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Analyzes target audience demographics, psychographics, behaviors, and platform preferences to inform influencer selection and campaign strategy. Essential foundation for effective influencer marketing.
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| name | audience_intelligence |
| router_kit | FullStackKit |
| description | Analyzes target audience demographics, psychographics, behaviors, and platform preferences to inform influencer selection and campaign strategy. Essential foundation for effective influencer marketing. |
| metadata | {"skillport":{"category":"auto-healed","tags":["agents","algorithms","analytics","artificial intelligence","audience intelligence","automation","behavior","chatbots","cognitive services","deep learning","demographics","embeddings","frameworks","generative ai","inference","large language models","llm","machine learning","market research","model fine-tuning","natural language processing","neural networks","nlp","openai","prompt engineering","rag","retrieval augmented generation","tools","user segments","vector databases","workflow automation"]}} |
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.
Analyze the target audience for [brand/product/category]
Who is the ideal customer for [product] and where do they spend time online?
Here's our customer data: [data]. Build an audience profile for influencer targeting.
Analyze the audience that follows [competitor brand] on social media
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
Profile Psychographics
## Psychographic Profile
### Values & Beliefs
| Value | Importance | How It Manifests |
|-------|------------|------------------|
| [Value 1] | High | [Behavior/preference] |
| [Value 2] | High | [Behavior/preference] |
| [Value 3] | Medium | [Behavior/preference] |
### Interests & Hobbies
**Primary Interests** (directly related to product):
- [Interest 1] - [relevance]
- [Interest 2] - [relevance]
**Adjacent Interests** (lifestyle/cultural):
- [Interest 1] - [connection to brand]
- [Interest 2] - [connection to brand]
### Lifestyle Characteristics
**Daily Life**:
- Morning routine: [description]
- Work/life balance: [description]
- Leisure time: [how they spend it]
- Social habits: [description]
**Aspiration Profile**:
- Who they aspire to be: [description]
- Brands they admire: [brands]
- Lifestyle they want: [description]
### Personality Traits
| Trait | Level | Impact on Content |
|-------|-------|-------------------|
| [Trait 1] | High/Med/Low | [How to appeal] |
| [Trait 2] | High/Med/Low | [How to appeal] |
**Implications for Influencer Selection**:
- Partner with creators who embody [values]
- Content should reflect [lifestyle aspirations]
- Avoid influencers who [misaligned traits]
Map Behavioral Patterns
## Behavioral Analysis
### Purchase Behavior
**Decision Journey**:
| Stage | Duration | Key Activities | Influencer Role |
|-------|----------|----------------|-----------------|
| Awareness | [time] | [activities] | [how influencers help] |
| Consideration | [time] | [activities] | [how influencers help] |
| Decision | [time] | [activities] | [how influencers help] |
| Post-Purchase | [time] | [activities] | [how influencers help] |
**Purchase Triggers**:
- [Trigger 1]: [description]
- [Trigger 2]: [description]
- [Trigger 3]: [description]
**Purchase Barriers**:
- [Barrier 1]: [how to overcome]
- [Barrier 2]: [how to overcome]
### Content Consumption
**Daily Media Diet**:
| Time | Activity | Platforms | Content Type |
|------|----------|-----------|--------------|
| Morning | [activity] | [platforms] | [content] |
| Commute | [activity] | [platforms] | [content] |
| Lunch | [activity] | [platforms] | [content] |
| Evening | [activity] | [platforms] | [content] |
| Weekend | [activity] | [platforms] | [content] |
**Content Engagement Patterns**:
- Most active time: [days/times]
- Average session length: [duration]
- Engagement style: [passive viewer/active commenter/sharer]
- Discovery method: [algorithm/search/recommendations]
### Social Behavior
**How They Interact with Influencers**:
- Follow count: [typical range]
- Engagement level: [lurker/occasional/active]
- Trust in recommendations: [low/medium/high]
- UGC creation: [never/occasionally/frequently]
Analyze Platform Preferences
## Platform Analysis
### Platform Priority Matrix
| Platform | Usage Level | Primary Purpose | Best Content Type |
|----------|-------------|-----------------|-------------------|
| Instagram | High/Med/Low | [purpose] | [format] |
| TikTok | High/Med/Low | [purpose] | [format] |
| YouTube | High/Med/Low | [purpose] | [format] |
| Twitter/X | High/Med/Low | [purpose] | [format] |
| LinkedIn | High/Med/Low | [purpose] | [format] |
| Pinterest | High/Med/Low | [purpose] | [format] |
| Twitch | High/Med/Low | [purpose] | [format] |
### Primary Platform Deep-Dive: [Platform]
**Usage Patterns**:
- Time spent: [hours/day]
- Sessions: [frequency]
- Primary activities: [discovery/entertainment/shopping/social]
**Content Preferences**:
- Preferred format: [Stories/Reels/Feed/etc.]
- Content length: [preference]
- Audio: [sound on/off]
**Influencer Relationship**:
- Influencer types followed: [mega/macro/micro/nano]
- Categories: [lifestyle/comedy/educational/etc.]
- Trust level: [how much they trust platform recommendations]
### Platform Recommendation
**Prioritize these platforms**:
1. [Platform 1]: [reason] - [% of budget recommended]
2. [Platform 2]: [reason] - [% of budget recommended]
3. [Platform 3]: [reason] - [% of budget recommended]
**Avoid or deprioritize**:
- [Platform]: [reason]
Identify Content Preferences
## Content Preference Analysis
### Format Preferences
| Format | Preference | Best For | Example |
|--------|------------|----------|---------|
| Short video (<60s) | High/Med/Low | [use case] | [example] |
| Long video (>3min) | High/Med/Low | [use case] | [example] |
| Static images | High/Med/Low | [use case] | [example] |
| Carousel posts | High/Med/Low | [use case] | [example] |
| Stories | High/Med/Low | [use case] | [example] |
| Live streams | High/Med/Low | [use case] | [example] |
| Podcasts | High/Med/Low | [use case] | [example] |
### Content Style Preferences
**Tone that resonates**:
- [Authentic/polished]
- [Humorous/serious]
- [Educational/entertaining]
- [Aspirational/relatable]
**Visual aesthetics**:
- [Minimalist/maximalist]
- [Bright/moody]
- [Professional/casual]
- [Trendy/timeless]
**Storytelling preferences**:
- [Personal stories/product focus]
- [Problem-solution/lifestyle integration]
- [Tutorial/review/unboxing]
### Topics That Engage
| Topic | Interest Level | Content Angle |
|-------|----------------|---------------|
| [Topic 1] | High | [angle] |
| [Topic 2] | High | [angle] |
| [Topic 3] | Medium | [angle] |
### Content Red Flags
**Avoid these approaches**:
- [Approach 1]: [why it fails]
- [Approach 2]: [why it fails]
Profile Influencer Affinity
## Influencer Affinity Analysis
### Influencer Types They Follow
| Type | Popularity | Trust Level | Example Categories |
|------|------------|-------------|-------------------|
| Mega (1M+) | [%] | [level] | [categories] |
| Macro (100K-1M) | [%] | [level] | [categories] |
| Micro (10K-100K) | [%] | [level] | [categories] |
| Nano (<10K) | [%] | [level] | [categories] |
### Why They Follow Influencers
| Motivation | Strength | Implications |
|------------|----------|--------------|
| Entertainment | High/Med/Low | [content strategy] |
| Education | High/Med/Low | [content strategy] |
| Aspiration | High/Med/Low | [content strategy] |
| Deals/Discounts | High/Med/Low | [content strategy] |
| Community | High/Med/Low | [content strategy] |
| FOMO | High/Med/Low | [content strategy] |
### Trust Factors
**What builds credibility**:
1. [Factor 1]: [explanation]
2. [Factor 2]: [explanation]
3. [Factor 3]: [explanation]
**What destroys trust**:
1. [Factor 1]: [why it fails]
2. [Factor 2]: [why it fails]
### Ideal Influencer Profile
Based on audience analysis, ideal influencers should:
- **Be aged**: [range]
- **Have aesthetic**: [style description]
- **Create content about**: [topics]
- **Communicate with**: [tone/style]
- **Have engagement rate**: [minimum %]
- **Be on**: [priority platforms]
- **Avoid**: [red flags]
Generate Audience Persona
## Audience Persona
### "[Persona Name]"
**Demographics**:
- Age: [X]
- Location: [city/region]
- Occupation: [job]
- Income: [range]
- Family: [status]
**Bio**:
[2-3 sentence description of who they are]
**A Day in Their Life**:
[Brief narrative of typical day including media consumption]
**Goals & Challenges**:
- Goals: [what they want to achieve]
- Challenges: [what stands in their way]
- How [product] helps: [connection]
**Media Consumption**:
- Primary platform: [platform]
- Content preferences: [types]
- Influencers they follow: [examples/types]
- Trust triggers: [what makes them believe]
**Purchase Journey**:
- Discovery: [how they find products]
- Research: [how they evaluate]
- Decision: [what tips them over]
- Loyalty: [what keeps them]
**Key Quote**:
> "[A quote this persona might say about the product/category]"
Summarize Influencer Selection Criteria
# Audience Analysis Summary
## Key Audience Insights
1. [Most important insight]
2. [Second insight]
3. [Third insight]
## Influencer Selection Criteria
Based on this audience analysis:
### Must-Have Criteria
| Criterion | Requirement | Reasoning |
|-----------|-------------|-----------|
| Audience age | [range] | Matches target demographic |
| Platform | [platforms] | Where audience is active |
| Content style | [style] | Resonates with preferences |
| Engagement rate | [min %] | Indicates active audience |
| Values alignment | [values] | Matches audience beliefs |
### Nice-to-Have Criteria
| Criterion | Preference | Reasoning |
|-----------|------------|-----------|
| [criterion] | [preference] | [reason] |
### Red Flags to Avoid
- [Red flag 1]
- [Red flag 2]
- [Red flag 3]
## Recommended Influencer Mix
| Tier | % of Budget | Quantity | Role |
|------|-------------|----------|------|
| Mega (1M+) | [%] | [#] | Awareness/credibility |
| Macro (100K-1M) | [%] | [#] | Reach + engagement |
| Micro (10K-100K) | [%] | [#] | Trust + conversion |
| Nano (<10K) | [%] | [#] | Authenticity + UGC |
## Next Steps
1. Use these criteria in [influencer-discovery](../../map/influencer-discovery/)
2. Score potential influencers with [fit-scorer](../../map/fit-scorer/)
3. Develop content strategy based on [content preferences]
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.]
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())
Kaynak: Data-Driven Marketing Guide
| Aşama | Doğrulama |
|---|---|
| 1 | Veri kaynağı güvenilir ve güncel |
| 2 | Segmentler birbirinden net ayrışıyor (Distinct) |
| 3 | Persona gerçekçi (hayali değil, veriye dayalı) |