13 data analysis 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 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

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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

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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.

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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)

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Output Template

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

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## 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]

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## 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] |

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## 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]

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## 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]

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## 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] |

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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

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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

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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