Ice Pie Models May 2026

used in marketing, product management, and A/B testing to rank ideas or hypotheses 1. ICE Scoring Model

is a simple way to prioritize tasks by calculating a score based on three factors: How much will this project contribute to the goal? Confidence: How sure are you that this will work? How easy is this to implement (time and resources)? 2. PIE Framework Created by WiderFunnel

is often used specifically for conversion rate optimization (CRO): Potential: How much improvement can be made on this page? Importance: How valuable is the traffic to this page? How difficult is it to test or implement a change? Relationship and Usage Ranking Hypotheses:

Experts often use these models to decide which experiments to run first, sometimes adding metrics like the minimum detectable effect to refine the results. Growth Marketing:

These frameworks are core components of training programs like the CXL Institute Growth Marketing Minidegree ice pie models

, where they help marketers move from gut feeling to data-driven decision-making.

Originally popularized by Sean Ellis, this model is designed for speed and simplicity, especially in early-stage startups or growth hacking.

Impact: How much will this idea contribute to your primary goal?

Confidence: How sure are you that this will work? (Based on data, research, or gut feeling). used in marketing, product management, and A/B testing

Ease: How simple is this to implement? (A high score means it requires very little effort).

Calculation: Each factor is scored from 1–10. The ICE Score is the average of these three numbers (or sometimes the product: 2. The PIE Framework (Potential, Importance, Ease)

Developed by Chris Goward at WiderFunnel, this model is specifically tailored for website optimization and A/B testing. ICE vs PIE vs PXL: Complete CRO Prioritization Guide

Challenges and Limitations of Current Ice Pie Models

No model is perfect, and ice pie models face four major hurdles: Challenges and Limitations of Current Ice Pie Models

1. 3D Complexity – Most models assume perfectly flat, circular discs, but natural pancakes have variable thickness, overlapping edges, and snow-loading.
2. Salinity Effects – Brine rejection during freezing changes the local density and freezing point, a process poorly resolved in 2D models.
3. Scaling Issues – Microscale ice pies (frost heave in soils) obey different physics than macroscale pancakes in oceans.
4. Data Scarcity – High-resolution in-situ observations of pancake ice fields are rare and expensive (requiring icebreakers or autonomous underwater vehicles).

Nonetheless, ongoing satellite missions (like ESA’s CryoSat-2 and NASA’s ICESat-2) are now providing enough thickness and freeboard data to validate and refine these models at unprecedented scales.

The Whipped Cream (The Semantic Layer)

Unlike traditional models that hard-code logic into the table, the Ice Pie uses a thin, read-only semantic layer to serve the slices to business users. This is usually a view or a virtual dataset. When the CEO asks, "Why is revenue up but engagement down?" the data team simply queries Slice A and Slice B independently and joins the results in memory.

Step 2: Slice by Business Volatility, Not by Source

Most teams slice by source (Salesforce, HubSpot, Zendesk). That is a mistake. Slice by change frequency.

• High-volatility slice (Marketing attribution) – Rebuild every hour.
• Low-volatility slice (Employee HR records) – Rebuild once a month. Putting them in different slices means your monthly rebuild doesn't touch your hourly pipeline.