marketing-analyst

Q: How do I install marketing-analyst?

Run npx skills add https://github.com/borghei/claude-skills --skill marketing-analyst in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/marketing-analyst/ (personal) or .claude/skills/marketing-analyst/ (project).

by borgheiMarketing & SEO

Marketing analytics covering campaign analysis, attribution and marketing mix modeling, ROI measurement, and performance reporting. Use when analyzing campaign ROI, comparing attribution models, or optimizing budget allocation.

Installs: 308
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/borghei/claude-skills --skill marketing-analyst

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads marketing-analyst automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Marketing Analyst

The agent operates as a senior marketing analyst, delivering campaign performance analysis, multi-touch attribution, marketing mix modeling, ROI measurement, and data-driven budget optimization.

Clarify First

Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

Campaigns/channels in scope — which campaigns or channels and the date range (defines the dataset and report boundaries)
KPIs and their targets — CPL, CAC, ROAS, pipeline, revenue, each with a target and a data source (drives the target-vs-actual performance table)
Sales-cycle length — short vs long B2B cycle (determines attribution model and whether to report pipeline vs closed revenue)
Report audience — exec summary vs ops deep-dive (sets the altitude and which sections matter most)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflow

Define measurement objectives - Identify which campaigns, channels, or initiatives require analysis. Confirm KPIs (CPL, CAC, ROAS, pipeline, revenue). Checkpoint: every KPI has a target and a data source.
Collect and validate data - Pull campaign data from ad platforms, CRM, and analytics tools. Validate completeness and consistency. Checkpoint: no channel has >5% missing data.
Run attribution analysis - Apply multiple attribution models (first-touch, last-touch, linear, time-decay, position-based) and compare channel credit allocation. Checkpoint: results are compared across at least 3 models.
Analyze campaign performance - Calculate ROI, ROAS, CPL, CAC, and conversion rates per campaign. Identify top and bottom performers. Checkpoint: performance table includes target vs. actual for every metric.
Optimize budget allocation - Use marketing mix modeling or ROI data to recommend budget shifts. Checkpoint: reallocation recommendations are backed by expected ROI per channel.
Build executive report - Summarize headline metrics, wins, challenges, and next-period focus. Checkpoint: report passes the "so what" test (every data point has an actionable insight).

Marketing Metrics Reference

Acquisition Metrics

Metric	Formula	Benchmark
CPL	Spend / Leads	Varies by industry
CAC	S&M Spend / New Customers	LTV/CAC > 3:1
CPA	Spend / Acquisitions	Target specific
ROAS	Revenue / Ad Spend	> 4:1

Engagement Metrics

Metric	Formula	Benchmark
Engagement Rate	Engagements / Impressions	1-5%
CTR	Clicks / Impressions	0.5-2%
Conversion Rate	Conversions / Visitors	2-5%
Bounce Rate	Single-page sessions / Total	< 50%

Retention Metrics

Metric	Formula	Benchmark
Churn Rate	Lost Customers / Total	< 5% monthly
NRR	(MRR - Churn + Expansion) / MRR	> 100%
LTV	ARPU x Gross Margin x Lifetime	3x+ CAC

Attribution Modeling

Model Comparison

The agent should apply multiple models and compare results to identify channel over/under-valuation:

Model	Logic	Best For
First-touch	100% credit to first interaction	Measuring awareness channels
Last-touch	100% credit to final interaction	Measuring conversion channels
Linear	Equal credit across all touches	Balanced view of full journey
Time-decay	More credit to recent touches	Short sales cycles
Position-based	40% first, 40% last, 20% middle	Most B2B scenarios

Attribution Calculator

def calculate_attribution(touchpoints, model='position'):
    """Calculate attribution credit for a conversion journey.

    Args:
        touchpoints: List of channel names in order of interaction
        model: One of 'first', 'last', 'linear', 'time_decay', 'position'

    Returns:
        Dict mapping channel -> credit (sums to 1.0)

    Example:
        >>> calculate_attribution(['paid_search', 'email', 'organic', 'direct'], 'position')
        {'paid_search': 0.4, 'email': 0.1, 'organic': 0.1, 'direct': 0.4}
    """
    n = len(touchpoints)
    credits = {}

    if model == 'first':
        credits[touchpoints[0]] = 1.0
    elif model == 'last':
        credits[touchpoints[-1]] = 1.0
    elif model == 'linear':
        for tp in touchpoints:
            credits[tp] = credits.get(tp, 0) + 1.0 / n
    elif model == 'time_decay':
        decay = 0.7
        total = sum(decay ** i for i in range(n))
        for i, tp in enumerate(reversed(touchpoints)):
            credits[tp] = credits.get(tp, 0) + (decay ** i) / total
    elif model == 'position':
        if n == 1:
            credits[touchpoints[0]] = 1.0
        elif n == 2:
            credits[touchpoints[0]] = 0.5
            credits[touchpoints[-1]] = credits.get(touchpoints[-1], 0) + 0.5
        else:
            credits[touchpoints[0]] = 0.4
            credits[touchpoints[-1]] = credits.get(touchpoints[-1], 0) + 0.4
            for tp in touchpoints[1:-1]:
                credits[tp] = credits.get(tp, 0) + 0.2 / (n - 2)

    return credits

Example: Campaign Analysis Report

# Campaign Analysis: Q1 2026 Product Launch

## Performance Summary
| Metric       | Target  | Actual  | vs Target |
|--------------|---------|---------|-----------|
| Impressions  | 500K    | 612K    | +22%      |
| Clicks       | 25K     | 28.4K   | +14%      |
| Leads        | 1,200   | 1,350   | +13%      |
| MQLs         | 360     | 410     | +14%      |
| Pipeline     | $1.2M   | $1.45M  | +21%      |
| Revenue      | $380K   | $425K   | +12%      |

## Channel Breakdown
| Channel      | Spend   | Leads | CPL   | Pipeline |
|--------------|---------|-------|-------|----------|
| Paid Search  | $45K    | 520   | $87   | $580K    |
| LinkedIn Ads | $30K    | 310   | $97   | $420K    |
| Email        | $5K     | 380   | $13   | $350K    |
| Content/SEO  | $8K     | 140   | $57   | $100K    |

## Key Insight
Email delivers lowest CPL ($13) and strong pipeline. Recommend shifting
10% of LinkedIn budget to email nurture sequences for Q2.

Budget Optimization Framework

Budget Allocation Recommendation
  Channel        Current    Optimal    Change    Expected ROI
  Paid Search    30%        35%        +5%       4.2x
  Social Paid    25%        20%        -5%       2.8x
  Display        15%        10%        -5%       1.5x
  Email          10%        15%        +5%       8.5x
  Content        10%        12%        +2%       5.2x
  Events         10%        8%         -2%       2.2x

  Projected Impact: +15% pipeline with same budget

A/B Test Statistical Analysis

from scipy import stats
import numpy as np

def analyze_ab_test(control_conv, control_total, treatment_conv, treatment_total, alpha=0.05):
    """Analyze A/B test for statistical significance.

    Example:
        >>> result = analyze_ab_test(150, 5000, 195, 5000)
        >>> result['significant']
        True
        >>> f"{result['lift_pct']:.1f}%"
        '30.0%'
    """
    p_c = control_conv / control_total
    p_t = treatment_conv / treatment_total
    p_pool = (control_conv + treatment_conv) / (control_total + treatment_total)
    se = np.sqrt(p_pool * (1 - p_pool) * (1/control_total + 1/treatment_total))
    z = (p_t - p_c) / se
    p_value = 2 * (1 - stats.norm.cdf(abs(z)))

    return {
        'control_rate': p_c,
        'treatment_rate': p_t,
        'lift_pct': ((p_t - p_c) / p_c) * 100,
        'p_value': p_value,
        'significant': p_value < alpha,
    }

Troubleshooting

Symptom	Likely Cause	Resolution
Attribution models give wildly different channel credit allocations	No single model captures full truth; each has structural bias	Run 3+ models (first-touch, last-touch, position-based) and compare; use position-based as default for B2B
ROAS calculations look great but pipeline is flat	Revenue attribution counting existing customers, not new pipeline	Separate new business attribution from expansion; report pipeline separately from revenue
Marketing reports and sales reports show different lead counts	Marketing counts MQLs at form fill, sales counts at CRM entry with different criteria	Align on shared definitions: document exact MQL, SQL, and opportunity criteria in a shared SLA
Forecast consistently over-predicts by 20%+	Model uses linear extrapolation without accounting for seasonality or saturation	Apply dampening factors for longer forecasts; use ensemble method (linear + growth rate + moving average)
Executive dashboard takes too long to build each month	Manual data pulls from 5+ platforms with different schemas	Automate data collection; standardize UTM and naming conventions so cross-platform analysis is consistent
Channel ROI is negative but still generating pipeline	Long B2B sales cycle means revenue attribution has not caught up to spend	Use pipeline-based attribution for channels with 3+ month sales cycles rather than closed-won revenue

Success Criteria

Multi-touch attribution model deployed comparing 3+ models with documented channel credit differences
Monthly marketing report delivered within 3 business days of month close
Budget reallocation recommendations backed by per-channel ROI data and implemented quarterly
Forecast accuracy within 15% of actual for 3-month projections
Campaign performance reports include target vs actual for every KPI
Every data point in executive reports has an actionable insight (passes "so what" test)
Channel data completeness above 95% (no channel has >5% missing data)

Scope & Limitations

In Scope: Campaign performance analysis, multi-touch attribution modeling, marketing mix optimization, ROI/ROAS calculation, budget allocation recommendations, executive reporting, cohort retention analysis, marketing forecasting.

Out of Scope: Analytics implementation and tracking setup (see analytics-tracking skill), product analytics (see product-team skills), financial modeling beyond marketing metrics (see finance skill), data engineering and warehouse management.

Limitations: Attribution models are approximations — no model perfectly captures the buyer journey, especially for high-touch B2B sales. Forecasting uses historical extrapolation with dampening; it does not account for market disruptions or competitive moves. Budget optimization assumes linear channel scaling; most channels have diminishing returns at scale.

Scripts

Script	Purpose	Usage
`scripts/channel_mix_optimizer.py`	Analyze channel performance and recommend optimal budget allocation	`python scripts/channel_mix_optimizer.py channels.json --budget 100000 --demo`
`scripts/cohort_analyzer.py`	Analyze user retention by cohort, identify trends and best/worst performers	`python scripts/cohort_analyzer.py cohort_data.json --demo`
`scripts/marketing_forecast_generator.py`	Generate marketing forecasts using linear, growth rate, and ensemble methods	`python scripts/marketing_forecast_generator.py historical.json --periods 6`

FAQ

What does the marketing-analyst skill do?

How do I install marketing-analyst?

Run npx skills add https://github.com/borghei/claude-skills --skill marketing-analyst in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/marketing-analyst/ (personal) or .claude/skills/marketing-analyst/ (project).

Which AI agents can use marketing-analyst?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is marketing-analyst safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains marketing-analyst?

It is published by borghei in borghei/claude-skills. It has 308 recorded installs.

More from borghei/claude-skills

View all →

product-designer

borghei/claude-skills

Product design across UI/UX, design systems, prototyping, and user research. Use when creating user journey maps, building wireframes, defining design tokens and component systems, planning usability tests, or establishing design principles.

Frontend & Design4.9K installs

business-intelligence

borghei/claude-skills

Business intelligence across dashboard design, visualization, and reporting automation. Use when designing dashboards, building KPI frameworks, automating reports, creating data stories, or optimizing BI tool performance.

Business & Finance1.5K installs

brand-strategist

borghei/claude-skills

Brand strategy covering positioning, identity, brand architecture, messaging frameworks, and governance. Use when developing brand positioning, creating messaging frameworks, auditing brand health, or building brand architecture.

Marketing & SEO776 installs

data-analyst

borghei/claude-skills

Data analysis across SQL, visualization, statistics, and reporting. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven recommendations.

Data & Databases606 installs

cfo-advisor

borghei/claude-skills

Open the skill to read its SKILL.md instructions.

454 installs

data-scientist

borghei/claude-skills

Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.

Education & Science440 installs

Popular marketing & seo skills

View category →

reddit-automation

flowkit-labs/skills

Find Reddit threads where people are genuinely asking for what you offer, then draft short, genuinely helpful replies — disclosing your affiliation honestly and naming your product only when it truly answers the question. Two moves: discovery — scan the right subreddits for real needs (recommendation asks, expressed pain, competitor mentions) and rank the few threads where you can actually help; drafting — write from real experience, respect each community's self-promo rules, and keep a human in the loop to review and post. This markets on Reddit the honest way: contribute value, disclose who you are, never astroturf. Distilled from the playbook behind doany.ai's Reddit agent. Triggers on "find reddit opportunities", "reddit marketing", "reddit automation", "reply on reddit for my product", "reddit community engagement", "reddit lead gen", or any ask to turn Reddit threads into genuine, disclosed marketing replies.

Marketing & SEO1.1M installs

twitter-automation

101-skills/superpowers

Automate Twitter/X with posting, engagement, and user management via inference.sh CLI. Apps: x/post-tweet, x/post-create (with media), x/post-like, x/post-retweet, x/dm-send, x/user-follow. Capabilities: post tweets, schedule content, like posts, retweet, send DMs, follow users, get profiles. Use for: social media automation, content scheduling, engagement bots, audience growth, X API. Triggers: twitter api, x api, tweet automation, post to twitter, twitter bot, social media automation, x automation, tweet scheduler, twitter integration, post tweet, twitter post, x post, send tweet

Marketing & SEO880K installs

twitter-automation

magentosh/superpowers

Marketing & SEO771.7K installs

twitter-automation

qu-skills/superpowers

Marketing & SEO564.3K installs

product-launch-video

heygen-com/hyperframes

Turn a product or marketing URL, pasted script, or brief into a product launch / promo video — SaaS promos, feature reveals, product demos, app and company launches. Use when the user wants to market, launch, promote, or reveal a product; the default for any commercial URL. Site tours / showcases of a website route here too — the brief carries the show-it-as-is intent. Unclear → /hyperframes.

Marketing & SEO538.7K installs

twitter-automation

skills-shell/superpowers

Marketing & SEO453.7K installs

marketing-analyst

Install

Install

Describe the task

Review the result

SKILL.md

Marketing Analyst

Clarify First

Workflow

Marketing Metrics Reference

Acquisition Metrics

Engagement Metrics

Retention Metrics

Attribution Modeling

Model Comparison

Attribution Calculator

Example: Campaign Analysis Report

Budget Optimization Framework

A/B Test Statistical Analysis

Troubleshooting

Success Criteria

Scope & Limitations

Scripts

FAQ

More from borghei/claude-skills

Popular marketing & seo skills