Skip to main content

analyzing-marketing-campaign

Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.

الانتقال إلى التثبيت

معلومات المصدر

المستودع
shunwuyu/ai_lesson
آخر نشاط في المصدر
٢٩ مايو ٢٠٢٦ في ٠٩:١٩
لغة SKILL.md المكتشفة
الإنجليزية
النجوم
٨
التفرعات
٥

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

مستكشف الملفات
3 ملفات

عرض SKILL.md

SKILL.md
تعليمات المصدر · معاينة للقراءة فقط
name
analyzing-marketing-campaign
description
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
input_file
campaign_data_week1.csv
allowed-tools
["Read","Grep","Glob","Write"]
# Marketing Campaign Analysis Automated analysis of multi-channel marketing campaign data. ## Input Requirements Expects campaign data in CSV format with these columns: - **date**: Campaign date - **campaign_name**: Campaign identifier - **channel**: Marketing channel - **segment**: Customer segment - **impressions**: Ad impressions (empty for Email channel) - **clicks**: Number of clicks - **conversions**: Number of conversions - **spend**: Marketing spend in dollars - **revenue**: Revenue generated in dollars - **orders**: Number of orders Default Input: This skill assumes input data is provided in campaign_data_week1.csv located in the root directory. The file must conform to the column schema below. ## Data Quality Check 1. Check for missing values and empty cells (Email channel won't have impressions) 2. Verify no negative values in numeric columns 3. Flag anomalies (e.g., conversions without clicks) ## Funnel Analysis Calculate per channel: - **Click Through Rate (CTR)** = clicks / impressions × 100 - **Conversion Rate (CVR)** = conversions / clicks × 100 Compare to user-provided benchmarks, report difference in percentage points and provide brief interpretation for each channel. If benchmarks are not provided, use these historical values: | Channel | CTR | CVR | |---------|-----|-----| | Facebook_Ads | 2.5% | 3.8% | | Google_Ads | 5.0% | 4.5% | | TikTok_Ads | 2.0% | 0.9% | | Email | 15.0% | 2.1% | ## Efficiency Analysis Calculate per channel: - **Return On Ad Spend (ROAS)** = revenue / spend - **Cost Per Acquisition (CPA)** = spend / conversions - **Net Profit** = revenue - Total Costs - Total Costs = spend + (orders × Shipping Cost) + (revenue × Product Cost %) - Unless user specifies different values, use: - **Shipping Cost**: $8 per order - **Product Cost**: 35% of revenue Compare to user-provided targets. If not provided, use these defaults: - **Target ROAS**: 4.0x minimum - **Max CPA**: $50 ## Output Format Present results as tables with status indicators: **Funnel Analysis Table:** | Channel | CTR Actual | CTR Benchmark | CTR Diff | CVR Actual | CVR Benchmark | CVR Diff | **Efficiency Analysis Table:** | Channel | ROAS | Status | CPA | Status | Net Profit | Status | Status indicators: - ROAS: "[OK] Above" if >= target, "[X] Below" if < target - CPA: "[OK] Below" if <= max, "[X] Above" if > max - Net Profit: "[OK] Positive" if > 0, "[X] Negative" if <= 0 Follow each table with brief channel-by-channel interpretation highlighting key insights and recommended actions. ## Budget Reallocation If user asks about budget reallocation, read `references/budget_reallocation_rules.md` for the complete decision framework including eligibility rules, performance-based actions, and constraints.
عرض على GitHub