Analytics Frameworks Skill

Core Philosophy

"What gets measured gets managed. What gets managed poorly gets measured excessively."

Analytics should drive action, not just reporting. Focus on metrics that inform decisions, not vanity metrics that feel good but don't guide strategy.

Fundamental Principles

1. Metrics Hierarchy

North Star Metric
└── Primary KPIs (3-5 max)
    └── Secondary Metrics
        └── Diagnostic Metrics

For a publication like tacosdedatos:

North Star: Active Engaged Subscribers
├── Newsletter subscribers
├── Open rate (engagement quality)
├── Click rate (content value)
└── Organic traffic (reach)

2. Leading vs. Lagging Indicators

Type	Description	Examples
Leading	Predict future outcomes	Social engagement, email signups, page views
Lagging	Measure past outcomes	Revenue, churn, lifetime value

Focus on leading indicators for day-to-day decisions.

3. Actionable Metrics

A good metric should:

• Be tied to a specific action you can take
• Have a clear threshold for success
• Be comparable over time
• Not be easily gamed

Newsletter Analytics

Key Metrics

Metric	Calculation	Healthy Benchmark
Open Rate	Opens / Delivered × 100	40%+ (industry avg: 43%)
Click Rate (CTR)	Clicks / Delivered × 100	2-5%
Click-to-Open Rate (CTOR)	Clicks / Opens × 100	10-15%
Unsubscribe Rate	Unsubscribes / Delivered × 100	<0.5%
Bounce Rate	Bounces / Sent × 100	<2%
List Growth Rate	(New - Unsubscribes) / Total × 100	2-5%/month

Apple Mail Privacy Protection (MPP) Considerations

⚠️ Important: Since iOS 15 (2021), Apple MPP pre-loads email content,
artificially inflating open rates.

What this means:
- Open rates are now ~10-20% higher than actual
- Apple Mail is ~46% of email clients
- Focus on click-based metrics instead

Adjusted Strategy:
1. Primary metric: Click rate (not open rate)
2. Secondary: Click-to-open rate
3. Segment Apple Mail users separately
4. Use multiple data points for decisions

Reporting Cadence

Daily (Quick Check):

- Open rate vs. 7-day average
- Click rate
- Unsubscribes (any spikes?)
- Bounce rate

Weekly (Performance Review):

## Newsletter Performance: Week of [Date]

### Overview
| Metric | This Week | Last Week | Change |
|--------|-----------|-----------|--------|
| Emails Sent | X | Y | +/-% |
| Open Rate | X% | Y% | +/-% |
| Click Rate | X% | Y% | +/-% |
| Unsubscribes | X | Y | +/-% |

### Top Performing Links
1. [Link] - X clicks (X% CTR)
2. [Link] - X clicks
3. [Link] - X clicks

### Insights
- [What worked]
- [What didn't]
- [Experiment to try]

Monthly (Strategic Review):

## Monthly Newsletter Report: [Month Year]

### Subscriber Growth
- Starting: X
- Ending: Y
- Net Growth: +/-Z (X%)
- Sources: [Organic: X, Referral: Y, Paid: Z]

### Engagement Trends
[Chart showing open/click rates over time]

### Content Performance
| Edition | Subject Line | Open Rate | Click Rate |
|---------|--------------|-----------|------------|
| [Date] | [Subject] | X% | Y% |

### Referral Program Performance
- Active referrers: X
- New subscribers from referrals: Y
- Top referrers: [List]

### Recommendations
1. [Strategic recommendation]
2. [Content adjustment]
3. [Experiment proposal]

Website/Blog Analytics

Key Metrics

Metric	What It Measures	Target
Sessions	Site visits	Growth month-over-month
Unique Visitors	Individual users	Growth month-over-month
Pageviews	Pages viewed	Higher than sessions
Avg. Session Duration	Engagement depth	2+ minutes for articles
Bounce Rate	Single-page visits	<70% for blog content
Pages/Session	Content discovery	1.5+

Google Analytics 4 Key Reports

Acquisition:

Traffic Sources:
- Organic Search: SEO effectiveness
- Direct: Brand recognition
- Referral: Partnership/link value
- Social: Social media ROI
- Email: Newsletter impact

Engagement:

Key Events to Track:
- page_view
- scroll (25%, 50%, 75%, 90%)
- click (external links, CTAs)
- newsletter_signup
- social_share

Content Performance:

## Content Report: [Period]

### Top Content by Pageviews
| Page | Views | Avg. Time | Bounce |
|------|-------|-----------|--------|
| [Page] | X | X:XX | X% |

### Top Entry Pages
[Where users start their journey]

### Top Organic Landing Pages
[SEO performance]

### Underperforming Content
[Pages with high traffic but high bounce or low time]

UTM Tracking

Standard UTM Structure:

URL: https://tacosdedatos.com/tutorial/pandas-groupby

With UTMs:
?utm_source=[platform]
&utm_medium=[type]
&utm_campaign=[campaign-name]
&utm_content=[specific-link]

Examples:
Newsletter link:
?utm_source=newsletter&utm_medium=email&utm_campaign=weekly-2024-01-15&utm_content=main-article

Twitter bio:
?utm_source=twitter&utm_medium=social&utm_campaign=bio-link

Referral partner:
?utm_source=partner-newsletter&utm_medium=referral&utm_campaign=cross-promo

UTM Naming Convention:

Sources: newsletter, twitter, linkedin, google, partner-name
Mediums: email, social, organic, paid, referral
Campaigns: [type]-[date or name]
Content: [descriptive-slug]

Social Media Analytics

Platform Metrics

Twitter/X:

Engagement Rate = (Likes + Retweets + Replies + Clicks) / Impressions × 100

Benchmarks:
- Good: 2%+
- Great: 4%+
- Viral: 10%+

Key Metrics:
- Impressions (reach)
- Profile visits (curiosity)
- Follower growth (sustained interest)
- Link clicks (conversion)

LinkedIn:

Engagement Rate = (Likes + Comments + Shares + Clicks) / Impressions × 100

Benchmarks:
- Good: 2%+
- Great: 5%+
- Excellent: 8%+

Note: First-hour engagement heavily affects reach

Social Media Report Template

## Social Media Report: [Period]

### Platform: Twitter/X

#### Overview
| Metric | This Period | Previous | Change |
|--------|-------------|----------|--------|
| Followers | X | Y | +/-Z |
| Impressions | X | Y | +/-% |
| Engagement Rate | X% | Y% | +/-% |
| Profile Visits | X | Y | +/-% |
| Link Clicks | X | Y | +/-% |

#### Top Performing Content
1. [Tweet text snippet]
   - Impressions: X
   - Engagement: X%
   - Why it worked: [Analysis]

2. [Tweet text snippet]
   - Impressions: X
   - Engagement: X%

#### Content Type Performance
| Type | Posts | Avg. Engagement | Best Day |
|------|-------|-----------------|----------|
| Thread | X | X% | [Day] |
| Single Tweet | X | X% | [Day] |
| Quote Tweet | X | X% | [Day] |

#### Recommendations
- [What to do more of]
- [What to stop doing]
- [Experiment to try]

Growth Experiment Framework

A/B Testing Structure

## Experiment: [Name]

### Hypothesis
If we [change X], then [metric Y] will [improve by Z%] because [reasoning].

### Variables
- **Control (A)**: [Current state]
- **Treatment (B)**: [New approach]

### Success Metrics
- **Primary**: [Metric] (minimum detectable effect: X%)
- **Secondary**: [Metric]
- **Guardrail**: [Metric we don't want to hurt]

### Sample Size & Duration
- Traffic/day: X
- Required sample: Y per variant
- Duration: Z days

### Results
| Variant | Sample | [Primary Metric] | [Secondary] |
|---------|--------|------------------|-------------|
| Control | X | Y% | Z% |
| Treatment | X | Y% | Z% |

**Statistical Significance**: X% confidence
**Winner**: [A/B/No clear winner]

### Learnings
- [What we learned]
- [What to test next]

### Decision
☑️ Implement Treatment
☐ Keep Control
☐ Run follow-up test

Common Growth Experiments

Newsletter:
- Subject line variations
- Send time optimization
- CTA placement and copy
- Content length
- Personalization

Website:
- Headline variations
- CTA button color/copy
- Form field count
- Social proof placement
- Content layout

Social:
- Posting times
- Content formats
- Hashtag strategies
- Hook variations
- Thread length

Reporting Templates

Executive Summary (Weekly)

# Growth Summary: Week of [Date]

## Headline Metrics
| Metric | This Week | Target | Status |
|--------|-----------|--------|--------|
| Newsletter Subs | X (+Y) | Z/week | 🟢/🟡/🔴 |
| Open Rate | X% | 40%+ | 🟢/🟡/🔴 |
| Organic Traffic | X | +5%/week | 🟢/🟡/🔴 |
| Social Followers | X (+Y) | +2%/week | 🟢/🟡/🔴 |

## Key Wins
1. [Win and why it matters]
2. [Win and why it matters]

## Challenges
1. [Challenge and proposed action]

## Next Week Focus
- [Priority 1]
- [Priority 2]

Monthly Growth Report

# Monthly Growth Report: [Month Year]

## Executive Summary
[2-3 sentence overview]

## Key Metrics Dashboard

### Newsletter
| Metric | [Month] | Previous | MoM Change | YoY |
|--------|---------|----------|------------|-----|
| Total Subscribers | X | Y | +Z% | +W% |
| Open Rate | X% | Y% | | |
| Click Rate | X% | Y% | | |
| Unsubscribe Rate | X% | Y% | | |

### Website
| Metric | [Month] | Previous | Change |
|--------|---------|----------|--------|
| Sessions | X | Y | +/-% |
| Unique Visitors | X | Y | +/-% |
| Organic Traffic | X | Y | +/-% |
| Avg. Session Duration | X:XX | Y:YY | |

### Social
| Platform | Followers | Engagement | Top Post |
|----------|-----------|------------|----------|
| Twitter | X (+Y) | Z% | [Link] |
| LinkedIn | X (+Y) | Z% | [Link] |

## Growth Initiatives

### What Worked
1. **[Initiative]**: [Result and learning]
2. **[Initiative]**: [Result and learning]

### What Didn't Work
1. **[Initiative]**: [Learning and adjustment]

### Experiments Run
| Experiment | Result | Decision |
|------------|--------|----------|
| [Name] | [Outcome] | Implement/Discard/Iterate |

## Recommendations for Next Month
1. **Priority**: [Recommendation]
2. **Test**: [Experiment proposal]
3. **Optimize**: [Improvement area]

## Goals for Next Month
| Goal | Target | Baseline |
|------|--------|----------|
| [Goal] | X | Y |

Quality Checklist for Analytics

Before making decisions based on data:

### Data Quality
- [ ] Sample size is sufficient
- [ ] Time period is representative
- [ ] No major external factors (holidays, outages)
- [ ] Data source is reliable

### Analysis Quality
- [ ] Compared to appropriate baseline
- [ ] Considered seasonality
- [ ] Checked for statistical significance
- [ ] Looked for confounding variables

### Decision Quality
- [ ] Metric is tied to business outcome
- [ ] Action is clear and feasible
- [ ] Success criteria defined
- [ ] Follow-up plan in place

Resources

• Google Analytics Academy
• GetResponse Benchmarks
• Mailchimp Benchmarks
• Buffer Analytics Guides