AI can suggest clusters, but source data and human review decide the map. A keyword string alone does not reveal exact demand, competition, or intent. Build clusters from owned Search Console data, approved research tools, SERP observation, and business relevance, then validate pages—not just labels.

// concept

Prepare the Dataset

Columns:

// prompt — copy me9 lines

query
source and date
impressions/clicks if owned
country/device
candidate intent
entity/topic
existing ranking/target URL
business relevance
sensitivity/seasonality notes

Remove or mask personal information and rare user queries that could identify someone before using an AI provider. Do not paste client exports without authorization and provider-data review.

// concept

Ask for a Controlled Output

Use an allowed schema:

// json8 lines

{
  "cluster_id": "service-ac-repair",
  "primary_topic": "AC repair service",
  "queries": ["..."],
  "intent": "local_service",
  "recommended_action": "merge|new_page|update|research",
  "uncertainty": "..."
}

The model must not invent search volume or difficulty. Use deterministic Sheets formulas for normalization and duplicates; use AI for semantic suggestions. Compare sample queries in live search results carefully by country/language and date to see whether the same page types satisfy them.

// concept

Map One Page per Need

Several phrasings can share a page when they represent the same task. Separate pages when intent, product/service, location with real presence, or required content differs materially. Avoid thin page-per-keyword or fake city variants.

// worked_example

Worked Example

A Lahore home-service site has Search Console queries around AC repair, installation, and general cleaning. AI groups them, but a human sees installation results require different proof, pricing logic, and FAQs. It becomes a separate service page. “AC repair Lahore” and “air conditioner repair Lahore” share one page.

The sheet maps current URLs and flags cannibalization: two old repair posts target the same task. The plan consolidates useful material and redirects only after link/traffic review. It does not create 40 neighborhood pages without distinct service evidence.

// failure_cases

Failure Cases to Diagnose

6 cases to diagnose

Model invents volume/difficulty
retain only sourced metrics.
Every cluster becomes new page
update/merge where appropriate.
Queries contain private details
aggregate/redact before AI.
One model run is final taxonomy
sample and review.
SERP observed without date/location
record context.
Existing URLs ignored
map consolidation and internal links.

// pakistan_angle

Pakistan Angle

Pakistani search phrasing can mix English, Urdu script, and Roman Urdu. Cluster by task, not merely language string. Validate whether users expect a service page, guide, marketplace, map/local result, video, or official source.

Do not create city pages for places the business does not serve. Where local terms differ—marla/kanal, COD, board/exam names—use verified terminology and useful content rather than stuffing variants.

// hands_on

Hands-On Exercise

5 steps

Export an authorized query sample or create synthetic data.
normalize/deduplicate in Sheets.
generate schema-bound cluster suggestions.
validate a sample against results and current URLs.
produce update/merge/new/research actions.

// completion_rubric

Completion Rubric

6 checks — tick as you verify

0/6

// sources

Sources

3 official sources — check every claim yourself

// check_yourself

Check yourself

4 questions · answers and options are taken word-for-word from this course

0/4

1 / 4 · diagnose
Your work shows this failure mode: “Model invents volume/difficulty.” What does the lesson tell you to do about it?