# Audience Targeting Reference
Detailed targeting strategies for each major ad platform.

- Contents
- Google Ads Audiences (Search Campaign Targeting, Display/YouTube Targeting)
- Meta Audiences (Core Audiences, Custom Audiences, Lookalike Audiences)

LinkedIn Audiences (Job-Based Targeting, Company-Based Targeting, High-Performing Combinations)

## Twitter/X Audiences

### TikTok Audiences

### Audience Size Guidelines

### Exclusion Strategy

### Google Ads Audiences

### Search Campaign Targeting

Keywords:

- Exact match: [keyword] — most precise, lower volume
- Phrase match: "keyword" — moderate precision and volume
- Broad match: keyword — highest volume, use with smart bidding

Audience layering:

- Add audiences in "observation" mode first
- Analyze performance by audience
- Switch to "targeting" mode for high performers

RLSA (Remarketing Lists for Search Ads):

- Bid higher on past visitors searching your terms
- Show different ads to returning searchers
- Exclude converters from prospecting campaigns

## Display/YouTube Targeting

Custom intent audiences:

- Based on recent search behavior
- Create from your converting keywords
- High intent, good for prospecting

In-market audiences:

- People actively researching solutions
- Pre-built by Google
- Layer with demographics for precision

Affinity audiences:

- Based on interests and habits
- Better for awareness
- Broad but can exclude irrelevant

Customer match:

- Upload email lists
- Retarget existing customers
- Create lookalikes from best customers

Similar/lookalike audiences:

- Based on your customer match lists
- Expand reach while maintaining relevance
- Best when source list is high-quality customers

## Meta Audiences

### Core Audiences (Interest/Demographic)

Interest targeting tips:

- Layer interests with AND logic for precision
- Use Audience Insights to research interests
- Start broad, let algorithm optimize
- Exclude existing customers always

Demographic targeting:

- Age and gender (if product-specific)
- Location (down to zip/postal code)
- Language
- Education and work (limited data now)

Behavior targeting:

- Purchase behavior
- Device usage
- Travel patterns
- Life events

## Custom Audiences

Website visitors:

- All visitors (last 180 days max)
- Specific page visitors
- Time on site thresholds
- Frequency (visited X times)

Customer list:

- Upload emails/phone numbers
- Match rate typically 30-70%
- Refresh regularly for accuracy

Engagement audiences:

- Video viewers (25%, 50%, 75%, 95%)
- Page/profile engagers
- Form openers
- Instagram engagers

App activity:

- App installers
- In-app events
- Purchase events

## Lookalike Audiences

Source audience quality matters:

- Use high-LTV customers, not all customers
- Purchasers > leads > all visitors
- Minimum 100 source users, ideally 1,000+

Size recommendations:

- 1% — most similar, smallest reach
- 1-3% — good balance for most
- 3-5% — broader, good for scale
- 5-10% — very broad, awareness only

Layering strategies:

- Lookalike + interest = more precision early
- Test lookalike-only as you scale
- Exclude the source audience

## LinkedIn Audiences

### Job-Based Targeting

Job titles:

- Be specific (CMO vs. "Marketing")
- LinkedIn normalizes titles, but verify
- Stack related titles
- Exclude irrelevant titles

Job functions:

- Broader than titles
- Combine with seniority level
- Good for awareness campaigns

Seniority levels:

- Entry, Senior, Manager, Director, VP, CXO, Partner
- Layer with function for precision

Skills:

- Self-reported, less reliable
- Good for technical roles
- Use as expansion layer

## Company-Based Targeting

Company size:

- 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5000+
- Key filter for B2B

Industry:

- Based on company classification
- Can be broad, layer with other criteria

Company names (ABM):

- Upload target account list
- Minimum 300 companies recommended
- Match rate varies

Company growth rate:

- Hiring rapidly = budget available
- Good signal for timing

## High-Performing Combinations

Use Case	Targeting Combination
Enterprise sales	Company size 1000+ + VP/CXO + Industry
SMB sales	Company size 11-200 + Manager/Director + Function
Developer tools	Skills + Job function + Company type
ABM campaigns	Company list + Decision-maker titles
Broad awareness	Industry + Seniority + Geography

## Twitter/X Audiences

Targeting options:

- Follower lookalikes (accounts similar to followers of X)
- Interest categories
- Keywords (in tweets)
- Conversation topics
- Events
- Tailored audiences (your lists)
- Best practices:
- Follower lookalikes of relevant accounts work well
- Keyword targeting catches active conversations
- Lower CPMs than LinkedIn/Meta
- Less precise, better for awareness

## TikTok Audiences

- Targeting options:
- Demographics (age, gender, location)
- Interests (TikTok's categories)
- Behaviors (video interactions)
- Device (iOS/Android, connection type)
- Custom audiences (pixel, customer file)
- Lookalike audiences
- Best practices:
- Younger skew (18-34 primarily)
- Interest targeting is broad
- Creative matters more than targeting
- Let algorithm optimize with broad targeting

## Audience Size Guidelines

Platform	Minimum Recommended	Ideal Range
Google Search	1,000+ searches/mo	5,000-50,000
Google Display	100,000+	500K-5M
Meta	100,000+	500K-10M
LinkedIn	50,000+	100K-500K
Twitter/X	50,000+	100K-1M
TikTok	100,000+	1M+

## Too narrow = expensive, slow learning Too broad = wasted spend, poor relevance

### Exclusion Strategy

Always exclude:

- Existing customers (unless upsell)
- Recent converters (7-14 days)
- Bounced visitors (<10 sec)
- Employees (by company or email list)
- Irrelevant page visitors (careers, support)
- Competitors (if identifiable)
