performance-report

Anthropic ｜ 行銷與 SEO ｜ 原文 ｜ Apache-2.0

整理行銷指標、趨勢和優化建議，產出成效報告

---
name: performance-report
description: 製作行銷成效報告，包含關鍵指標、趨勢分析、成效與落差，以及排好優先順序的優化建議。在活動收尾、為利害關係人準備每週／每月／每季的通路摘要，或需要把資料轉成附下一期優先事項的主管摘要時使用。
argument-hint: "<time period or campaign>"
---

# 成效報告（Performance Report）

> 如果看到不熟的佔位符，或需要確認目前連了哪些工具，請見 [CONNECTORS.md](../../CONNECTORS.md)。

產生行銷成效報告，包含關鍵指標、趨勢分析、洞察和優化建議。

## 觸發

使用者執行 `/performance-report`，或要求行銷報告、成效分析、活動結果或指標摘要。

## 輸入

1. **報告類型**：判斷使用者需要哪一種報告：
   - **活動報告（Campaign report）**：某個特定活動的成效
   - **通路報告（Channel report）**：某個特定通路（email、社群、付費、SEO 等）的整體成效
   - **內容成效（Content performance）**：各內容的表現如何
   - **整體行銷報告（Overall marketing report）**：跨通路摘要（每週、每月、每季）
   - **自訂（Custom）**：使用者自訂的範圍

2. **時間區間**：報告的涵蓋期間（上週、上個月、上一季、自訂日期範圍）

3. **資料來源**：
   - 如果 ~~marketing analytics 已連接，先找出有哪些帳號和平台可用，再自動拉取成效資料
   - 如果 ~~product analytics 已連接：自動拉取成效資料
   - 如果沒連接：請使用者提供指標。提示語：「Please paste or share your performance data. I can work with spreadsheets, CSV data, dashboard screenshots described in text, or just the key numbers.」

4. **比較期間**（選填）：前一期或年對年，提供趨勢脈絡

5. **利害關係人受眾**（選填）：誰會讀這份報告（主管摘要風格 vs. 詳細的分析師視角）

## 報告結構

### 1. 主管摘要
- 用 2 到 3 句話概述該期間的成效
- 重點指標，附趨勢方向（跟前一期比上升／下降／持平）
- 一個關鍵的成果，和一個需要關注的地方

### 2. 關鍵指標 Dashboard

用摘要表呈現核心指標：

| Metric | This Period | Prior Period | Change | Target | Status |
|--------|------------|--------------|--------|--------|--------|

狀態指示：
- On track（達標或超越目標）
- At risk（低於目標但在可接受範圍內）
- Off track（明顯低於目標）

#### 依報告類型的指標

**活動報告：**
- 曝光與觸及
- 點擊率（CTR）
- 轉換率
- 每次獲客成本（CPA）
- 廣告投資報酬率（ROAS）或 ROI
- 總轉換數／註冊數／名單數

**通路報告（Email）：**
- 寄出、送達、退信的 email 數
- 開信率
- 點擊率
- 退訂率
- 轉換率

**通路報告（社群）：**
- 曝光與觸及
- 互動率（按讚、留言、分享）
- 追蹤者成長
- 點擊率
- 表現最好的貼文

**通路報告（付費）：**
- 花費
- 曝光與點擊
- CTR
- CPC 與 CPM
- 轉換與 CPA
- ROAS

**通路報告（SEO／自然流量）：**
- 自然搜尋工作階段（sessions）
- 關鍵字排名（變動）
- 已索引的頁面數
- 新獲得的反向連結
- 表現最好的頁面

**內容成效：**
- 頁面瀏覽量與不重複訪客
- 頁面停留時間
- 跳出率
- 社群分享
- 歸因於內容的轉換
- 表現最好和最差的內容

**整體行銷報告：**
- 產生的總名單數
- 行銷合格名單（MQL）
- 對 pipeline 的貢獻
- 客戶獲取成本（CAC）
- 逐通路摘要

### 3. 趨勢分析
- 該期間的成效趨勢（週對週或月對月）
- 值得注意的轉折點及成因
- 觀察到的季節性或週期性模式
- 與標竿或目標比較

### 4. 有效的做法
- 前 3 到 5 個成果，附具體資料
- 這些為什麼表現好（假設）
- 如何複製或擴大

### 5. 需要改進的地方
- 後 3 到 5 個表現不佳的項目，附具體資料
- 表現不佳的假設
- 建議的修正

### 6. 洞察與觀察
- 光看指標看不出來的資料模式
- 受眾行為洞察
- 引起共鳴的內容或創意主題
- 可能影響成效的外部因素（季節性、新聞、競爭對手動作）

### 7. 建議
每一項建議：
- 要做什麼
- 為什麼（連結到資料中的某個具體洞察）
- 預期影響（高、中、低）
- 實作所需的努力（高、中、低）
- 優先順序（立即、下一個 sprint、下一季）

用 2x2 矩陣格式排列建議的優先順序：

| | Low Effort | High Effort |
|---|---|---|
| **High Impact** | Do first | Plan for next sprint |
| **Low Impact** | Do if time allows | Deprioritize |

### 8. 下一期的重點
- 接下來這一期的前 3 個優先事項
- 要執行的測試或實驗
- 關鍵指標的目標

## 指標定義與標竿

### Email 行銷

| 指標 | 定義 | 標竿範圍 | 它告訴你什麼 |
|--------|-----------|----------------|-------------------|
| 送達率 | 送達的 email / 寄出的 email | 95-99% | 名單健康度與寄件者信譽 |
| 開信率 | 不重複開信數 / 送達的 email | 15-30% | 主旨和寄件者的效果 |
| 點擊率（CTR） | 不重複點擊數 / 送達的 email | 2-5% | 內容相關性與 CTA 效果 |
| 開信點擊率（CTOR） | 不重複點擊數 / 不重複開信數 | 10-20% | Email 內容品質（針對有開信的人） |
| 退訂率 | 退訂數 / 送達的 email | <0.5% | 內容與受眾的契合度及寄送頻率的容忍度 |
| 退信率 | 退信數 / 寄出的 email | <2% | 名單品質與資料整潔度 |
| 轉換率 | 轉換數 / 送達的 email | 1-5% | Email 端到端的效果 |
| 每封 email 營收 | 總營收 / 寄出的 email | 不一定 | 直接的營收歸因 |
| 名單成長率 | （新訂閱者 - 退訂者）/ 總名單 | 每月 2-5% | 受眾經營的健康度 |

### 社群媒體

| 指標 | 定義 | 它告訴你什麼 |
|--------|-----------|-------------------|
| 曝光（Impressions） | 內容被顯示的次數 | 內容的分發與觸及 |
| 觸及（Reach） | 看過內容的不重複使用者數 | 受眾廣度 |
| 互動率 | （按讚 + 留言 + 分享）/ 觸及 | 內容引起的共鳴 |
| 點擊率 | 連結點擊數 / 曝光 | 導流的效果 |
| 追蹤者成長率 | 每期淨新增追蹤者 / 總追蹤者 | 受眾經營 |
| 分享／轉貼率 | 分享數 / 觸及 | 內容的病毒傳播力與擁護度 |
| 影片觀看率 | 觀看數 / 曝光 | 影片內容開頭鉤子的效果 |
| 影片完播率 | 完整觀看數 / 總觀看數 | 影片內容品質與長度是否合適 |
| 社群聲量占比 | 你的提及數 / 該類別總提及數 | 品牌相對於競爭對手的能見度 |

### 付費廣告（搜尋與社群）

| 指標 | 定義 | 它告訴你什麼 |
|--------|-----------|-------------------|
| 曝光 | 廣告被顯示的次數 | 預算使用情況與目標對象的廣度 |
| 點擊率（CTR） | 點擊數 / 曝光 | 廣告創意與目標對象的相關性 |
| 每次點擊成本（CPC） | 總花費 / 點擊數 | 導流的成本效率 |
| 每千次曝光成本（CPM） | 每 1,000 次曝光的成本 | 知名度的成本效率 |
| 轉換率 | 轉換數 / 點擊數 | 登陸頁與優惠的效果 |
| 每次獲客成本（CPA） | 總花費 / 轉換數 | 全漏斗的成本效率 |
| 廣告投資報酬率（ROAS） | 營收 / 廣告花費 | 創造營收的效率 |
| 品質分數（搜尋） | Google 的相關性評分（1-10） | 廣告、關鍵字與登陸頁的一致性 |
| 頻率（Frequency） | 使用者平均看到廣告的次數 | 廣告疲乏的風險 |
| 觀看後轉換（View-through conversions） | 看過但沒點擊的使用者所產生的轉換 | 展示型／知名度活動的影響力 |

### SEO／自然搜尋

| 指標 | 定義 | 它告訴你什麼 |
|--------|-----------|-------------------|
| 自然搜尋工作階段 | 來自自然搜尋的造訪 | SEO 效果與內容觸及 |
| 關鍵字排名 | 目標關鍵字的名次 | 搜尋能見度 |
| 自然搜尋 CTR | 搜尋結果中的點擊 / 曝光 | 標題與 meta description 的效果 |
| 已索引頁面 | 搜尋索引中的頁面數 | 可爬取性與網站健康度 |
| Domain authority | 第三方的權威分數 | 網站整體實力 |
| 反向連結 | 連結到你的外部網站數量 | 內容權威度與站外 SEO |
| 頁面載入速度 | 可互動的時間 | 使用者體驗與排名因素 |
| 自然搜尋轉換率 | 自然搜尋轉換數 / 自然搜尋工作階段 | 內容品質與搜尋意圖的契合度 |
| 熱門進站頁面 | 從自然搜尋被造訪最多的頁面 | 帶來最多自然流量的內容 |

### 內容行銷

| 指標 | 定義 | 它告訴你什麼 |
|--------|-----------|-------------------|
| 頁面瀏覽量 | 內容頁面的總瀏覽次數 | 內容的觸及與分發 |
| 不重複訪客 | 瀏覽內容的不同使用者 | 受眾規模 |
| 平均頁面停留時間 | 在內容頁面上花的時間 | 內容的參與度與深度 |
| 跳出率 | 單頁工作階段 / 總工作階段 | 內容與受眾的契合度和 UX |
| 捲動深度 | 使用者在頁面上捲動多遠 | 對整篇內容的參與度 |
| 社群分享 | 內容在社群上被分享的次數 | 內容引起的共鳴與病毒傳播力 |
| 獲得的反向連結 | 連到內容的外部連結 | 內容權威度與 SEO 價值 |
| 名單產生 | 歸因於內容的名單 | 內容的轉換效果 |
| 內容 ROI | 歸因的營收 / 內容製作成本 | 內容投資的整體報酬 |

### 整體行銷／Pipeline

| 指標 | 定義 | 它告訴你什麼 |
|--------|-----------|-------------------|
| 行銷合格名單（MQL） | 符合行銷資格標準的名單 | 漏斗頂端的效果 |
| 銷售合格名單（SQL） | 被業務接受的 MQL | 名單品質 |
| MQL 轉 SQL 轉換率 | SQL / MQL | 行銷與業務的對齊程度及名單品質 |
| 產生的 pipeline | 所建立商機的金額 | 行銷對營收的影響 |
| Pipeline 速度 | 案子在 pipeline 中移動的速度 | 活動的急迫性與品質 |
| 客戶獲取成本（CAC） | 總行銷加業務成本 / 新客戶數 | 獲客的效率 |
| CAC 回收期 | 從營收回收 CAC 所需的月數 | 單位經濟（unit economics）的健康度 |
| 行銷來源營收 | 來自行銷發起的案子的營收 | 行銷的直接貢獻 |
| 行銷影響營收 | 行銷有接觸過的案子的營收 | 行銷更廣泛的影響 |

## 依頻率分類的報告範本

### 每週行銷報告
給團隊站會快速掃過的格式：
- **前 3 個指標**，附週對週變化
- 本週**有效的做法**（1 到 2 個重點，附資料）
- **需要關注的地方**（1 到 2 個重點，附資料）
- **本週優先事項**（3 到 5 個行動項目）

### 每月行銷報告
標準的利害關係人報告：
1. 主管摘要（3 到 5 句）
2. 關鍵指標 dashboard（表格，附月對月和目標比較）
3. 逐通路的成效摘要
4. 活動亮點與結果
5. 哪些有效、哪些沒效（附假設）
6. 建議與下個月的優先事項
7. 預算花費 vs. 計畫

### 季度業務檢視（QBR）
給管理層的策略檢視：
1. 本季成效 vs. 目標
2. 年初至今的軌跡
3. 通路 ROI 分析
4. 活動成效摘要
5. 競爭與市場觀察
6. 下一季的策略建議
7. 預算申請與分配計畫
8. 關鍵實驗與學到的事

### Dashboard 設計原則
- 以對應到商業目標的指標領頭（不是虛榮指標）
- 呈現隨時間的趨勢，不要只有單一時間點的快照
- 納入比較脈絡：前一期、目標、標竿
- 使用一致的顏色編碼：綠色（on track）、黃色（at risk）、紅色（off track）
- 依漏斗階段或商業問題為指標分組
- Dashboard 維持在一頁／一個畫面，細節放進附錄
- 更新頻率要配合決策頻率（付費是即時，內容是每週）

## 趨勢分析與預測

### 辨識趨勢
分析成效資料時，要找：

1. **方向性趨勢**：這個指標在 4 期以上是否持續上升、下降或持平？
2. **轉折點**：成效在哪裡改變方向，當時發生了什麼？
3. **季節性**：依星期幾、月份或季度是否有可預測的模式？
4. **異常**：一次性的暴增或暴跌，成因是什麼，會不會重現？
5. **領先指標**：哪些指標先動，並能預測未來結果？

### 趨勢分析流程
1. 把指標隨時間畫成圖（至少 8 到 12 個資料點才算有意義的趨勢）
2. 找出整體方向（向上、向下、持平、週期性）
3. 計算變化速率（是加速還是減速？）
4. 疊上重要事件（推出的活動、產品變更、市場事件）
5. 跟標竿或目標比較
6. 找出與其他指標的相關性
7. 對因果關係提出假設（並規劃測試去驗證）

### 簡單的預測方法
- **線性外推**：把目前的趨勢線往前延伸（適用於穩定的指標）
- **移動平均**：平均最近 3 到 6 期，把雜訊平滑掉
- **年對年比較**：用去年的模式當基準，再依成長率調整
- **漏斗運算**：從輸入預測輸出（例如，如果我們以 Y 的轉換率產生 X 個名單，就會得到 Z 位客戶）
- **情境建模**：建立最佳、預期和最差三種情況的推估

### 預測的注意事項
- 短期預測（1 到 3 個月）比長期預測可靠
- 根據少於 12 個資料點做的預測，要標示為低信心
- 外部因素（市場變化、競爭對手動作、經濟變動）可能讓依趨勢做的預測失效
- 預測一律以區間呈現，不要給精確數字

## 歸因建模基礎

### 什麼是歸因？
歸因（Attribution）決定哪些行銷接觸點該得到轉換的功勞。這很重要，因為買家在轉換之前，通常會接觸多個通路。

### 常見的歸因模型

| 模型 | 運作方式 | 最適合 | 限制 |
|-------|-------------|----------|------------|
| 最後接觸（Last touch） | 100% 功勞給轉換前的最後一次互動 | 了解最終的轉換觸發點 | 忽略知名度和培養 |
| 首次接觸（First touch） | 100% 功勞給第一次互動 | 了解漏斗頂端的效果 | 忽略培養和轉換的推動力 |
| 線性（Linear） | 所有接觸點平均分配功勞 | 公平呈現所有通路 | 無法反映相對影響力 |
| 時間衰減（Time decay） | 越接近轉換的接觸點得到越多功勞 | 偏重近期互動的平衡視角 | 可能低估知名度的貢獻 |
| 位置式（U 型） | 40% 給首次、40% 給最後、20% 分給中間 | 同時重視發現與轉換 | 權重有些武斷 |
| 資料驅動（Data-driven） | 依轉換模式用演算法分配功勞 | 最準確的呈現 | 需要大量資料 |

### 歸因的實務指引
- 如果還沒有任何模型，就從最後接觸歸因開始，它最簡單，也最有可行動性
- 比較首次接觸和最後接觸，了解哪些通路帶動知名度、哪些帶動轉換
- 對大多數 B2B 公司，位置式（U 型）是一個合理的折衷
- 資料驅動歸因需要很高的轉換量，才具有統計意義
- 沒有任何模型是完美的，歸因要當作方向參考，不要當作絕對真理
- 多點接觸歸因比單點接觸好，但任何模型都比沒有好

### 歸因的陷阱
- 不要根據單點接觸歸因，孤立地優化某一個通路
- 知名度通路（展示廣告、社群、公關）在最後接觸模型裡永遠看起來很差
- 轉換通路（搜尋、再行銷）在首次接觸模型裡永遠看起來很差
- 自述式歸因（「你是怎麼知道我們的？」）能提供有用的質性色彩，但作為量化資料並不可靠
- 跨裝置和跨通路追蹤的缺口，代表歸因資料永遠是不完整的

## 優化建議框架

### 優化流程
1. **辨識**：哪些指標低於目標或標竿？
2. **診斷**：問題出在漏斗的哪裡？（曝光、點擊、轉換、留存）
3. **假設**：是什麼造成表現不佳？（受眾、訊息、創意、優惠、時機、技術）
4. **排序**：哪些修正能以最少的努力產生最大的影響？
5. **測試**：設計實驗來驗證假設
6. **衡量**：這個改動有改善指標嗎？
7. **擴大或迭代**：有效的廣泛推行；沒定論或失敗的測試就迭代

### 依漏斗階段的優化槓桿

| 漏斗階段 | 問題訊號 | 優化槓桿 |
|-------------|---------------|---------------------|
| 知名度（Awareness） | 低曝光、低觸及 | 預算、目標對象、通路組合、創意形式 |
| 興趣（Interest） | 低 CTR、低互動 | 廣告創意、標題、內容鉤子、受眾定位 |
| 考慮（Consideration） | 高跳出率、低頁面停留時間 | 登陸頁內容、頁面速度、內容相關性、UX |
| 轉換（Conversion） | 低轉換率 | 優惠、CTA、表單長度、信任訊號、頁面版面 |
| 留存（Retention） | 高流失、低重複互動 | 引導、email 培養、產品體驗、客服支援 |

### 測試的最佳實務
- 一次只測一個變數，結果才乾淨
- 在啟動測試之前先定義成功指標
- 開始之前先算好需要的樣本數（不要提早結束測試）
- 測試至少跑滿一個完整的商業週期（B2B 通常是一週）
- 無論結果如何，都要記錄所有測試和結果
- 在團隊內分享學到的事，失敗的測試是有價值的資訊
- 確認現狀的測試不算失敗，它會增加你對目前做法的信心

### 持續優化的節奏
- **每天**：監控付費活動的預算進度、異常和被拒的廣告
- **每週**：檢視通路成效，暫停表現差的，擴大表現好的
- **每兩週**：更新廣告創意並測試新的變體
- **每月**：完整的成效檢視，找出新的優化機會，更新預測
- **每季**：策略性檢視通路組合、預算分配和目標對象策略

## 輸出格式

- 資料用表格呈現
- 關鍵數字和趨勢用粗體
- 主管摘要要精簡（適合轉寄給管理層）
- 如果使用者提供了大量指標，加入一個「詳細附錄」區段放細部資料

## 報告之後

問：「Would you like me to:
- Create a slide-ready summary of these results?
- Draft a stakeholder email with the key takeaways?
- Dive deeper into any specific metric or channel?
- Set up a reporting template you can reuse next period?」

---
name: performance-report
description: Build a marketing performance report with key metrics, trend analysis, wins and misses, and prioritized optimization recommendations. Use when wrapping a campaign, when preparing weekly, monthly, or quarterly channel summaries for stakeholders, or when you need data translated into an executive summary with next-period priorities.
argument-hint: "<time period or campaign>"
---

# Performance Report

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).

Generate a marketing performance report with key metrics, trend analysis, insights, and optimization recommendations.

## Trigger

User runs `/performance-report` or asks for a marketing report, performance analysis, campaign results, or metrics summary.

## Inputs

1. **Report type** — determine which type of report the user needs:
   - **Campaign report** — performance of a specific campaign
   - **Channel report** — performance across a specific channel (email, social, paid, SEO, etc.)
   - **Content performance** — how content pieces are performing
   - **Overall marketing report** — cross-channel summary (weekly, monthly, quarterly)
   - **Custom** — user-defined scope

2. **Time period** — the reporting window (last week, last month, last quarter, custom date range)

3. **Data source**:
   - If ~~marketing analytics is connected, discover what accounts and platforms are available, then pull performance data automatically
   - If ~~product analytics is connected: pull performance data automatically
   - If not connected: ask the user to provide metrics. Prompt with: "Please paste or share your performance data. I can work with spreadsheets, CSV data, dashboard screenshots described in text, or just the key numbers."

4. **Comparison period** (optional) — prior period or year-over-year for trend context

5. **Stakeholder audience** (optional) — who will read this report (executive summary style vs. detailed analyst view)

## Report Structure

### 1. Executive Summary
- 2-3 sentence overview of performance in the period
- Headline metric with trend direction (up/down/flat vs. prior period)
- One key win and one area of concern

### 2. Key Metrics Dashboard

Present core metrics in a summary table:

| Metric | This Period | Prior Period | Change | Target | Status |
|--------|------------|--------------|--------|--------|--------|

Status indicators:
- On track (meeting or exceeding target)
- At risk (below target but within acceptable range)
- Off track (significantly below target)

#### Metrics by Report Type

**Campaign Report:**
- Impressions and reach
- Click-through rate (CTR)
- Conversion rate
- Cost per acquisition (CPA)
- Return on ad spend (ROAS) or ROI
- Total conversions/signups/leads

**Channel Report (Email):**
- Emails sent, delivered, bounced
- Open rate
- Click-through rate
- Unsubscribe rate
- Conversion rate

**Channel Report (Social):**
- Impressions and reach
- Engagement rate (likes, comments, shares)
- Follower growth
- Click-through rate
- Top-performing posts

**Channel Report (Paid):**
- Spend
- Impressions and clicks
- CTR
- CPC and CPM
- Conversions and CPA
- ROAS

**Channel Report (SEO/Organic):**
- Organic sessions
- Keyword rankings (movement)
- Pages indexed
- Backlinks acquired
- Top-performing pages

**Content Performance:**
- Pageviews and unique visitors
- Time on page
- Bounce rate
- Social shares
- Conversions attributed to content
- Top and bottom performers

**Overall Marketing Report:**
- Total leads generated
- Marketing qualified leads (MQLs)
- Pipeline contribution
- Customer acquisition cost (CAC)
- Channel-by-channel summary

### 3. Trend Analysis
- Performance trend over the period (week-over-week or month-over-month)
- Notable inflection points and what caused them
- Seasonal or cyclical patterns observed
- Comparison to benchmarks or targets

### 4. What Worked
- Top 3-5 wins with specific data
- Why these performed well (hypothesis)
- How to replicate or scale

### 5. What Needs Improvement
- Bottom 3-5 performers with specific data
- Hypotheses for underperformance
- Recommended fixes

### 6. Insights and Observations
- Patterns in the data that are not obvious from the metrics alone
- Audience behavior insights
- Content or creative themes that resonated
- External factors that may have influenced performance (seasonality, news, competitive moves)

### 7. Recommendations
For each recommendation:
- What to do
- Why (linked to a specific insight from the data)
- Expected impact (high, medium, low)
- Effort to implement (high, medium, low)
- Priority (immediate, next sprint, next quarter)

Prioritize recommendations in a 2x2 matrix format:

| | Low Effort | High Effort |
|---|---|---|
| **High Impact** | Do first | Plan for next sprint |
| **Low Impact** | Do if time allows | Deprioritize |

### 8. Next Period Focus
- Top 3 priorities for the upcoming period
- Tests or experiments to run
- Targets for key metrics

## Metric Definitions and Benchmarks

### Email Marketing

| Metric | Definition | Benchmark Range | What It Tells You |
|--------|-----------|----------------|-------------------|
| Delivery rate | Emails delivered / emails sent | 95-99% | List health and sender reputation |
| Open rate | Unique opens / emails delivered | 15-30% | Subject line and sender effectiveness |
| Click-through rate (CTR) | Unique clicks / emails delivered | 2-5% | Content relevance and CTA effectiveness |
| Click-to-open rate (CTOR) | Unique clicks / unique opens | 10-20% | Email content quality (for those who opened) |
| Unsubscribe rate | Unsubscribes / emails delivered | <0.5% | Content-audience fit and frequency tolerance |
| Bounce rate | Bounces / emails sent | <2% | List quality and data hygiene |
| Conversion rate | Conversions / emails delivered | 1-5% | End-to-end email effectiveness |
| Revenue per email | Total revenue / emails sent | Varies | Direct revenue attribution |
| List growth rate | (New subscribers - unsubscribes) / total list | 2-5% monthly | Audience building health |

### Social Media

| Metric | Definition | What It Tells You |
|--------|-----------|-------------------|
| Impressions | Number of times content was displayed | Content distribution and reach |
| Reach | Number of unique users who saw content | Audience breadth |
| Engagement rate | (Likes + comments + shares) / reach | Content resonance |
| Click-through rate | Link clicks / impressions | Traffic driving effectiveness |
| Follower growth rate | Net new followers / total followers per period | Audience building |
| Share/Repost rate | Shares / reach | Content virality and advocacy |
| Video view rate | Views / impressions | Video content hook effectiveness |
| Video completion rate | Completed views / total views | Video content quality and length fit |
| Social share of voice | Your mentions / total category mentions | Brand visibility vs. competitors |

### Paid Advertising (Search and Social)

| Metric | Definition | What It Tells You |
|--------|-----------|-------------------|
| Impressions | Times ad was shown | Budget utilization and targeting breadth |
| Click-through rate (CTR) | Clicks / impressions | Ad creative and targeting relevance |
| Cost per click (CPC) | Total spend / clicks | Cost efficiency of traffic generation |
| Cost per mille (CPM) | Cost per 1,000 impressions | Awareness cost efficiency |
| Conversion rate | Conversions / clicks | Landing page and offer effectiveness |
| Cost per acquisition (CPA) | Total spend / conversions | Full-funnel cost efficiency |
| Return on ad spend (ROAS) | Revenue / ad spend | Revenue generation efficiency |
| Quality Score (search) | Google's relevance rating (1-10) | Ad-keyword-landing page alignment |
| Frequency | Average times a user sees the ad | Ad fatigue risk |
| View-through conversions | Conversions from users who saw but did not click | Display/awareness campaign influence |

### SEO / Organic Search

| Metric | Definition | What It Tells You |
|--------|-----------|-------------------|
| Organic sessions | Visits from organic search | SEO effectiveness and content reach |
| Keyword rankings | Position for target keywords | Search visibility |
| Organic CTR | Clicks / impressions in search results | Title and meta description effectiveness |
| Pages indexed | Number of pages in search index | Crawlability and site health |
| Domain authority | Third-party authority score | Overall site strength |
| Backlinks | Number of external sites linking to you | Content authority and off-page SEO |
| Page load speed | Time to interactive | User experience and ranking factor |
| Organic conversion rate | Organic conversions / organic sessions | Content quality and intent alignment |
| Top entry pages | Most-visited pages from organic search | Content driving the most organic traffic |

### Content Marketing

| Metric | Definition | What It Tells You |
|--------|-----------|-------------------|
| Pageviews | Total views of content pages | Content reach and distribution |
| Unique visitors | Distinct users viewing content | Audience size |
| Average time on page | Time spent on content pages | Content engagement and depth |
| Bounce rate | Single-page sessions / total sessions | Content-audience fit and UX |
| Scroll depth | How far users scroll on a page | Content engagement through the piece |
| Social shares | Times content was shared on social | Content resonance and virality |
| Backlinks earned | External links to content | Content authority and SEO value |
| Lead generation | Leads attributed to content | Content conversion effectiveness |
| Content ROI | Revenue attributed / content production cost | Overall content investment return |

### Overall Marketing / Pipeline

| Metric | Definition | What It Tells You |
|--------|-----------|-------------------|
| Marketing qualified leads (MQLs) | Leads meeting marketing qualification criteria | Top-of-funnel effectiveness |
| Sales qualified leads (SQLs) | MQLs accepted by sales | Lead quality |
| MQL to SQL conversion rate | SQLs / MQLs | Marketing-sales alignment and lead quality |
| Pipeline generated | Dollar value of opportunities created | Marketing impact on revenue |
| Pipeline velocity | How fast deals move through pipeline | Campaign urgency and quality |
| Customer acquisition cost (CAC) | Total marketing + sales cost / new customers | Efficiency of customer acquisition |
| CAC payback period | Months to recover CAC from revenue | Unit economics health |
| Marketing-sourced revenue | Revenue from marketing-originated deals | Direct marketing contribution |
| Marketing-influenced revenue | Revenue from deals where marketing touched | Broader marketing impact |

## Reporting Templates by Cadence

### Weekly Marketing Report
Quick-scan format for team standups:
- **Top 3 metrics** with week-over-week change
- **What worked** this week (1-2 bullet points with data)
- **What needs attention** (1-2 bullet points with data)
- **This week's priorities** (3-5 action items)

### Monthly Marketing Report
Standard stakeholder report:
1. Executive summary (3-5 sentences)
2. Key metrics dashboard (table with MoM and target comparison)
3. Channel-by-channel performance summary
4. Campaign highlights and results
5. What worked and what did not (with hypotheses)
6. Recommendations and next month priorities
7. Budget spend vs. plan

### Quarterly Business Review (QBR)
Strategic review for leadership:
1. Quarter performance vs. goals
2. Year-to-date trajectory
3. Channel ROI analysis
4. Campaign performance summary
5. Competitive and market observations
6. Strategic recommendations for next quarter
7. Budget request and allocation plan
8. Key experiments and learnings

### Dashboard Design Principles
- Lead with the metrics that map to business objectives (not vanity metrics)
- Show trends over time, not just point-in-time snapshots
- Include comparison context: prior period, target, benchmark
- Use consistent color coding: green (on track), yellow (at risk), red (off track)
- Group metrics by funnel stage or business question
- Keep dashboards to one page/screen — detail goes in appendix
- Update cadence should match decision cadence (real-time for paid, weekly for content)

## Trend Analysis and Forecasting

### Trend Identification
When analyzing performance data, look for:

1. **Directional trends**: is the metric consistently going up, down, or flat over 4+ periods?
2. **Inflection points**: where did performance change direction and what happened then?
3. **Seasonality**: are there predictable patterns by day of week, month, or quarter?
4. **Anomalies**: one-time spikes or drops — what caused them and are they repeatable?
5. **Leading indicators**: which metrics change first and predict future outcomes?

### Trend Analysis Process
1. Chart the metric over time (at least 8-12 data points for meaningful trends)
2. Identify the overall direction (upward, downward, flat, cyclical)
3. Calculate the rate of change (is it accelerating or decelerating?)
4. Overlay key events (campaigns launched, product changes, market events)
5. Compare to benchmarks or targets
6. Identify correlations with other metrics
7. Form hypotheses about causation (and plan tests to validate)

### Simple Forecasting Approaches
- **Linear projection**: extend the current trend line forward (useful for stable metrics)
- **Moving average**: smooth out noise by averaging the last 3-6 periods
- **Year-over-year comparison**: use last year's pattern as a baseline, adjusted for growth rate
- **Funnel math**: forecast outputs from inputs (e.g., if we generate X leads at Y conversion rate, we will get Z customers)
- **Scenario modeling**: create best case, expected case, and worst case projections

### Forecasting Caveats
- Short-term forecasts (1-3 months) are more reliable than long-term
- Forecasts based on fewer than 12 data points should be flagged as low confidence
- External factors (market shifts, competitive moves, economic changes) can invalidate trend-based forecasts
- Always present forecasts as ranges, not exact numbers

## Attribution Modeling Basics

### What Is Attribution?
Attribution determines which marketing touchpoints get credit for a conversion. This matters because buyers typically interact with multiple channels before converting.

### Common Attribution Models

| Model | How It Works | Best For | Limitation |
|-------|-------------|----------|------------|
| Last touch | 100% credit to last interaction before conversion | Understanding final conversion triggers | Ignores awareness and nurture |
| First touch | 100% credit to first interaction | Understanding top-of-funnel effectiveness | Ignores nurture and conversion drivers |
| Linear | Equal credit to all touchpoints | Fair representation of all channels | Does not reflect relative impact |
| Time decay | More credit to touchpoints closer to conversion | Balanced view favoring recent interactions | May undervalue awareness |
| Position-based (U-shaped) | 40% first, 40% last, 20% split among middle | Valuing both discovery and conversion | Somewhat arbitrary weighting |
| Data-driven | Algorithmic credit based on conversion patterns | Most accurate representation | Requires significant data volume |

### Attribution Practical Guidance
- Start with last-touch attribution if you have no model in place — it is the simplest and most actionable
- Compare first-touch and last-touch to understand which channels drive awareness vs. conversion
- Use position-based (U-shaped) as a reasonable middle ground for most B2B companies
- Data-driven attribution requires high conversion volume to be statistically meaningful
- No model is perfect — use attribution directionally, not as absolute truth
- Multi-touch attribution is better than single-touch, but any model is better than none

### Attribution Pitfalls
- Do not optimize one channel in isolation based on single-touch attribution
- Awareness channels (display, social, PR) will always look bad in last-touch models
- Conversion channels (search, retargeting) will always look bad in first-touch models
- Self-reported attribution ("how did you hear about us?") provides useful qualitative color but is unreliable as quantitative data
- Cross-device and cross-channel tracking gaps mean attribution data is always incomplete

## Optimization Recommendations Framework

### Optimization Process
1. **Identify**: which metrics are underperforming vs. target or benchmark?
2. **Diagnose**: where in the funnel is the problem? (impressions, clicks, conversions, retention)
3. **Hypothesize**: what is causing the underperformance? (audience, message, creative, offer, timing, technical)
4. **Prioritize**: which fixes will have the biggest impact with the least effort?
5. **Test**: design an experiment to validate the hypothesis
6. **Measure**: did the change improve the metric?
7. **Scale or iterate**: roll out wins broadly; iterate on inconclusive or failed tests

### Optimization Levers by Funnel Stage

| Funnel Stage | Problem Signal | Optimization Levers |
|-------------|---------------|---------------------|
| Awareness | Low impressions, low reach | Budget, targeting, channel mix, creative format |
| Interest | Low CTR, low engagement | Ad creative, headlines, content hooks, audience targeting |
| Consideration | High bounce rate, low time on page | Landing page content, page speed, content relevance, UX |
| Conversion | Low conversion rate | Offer, CTA, form length, trust signals, page layout |
| Retention | High churn, low repeat engagement | Onboarding, email nurture, product experience, support |

### Testing Best Practices
- Test one variable at a time for clean results
- Define the success metric before launching the test
- Calculate required sample size before starting (do not end tests early)
- Run tests for a minimum of one full business cycle (typically one week for B2B)
- Document all tests and results, regardless of outcome
- Share learnings across the team — failed tests are valuable information
- A test that confirms the status quo is not a failure — it builds confidence in your current approach

### Continuous Optimization Cadence
- **Daily**: monitor paid campaigns for budget pacing, anomalies, and disapproved ads
- **Weekly**: review channel performance, pause underperformers, scale winners
- **Bi-weekly**: refresh ad creative and test new variants
- **Monthly**: full performance review, identify new optimization opportunities, update forecasts
- **Quarterly**: strategic review of channel mix, budget allocation, and targeting strategy

## Output Formatting

- Use tables for data presentation
- Bold key numbers and trends
- Keep the executive summary concise (suitable for forwarding to leadership)
- Include a "detailed appendix" section for granular data if the user provided a lot of metrics

## After the Report

Ask: "Would you like me to:
- Create a slide-ready summary of these results?
- Draft a stakeholder email with the key takeaways?
- Dive deeper into any specific metric or channel?
- Set up a reporting template you can reuse next period?"