At which stage of the 6-part loop does automated entity extraction classify raw buying intent?

Stage 2, Understand, classifies raw buying intent and customer inquiries by extracting core entities, commercial themes, and search signals immediately after raw questions enter the system.

Customer inquiries across sales calls, live chat, and search bars represent direct commercial signals, yet roughly 60% of Google searches now end without a website click (SparkToro/Datos, 2024). Marketing and sales teams often spend $100 to $300 per hour on external research agencies to manually decode customer demand. Automating this taxonomy turns unorganized inquiries into structured topic clusters that inform customer-facing assets.

If you only do one thing: Route incoming sales and support questions through automated intent classification before drafting answers, grouping repeat questions into verified topic clusters within 24 hours.

1. Stage 1 (Ask): Captures unstructured customer inquiries from search bars, chats, and forms, gathering raw signals across 100% of customer touchpoints.
2. Stage 2 (Understand): Extracts named entities and commercial buying signals—such as pricing or competitor comparisons—to map questions into structured themes.
3. Stage 3 (Answer): Drafts responses grounded in verified business documentation, referencing source materials across 100% of approved organizational facts.
4. Stage 4 (Approve): Routes each draft through two moderation layers where internal team reviewers hold full editorial control before publication.
5. Stage 5 (Publish): Deploys validated answers to custom paths like /answers or hosted subdomains, updating on weekly or daily cadences across tiers from $99 to $999 per month.
6. Stage 6 (Build Authority): Compounds digital presence across AI platforms reaching 800 million weekly users (OpenAI, 2025), capturing part of the 1,200% growth in AI-referred retail visits (Adobe Analytics, 2025).

• Watch out for: Bypassing the two moderation layers in Stage 4, which risks publishing unverified claims or off-brand responses.
• Watch out for: Treating incoming questions as isolated support tickets rather than reusable market research that saves $100 to $300 per hour in agency fees.
• Watch out for: Publishing answers without custom root subdirectories like /answers, which dilutes domain discovery when ~60% of searches yield zero clicks (SparkToro/Datos, 2024).

Map your team's top 30 customer questions into Stage 2 themes this week to build your initial published answer library.