Y% of team time (reason)
- [Channel B]: Maintain at X% (reason)
- [Channel C]: Reduce from X% → Y% or eliminate (reason)
- [New Channel]: Allocate Z% for testing (reason)

Output Contract

insights: {
  double_down: [
    { initiative: string, recommendation: string, expected_impact: string, data_point: string }
  ]
  fix_or_kill: [
    { initiative: string, recommendation: string, deadline: string, data_point: string }
  ]
  start: [
    { recommendation: string, rationale: string, effort_estimate: string }
  ]
  resource_allocation: {
    current: [ { channel: string, effort_percentage: percentage } ]
    recommended: [ { channel: string, effort_percentage: percentage, change: string, reason: string } ]
  }
  key_risks: string[]                 # Top 3 risks to next period's targets
}

Step 5: Generate Report

Purpose: Produce a presentation-ready report that the team can review together. Two formats: the full detailed report and a slide-ready summary.

Input Contract

initiative_inventory: [...]           # From Step 1
initiative_metrics: [...]             # From Step 2
analysis: { ... }                     # From Step 3
insights: { ... }                     # From Step 4
targets: { ... }                      # From config
report_audience: string               # From config
period: { ... }

Report Structure

Adapt depth and tone based on report_audience:

# Sales Performance Review — [Period Type]: [Start Date] to [End Date]

## Executive Summary

**Period overview:** [2-3 sentences: what happened, headline result]

### Scorecard
| Metric | Target | Actual | Status | Trend |
|--------|--------|--------|--------|-------|
| Meetings booked | X | Y | [ahead/behind] | [+/-Z% vs prior] |
| Qualified meetings | X | Y | [ahead/behind] | [+/-Z%] |
| Pipeline created | $X | $Y | [ahead/behind] | [+/-Z%] |
| Revenue closed | $X | $Y | [ahead/behind] | [+/-Z%] |

### Headline Insights
1. **Best performer:** [Initiative] — [one-line result with key number]
2. **Biggest concern:** [Issue] — [one-line description]
3. **Biggest opportunity:** [What to do next] — [expected impact]

---

## Initiative Performance

### Overview
| # | Initiative | Channel | Grade | Meetings | Pipeline | Verdict |
|---|-----------|---------|-------|----------|----------|---------|
| 1 | [name] | Email | A | 18 | $240K | Scale up |
| 2 | [name] | LinkedIn | B | 8 | $120K | Optimize |
| 3 | [name] | Event | B+ | 12 | $180K | Repeat |
| 4 | [name] | Content | C | 4 | $50K | Needs time |
| 5 | [name] | Referral | A | 12 | $200K | Invest more |

### Detailed Scorecards

#### [Initiative 1 Name] — Grade: [X]
**What it is:** [1-sentence description]
**Target audience:** [Who]

| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| [Activity] | X | — | — |
| Response rate | X% | Y% | [above/below] |
| Meetings booked | X | — | — |
| Meeting conversion | X% | Y% | [above/below] |
| Pipeline created | $X | — | — |
| Cost per meeting | $X | — | [if available] |

**What worked:** [Specific things that performed well]
**What didn't:** [Specific issues]
**Recommendation:** [Scale / Optimize / Fix / Kill]

[Repeat for each initiative]

---

## Cross-Initiative Comparison

### Efficiency Rankings
| Rank | Initiative | Meetings Booked | Meeting Conversion | Pipeline per Meeting | Cost per Meeting |
|------|-----------|----------------|-------------------|---------------------|-----------------|
| 1 | [name] | X | Y% | $Z | $W |
| 2 | [name] | X | Y% | $Z | $W |
| ... |

### Effort vs. Impact Matrix

SCALE UP (high impact, efficient): • [Initiative] — [why] • [Initiative] — [why]

OPTIMIZE (high impact, resource-heavy): • [Initiative] — [why]

MAINTAIN (low effort, moderate results): • [Initiative] — [why]

QUESTION (high effort, low results): • [Initiative] — [why]

---

## Full Funnel Analysis

### Aggregate Funnel

[Total Activity] → [Responses] → [Meetings] → [Qualified] → [Pipeline] → [Won] X,XXX XXX XX XX $XXX K $XX K X.X% X.X% XX% XX% XX%

### Funnel by Channel
| Channel | Activity | → Response | → Meeting | → Qualified | → Pipeline | → Won |
|---------|----------|-----------|-----------|-------------|-----------|-------|
| Email | X | Y% | Z% | W% | $V | $U |
| LinkedIn | X | Y% | Z% | W% | $V | $U |
| Events | X | Y% | Z% | W% | $V | $U |
| Inbound | X | Y% | Z% | W% | $V | $U |
| Referral | X | Y% | Z% | W% | $V | $U |

**Biggest drop-off:** [Stage] — [X% of pipeline is lost here. Why and what to do.]

---

## Gap Analysis (vs. Targets)

| Target | Goal | Actual | Gap | To Close the Gap |
|--------|------|--------|-----|-----------------|
| Meetings | X | Y | -Z | Need Z more from [best-converting channel] |
| Pipeline | $X | $Y | -$Z | At avg deal size of $W, need Z more qualified meetings |
| Revenue | $X | $Y | -$Z | Need to close X more deals from current pipeline |

**Path to target:** [Specific plan — "If we increase [channel] volume by X% and maintain current conversion, we close the gap in Y weeks."]

---

## Trends (vs. Prior Period)

| Metric | Prior Period | Current Period | Change | Signal |
|--------|-------------|---------------|--------|--------|
| Total meetings | X | Y | +/-Z% | [Improving/Declining] |
| Qualification rate | X% | Y% | +/-Z pts | [context] |
| Pipeline created | $X | $Y | +/-Z% | [context] |
| Best channel | [channel] | [channel] | [Shifted?] | [context] |
| Avg deal size | $X | $Y | +/-Z% | [context] |

[Commentary on what the trends mean]

---

## What's Working (Double Down)

1. **[Initiative/Channel]** — [Evidence with numbers]. Recommendation: [Specific action + expected impact].
2. **[Initiative/Channel]** — [Evidence]. Recommendation: [Action].
3. **[Pattern]** — [Evidence]. Recommendation: [Action].

## What's Not Working (Fix or Kill)

1. **[Initiative/Channel]** — [Evidence]. Recommendation: [Fix plan with deadline OR kill decision].
2. **[Initiative/Channel]** — [Evidence]. Recommendation: [Action].

## What's Missing (Start)

1. **[New initiative]** — [Rationale]. Expected effort: [X]. Expected impact: [Y].

---

## Recommended Resource Allocation (Next Period)

| Channel | Current Effort | Recommended | Change | Reason |
|---------|---------------|-------------|--------|--------|
| Outbound email | X% | Y% | [+/-Z%] | [reason] |
| LinkedIn | X% | Y% | [+/-Z%] | [reason] |
| Events | X% | Y% | [+/-Z%] | [reason] |
| Content/Inbound | X% | Y% | [+/-Z%] | [reason] |
| Referrals | X% | Y% | [+/-Z%] | [reason] |

---

## Action Items

### This Week
| # | Action | Owner | Deadline | Expected Impact |
|---|--------|-------|----------|----------------|
| 1 | [action] | [name] | [date] | [impact] |
| 2 | [action] | [name] | [date] | [impact] |

### This Month
| # | Action | Owner | Expected Impact |
|---|--------|-------|----------------|
| 1 | [action] | [name] | [impact] |

### Key Risks
1. [Risk with mitigation plan]
2. [Risk with mitigation plan]

Step 6: Export & Distribute

Purpose: Save and optionally distribute the report.

Output Options

Execution Summary

Total human review time: ~15-25 minutes for a report that would take 2-4 hours of manual data gathering, spreadsheet building, and analysis.

Cadence Guide

The report depth scales with period length. A weekly review focuses on activity and blockers. A quarterly review focuses on trends, ROI, and strategic allocation.

Tips

• Attribution is the hardest part. Don't let perfect be the enemy of good. If you can't do multi-touch attribution, first-touch is fine. If CRM source fields are empty, use email/name matching. Some attribution is infinitely better than none.
• Compare channels on the same metric. "We sent 2,500 emails and attended 1 conference" is meaningless. "Email produced 18 meetings at $0.50/meeting. The conference produced 12 meetings at $400/meeting" is actionable.
• The effort-impact matrix drives resource allocation. This is the most important visual in the report. It turns a complex multi-channel discussion into four simple quadrants.
• Include cost data when possible. Revenue numbers without cost data tell half the story. A $200K pipeline from referrals (free) is very different from $200K from events ($50K cost).
• Trend direction matters more than absolute numbers. A 3% reply rate that was 1.5% last month is great. A 5% reply rate that was 8% last month is concerning. Always show the trajectory.
• End every report with specific action items and owners. A beautiful report with no actions is a waste. Every insight should map to "who does what by when."
• Save historical reports. The real power comes from comparing Q1 review to Q2 to Q3. Trends across review periods reveal strategic shifts that no single report can show.