marketingskills/skills/ads/references/audience-targeting.md

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

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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

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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

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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

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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

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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

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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)