programmatic-seo

Programmatic SEO

Production-grade framework for building SEO page sets at scale. Covers the full lifecycle from keyword pattern discovery through template design, data pipeline construction, quality assurance, and post-launch optimization. Designed for deployments ranging from 50 to 100,000+ pages.

Table of Contents

• [When to Use vs When Not To](#when-to-use-vs-when-not-to)
• [Initial Assessment](#initial-assessment)
• [The 14 Playbooks](#the-14-playbooks)
• [Playbook Selection Matrix](#playbook-selection-matrix)
• [Keyword Pattern Mining](#keyword-pattern-mining)
• [Data Pipeline Architecture](#data-pipeline-architecture)
• [Template Design System](#template-design-system)
• [Quality Control Framework](#quality-control-framework)
• [Internal Linking Architecture](#internal-linking-architecture)
• [Indexation Strategy](#indexation-strategy)
• [Launch Sequence](#launch-sequence)
• [Post-Launch Optimization](#post-launch-optimization)
• [Anti-Patterns and Penalty Avoidance](#anti-patterns-and-penalty-avoidance)
• [Decision Matrix: Build vs Skip](#decision-matrix-build-vs-skip)
• [Output Artifacts](#output-artifacts)
• [Related Skills](#related-skills)

When to Use vs When Not To

Use this skill when:

• You have a repeating keyword pattern with 50+ variations
• You have (or can acquire) structured data to populate pages
• The search intent is consistent across variations
• Your domain has sufficient authority to compete

Do NOT use when:

• Each page requires unique editorial content (use content-creator instead)
• Total addressable pages < 30 (manual content is more effective)
• You lack a data source and would be generating thin placeholder content
• Your domain authority is below DR 20 and competitors are DR 60+

Initial Assessment

Before designing any pSEO strategy, answer these questions. Skip nothing.

1. Opportunity Validation

| Question | Why It Matters | Red Flag | |----------|---------------|----------| | What is the repeating keyword pattern? | Defines the template structure | Pattern is vague or inconsistent | | What is the aggregate monthly search volume? | Determines ROI ceiling | < 5,000 aggregate monthly searches | | How many unique pages can you generate? | Scope the project | < 50 pages (too few) or > 50K without data infrastructure | | What does the SERP look like for sample queries? | Competitive feasibility | Page 1 dominated by DR 80+ editorial content | | Is intent informational, navigational, or transactional? | Template design | Mixed intent across the same pattern |

2. Data Source Evaluation

Rate your data source on this scale:

| Tier | Source Type | Defensibility | Example | |------|-----------|---------------|---------| | S | Proprietary first-party | Unbeatable | Your product usage data, internal benchmarks | | A | Product-derived | Strong | Aggregated user analytics, customer outcomes | | B | User-generated | Moderate | Community reviews, submitted content | | C | Licensed exclusive | Moderate | Paid data feed no competitor has | | D | Public aggregated | Weak | Government data, public APIs | | F | Scraped commodity | None | Wikipedia rewrites, copied listings |

Rule: Do not build pSEO on Tier F data. Google penalizes commodity rewrites. If your only data source is public and easily replicable, invest in acquiring Tier A-C data first.

3. Competitive Moat Assessment

For 5 sample queries in your pattern, analyze page 1 results:

• What is the average Domain Rating of ranking pages?
• Are existing results programmatic or editorial?
• What unique data do ranking pages provide?
• What is the content depth (word count, data richness, UX quality)?

Go/No-Go threshold: If the average DR gap between you and page 1 is > 30 AND existing results have proprietary data, the opportunity requires either a differentiated approach or domain authority building first.

The 14 Playbooks

| # | Playbook | Pattern | Example | Data Requirement | |---|----------|---------|---------|-----------------| | 1 | Templates | "[Type] template" | "resume template", "invoice template" | Template files + metadata | | 2 | Curation | "best [category]" | "best CRM for startups" | Product/service reviews + ratings | | 3 | Conversions | "[X] to [Y]" | "100 USD to EUR" | Conversion logic/API | | 4 | Comparisons | "[X] vs [Y]" | "Notion vs Confluence" | Feature data for both products | | 5 | Examples | "[type] examples" | "landing page examples" | Curated example collection | | 6 | Locations | "[service] in [city]" | "coworking in Austin" | Location-specific data | | 7 | Personas | "[product] for [audience]" | "CRM for real estate" | Audience-specific use cases | | 8 | Integrations | "[A] + [B] integration" | "Slack Asana integration" | Integration documentation | | 9 | Glossary | "what is [term]" | "what is churn rate" | Domain expertise | | 10 | Translations | Content in N languages | Localized guides | Translation + localization data | | 11 | Directory | "[category] tools" | "AI writing tools" | Tool listings + evaluations | | 12 | Profiles | "[entity name]" | "Stripe company profile" | Entity-level data | | 13 | Statistics | "[topic] statistics" | "SaaS churn statistics 2026" | Verified statistical data | | 14 | Calculators | "[topic] calculator" | "LTV calculator" | Calculation logic + inputs |

Playbook Selection Matrix

| If you have... | Primary Playbook | Secondary Layer | |----------------|-----------------|-----------------| | A product with many integrations | Integrations | Comparisons | | A design/creative tool | Templates + Examples | Personas | | A multi-segment audience | Personas | Comparisons | | Local/regional presence | Locations | Directory | | A tool/utility product | Calculators + Conversions | Glossary | | Deep domain expertise | Glossary + Statistics | Curation | | A competitor landscape to exploit | Comparisons + Curation | Directory | | User-generated content | Examples + Directory | Profiles |

Layering rule: Combine up to 2 playbooks per page set. Example: "Best coworking spaces in [city]" = Curation + Locations.

Keyword Pattern Mining

Step 1: Pattern Identification

Extract the repeating structure from seed keywords:

Seed: "react developer salary san francisco"
Pattern: [role] salary [city]
Variables: role (200+ options), city (500+ options)
Max pages: 200 x 500 = 100,000

Step 2: Volume Distribution Analysis

Not all variable combinations have search volume. Map the distribution:

| Tier | Volume Range | Typical % of Total Pages | Strategy | |------|-------------|-------------------------|----------| | Head | 1,000+ monthly | 2-5% | Priority indexation, highest content quality | | Torso | 100-999 monthly | 15-25% | Standard template, full deployment | | Long-tail | 10-99 monthly | 40-50% | Template with conditional content blocks | | Zero-volume | < 10 monthly | 20-40% | Noindex OR skip unless data is uniquely valuable |

Step 3: Intent Classification

For each pattern, verify intent consistency:

| Intent Type | Template Implications | CTA Strategy | |------------|----------------------|--------------| | Informational | Data-heavy, educational content | Newsletter, related content | | Commercial investigation | Comparison tables, pros/cons | Free trial, demo | | Transactional | Pricing, availability, features | Buy now, sign up | | Navigational | Brand-specific, direct answer | Product page link |

Data Pipeline Architecture

Pipeline Design

[Data Source] → [Extraction] → [Transformation] → [Enrichment] → [Validation] → [Template Population] → [Quality Check] → [Publish]

Data Quality Gates

Every record must pass these gates before page generation:

| Gate | Check | Failure Action | |------|-------|---------------| | Completeness | All required fields populated | Skip page, log for manual review | | Accuracy | Data matches source, no staleness > 90 days | Flag for refresh | | Uniqueness | No