AI advisors modelled on practitioners
@avinashkaushik
AI interpretation, not the person. This advisor is modelled on Avinash Kaushik's publicly available work and documented frameworks. It is not Avinash Kaushik. It does not speak as Avinash Kaushik, it does not fabricate quotes attributed to Avinash Kaushik, and it will not reproduce copyrighted material. Every response cites the public source it draws from.
The 10/90 rule: invest 10% in tools, 90% in people who can create actionable insights
This is an AI interpretation of Avinash Kaushik's public work. Avinash Kaushik is a digital marketing evangelist and author of Web Analytics 2.0 and Web Analytics: An Hour A Day. His published work argues that analytics should drive actionable decisions, not vanity metrics. He is known for the 10/90 rule and for advocating measurement frameworks grounded in business outcomes rather than page views.
Avinash Kaushik's 10/90 rule (Web Analytics 2.0, 2009): for every $10 spent on analytics tools, spend $90 on people who can interpret and act on the data. Most companies invert this ratio, spending heavily on platforms and leaving analysis to whoever is closest to the export button. The rule reflects the insight that data collection is cheap but actionable interpretation is rare and valuable. A smaller, well-interpreted dataset almost always outperforms a large, uninterpreted one.
Kaushik's micro/macro conversion framework separates ultimate business outcomes (macro conversions: purchase, sign-up, trial start) from supporting actions that signal intent (micro conversions: video view, PDF download, email capture). Measuring only macro conversions leaves you blind to the customer journey. Measuring only micro conversions leads to vanity metric optimisation. The framework connects both layers: micro conversions are leading indicators, macro conversions are the scoreboard.
In domain
When a question falls within analytics, measurement, data-strategy and related areas, this advisor answers grounded in Avinash Kaushik's documented frameworks, with citations to the public source.
Out of domain
Out of domain, this advisor explicitly flags low confidence and defers rather than guessing. It never answers authoritatively outside its declared specialisation.
Example
“How do I build a paid social campaign on Meta for DTC?”
This question is about paid-social which is outside this advisor's declared domains.
Capabilities
2Schedule
Source scraping
onWeb research
onAdd to knowledge