Marketing Data Analysis (Global)

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

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).

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 |

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

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 early; lock LPs | | Chinese New Year | Asia logistics paused, CPM +20% in APAC | Move launches before/after; warn customers about shipping | | Back-to-school (US: Aug; UK: Sep) | CPM +10–15% (education/electronics) | Plan from June | | Valentine's, Mother's Day, Father's Day | CPM +15–25% (gifting niches) | Run campaigns 1 week before | | Summer (Northern hemisphere: Jun–Aug) | CPM dips 10–15% in many verticals | Test creative, scale new channels | | Ramadan / Eid (varies by year) | MENA conversion shifts | Adjust tone, timing — engagement spikes after iftar |

Anomaly Detection (Decision Trees)

CPL Spike

CPL up
├── CTR down? → Creative fatigue → Refresh creative
├── CTR normal + Conv rate down? → LP issue
│   ├── Slow load? → Check PageSpeed
│   ├── Form broken? → Test form on mobile
│   └── Wrong intent traffic? → Audit audience targeting
└── CPM up? → Auction pressure or seasonality
    ├── Holiday / sale season? → Increase budget or pause
    └── Competitor spend up? → Switch audience or channel

ROAS Drop

ROAS down
├── Revenue down + spend flat? → Conversion problem
│   ├── Lead quality poor? → Check audience
│   ├── Sales team slow? → Check response time
│   └── Pricing changed? → Audit pricing
├── Revenue flat + spend up? → Over-spending
│   ├── Scaled too fast? → Reduce, max 20%/day increase
│   └── New channel not optimized? → Stop scaling, optimize first
└── Both down? → Systemic issue
    ├── Competitor running big promo? → Competitor scan
    └── Off-season? → Check seasonality

Engagement Drop

Engagement down
├── Reach down? → Algo de-prioritized
│   ├── Too many promo posts? → Increase educational/entertainment ratio
│   └── Posting too often? → Reduce frequency
├── Reach normal + ER down? → Content not compelling
│   ├── Stale format? → Try new formats (carousel, POV, duet)
│   └── Repetitive topics? → Rotate angles per content matrix
└── Reach up + ER down? → Wrong audience reaching

Cohort Analysis

Monthly Cohort Template

| Cohort (signup month) | Month 1 | Month 2 | Month 3 | Month 6 | Month 12 | |-----------------------|---------|---------|---------|---------|----------| | Jan 2026 (100 customers) | 100% | X%] active | X%] | X%] | X%] | | Feb 2026 (120 customers) | 100% | X%] | X%] | X%] | — | | Mar 2026 (95 customers) | 100% | X%] | X%] | — | — |

Reading:

• Steady decline across months → natural churn, build retention program
• Sharp drop in month 2 → bad first experience, fix onboarding
• Stable from month 3 → retention floor reached, focus on this segment

Cohort by Acquisition Source

| Source | Customers | CAC | LTV 90 days | LTV:CAC | |--------|-----------|-----|-------------|---------| | Meta Ads | X] | X] | X] | X:1] | | TikTok Ads | X] | X] | X] | X:1] | | Organic | X] | X] | X] | X:1] | | Referral | X] | X] | X] | X:1] | | Email | X] | X] | X] | X:1] |

Healthy LTV:CAC is generally 3:1 or better.

Attribution Models

Comparing 3 Models

| Model | How it credits | When to use | |-------|---------------|-------------| | Last Click | 100% to final touch | Default, simple, short funnels | | First Click | 100% to first touch | Evaluating TOFU/awareness channels | | Linear | Equal split across all touches | Long funnels, multi-channel, fair credit |

Attribution comparison template:

| Channel | Last Click | First Click | Linear | Note | |---------|-----------|-------------|--------|------| | Meta Ads | X orders] | X orders] | X orders] | Role: TOFU/BOFU?] | | TikTok Ads | X orders] | X orders] | X orders] | Role?] | | Google Search | X orders] | X orders] | X orders] | Role?] | | Organic | X orders] | X orders] | X orders] | Role?] | | Email | X orders] | X orders] | X orders] | Role?] |

Recommendations:

• Short funnel (1–3 days): Last Click works
• Medium funnel (7–14 days): use Linear
• Long funnel (30+ days): First Click for TOFU, Last Click for BOFU
• DTC/dropshipping at scale: switch to a dedicated tool (Triple Whale, Hyros, Northbeam)

Output Template

markdown
# Data Analysis Report — [Brand/Campaign]
Period: [Start] — [End]
Data sources: [Meta Ads / TikTok Ads / GA4 / Shopify / ...]
Analysis date: [YYYY-MM-DD]

---

## 1. Executive Summary

**3 most important insights:**
1. [Insight 1 — written as judgment, not raw numbers]
2. [Insight 2]
3. [Insight 3]

**Overall status:** [Green = stable | Yellow = monitor | Red = urgent action]

---

## 2. Descriptive — What happened?

### Top-line metrics

| Metric | This period | Prior period | Change | Industry benchmark | Status |
|--------|-------------|--------------|--------|--------------------|--------|
| Spend | [X] | [X] | [+/- %] | — | [icon] |
| Impressions | [X] | [X] | [+/- %] | — | [icon] |
| Clicks | [X] | [X] | [+/- %] | — | [icon] |
| CTR | [X%] | [X%] | [+/- %] | [X%] | [icon] |
| Leads | [X] | [X] | [+/- %] | — | [icon] |
| CPL | [X] | [X] | [+/- %] | [X] | [icon] |
| ROAS | [Xx] | [Xx] | [+/- %] | [Xx] | [icon] |

### Performance by channel

| Channel | Spend | Leads | CPL | ROAS | % of budget | Note |
|---------|-------|-------|-----|------|-------------|------|
| Meta Ads | [X] | [X] | [X] | [Xx] | [X%] | [1 sentence] |
| TikTok Ads | [X] | [X] | [X] | [Xx] | [X%] | [1 sentence] |
| Google Ads | [X] | [X] | [X] | [Xx] | [X%] | [1 sentence] |

### Top 5 campaigns

| Campaign | Spend | Leads | CPL | ROAS | Note |
|----------|-------|-------|-----|------|------|
| 1. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 2. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 3. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 4. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 5. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |

### Top 3 creatives

| Creative | Format | Hook rate | CTR | CPL | Days running | Note |
|----------|--------|-----------|-----|-----|--------------|------|
| 1. [Name/desc] | [Video/Image/Carousel] | [X%] | [X%] | [X] | [X days] | [1 sentence] |
| 2. [Name/desc] | [Format] | [X%] | [X%] | [X] | [X days] | [1 sentence] |
| 3. [Name/desc] | [Format] | [X%] | [X%] | [X] | [X days] | [1 sentence] |

---

## 3. Diagnostic — Why?

### What's working — why?
- [Cause 1 + supporting data]
- [Cause 2 + supporting data]

### What's not — why?
- [Cause 1 + supporting data + remedy]
- [Cause 2 + supporting data + remedy]

### Anomalies to investigate
- [Anomaly 1 — description + likely cause + investigation step]
- [Anomaly 2]

---

## 4. Predictive — Forecast

### Next period (3 scenarios)

| Metric | Bear | Base | Bull |
|--------|------|------|------|
| Spend | [X] | [X] | [X] |
| Leads | [X] | [X] | [X] |
| CPL | [X] | [X] | [X] |
| ROAS | [Xx] | [Xx] | [Xx] |
| Revenue | [X] | [X] | [X] |

### Forecast drivers
- [Driver 1: seasonality, competitor, algo change, ...]
- [Driver 2]

---

## 5. Prescriptive — Actions

### Act now (next 48h)
| # | Action | Owner | Deadline | Measure by |
|---|--------|-------|----------|------------|
| 1 | [Specific action] | [Role] | [Date] | [Metric] |
| 2 | [Specific action] | [Role] | [Date] | [Metric] |

### This week
| # | Action | Owner | Deadline | Measure by |
|---|--------|-------|----------|------------|
| 1 | [Specific action] | [Role] | [Date] | [Metric] |
| 2 | [Specific action] | [Role] | [Date] | [Metric] |

### This month
| # | Action | Owner | Deadline | Measure by |
|---|--------|-------|----------|------------|
| 1 | [Specific action] | [Role] | [Date] | [Metric] |
| 2 | [Specific action] | [Role] | [Date] | [Metric] |

Auto-Diagnostics

When analyzing, automatically check these conditions:

| Condition | Check | Action | |-----------|-------|--------| | CPL up > 30% WoW | Creative running > 14 days? Frequency > 3? | Refresh creative, rotate audience | | CTR < 0.8% | Strong 3s hook? Eye-catching imagery? | A/B test hooks, change opening frame | | ROAS < 2x for 7 days | Right audience? LP conv rate? | Narrow audience, audit LP | | LP conv rate < 3% | Load time? Form length? CTA clarity? | Trigger skill 12-landing-page-brief-global | | Frequency > 4 | Audience saturated | Expand audience or switch channel | | Spend < 70% of budget | Audience too narrow or bid too low | Expand audience, raise bid | | One channel > 60% spend | Single-channel dependency risk | Reallocate, test new channel |

Skill Cross-references

• 03-performance-review-global — broader marketing performance review
• 07-marketing-report-global — turn analysis into stakeholder-ready monthly/quarterly report
• 10-reverse-kpi-calc-global — recompute KPIs and budget from real data
• 12-landing-page-brief-global — when LP conversion is the bottleneck
• 05-ad-copy-global — when creative is the bottleneck
• 15-social-listening-global — add qualitative data (sentiment, trends) alongside quantitative

Quality Checklist

Before delivering the report

• ] Every insight has supporting data
• ] Every number is compared (WoW, MoM, or vs benchmark)
• ] Anomalies (> 20% change) flagged and explained
• ] Recommendations specify: owner, deadline, success metric
• ] Forecast includes 3 scenarios (bear, base, bull)
• ] No raw numbers without interpretation
• ] Source and time window are clearly stated
• ] Cross-checked: ad-platform spend matches actual spend
• ] For dropshipping/DTC: revenue cross-checked between Shopify and ad platform