Decision-first workflow

Problem

The product had two strong but disconnected experiences:

• a simplified overview designed for quick decisions

• detailed metric and AOI pages designed for expert users

In practice, this created a significant gap in understanding.

Non-technical users struggled to interpret raw metrics without guidance, while expert users still had to manually structure insights when preparing reports or communicating results.

This led to three key issues:

• High cognitive load: users had to interpret multiple metrics independently

• Fragmented understanding: insights were spread across multiple pages and contexts

• External translation work: teams often recreated summaries outside the product

As a result, a large part of the analytical value was not directly consumable inside the product experience itself.

Insight

The core issue wasn’t lack of data, it was lack of clarity.

Users didn’t need more analysis. They needed a clear understanding of performance and what to do next.

Approach

I treated this as a system problem, not a UI problem, a mismatch between how the product collects context and how users are expected to make decisions from it.

This led me to redesign the experience across two layers: input and output.

I introduced a structured setup flow for Objectives and AOIs to ensure every analysis starts with clear intent. This made results more consistent and meaningful, and reinforced that context is a prerequisite for evaluation.

On the output side, I reduced reliance on interpretation by consolidating multiple different metrics into a single performance score and adding clear recommendations for next steps.

Finally, I structured the experience into a simple flow: define intent, evaluate performance, and guide action, turning analysis into a decision-making system.

Key Decisions

1. Introduced a decision-first overview layer

I designed a new Overview Page that:

• summarizes performance in a single view

• surfaces key signals upfront

• provides explicit recommendations (Launch / Iterate / Optimize)

This shifted the product from exploration to guided decision-making.

2. Created the Creative Impact Score (1–10)

To replace fragmented metrics and a low-priority color gauge, I introduced a unified score that:

• aggregates key performance indicators

• simplifies interpretation

• enables fast comparison across assets

This became the primary entry point for understanding performance.

3. Added structured recommendations

Instead of leaving interpretation to the user, the system now provides a clear recommendation layer:

What to do next with the asset, based on performance and objectives

This removed ambiguity from the decision-making process.

4. Introduced a two-step setup flow (Objectives + AOIs)

A major structural change was moving setup into a dedicated flow before analysis:

• Step 1: Objectives setup
  Users define what they are trying to achieve (e.g. brand building, conversion)

• Step 2: AOI setup
  Users define Areas of Interest to ensure analysis is aligned with intent

This ensured that:

• analysis always has correct context

• results are meaningful and comparable

• users cannot bypass critical setup steps

It also reinforced that intent defines evaluation in Neurons.

5. Prioritised metrics based on user intent

Metrics were grouped into:

• primary (driving score)

• secondary (supporting context)

This reduced cognitive load and aligned analysis with user goals.

Outcome

The redesign transformed Neurons from a tool that presents analysis into a system that supports structured decision-making from input to output.

It improved:

• clarity of performance understanding

• confidence in decision-making

• consistency of analysis context

• overall usability for non-expert users

The experience now guides users from:

defining intent → interpreting results → making decisions