tiktok-audience-strategy

TikTok Audience Strategy

Purpose

Help advertisers build and manage audience targeting strategies on TikTok that balance reach with relevance. TikTok's algorithm is exceptionally good at finding the right users within broad audiences — often outperforming narrow targeting. This skill provides frameworks for when to go broad vs narrow, how to build and layer audiences, lookalike sizing guidance, and refresh cadence to prevent audience fatigue.

When to Use This Skill

Invoke when user mentions:

• Targeting: "How should I target my TikTok ads?"
• Audiences: "How do I build audiences on TikTok?"
• Lookalikes: "What lookalike size should I use on TikTok?"
• Broad vs narrow: "Should I use interest targeting or go broad?"
• Custom audiences: "How do I create a custom audience from my pixel?"
• Audience refresh: "When should I refresh my TikTok audiences?"

Required Tools

Tool	Purpose
tiktok_get_audiences	Retrieve existing audiences, their sizes, and configurations
tiktok_query	Pull campaign targeting configurations and audience performance

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Part 1: TikTok Targeting Architecture

Targeting Layer Overview

TIKTOK TARGETING HIERARCHY:
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LAYER 0: SEARCH ADS (highest intent — new as of Sep 2025)
├── Keyword-based targeting in TikTok's built-in search
├── Reaches users actively searching on TikTok
├── Formats: text ads and video ads in search results
├── Best for: high-intent users, product discovery
└── Note: TikTok Search Ads available in most markets since Q3 2025

LAYER 1: DEMOGRAPHICS (broadest)
├── Location (country, region, DMA)
├── Age (13-17, 18-24, 25-34, 35-44, 45-54, 55+)
├── Gender (male, female, all)
├── Language
└── These are always set, even in "broad" campaigns

LAYER 2: INTERESTS (medium precision)
├── Interest categories (predefined by TikTok)
├── Based on: content consumed, accounts followed, interactions
├── Examples: "Fashion", "Cooking", "Fitness", "Technology"
├── Can select multiple interests (OR logic within category)
└── Refreshes: Updates dynamically based on recent behavior

LAYER 3: BEHAVIORS (higher precision)
├── Video interactions (liked, commented, shared specific content types)
├── Creator interactions (followed certain creator categories)
├── Hashtag interactions (engaged with specific hashtags)
└── More recent signal than interests (last 7-15 days of activity)

LAYER 4: CUSTOM AUDIENCES (highest precision)
├── Pixel-based (website visitors, converters)
├── Engagement-based (ad interactions, video views)
├── Customer file (email/phone upload)
├── App activity (install, in-app events)
└── Lead generation (people who submitted lead forms)

LAYER 5: LOOKALIKE AUDIENCES (algorithmic expansion)
├── Built from any custom audience
├── Sizes: Narrow (1%), Balanced (2-3%), Broad (5-10%)
├── TikTok finds users similar to your seed audience
└── Quality depends heavily on seed audience quality