Pie Models: Ice

The power of the model lies in its simplicity. Each initiative is rated on a scale of 1 to 10 across three distinct dimensions:

The final ICE score is the average of these three numbers. By multiplying the scores or averaging them, the team creates a ranked list. The projects with the highest scores naturally float to the top, representing the optimal intersection of high reward, high certainty, and low effort.

Consider "LedgerX," a cryptocurrency payment processor. They started with a classic Snowflake warehouse. Two months before a Series B audit, their compliance team needed a new report on "cross-chain wallet clustering."

In the old model, this would require altering the entire transaction model, risking production downtime for their real-time dashboard.

LedgerX pivoted to an Ice Pie model:

The result? The Real-time slice never paused. The Compliance slice was built in 48 hours. The audit passed. The CEO later joked, "We didn't fix the engine; we just built a new slice of pie." ice pie models

For decades, the Kimball and Inmon methodologies reigned. Data flows from raw (bottom layer) to staging, to integration, to presentation (top layer). The problem? It is rigid. If you want to change how "Customer Lifetime Value" is calculated, you must rebuild all layers above it.

Of course, a perfect circle of ice is a fiction. Real ice floes are irregular, have varying thickness, and exist in swarms that interact non-linearly. The biggest challenge is scale: modeling every single ice pie in the Arctic for a century is computationally impossible. Therefore, modern models are hybrid. They use the ice pie physics for small-scale interactions (meters to kilometers) and then "parameterize" (approximate) the large-scale behavior.

In summary, the humble ice pie model is a powerful example of scientific abstraction. By stripping a chaotic, frozen landscape down to a single, drifting disc, researchers have unlocked the ability to predict sea ice, prevent floods, and even reconstruct the geology of distant, ocean-bearing moons. The next time you see a picture of Jupiter’s cracked, white surface, remember: you are likely looking at the leftovers of a planetary-scale ice pie.

The ICE and PIE Frameworks: Navigating Prioritization in Product Growth Introduction

In fast-paced development environments, the challenge is rarely a lack of ideas—it is determining which ideas to execute first. Product managers often use scoring models like ICE (Impact, Confidence, Ease) and PIE (Potential, Importance, Ease) to objectively rank tasks and features. The ICE Framework The power of the model lies in its simplicity

The ICE model is a popular methodology used by growth teams to quickly estimate the value of an experiment or feature. It scores items based on three criteria, usually on a scale of 1–10: Impact: How much will this contribute to our key objective? Confidence: How sure are we that this will actually work?

Ease: How simple is this to build or launch? (Higher scores often mean "easier" or "lower effort")

By multiplying or averaging these three scores, teams can identify "low-hanging fruit"—high-impact tasks that are easy to implement. The PIE Framework

Created by WiderFunnel, the PIE model is frequently used for A/B testing and conversion rate optimization (CRO). It consists of:

Potential: How much improvement can be made on this specific page or feature? The final ICE score is the average of these three numbers

Importance: How valuable is the traffic or user base being affected? (e.g., a checkout page is more "important" than a blog post)

Ease: How much technical or creative effort is required to launch the test? Comparison and Limitations

Both models aim to reduce "HIPPO" (Highest Paid Person's Opinion) decision-making. However, they are subjective by nature. To combat this, many modern teams are moving toward more rigorous frameworks like PXL, which asks specific binary questions (e.g., "Is this above the fold?") to generate a more objective score. Conclusion

Whether you choose ICE or PIE, the goal is the same: creating a structured way to say "no" to distractions and "yes" to the most valuable work. These models transform gut feelings into actionable, data-informed roadmaps.

While prioritization models are the most likely intent, "ice models" can also refer to geological ice sheet modeling used to predict sea level rise.

Ice pie models use a circular, segmented representation where each "slice" denotes a component, phase, or temporal segment; the ice metaphor emphasizes phase transitions (solid/liquid), fragility, and melting/refreezing dynamics. They are useful for systems with repeating cycles, seasonal effects, or where discrete compartments interact and change state over time.

Every Ice Pie needs a crust to hold the slices together. In data terms, this is your cloud storage bucket (S3, GCS, Azure Blob). The crust is passive—it does no computing. It simply holds the immutable raw logs.