/13-data-analysis-global

name: 13-data-analysis-global
description: Turn raw marketing data into actionable insight — analysis by channel, campaign, creative, audience, time. Descriptive → Diagnostic → Predictive → Prescriptive.
metadata:
  version: 2.5.0
  category: performance
  language: en
triggers:
  - "data analysis"
  - "analyze data"
  - "marketing analytics"
  - "Meta Ads analysis"
  - "TikTok Ads analysis"
  - "GA4 report"
  - "performance analysis"
  - "Triple Whale"
  - "Hyros"
  - "Northbeam"
output: A .md report structured as Descriptive, Diagnostic, Predictive, Prescriptive — with tables and concrete recommendations
related:
  - 03-performance-review-global
  - 07-marketing-report-global
  - 10-reverse-kpi-calc-global
  - 12-landing-page-brief-global

Marketing Data Analysis (Global)

> Insight before numbers. Lead with judgment, illustrate with data — never list numbers without interpretation.

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Information Gathering

Ask up to 4 questions:

1. **Data source?** Meta Ads, TikTok Ads, GA4, Shopify, Triple Whale/Hyros/Northbeam, Google Sheets — single source or combined? 2. **Time window?** This week, this month, A vs B (e.g. March vs April)? 3. **Current business goal?** Increase leads, lower CPL, raise ROAS, or a specific issue to fix? 4. **Paste data here** — drop a table, or describe core metrics (spend, impressions, clicks, leads, revenue).

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Analysis Principles

Reading Order

1. DESCRIPTIVE   — What happened? (numbers, trends)
2. DIAGNOSTIC    — Why? (root cause)
3. PREDICTIVE    — What's next? (forecast)
4. PRESCRIPTIVE  — What to do? (concrete actions)

Presentation Rules

| Rule | Explanation | |------|-------------| | Insight first, numbers second | "CPL up 40% due to creative fatigue" — not "CPL went from $5 to $7" | | Compare, don't quote absolutes | Always compare with: prior week (WoW), prior month (MoM), or industry benchmark | | Flag anomalies | Any metric moving > 20% vs prior period → flag for investigation | | Recommendations have deadlines | Each recommendation specifies: action, when, owner, success metric |

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Analysis Frameworks by Source

Meta Ads

| Level | Primary metrics | Secondary metrics | |-------|-----------------|-------------------| | Account | Spend, ROAS, CPA | Frequency, Reach | | Campaign | CPM, CPL, Conv rate | Budget utilization | | Ad Set | CPC, CTR, CPM | Audience size, overlap | | Ad (Creative) | Hook rate (3s view), Hold rate, CTR | Engagement rate, save rate |

**Reading Meta Ads:**

High spend + low impressions → CPM high → audience too narrow or auction-pressured
High impressions + low clicks → CTR low → creative not compelling
High clicks + low leads → LP problem or form too long
High leads + low bookings → poor lead quality or weak nurture

TikTok Ads

| Level | Primary metrics | Secondary metrics | |-------|-----------------|-------------------| | Account | Spend, CPA, ROAS | Total impressions | | Campaign | CPM, Cost per result | Campaign type performance | | Ad Group | CPC, CTR, Conv rate | Audience size, age/gender split | | Ad (Video) | 2s view rate, 6s view rate, completion rate | Like, comment, share |

**Reading TikTok Ads:**

2s view rate low → weak hook — first 3 seconds aren't strong enough
6s view rate low → losing attention after the hook
Completion rate low + CTR low → video doesn't drive action
CPV high → wrong audience, or video doesn't fit TikTok format

Google Analytics 4

| Metric group | Metric | Meaning | |--------------|--------|---------| | Acquisition | Users, Sessions, Source/Medium | Traffic origin | | Engagement | Engagement rate, Time on page, Pages/session | Traffic quality | | Conversion | Conv rate, Events (form submit, click CTA) | Conversion effectiveness | | Retention | Returning users, User retention | Stickiness |

**Reading GA4:**

Traffic up + engagement down → low-quality traffic, filter sources
Traffic up + conversions down → LP problem or wrong-intent traffic
Bounce rate high (>70%) on one page → mismatch with ad copy or slow load

E-commerce Attribution Tools (Dropshipping/DTC)

For dropshipping or DTC stores, native ad-platform metrics often diverge from real revenue. Use one of these:

| Tool | Best for | Key feature | |------|----------|-------------| | **Triple Whale** | Shopify DTC | Pixel-based attribution, blended ROAS, AI insights | | **Hyros** | Info products + DTC | Server-side tracking, long-window attribution | | **Northbeam** | High-spend DTC ($100K+/mo) | MTA + MMM, incrementality testing | | **Polar Analytics** | Mid-market DTC | All-in-one dashboards, source-of-truth tracking | | **Wicked Reports** | Email-heavy DTC | Multi-touch attribution including email |

**Cross-checking:** when Meta reports 5x ROAS but Shopify reports 2x ROAS, trust the platform-of-record (Shopify). The gap is usually iOS 14+ attribution loss.

Spreadsheet Data (Manual)

When user pastes data from a sheet:

1. Identify core columns: date, channel, spend, units (impressions/clicks/leads/orders), revenue 2. Compute derived metrics: CPL, CPA, ROAS, conversion rate 3. Sort by time to surface trends 4. Group by channel/campaign for comparison

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Trend Detection

Week over Week (WoW)

| Metric | Prior week | This week | Change | Status | |--------|-----------|-----------|--------|--------| | [Metric] | [Value] | [Value] | [+/- %] | [Normal / Watch / Alert] |

**Alert thresholds:**

• 10–20% change → monitor, no action yet
• 20–40% change → investigate, prepare a response
• > 40% change → act now

Month over Month (MoM)

| Metric | Prior month | This month | Change | vs Industry benchmark | |--------|------------|-----------|--------|----------------------| | [Metric] | [Value] | [Value] | [+/- %] | [Above/Below industry avg] |

Seasonality (Global)

| Period | Impact | Adjustment | |--------|--------|-----------| | Q4 holiday (US: Black Friday → Christmas) | CPM +30–50%, conversion up | Increase budget; book inventory ea