51-audience-research-global v1.0.0A

Immutable. This exact content is served forever at /api/v1/blob/aac085f381f4c3ca.

---
name: 51-audience-research-global
description: "Use when the user needs to research and define paid ad audiences before a campaign launches: target profile, interest and behavior mapping per platform, audience sizing, cold/warm/hot tiering, lookalike seeds, and targeting hypotheses to test. Trigger on 'audience research', 'target audience', 'interest targeting', 'audience for campaign', 'who should I target', 'Meta audience', 'TikTok audience', or 'lookalike seed'."
metadata:
  version: 1.0.0
  category: performance
license: MIT
triggers:
  - "audience research"
  - "target audience"
  - "interest targeting"
  - "audience for campaign"
  - "who should I target"
  - "Meta audience"
  - "TikTok audience"
  - "lookalike seed"
output: "File .md — audience profile, ready-to-paste targeting settings per platform, cold/warm/hot tiers, lookalike seed brief, and a ranked list of targeting hypotheses to test"
related:
  - product-marketing-context-global
  - 09-customer-insight-global
  - 08-competitor-research-global
  - 10-reverse-kpi-global
  - 52-account-structure-global
  - 54-media-plan-global
  - 56-retargeting-plan-global
---

# Audience Research (Global)

Wrong targeting burns budget no matter how good the copy or creative is. This is the first step of the performance chain: 51 -> `10-reverse-kpi-global` -> `54-media-plan-global` -> `53-tracking-setup-global` -> `52-account-structure-global`. If there is no customer insight yet, run `09-customer-insight-global` first.

## Information gathering

Read `.agents/product-marketing-context-global.md` and any output from `09-customer-insight-global`. If information is missing, ask up to 4 questions:

1. **What product or service will run ads?** Price point and market tier (mass / mid / premium)?
2. **What existing customer data is available?** Age, gender, geography, purchase behavior, past customer list, CRM export, pixel data.
3. **Which platforms and which markets?** Meta / Google / TikTok / YouTube / LinkedIn / Pinterest — and which countries. Primary objective: lead gen / conversion / traffic / awareness?
4. **Planned budget and target CPA/CPL?** If not calculated yet, run `10-reverse-kpi-global`.

## Principles

1. **Geography is the biggest cost lever, before interests.** Per `references/benchmarks-global.md`, Tier 1 markets (US, Canada, Australia, Western EU) run 6-7x the CPM of Tier 2 (SEA, LATAM). US Meta CPM sits at $15-25 vs $2-6 in Brazil/LATAM. Decide the market before debating interest stacks.
2. **Research before copy and before campaign build.** Never the reverse.
3. **Broad first, narrow only with evidence.** Modern delivery algorithms optimize well on a wide pool plus strong creative. Narrow only when segment data proves it.
4. **One clear interest theme per ad set.** Stacking many unrelated interests makes it impossible to tell which one worked.
5. **Write pain points in the customer's own words**, not marketer language.
6. **Size for spend, not for a magic number.** A cold ad set must be large enough to spend its daily budget for at least 7 days without frequency passing 2.5. A tight interest stack saturates fast in a Tier 1 market where CPM is $13-20.
7. **This is a living document.** Update it after 3-5 days of live data by comparing CPA per segment.

## Workflow

### 1. Core audience profile

Build from real data (CRM, analytics, order history, platform audience insights) plus `09-customer-insight-global`:

| Field | Detail |
|-------|--------|
| Age | [primary band + secondary band] |
| Gender | [actual split from data, not assumption] |
| Markets | [countries/regions you can actually ship to or serve] |
| Market tier | Tier 1 (US/CA/AU/W.EU) / Tier 2 (SEA/LATAM) / mixed |
| Income / budget band | [must match the price point] |
| Job or role | [primary segment; required for B2B] |
| Primary device | Mobile / Desktop / Both |
| Language | [ad language per market] |

For B2B, add company size, industry, seniority, and buying committee role.

### 2. Psychographics and behavior

- **Top pain points:** 3-5, phrased the way customers say them.
- **Buying motivation:** what they want to gain, what they want to avoid.
- **Purchase context:** card-first checkout, subscription comfort, review dependence, return-policy sensitivity.
- **Online behavior:** platforms used, peak hours per market timezone, content formats they engage with.

### 3. Targeting settings per platform

Only build blocks for platforms that will actually run. Each block should be paste-ready into the ads manager.

- **Meta Ads:** age, gender, locations (list countries explicitly, exclude where you cannot fulfill); detailed targeting with 10-15 related interests grouped into 2-3 themes for separate testing; behaviors; exclusions (past purchasers, submitted leads); a broad-vs-narrow recommendation. Note whether Advantage+ audience will be used as a control.
- **Google Ads:** in-market audiences; custom segments built from 10-15 high-intent search terms; affinity; Customer Match if a consented list exists. Search intent beats demographic targeting here.
- **TikTok Ads:** age, gender, interests, behaviors (recent video interactions), device OS, creator-adjacent targeting.
- **YouTube Ads:** custom segments from search terms, placements (specific channels/videos), topics, life events.
- **LinkedIn Ads (B2B only):** job title, function, seniority, company size, industry, member skills, matched company lists. Expect CPM $30-100+ per `references/benchmarks-global.md` — validate the economics with `10-reverse-kpi-global` before committing.
- **Pinterest Ads:** interests, keywords, actalike audiences. Strongest for home, fashion, DIY, and female 25-54.

### 4. Cold / warm / hot tiers

| Tier | Definition | Signal | Source |
|------|-----------|--------|--------|
| Hot | Landing page visit, add to cart, checkout started, form opened, open sales conversation | Pixel/CAPI events, CRM | Meta, Google, TikTok, CRM |
| Warm | Video watched >50%, page or post engagement, link click, follow, email opened | Engagement custom audiences, ESP segments | Meta, TikTok, email platform |
| Cold | Does not know the brand | Interest, behavior, broad, lookalike, search intent | All platforms |

Rule: cold and warm/hot must live in separate campaigns with different messages (see `56-retargeting-plan-global`). Always exclude warm and hot from cold campaigns so the data stays clean.

### 5. Lookalike and seed audiences

| Seed | Minimum seed size | Lookalike % | Platform | Purpose |
|------|------------------:|-------------|----------|---------|
| Past purchasers | >= 100 | 1-3% | Meta | Find people like the best customers |
| Qualified leads | >= 500 | 1-5% | Meta | Scale lead generation |
| Video viewers 75% | >= 1000 | Broad | TikTok | Scale awareness |
| Email / CRM list | >= 300 | 1-5% | Meta, Google Customer Match | Extend from first-party data |

Seed quality beats seed size: purchasers outperform leads, leads outperform viewers. Do not build a lookalike from a seed below the minimum. Any uploaded customer list must have marketing consent on record — see the consent section in `53-tracking-setup-global`.

### 6. Targeting hypotheses to test

One line each, in the form "If we target [X], then [metric] will [Y], because [Z]". Hand these to `19-ab-test-setup-global` and `52-account-structure-global`.

| # | Hypothesis | Test variable | Metric | Priority |
|---|-----------|---------------|--------|----------|
| 1 | Broad plus strong creative is cheaper than a manual interest stack | Broad vs interest | CPA | High |
| 2 | [Interest theme A] sits closer to the pain than [theme B] | Interest A vs B | CPA, CTR | High |
| 3 | 1% purchaser lookalike converts better than 3% | Lookalike % | CPA, close rate | Medium |
| 4 | [Market X] delivers acceptable CPA despite higher CPM | Geo split | CPA, ROAS | Medium |
| 5 | [Age segment] converts better | Age split | CPA | Low |

Maximum 3-5 test ad sets at once. More than that splits budget too thin to reach a conclusion.

## Output structure

File name: `audience-research-[product]-[YYYYMMDD].md`

```markdown
# Audience Research — [Product]
Date: [YYYY-MM-DD] · Platforms: [list] · Markets: [countries] · Objective: [Lead/Conversion]

## 1. Core audience profile
| Age | Gender | Markets | Tier | Income band | Role | Device | Language |

## 2. Psychographics and behavior
- Pain points: [3-5, customer wording]
- Motivation: [gain / avoid]
- Purchase context: [payment, reviews, returns, subscription]
- Online behavior: [platforms, peak hours, preferred formats]

## 3. Targeting settings per platform
### Meta: [age/gender/geo/interest themes/behaviors/exclusions/Advantage+ control]
### Google: [...] · TikTok: [...] · LinkedIn: [...] · Pinterest: [...]

## 4. Audience tiers
| Tier | Audience | Signal | Campaign that uses it |

## 5. Lookalike and seed audiences
| Seed | Size | Lookalike % | Platform | Purpose | Consent status |

## 6. Targeting hypotheses (hand to A/B test)
| # | Hypothesis | Variable | Metric | Priority |

## 7. Sizing and overlap notes
- Estimated reach per ad set: [number]
- Days of spend the pool supports at planned budget: [number]
- Overlaps to avoid: [audiences likely to collide]
- Regional CPM expectation per `references/benchmarks-global.md`: [range]
```

## After the first live data

- After 3-5 days: compare CPA and lead quality per audience segment. Record winners and losers in the profile.
- Winning audiences expand into similar lookalike or broad pools (see `55-scaling-ads-global`). Losing audiences get a written reason so nobody retests the same thing.
- Monthly, reconcile the profile against CRM data: which source closes best. Cheap leads are not always good leads.

## Related skills

- `09-customer-insight-global`: run first — supplies insight, pain, and customer language.
- `08-competitor-research-global`: see who competitors target and with which angles (ad libraries).
- `10-reverse-kpi-global`: max CPA and budget before planning.
- `54-media-plan-global`: turns this audience map into channel and budget allocation.
- `52-account-structure-global`: turns targeting settings into ad set structure.
- `56-retargeting-plan-global`: detailed warm/hot tiering and messaging.

## Quality checklist

- [ ] Profile built from real data (CRM, analytics, surveys), not guesses
- [ ] Market tier stated, with the CPM expectation from `references/benchmarks-global.md`
- [ ] Pain points written in customer language, not marketer language
- [ ] Targeting settings complete and paste-ready for every platform in the plan
- [ ] Three tiers defined (cold/warm/hot) with cross-exclusion rules
- [ ] Lookalike seeds meet minimum size and have documented consent
- [ ] 3-5 targeting hypotheses, each with a test variable and a metric
- [ ] Cold audience large enough to sustain 7 days of planned spend below frequency 2.5
- [ ] Plan in place to update the profile after 3-5 days of live data