audience_intelligence

•

43126 months ago

Analyzes target audience demographics, psychographics, behaviors, and platform preferences to inform influencer selection and campaign strategy. Essential foundation for effective influencer marketing.

Quick Install

npxskills add vuralserhat86/antigravity-agentic-skills--skill audience_intelligence

Instructions

Audience Analyzer

This skill helps you deeply understand your target audience before selecting influencers. It analyzes demographics, behaviors, content preferences, and platform habits to ensure influencer partnerships reach the right people.

When to Use This Skill

Starting a new influencer marketing program
Launching a product to a new audience segment
Refining your influencer selection criteria
Understanding why previous campaigns underperformed
Identifying audience overlap between brand and influencers
Developing audience personas for briefing

What This Skill Does

Demographic Analysis: Age, gender, location, income, education
Psychographic Profiling: Values, interests, lifestyle, attitudes
Behavioral Mapping: Purchase habits, content consumption, decision journey
Platform Analysis: Where they spend time, how they engage
Content Preferences: Formats, topics, styles that resonate
Influencer Affinity: Types of creators they follow and trust

How to Use

Basic Audience Analysis

Analyze the target audience for [brand/product/category]

Who is the ideal customer for [product] and where do they spend time online?

From Customer Data

Here's our customer data: [data]. Build an audience profile for influencer targeting.

Competitive Analysis

Analyze the audience that follows [competitor brand] on social media

Instructions

When a user requests audience analysis:

Gather Context

### Analysis Parameters

**Brand/Product**: [name]
**Category**: [industry/vertical]
**Current Customer Base**: [description if available]
**Geographic Focus**: [regions/countries]
**Price Point**: [budget/mid/premium]
**Campaign Objective**: [awareness/consideration/conversion]

Analyze Demographics

## Demographic Profile

### Primary Audience

| Attribute | Profile | Confidence |
|-----------|---------|------------|
| Age Range | [X-Y years] | High/Med/Low |
| Gender | [distribution] | High/Med/Low |
| Location | [primary markets] | High/Med/Low |
| Income | [range] | High/Med/Low |
| Education | [level] | High/Med/Low |
| Occupation | [types] | High/Med/Low |
| Family Status | [single/married/parents] | High/Med/Low |

### Secondary Audience

| Attribute | Profile | Notes |
|-----------|---------|-------|
| [attributes] | [values] | [notes] |

### Demographic Insights

**Key Findings**:
1. [Insight about age/generation]
2. [Insight about location/culture]
3. [Insight about life stage]

**Implications for Influencer Selection**:
- Look for influencers aged [range] who resonate with [demographic]
- Prioritize creators in [locations/markets]
- Consider [family/lifestyle] focused content creators

Example

User: "Analyze the target audience for a premium skincare brand targeting millennial women"

Output: [Comprehensive audience analysis following the structure above, with specific insights about millennial women's skincare habits, social media behavior, influencer preferences, etc.]

Tips for Success

Use real data when available - Customer surveys, social insights, sales data
Don't assume - Validate hypotheses with research
Consider micro-segments - Not all customers are the same
Update regularly - Audiences evolve
Connect to influencer criteria - Every insight should inform selection

Related Skills

trend-spotter - Identify trends relevant to audience
niche-researcher - Deep-dive into specific communities
influencer-discovery - Find influencers matching criteria
fit-scorer - Score influencer-audience alignment

🤖 Advanced: Data-Driven Segmentation

Use Python to find hidden patterns in customer data.

import pandas as pd
from sklearn.cluster import KMeans

# 1. Load Data
df = pd.read_csv('customers.csv')
features = df[['age', 'spending_score', 'visit_frequency']]

# 2. Find Segments (K-Means)
kmeans = KMeans(n_clusters=4, random_state=42)
df['segment'] = kmeans.fit_predict(features)

# 3. Analyze Profiles
print(df.groupby('segment').mean())

🔄 Workflow

Kaynak: Data-Driven Marketing Guide

Aşama 1: Data Gathering

Quantitative: Google Analytics, CRM data, Sales history.
Qualitative: Social listening, customer interviews.
Competitor: Analyze who interacts with rival brands.

Aşama 2: Segmentation (AI/Manual)

Demographic: Yaş, Konum, Gelir (Geleneksel).
Psychographic: Değerler, İlgi alanları (Modern).
Behavioral: Satın alma sıklığı, Sadakat (Data-driven).

Aşama 3: Persona Creation

Draft Profile: "Tech-Savvy Tina" gibi isimler ver.
Empathy Map: Ne görür, duyar, düşünür, hisseder?
Influencer Match: Bu persona kimi takip eder?

Kontrol Noktaları

Aşama	Doğrulama
1	Veri kaynağı güvenilir ve güncel
2	Segmentler birbirinden net ayrışıyor (Distinct)
3	Persona gerçekçi (hayali değil, veriye dayalı)

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Audience Analyzer

When to Use This Skill

Starting a new influencer marketing program
Launching a product to a new audience segment
Refining your influencer selection criteria
Understanding why previous campaigns underperformed
Identifying audience overlap between brand and influencers
Developing audience personas for briefing

What This Skill Does

Demographic Analysis: Age, gender, location, income, education
Psychographic Profiling: Values, interests, lifestyle, attitudes
Behavioral Mapping: Purchase habits, content consumption, decision journey
Platform Analysis: Where they spend time, how they engage
Content Preferences: Formats, topics, styles that resonate
Influencer Affinity: Types of creators they follow and trust

How to Use

Basic Audience Analysis

Analyze the target audience for [brand/product/category]

Who is the ideal customer for [product] and where do they spend time online?

From Customer Data

Here's our customer data: [data]. Build an audience profile for influencer targeting.

Competitive Analysis

Analyze the audience that follows [competitor brand] on social media

Instructions

When a user requests audience analysis:

Gather Context

### Analysis Parameters

**Brand/Product**: [name]
**Category**: [industry/vertical]
**Current Customer Base**: [description if available]
**Geographic Focus**: [regions/countries]
**Price Point**: [budget/mid/premium]
**Campaign Objective**: [awareness/consideration/conversion]

Analyze Demographics

## Demographic Profile

### Primary Audience

| Attribute | Profile | Confidence |
|-----------|---------|------------|
| Age Range | [X-Y years] | High/Med/Low |
| Gender | [distribution] | High/Med/Low |
| Location | [primary markets] | High/Med/Low |
| Income | [range] | High/Med/Low |
| Education | [level] | High/Med/Low |
| Occupation | [types] | High/Med/Low |
| Family Status | [single/married/parents] | High/Med/Low |

### Secondary Audience

| Attribute | Profile | Notes |
|-----------|---------|-------|
| [attributes] | [values] | [notes] |

### Demographic Insights

**Key Findings**:
1. [Insight about age/generation]
2. [Insight about location/culture]
3. [Insight about life stage]

**Implications for Influencer Selection**:
- Look for influencers aged [range] who resonate with [demographic]
- Prioritize creators in [locations/markets]
- Consider [family/lifestyle] focused content creators

Example

User: "Analyze the target audience for a premium skincare brand targeting millennial women"

Output: [Comprehensive audience analysis following the structure above, with specific insights about millennial women's skincare habits, social media behavior, influencer preferences, etc.]

Tips for Success

Use real data when available - Customer surveys, social insights, sales data
Don't assume - Validate hypotheses with research
Consider micro-segments - Not all customers are the same
Update regularly - Audiences evolve
Connect to influencer criteria - Every insight should inform selection

Related Skills

trend-spotter - Identify trends relevant to audience
niche-researcher - Deep-dive into specific communities
influencer-discovery - Find influencers matching criteria
fit-scorer - Score influencer-audience alignment

🤖 Advanced: Data-Driven Segmentation

Use Python to find hidden patterns in customer data.

import pandas as pd
from sklearn.cluster import KMeans

# 1. Load Data
df = pd.read_csv('customers.csv')
features = df[['age', 'spending_score', 'visit_frequency']]

# 2. Find Segments (K-Means)
kmeans = KMeans(n_clusters=4, random_state=42)
df['segment'] = kmeans.fit_predict(features)

# 3. Analyze Profiles
print(df.groupby('segment').mean())

🔄 Workflow

Kaynak: Data-Driven Marketing Guide

Aşama 1: Data Gathering

Quantitative: Google Analytics, CRM data, Sales history.
Qualitative: Social listening, customer interviews.
Competitor: Analyze who interacts with rival brands.

Aşama 2: Segmentation (AI/Manual)

Demographic: Yaş, Konum, Gelir (Geleneksel).
Psychographic: Değerler, İlgi alanları (Modern).
Behavioral: Satın alma sıklığı, Sadakat (Data-driven).

Aşama 3: Persona Creation

Draft Profile: "Tech-Savvy Tina" gibi isimler ver.
Empathy Map: Ne görür, duyar, düşünür, hisseder?
Influencer Match: Bu persona kimi takip eder?

Kontrol Noktaları

Aşama	Doğrulama
1	Veri kaynağı güvenilir ve güncel
2	Segmentler birbirinden net ayrışıyor (Distinct)
3	Persona gerçekçi (hayali değil, veriye dayalı)