The Challenge
A pricing software company serving the hospitality industry was preparing to launch a next-generation AI forecasting engine. To drive adoption, it needed an interface and feature set grounded in real-world workflows rather than theoretical models.
The missing foundation was understanding: the team lacked a clear, structured picture of how actual revenue managers make decisions across departments and roles, so there was no reliable basis for designing features people would trust and use.
The Solution
We led a research and UX discovery project to capture how demand forecasting really works in the field, then turned those insights into a concrete design direction. The work included:
- In-depth interviews with dozens of revenue managers and department stakeholders
- Mapping the decision-making process across Revenue, Sales, Marketing, and Operations
- Documenting the key forecasting questions, friction points, and organizational hand-off moments
- Co-designing a user interface concept aligned with real workflows rather than feature lists
- Prioritizing functional use cases grouped by job role and business objective
- Building and testing clickable wireframes with real users to validate fit
The Results
The engagement delivered a comprehensive user journey and a feature roadmap aligned with what customers actually need, giving the product team a clear, evidence-based direction.
It produced actionable design artifacts, including use cases, interface logic, and visual wireframes ready for developer handoff, and deepened user empathy across product and engineering teams.
Most importantly, it positioned the new AI module for stronger adoption and clearer differentiation in a competitive market, grounded in the reality of the people who would use it.
What made it unique
Every design decision traced back to the field, with dozens of real revenue managers shaping the interface instead of theoretical assumptions.