1# Audience Targeting Reference
2 
3Detailed targeting strategies for each major ad platform.
4 
5## Contents
6- Google Ads Audiences (Search Campaign Targeting, Display/YouTube Targeting)
7- Meta Audiences (Core Audiences, Custom Audiences, Lookalike Audiences)
8- LinkedIn Audiences (Job-Based Targeting, Company-Based Targeting, High-Performing Combinations)
9- Twitter/X Audiences
10- TikTok Audiences
11- Audience Size Guidelines
12- Exclusion Strategy
13 
14## Google Ads Audiences
15 
16### Search Campaign Targeting
17 
18**Keywords:**
19- Exact match: [keyword] — most precise, lower volume
20- Phrase match: "keyword" — moderate precision and volume
21- Broad match: keyword — highest volume, use with smart bidding
22 
23**Audience layering:**
24- Add audiences in "observation" mode first
25- Analyze performance by audience
26- Switch to "targeting" mode for high performers
27 
28**RLSA (Remarketing Lists for Search Ads):**
29- Bid higher on past visitors searching your terms
30- Show different ads to returning searchers
31- Exclude converters from prospecting campaigns
32 
33### Display/YouTube Targeting
34 
35**Custom intent audiences:**
36- Based on recent search behavior
37- Create from your converting keywords
38- High intent, good for prospecting
39 
40**In-market audiences:**
41- People actively researching solutions
42- Pre-built by Google
43- Layer with demographics for precision
44 
45**Affinity audiences:**
46- Based on interests and habits
47- Better for awareness
48- Broad but can exclude irrelevant
49 
50**Customer match:**
51- Upload email lists
52- Retarget existing customers
53- Create lookalikes from best customers
54 
55**Similar/lookalike audiences:**
56- Based on your customer match lists
57- Expand reach while maintaining relevance
58- Best when source list is high-quality customers
59 
60---
61 
62## Meta Audiences
63 
64### Core Audiences (Interest/Demographic)
65 
66**Interest targeting tips:**
67- Layer interests with AND logic for precision
68- Use Audience Insights to research interests
69- Start broad, let algorithm optimize
70- Exclude existing customers always
71 
72**Demographic targeting:**
73- Age and gender (if product-specific)
74- Location (down to zip/postal code)
75- Language
76- Education and work (limited data now)
77 
78**Behavior targeting:**
79- Purchase behavior
80- Device usage
81- Travel patterns
82- Life events
83 
84### Custom Audiences
85 
86**Website visitors:**
87- All visitors (last 180 days max)
88- Specific page visitors
89- Time on site thresholds
90- Frequency (visited X times)
91 
92**Customer list:**
93- Upload emails/phone numbers
94- Match rate typically 30-70%
95- Refresh regularly for accuracy
96 
97**Engagement audiences:**
98- Video viewers (25%, 50%, 75%, 95%)
99- Page/profile engagers
100- Form openers
101- Instagram engagers
102 
103**App activity:**
104- App installers
105- In-app events
106- Purchase events
107 
108### Lookalike Audiences
109 
110**Source audience quality matters:**
111- Use high-LTV customers, not all customers
112- Purchasers > leads > all visitors
113- Minimum 100 source users, ideally 1,000+
114 
115**Size recommendations:**
116- 1% — most similar, smallest reach
117- 1-3% — good balance for most
118- 3-5% — broader, good for scale
119- 5-10% — very broad, awareness only
120 
121**Layering strategies:**
122- Lookalike + interest = more precision early
123- Test lookalike-only as you scale
124- Exclude the source audience
125 
126---
127 
128## LinkedIn Audiences
129 
130### Job-Based Targeting
131 
132**Job titles:**
133- Be specific (CMO vs. "Marketing")
134- LinkedIn normalizes titles, but verify
135- Stack related titles
136- Exclude irrelevant titles
137 
138**Job functions:**
139- Broader than titles
140- Combine with seniority level
141- Good for awareness campaigns
142 
143**Seniority levels:**
144- Entry, Senior, Manager, Director, VP, CXO, Partner
145- Layer with function for precision
146 
147**Skills:**
148- Self-reported, less reliable
149- Good for technical roles
150- Use as expansion layer
151 
152### Company-Based Targeting
153 
154**Company size:**
155- 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5000+
156- Key filter for B2B
157 
158**Industry:**
159- Based on company classification
160- Can be broad, layer with other criteria
161 
162**Company names (ABM):**
163- Upload target account list
164- Minimum 300 companies recommended
165- Match rate varies
166 
167**Company growth rate:**
168- Hiring rapidly = budget available
169- Good signal for timing
170 
171### High-Performing Combinations
172 
173| Use Case | Targeting Combination |
174|----------|----------------------|
175| Enterprise sales | Company size 1000+ + VP/CXO + Industry |
176| SMB sales | Company size 11-200 + Manager/Director + Function |
177| Developer tools | Skills + Job function + Company type |
178| ABM campaigns | Company list + Decision-maker titles |
179| Broad awareness | Industry + Seniority + Geography |
180 
181---
182 
183## Twitter/X Audiences
184 
185### Targeting options:
186- Follower lookalikes (accounts similar to followers of X)
187- Interest categories
188- Keywords (in tweets)
189- Conversation topics
190- Events
191- Tailored audiences (your lists)
192 
193### Best practices:
194- Follower lookalikes of relevant accounts work well
195- Keyword targeting catches active conversations
196- Lower CPMs than LinkedIn/Meta
197- Less precise, better for awareness
198 
199---
200 
201## TikTok Audiences
202 
203### Targeting options:
204- Demographics (age, gender, location)
205- Interests (TikTok's categories)
206- Behaviors (video interactions)
207- Device (iOS/Android, connection type)
208- Custom audiences (pixel, customer file)
209- Lookalike audiences
210 
211### Best practices:
212- Younger skew (18-34 primarily)
213- Interest targeting is broad
214- Creative matters more than targeting
215- Let algorithm optimize with broad targeting
216 
217---
218 
219## Audience Size Guidelines
220 
221| Platform | Minimum Recommended | Ideal Range |
222|----------|-------------------|-------------|
223| Google Search | 1,000+ searches/mo | 5,000-50,000 |
224| Google Display | 100,000+ | 500K-5M |
225| Meta | 100,000+ | 500K-10M |
226| LinkedIn | 50,000+ | 100K-500K |
227| Twitter/X | 50,000+ | 100K-1M |
228| TikTok | 100,000+ | 1M+ |
229 
230Too narrow = expensive, slow learning
231Too broad = wasted spend, poor relevance
232 
233---
234 
235## Exclusion Strategy
236 
237Always exclude:
238- Existing customers (unless upsell)
239- Recent converters (7-14 days)
240- Bounced visitors (<10 sec)
241- Employees (by company or email list)
242- Irrelevant page visitors (careers, support)
243- Competitors (if identifiable)
244