Pricing Simplified Across Fragmented Systems

At Norwegian Cruise Lines, Pricing decisions were slowed by fragmented dashboards and competing signals.

In 4 months, I designed within tightly coupled data systems, prioritizing decision clarity over raw data visibility to reduce redundancy and support real-time pricing actions.

The approach:

  1. Prioritizing speed over completeness
    Reduced competing signals across dashboards—focusing on fewer inputs to support real-time decision-making.

  2. Aligning teams on logic before interface design
    Mapped pricing logic upfront—surfacing misalignment across teams and aligning on how decisions should resolve before design.

  3. Balanced analyst mental models with system constraints
    Built on familiar workflows—avoiding disruption while improving decision speed and clarity.


My Role:

  • Led end-to-end redesign of pricing workflows across fragmented systems

  • Aligned product, engineering, and analytics on decision logic through multiple sessions

  • Improved decision speed and reduced redundancy through system consolidation

 

The Challenge

How might we streamline how tours are priced across multiple internal tools?

Pricing workflows spanned fragmented tools—leaving analysts to manually piece together decisions under time pressure.

Analysts couldn’t confidently set pricing in real time, impacting revenue decisions.

The Costs

  • Missed revenue → delayed pricing decisions in time-sensitive moments

  • Higher error risk → inconsistent pricing outcomes

  • Longer ramp time → increased operational cost

I led the design to streamline how key pricing signals were surfaced on a unified dashboard.

 

Defining the problem

Research Findings: Inventory levels unknown

Analysts were continually flipping between 4 different screens to set prices.

Analysts were struggling to reconcile conflicting signals across tools.

It made it hard to act to confidently and answer key questions:

  1. How well am I selling right now?

  2. What should my ideal rate be?

The challenge wasn’t just clarity—it was achieving it within constraints we couldn’t change.

Key Constraints

  • No system rebuild
    Pricing logic and data structures were fixed—improvements had to come from how signals were surfaced, not changed.

  • Decisions happened live
    Analysts needed clarity in the moment—no time to reconcile across tools.

  • Too many signals, no prioritization
    The problem wasn’t missing data, but determining what actually mattered.

Given these boundaries, I started by defining a decision model that clarified how pricing decisions actually worked.

 

Shaping the decision model

Logic Diagramming

Pricing actions carried edge cases and failure states. I negotiated with product to focus on high-frequency error states over exhaustive edge cases.

Over multiple sessions, I worked with product, engineering, and analytics to map the logic flow.

The model revealed gaps between how the system worked and how teams thought it worked—aligning stakeholders before moving into UI.

 

Visualizing the solution

Low Fidelity: Decision points

Spreadsheet prototypes enabled rapid testing of the information hierarchy.

A card-sorting exercise based on how frequently data points were referenced informed how I prioritized the data, surfacing what mattered most for real-time decision-making.

I made 3 key judgement calls to simplify decision-making as I experimented with the design:

Constraint What I chose—and why
Analysts needed to maintain existing workflows Cut redundant signals—faster decisions without disruption
High variability in edge cases Deferred long-tail scenarios by pushing against product—stabilizing common paths first
System could not be rebuilt from scratch Reworked decision flow within existing systems—no rebuild required
 

High fidelity

I oversaw the design, working with a junior designer to refine the prototype flow and carefully source accurate data from stakeholders to ensure realistic user feedback.

It supports key actions—understanding metrics, exploring detail, and adjusting pricing in place.

Decision flow

Check performance → Assess urgency → Set rate

The After

The Before

Conceptual visualization created for this case study via Nano Banana Pro AI. Not an actual representation of analyst workflows.

 

Outcome

"It’s well designed and pretty intuitive. It incorporates a lot of elements we are used to.

The new workflow made pricing decisions faster, clearer, and easier to learn:

  • Reduced decision time by 20% through unifying pricing signals—easing cross-tool reconciliation

  • Increased confidence by prioritizing high-signal inputs over noise

    • Value: 8.8/10

    • Usability 9.2/10

 

Design Evolution

Affinity Mapping.png

I led the research analysis, while the junior designer built upon the design based on analysts’ feedback:

  • Toggling to show/hide nested tour packages

  • Mark price changes above 30% visually

EU pricing adjustments were identified early—but added regional complexity that slowed initial delivery.

I scoped this to the next iteration after establishing a stable core workflow.

 

What I’d do differently

  • Focus on high-frequency scenarios sooner
    Avoiding over-investing in error edge cases early would’ve saved feature churn

  • Engage cross-functional partners sooner
    Co-defining constraints and priorities upfront to prevent misalignment downstream (which informed our next approach)

 

Key Learnings

  • Dashboards don’t need more data—they need to support a clear next action

  • Edge cases aren’t exceptions in pricing systems, they’re part of the core experience

  • Visualizing data relationships early prevents downstream misalignment

 

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