Mmcp.market

influential-factors-detective skill

by florianbonnet14·florianbonnet14/ThePowerOfAnalytics_ClaudeSkills·29 stars·MIT

Identify and analyze qualitative factors that influence KPIs but can't be measured as numbers. Use when users need to find non-quantifiable drivers affecting metrics (like copy quality, design aesthetics, timing), plan A/B tests for subjective elements, bridge the gap between data and design/content decisions, understand why quantitative analysis is incomplete, optimize messaging or UX elements, or identify the actual levers to pull for improving KPIs at the leaf nodes of a KPI tree.

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Install the influential-factors-detective skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/florianbonnet14/ThePowerOfAnalytics_ClaudeSkills.git /tmp/ThePowerOfAnalytics_ClaudeSkills
mkdir -p ~/.claude/skills
cp -r /tmp/ThePowerOfAnalytics_ClaudeSkills/influential-factors-detective ~/.claude/skills/influential-factors-detective
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Influential Factors Detective

Systematically identify and test qualitative factors that drive KPI performance but can't be directly measured.

Core Concept

Influential Factors = Elements you can change that affect KPIs, but whose "quality" can't be directly measured—only their impact on downstream metrics.

Location in KPI Tree:

North Star Metric
├─ Quantifiable KPI
│  └─ Quantifiable KPI
│     └─ 🔍 Influential Factor (qualitative leaf node)

Key insight: Quantitative analysis tells you WHERE the problem is. Influential factors tell you WHAT to change.

When to Use This Skill

Trigger this skill when:

  • User asks to identify influential factors for a KPI
  • KPI changed but quantitative drivers don't explain why
  • User needs to optimize subjective elements (copy, design, UX)
  • User wants to plan A/B tests
  • User asks "what can we change?" after reaching KPI tree leaf nodes
  • User needs to bridge analytics and creative decisions

Six Categories of Influential Factors

1. Copy & Messaging

Word choice, tone, length, clarity, emotional appeal, value prop framing, CTA phrasing, urgency language

Example: Email subject line—can't measure "quality" but can test which one drives higher open rates

2. Visual Design

Layout, colors, typography, visual hierarchy, images, icons, button design, white space

Example: CTA button color—can't measure "effectiveness" but can test which color drives more clicks

3. User Experience

Navigation, form fields, progress indicators, error messages, loading states, micro-interactions

Example: Form length—can't measure "optimal length" but can test 5 vs 10 vs 15 fields

4. Timing & Frequency

Time of day, day of week, contact frequency, delay periods, pacing, response expectations

Example: Email send time—can't measure "best time" universally but can test 8am vs 12pm vs 6pm

5. Content & Media

Format (image/video/text), storytelling approach, depth, technical level, personalization

Example: Product demo format—can't measure "best format" but can test video vs screenshots vs interactive

6. Positioning & Placement

Above/below fold, sidebar vs main content, modal vs banner, navigation placement, feature prominence

Example: CTA placement—can't measure "ideal position" but can test top vs bottom vs sticky

Four Methods to Identify Influential Factors

Method 1: KPI Tree Leaf Analysis

When: Working with an existing KPI tree

Process:

  1. Navigate to bottom-level quantifiable KPIs
  2. Ask: "What elements can I change that affect this metric?"
  3. List all changeable but non-measurable elements
  4. Document as 🔍 influential factors

Template:

For KPI: [Name]

Influential Factors:
1. [Factor] - [How it affects KPI] - [Current state] - [Alternatives]
2. [Factor] - [How it affects KPI] - [Current state] - [Alternatives]

Method 2: Comparison Analysis

When: Two similar things perform differently

Process:

  1. Identify comparable elements (Feature A vs B, Page X vs Y)
  2. List quantitative differences (traffic, engagement)
  3. List qualitative differences (copy, design, placement)
  4. Hypothesize which qualitative differences drive performance gap

Example:

Landing Page A: 15% conversion | Landing Page B: 8% conversion
Qualitative differences:
- Headline (benefit vs feature)
- Hero image (people vs product)
- CTA color, Form length, Social proof placement
→ Test these factors systematically

Method 3: User Research Insights

Sources: Interviews, usability tests, session recordings, support tickets, surveys

Process:

  1. Collect qualitative feedback ("confusing," "overwhelming," "unclear")
  2. Map feedback to potential factors
  3. Form hypotheses about improvements

Example:

Feedback: "Wasn't sure if plan included X feature"
→ Factors: Feature list completeness, description clarity, comparison design
→ Hypothesis: Adding use case examples will increase conversion

Method 4: Best Practice Benchmarking

Process:

  1. Research industry best practices
  2. Compare your implementation
  3. Identify gaps as testable factors

Four Testing Approaches

A/B Testing (sufficient traffic)

  • Test one factor at a time
  • Create control + variant(s)
  • Run to statistical significance
  • Document and implement winner

Sequential Testing (low traffic)

  • Measure baseline (2 weeks)
  • Make change
  • Measure new performance (2 weeks)
  • Compare accounting for seasonality

Multivariate Testing (high traffic, multiple factors)

  • Test combinations of factors
  • Requires 8x+ traffic vs simple A/B
  • Reveals interaction effects
  • Use sparingly

Qualitative Testing (early exploration)

  • Usability testing (5-10 users)
  • Preference testing (side-by-side)
  • 5-second tests (recall measurement)
  • Inform quantitative test priorities

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