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Prof. Robert Thornton

CEXRES Academy

RICE+ Framework

Brand Analytics Fundamentals

The proprietary RICE+ methodology for comprehensive brand measurement

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CEXRES
CEXRES © 2026
CEXRES

RICE+ Framework

The proprietary RICE+ methodology for comprehensive brand measurement

Key Concepts

Instrumentation and Data Pipeline Design

Instrumentation and Data Pipeline Design is the discipline of turning activity data into decisions in a way that compounds — specifically as it applies to rice+ framework. In data analytics work this shows up as decisions about north-star metric, evaluated through Multi-touch attribution modeling so the team is never relying on taste alone. The goal of this concept is to give you a repeatable way to make the call when the data is incomplete and the stakes are real.

Netflix applied this idea by anchoring its north-star metric decisions to a measurable outcome rather than a quarterly deliverable; the result was a faster, cheaper path to the same business goal, and a model other teams inside the company could reuse.

Attribution: From Last-Click to Full-Path

Attribution: From Last-Click to Full-Path is the discipline of turning activity data into decisions in a way that compounds — specifically as it applies to rice+ framework. In data analytics work this shows up as decisions about dashboarding, evaluated through Sentiment & perception mining so the team is never relying on taste alone. The goal of this concept is to give you a repeatable way to make the call when the data is incomplete and the stakes are real.

Airbnb applied this idea by anchoring its dashboarding decisions to a measurable outcome rather than a quarterly deliverable; the result was a faster, cheaper path to the same business goal, and a model other teams inside the company could reuse.

North-Star vs. Vanity Metrics

North-Star vs. Vanity Metrics is the discipline of turning activity data into decisions in a way that compounds — specifically as it applies to rice+ framework. In data analytics work this shows up as decisions about data pipeline, evaluated through Experimentation & causal inference so the team is never relying on taste alone. The goal of this concept is to give you a repeatable way to make the call when the data is incomplete and the stakes are real.

Amazon applied this idea by anchoring its data pipeline decisions to a measurable outcome rather than a quarterly deliverable; the result was a faster, cheaper path to the same business goal, and a model other teams inside the company could reuse.

Experimentation and Causal Inference

Experimentation and Causal Inference is the discipline of turning activity data into decisions in a way that compounds — specifically as it applies to rice+ framework. In data analytics work this shows up as decisions about statistical significance, evaluated through Cohort & retention analysis so the team is never relying on taste alone. The goal of this concept is to give you a repeatable way to make the call when the data is incomplete and the stakes are real.

Netflix applied this idea by anchoring its statistical significance decisions to a measurable outcome rather than a quarterly deliverable; the result was a faster, cheaper path to the same business goal, and a model other teams inside the company could reuse.

Methodology

1. Instrumentation and Data Pipeline Design

2. Attribution: From Last-Click to Full-Path

3. North-Star vs. Vanity Metrics

4. Experimentation and Causal Inference

Case Study

Challenge

A vast retail catalog where customers were overwhelmed and conversion suffered from choice paralysis.

Approach

Amazon instrumented every click and deployed real-time personalization — "customers who bought this also bought," dynamic search ranking, and demand forecasting.

Outcome

Recommendation and search systems are credited with 30%+ of incremental revenue and are the backbone of Amazon's retail flywheel.

Lesson

Lesson for you: the same RICE+ (Brand Performance Metrics) discipline that worked for Amazon is available to any team — the difference is having the method, not the budget.

Chapter Takeaways

  • Start every rice+ framework effort by naming a measurable outcome in one sentence.
  • Use RICE+ (Brand Performance Metrics) to score options against that outcome — never against taste or volume of activity.
  • Anchor the work in instrumentation and data pipeline design, which gives you a repeatable way to make the call under uncertainty.
  • Ship a small, measurable experiment in under two weeks; the team that learns fastest wins.
  • Remember: Instrumentation and Data Pipeline Design — Instrumentation and Data Pipeline Design is the discipline of turning activity data into decisions in a way that compounds — specifically as it applies to rice+ framework.

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