Who should use the Classify search intent workflow?
Teams or solo builders working on marketing tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Marketing
Practical execution plan for classify search intent with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A validated, data-driven intent classification system that improves over time.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A validated, data-driven intent classification system that improves over time.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Formulas HQ to a clean, deduplicated list of raw search queries ready for labeling. Then, you pass the output to Notion AI 3.0 to a documented classification framework with clear definitions and examples. Then, you pass the output to Moz AI to every query in the dataset has a validated intent label. Then, you pass the output to Formulas HQ to a clear report showing intent mix, channel differences, and priority query segments. Then, you pass the output to Asana to a prioritized action plan linking each query to a specific content or campaign output. Finally, Moz AI is used to a validated, data-driven intent classification system that improves over time.
Collect and clean raw search query data
A clean, deduplicated list of raw search queries ready for labeling.
Define intent categories and labeling criteria
A documented classification framework with clear definitions and examples.
Label queries manually or via AI-assisted classification
Every query in the dataset has a validated intent label.
Analyze intent distribution and identify patterns
A clear report showing intent mix, channel differences, and priority query segments.
Map intent to content and campaign actions
A prioritized action plan linking each query to a specific content or campaign output.
Validate and iterate with performance data
A validated, data-driven intent classification system that improves over time.
Gather all search queries from your sources (e.g., Google Search Console, PPC campaigns, site search logs). Remove duplicates, non-text characters, and bot traffic to ensure clean input for classification.
Why Formulas HQ: Formulas HQ can generate formulas for Excel/Google Sheets to clean and transform raw query data from Google Search Console and Google Ads.
Establish clear, mutually exclusive intent categories (e.g., Informational, Navigational, Commercial, Transactional) and write a short definition for each. This ensures consistent labeling across the dataset.
Why Notion AI 3.0: Notion AI 3.0 can be used to define intent categories and labeling criteria in a collaborative document editor with AI assistance.
Apply the intent categories to each query. For small datasets (<500 queries), label manually. For larger sets, use an LLM (e.g., GPT-4) with a prompt that includes your definitions and asks for a category per query, then manually review a random sample for accuracy.
Why Moz AI: Moz AI directly offers Search Intent Classification, which can automate or assist in labeling queries by intent.
Aggregate the labeled data to see the percentage breakdown of each intent category. Look for patterns by query length, keyword presence, or source (organic vs. paid) to inform content and campaign strategy.
Why Formulas HQ: Formulas HQ can generate Excel/Google Sheets formulas and Python code to analyze intent distribution and identify patterns in pivot tables.
Translate the classified intent into concrete marketing actions: Informational queries → blog posts or guides; Commercial → comparison pages or case studies; Transactional → product pages or ad copy. Document the mapping for each intent category.
Why Asana: Asana is a project management tool that can map intent categories to content and campaign actions with task tracking.
After implementing actions, measure how well the classified intent predicted user behavior. Compare click-through rates, conversion rates, or bounce rates per intent category. Refine your labeling criteria or action mapping based on real performance.
Why Moz AI: Moz AI offers Predictive Domain Authority Scoring and can help validate intent classification against performance data.
§ Before you start
Teams or solo builders working on marketing tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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