Keyword Research

# Keyword Research

Part of [Agent Skills™](https://github.com/itallstartedwithaidea/agent-skills) by [googleadsagent.ai™](https://googleadsagent.ai)

## Description

The Keyword Research skill delivers a systematic approach to keyword discovery, expansion, and optimization for Google Ads campaigns. Starting from seed keywords, it builds comprehensive keyword universes through multiple expansion vectors: semantic variations, competitor keyword mining, search term report analysis, long-tail discovery, and intent-based grouping. The result is a structured keyword strategy that maximizes relevant coverage while minimizing wasted spend.

Match type selection is a critical component. The skill evaluates each keyword against conversion probability, search volume, competition intensity, and cost-per-click economics to recommend the optimal match type. Broad match keywords are paired with smart bidding strategies, phrase match captures high-intent variations, and exact match locks in proven converters. The skill continuously refines match type assignments based on search term report feedback loops.

Negative keyword management is equally important. The skill mines search term reports for irrelevant queries, builds hierarchical negative keyword lists (account-level, campaign-level, ad-group-level), and maintains shared negative keyword lists across campaigns. Proactive negative keyword discovery prevents budget waste before it occurs by identifying common irrelevant query patterns for each industry vertical.

## Use When

- User asks for "keyword research" or "keyword ideas"
- User wants to "expand keywords" or "find new keywords"
- User mentions "negative keywords" or "search term mining"
- User asks about "match types" (broad, phrase, exact)
- User wants to "reduce wasted spend" on irrelevant queries
- User asks to "build a keyword list" or "keyword strategy"
- User mentions "keyword grouping" or "ad group structure"
- User wants "long-tail keywords" or "low competition keywords"
- User asks to "analyze search terms" or "query mining"

## Architecture

```mermaid
flowchart TD
    A[Seed Keywords] --> B[Expansion Engine]

    B --> C[Semantic Expansion]
    B --> D[Competitor Mining]
    B --> E[Search Term Report]
    B --> F[Long-Tail Discovery]
    B --> G[Question Queries]

    C --> H[Raw Keyword Universe]
    D --> H
    E --> H
    F --> H
    G --> H

    H --> I[Deduplication & Normalization]
    I --> J[Intent Classification]

    J --> K[Informational]
    J --> L[Commercial]
    J --> M[Transactional]
    J --> N[Navigational]

    L --> O[Match Type Assignment]
    M --> O
    N --> O

    K --> P[Negative Keyword Candidates]

    O --> Q[Keyword Grouping Engine]
    Q --> R[Themed Ad Groups]

    P --> S[Negative Keyword Lists]
    S --> T[Account-Level Negatives]
    S --> U[Campaign-Level Negatives]
    S --> V[Ad Group-Level Negatives]

    R --> W[Final Keyword Strategy]
    T --> W
    U --> W
    V --> W
```

## Implementation

Keyword expansion and match type assignment engine:

```javascript
const MATCH_TYPES = {
  BROAD: 'BROAD',
  PHRASE: 'PHRASE',
  EXACT: 'EXACT'
};

const INTENT_CATEGORIES = ['informational', 'commercial', 'transactional', 'navigational'];

async function expandKeywords(seedKeywords, config) {
  const { customerId, industry, maxKeywords = 500 } = config;

  const expansionResults = await Promise.all([
    semanticExpansion(seedKeywords),
    competitorKeywordMining(seedKeywords, industry),
    searchTermReportMining(customerId),
    longTailDiscovery(seedKeywords),
    questionQueryExpansion(seedKeywords)
  ]);

  const rawKeywords = deduplicateAndNormalize(expansionResults.flat());
  const classifiedKeywords = rawKeywords.map(kw => ({
    ...kw,
    intent: classifyIntent(kw.text),
    suggestedMatchType: assi