Author: Chris Han

Learn how to attribute pipeline to AI search engines using CRM fields, evidence weights, account matching, and a defensible reporting model. Build it now.

Learn how to track domain citations in Perplexity with repeatable prompts, URL-level metrics, competitor benchmarks, and action rules. Start auditing.

Share of model vs share of search explained with formulas, a SaaS example, and a dual-track framework for forecasting demand. Build your scorecard.

Measuring AI search impact for B2B marketers requires visibility, perception, traffic, and pipeline evidence. Use this practical scorecard.

Use this generative engine visibility reporting framework to standardize AI mentions, citations, sentiment, competitors, and actions. Build your report.

LLM share of model data cleansing best practices for deduplicating prompts, resolving brands, filtering noise, and protecting trend accuracy. Use the checklist.

AI search visibility metrics for SaaS boards connect mentions, recommendations, citations, accuracy, and pipeline. Build a defensible scorecard.

Use a Perplexity AI referral tracking tool to connect citations, GA4 sessions, and SaaS conversions without overstating attribution. Build your stack.

Learn how to calculate AI brand mention conversion rate using referrals, self-reported attribution, CRM evidence, and cohort tests. Build your scorecard.

Weighted AI visibility scoring combines engine reach, buyer intent, and recommendation position into an auditable 0–100 metric. Build your model.