Marketing Analytics Dashboard

Marketing Analytics Dashboard

Overview

A comprehensive marketing analytics and reporting methodology covering KPI selection by business type (e-commerce: CAC/LTV/AOV/ROAS, SaaS: MRR/churn/NPS/Net Revenue Retention, lead gen: MQL→SQL→SAL conversion rates), dashboard design principles (hierarchy, context, actionability), funnel visualization techniques, cohort analysis for retention and LTV modeling, attribution models (first-touch, last-touch, linear, time-decay, U-shaped, data-driven), and reporting cadence. This skill provides templates and decision frameworks for building performance marketing dashboards that drive data-informed decisions.

When to Use

• Building a marketing analytics dashboard from scratch (any BI tool)
• Selecting the right KPIs for a specific business model (e-commerce, SaaS, lead gen, content)
• Setting up funnel and cohort analyses in GA4, Mixpanel, Amplitude, or similar
• Evaluating and choosing an attribution model
• Creating weekly/monthly executive marketing reports
• Auditing existing dashboards for actionability gaps
• Standardizing marketing metrics across teams and channels
• Setting up automated anomaly detection and alerting

Body

1. KPI Selection by Business Type

1.1 E-commerce / Retail

North Star Metric: Orders per week (or Revenue per visitor)

1.2 SaaS / Subscription

North Star Metric: Weekly Active Users (WAU) or Net Revenue Retention (NRR)

1.3 Lead Generation / B2B

North Star Metric: Pipeline Generated ($) or SQLs per month

1.4 Content / Media

2. Dashboard Design Principles

2.1 Dashboard Hierarchy

Level 1: Executive Summary (1 page)
├── North Star Metric (big number + trend)
├── 4–6 Tier 1 KPIs (revenue, new customers, avg CVR, CAC)
├── Last 30 days trend line (or comparison vs. prior period)
└── Top 3 alerts / highlights

Level 2: Channel Performance (1 page per channel)
├── Channel-specific KPIs (e.g., SEO: organic traffic, keyword rankings)
├── Performance vs. budget
├── Trend: last 90 days
└── Top/bottom performers (campaigns, keywords, content)

Level 3: Deep Dive (ad hoc / weekly review)
├── Funnel analysis (stage-by-stage drop-off)
├── Cohort analysis (retention, LTV)
├── Segmentation analysis (by device, source, geography, persona)
└── Attribution model comparison

2.2 Dashboard Design Rules

2.3 Dashboard Types by Audience

3. Funnel Visualization

3.1 Marketing Funnel Stages

┌─────────────────────────────────┐
│       AWARENESS (Reach)         │
│   Visitors, Impressions, Reach  │
├─────────────────────────────────┤
│       INTEREST (Traffic)        │
│   Page views, Sessions, Clicks  │
├─────────────────────────────────┤
│     CONSIDERATION (Engage)      │
│   Time on site, Pages/session   │
├─────────────────────────────────┤
│       INTENT (Leads)            │
│   Form fills, Email signups     │
├─────────────────────────────────┤
│       PURCHASE (Conversion)     │
│   Sales, Subscriptions, Deals   │
├─────────────────────────────────┤
│      RETENTION (Loyalty)        │
│   Repeat purchases, Retention   │
├─────────────────────────────────┤
│      ADVOCACY (Referral)        │
│   NPS, Referrals, Reviews       │
└─────────────────────────────────┘

3.2 Funnel CVR by Channel

3.3 Funnel Visualization Template

                    ┌──────────────────────────────────────────────┐
                    │     ALL VISITORS: 100,000                    │
                    │     ──────────────────────                   │
                    │     ┌─────┐ ┌─────┐ ┌─────┐                │
                    │     │Org  │ │Paid │ │Social│                │
                    │     │45%  │ │25%  │ │15%  │                │
                    │     └─────┘ └─────┘ └─────┘                │
                    └──────────────────────────────────────────────┘
                                       │
                                       ▼
                    ╔══════════════════════════════════════════╗
                    ║   ENGAGED VISITORS: 45,000 (45%)        ║
                    ║   (≥ 30s on site, > 50% scroll)         ║
                    ╚══════════════════════════════════════════╝
                                       │
                                       ▼
                    ╔══════════════════════════════════════════╗
                    ║   LEADS: 3,000 (3% of all visitors)     ║
                    ║   (form fills, email signups, downloads) ║
                    ║   CPL: $25                               ║
                    ╚══════════════════════════════════════════╝
                                       │
                                       ▼
                    ╔══════════════════════════════════════════╗
                    ║   MQLs: 1,500 (50% of leads)            ║
                    ║   Cost per MQL: $50                      ║
                    ╚══════════════════════════════════════════╝
                                       │
                                       ▼
                    ╔══════════════════════════════════════════╗
                    ║   SQLs: 750 (50% of MQLs)               ║
                    ║   Cost per SQL: $100                     ║
                    ╚══════════════════════════════════════════╝
                                       │
                                       ▼
                    ╔══════════════════════════════════════════╗
                    ║   OPPORTUNITIES: 300 (40% of SQLs)      ║
                    ║   Pipeline Value: $450,000               ║
                    ╚══════════════════════════════════════════╝
                                       │
                                       ▼
                    ╔══════════════════════════════════════════╗
                    ║   CLOSED WON: 90 (30% of opportunities) ║
                    ║   Revenue: $180,000                      ║
                    ║   CAC: $500                              ║
                    ╚══════════════════════════════════════════╝

4. Cohort Analysis

4.1 Retention Cohort Table

Insights from this cohort table:

• Month 1 retention (42–48%) is stable — check onboarding flow for consistency
• Month 2→3 drop (32–34%) is a key inflection point — investigate what happens between months 2–3
• 12-month retention (18%) needs improvement — target 25%+

4.2 Revenue / LTV Cohort

LTV projection formula (for incomplete cohorts):

Projected LTV = (Cumulative Revenue to Date) / (Retention Rate to Date × Expected Annual Retention)

4.3 Behavior Cohort Analysis

Group users not by acquisition date, but by action date:

Key insight: If 2nd purchase rate improves over time (35% → 38%), recent changes to the post-purchase experience are working.

5. Attribution Models

5.1 Model Comparison

5.2 Attribution Model Selection by Business Type

5.3 Attribution Comparison Dashboard

CHANNEL ATTRIBUTION COMPARISON
┌─────────────┬──────────┬──────────┬──────────┬──────────┐
│ Channel     │ First    │ Last     │ Linear   │ Data     │
│             │ Touch    │ Touch    │          │ Driven   │
├─────────────┼──────────┼──────────┼──────────┼──────────┤
│ Organic     │ 45%      │ 20%      │ 32%      │ 30%      │
│ Paid Search │ 20%      │ 35%      │ 28%      │ 31%      │
│ Social      │ 15%      │ 8%       │ 12%      │ 11%      │
│ Email       │ 5%       │ 22%      │ 14%      │ 16%      │
│ Direct      │ 10%      │ 12%      │ 10%      │ 9%       │
│ Referral    │ 5%       │ 3%       │ 4%       │ 3%       │
└─────────────┴──────────┴──────────┴──────────┴──────────┘

IF first-touch dominated → Brand/awareness channels over-attributed
IF last-touch dominated → Conversion/nurture channels over-attributed
Data-Driven is most accurate (if data volume sufficient)

6. Reporting Cadence & Templates

6.1 Reporting Calendar

6.2 Weekly Marketing Report Template

# Weekly Marketing Report — [Date] to [Date]

## Executive Summary
- Revenue: $XXX,XXX (+X% vs prior week, X% vs target) 
- New Customers: XXX (+X%)
- Avg. CAC: $XXX (X% vs target)
- Top Highlight: [What went well]
- Top Flag: [What needs attention]

## Channel Performance

### Organic Search
- Visits: XX,XXX (+X%)
- Leads: XXX (+X%)
- Conversion Rate: X.XX%
- Top Pages: [Page 1], [Page 2]
- Notes: [Any keyword movements, algo changes]

### Paid Search
- Spend: $X,XXX (+X%)
- Clicks: XXX (+X%)
- CVR: X.XX% (+X%)
- CPA: $XXX (+X%)
- Top Campaigns: [Campaign 1], [Campaign 2]

### Email
- Sent: XX,XXX
- Open Rate: XX.X% (+X% vs benchmark)
- CTR: X.XX% (+X% vs benchmark)
- Unsubscribes: XX (X.XX%)
- Top Sends: [Campaign 1], [Campaign 2]

## Pipeline (B2B)
- New MQLs: XXX 
- New SQLs: XXX
- Opportunities Created: XX ($XX,XXX)
- Closed Won: XX ($XX,XXX)

## Tests in Flight
- [Test 1]: Running since [date], results so far: X
- [Test 2]: Scheduled to launch [date]

## Action Items
- [ ] [Action] — Owner — Due date
- [ ] [Action] — Owner — Due date

6.3 Monthly Executive Dashboard Template

# Monthly Marketing Dashboard — [Month Year]

## North Star
**Orders per Week (Ecom)** or **SQLs/Month (B2B)** or **NRR (SaaS)**
Current: X,XXX | Prior Month: X,XXX | Target: X,XXX | Status: ✅ / ⚠️ / ❌

## Revenue & Growth
| Metric | Current | Prior Mo | MoM | Target | Status |
|--------|---------|----------|-----|--------|--------|
| Total Revenue | $X | $X | +X% | $X | ✅ |
| New Customers | X | X | +X% | X | ✅ |
| Avg. CAC | $X | $X | -X% | $X | ✅ |
| LTV:CAC | X:1 | X:1 | +X | 3:1 | ⚠️ |
| Gross Margin | X% | X% | +X% | X% | ✅ |

## Channel Breakdown
| Channel | Spend | Leads/Conversions | CPA/CVR | ROAS | MoM Change |
|---------|-------|-------------------|---------|------|------------|
| Organic | $0 | X | X% | N/A | +X% |
| Paid Search | $X | X | $X | X:1 | +X% |
| Social Paid | $X | X | $X | X:1 | -X% |
| Email | $X | X | $X | X:1 | +X% |
| **Total** | **$X** | **X** | **$X** | **X:1** | |

## Key Highlights
1. [Positive outcome with specific data]
2. [Positive outcome with specific data]
3. [Area of concern with specific data]

## Recommendations for Next Month
1. [Actionable recommendation]
2. [Actionable recommendation]
3. [Actionable recommendation]

7. Anomaly Detection & Alerting

7.1 Alert Triggers

7.2 Alert Severity Levels

Common Pitfalls

1. Vanity metrics over actionable KPIs: Impressions, page views, and social likes feel good but don't drive decisions. Focus on metrics that lead to action: CAC, CVR, churn, pipeline velocity.
2. Too many KPIs on one dashboard: A dashboard with 50+ metrics is a report, not a dashboard. Limit to 5–7 KPIs per page and drill down for detail.
3. No context or benchmarks: "3,412 conversions" is noise. "3,412 conversions (+12% MoM, -5% vs target)" is a signal. Always include comparison periods and targets.
4. Ignoring data quality: Bad tracking in = bad decisions out. Verify UTM parameters, conversion tags, and integration points monthly.
5. Attributing everything to the last click: Last-click attribution over-values bottom-of-funnel channels and under-values brand-building. Use multi-touch or data-driven models.
6. Mixed cohort comparisons: Comparing different acquisition cohorts (e.g., social vs. email) without segmentation masks real performance differences. Always segment cohorts by source.
7. No alerting system: Dashboards that nobody checks daily are useless. Set up automated alerts for critical metric changes.
8. Data silos: Marketing data in GA4, ad platform data in their native dashboards, revenue data in the CRM — building a dashboard that connects them is essential for full-funnel visibility.
9. Over-reliance on platform attribution: Google and Meta's in-platform attribution over-attribute themselves. Use a third-party attribution tool or a statistical model for unbiased measurement.
10. Not reviewing and updating dashboards: Business models change, new channels emerge, old KPIs become irrelevant. Review dashboard structure quarterly.

Verification Checklist

• North Star Metric defined and aligned with business goals
• Tier 1 KPIs selected (5–7 max) matching business type (ecom/SaaS/lead gen/content)
• Tier 2/3 KPIs defined for drill-down analyses
• Dashboard hierarchy designed (Executive → Channel → Deep Dive)
• Context/comparison logic built into every KPI visualization
• Funnel visualization built with stage-by-stage conversion rates
• Cohort analysis configured (retention cohorts and/or revenue cohorts)
• Attribution model selected and documented (with justification)
• Attribution comparison dashboard built (first/last/linear/data-driven)
• Data quality audit completed (UTM consistency, conversion tracking)
• Reporting calendar defined (daily/weekly/monthly/quarterly)
• Weekly report template finalized and automated where possible
• Monthly executive dashboard built in BI tool
• Automated alerts configured for critical and high-severity triggers
• Dashboard reviewed with stakeholders for actionability feedback
• Data sources connected and verified (GA4, ad platforms, CRM, payment)
• Access controls and permissions set (who sees what)
• Dashboard refresh schedule set (real-time, hourly, daily)
• CCPA/GDPR compliance: no PII in dashboards
• Mobile-friendly dashboard view tested