Cirium says their Diio Signals “improves schedule planning and revenue management by collecting demand indicators.” The new demand forecasting data uses machine learning to identify which indicators will create increases in demand and on what routes.
The traditional models used at most airlines and airports apply trends to historical data to project demand. As markets became disrupted during the pandemic, this method begins to break down without the addition of contextual data representing real and dynamic factors influencing travel. Over the last two years building accurate demand forecasts has become increasingly difficult. Using data from Diio Schedule Snapshot, we know airline network planners are adjusting their schedules with much more frequency than they did two years ago.
Diio Signals for airlines and airports
- Build higher confidence in demand models
- Interpret contemporary data to evaluate changes in demand
- Understand demand spikes to maximize revenue
- Improve target marketing by identifying real opportunities
How Cirium Signals schedule demand forecasting works
Cirium automatically captures unstructured web data as well as closed unique sources of insights and stores it in The Cirium Core, our cloud-based hub for aviation data. It pairs it with high quality industry data like search, bookings and fares. This allows Cirium to estimate travel demand and willingness-to-pay changes over long and short-term horizons allowing adjustment of the commercial levers within airlines like pricing, planning and marketing and sales.
The first Cirium Diio Signals data set identifies business events and conferences that will have demand impact. Are there a series of major trade shows in Orlando? Diio Signals finds them and forecasts their impact on demand.
Demand is dynamic. Barcelona hosted 10 major business events impacting travel in January 2019, 23 events in January 2020 and only two in January 2021. Cirium data shows there will be four events in January impacting business travel.
Diio Signals maps each event to its corresponding airport and forecasts traffic to that airport. Diio Signals also provides projections on the actual routes which will see increases in demand because of the event.
Airlines and airports can validate or supplement existing demand models and forecasts. With this data, airlines and airports gain understanding and insight into what may be causing fluctuations in demand.
Customers who subscribe to Diio Signals receive a bi-weekly flat file data feed delivered via SFTP.
Why business events?
Organizational meetings, conferences and conventions are a significant source of premium and business travel. However, the size of these events and their locations changes every year. This dynamic makes it a very poor static input into year-over-year based demand models.
These events are too frequent and numerous to track by hand. Machine learning enables Cirium to identify capture and analyze unstructured web data and turn it into demand events. Further analysis determines where the attendees will be coming from and their likely travel behavior.
Diio Signals is the latest add-on for Diio Mi and SRS Analyzer. Cirium provides the most accurate and reliable data and analytics for airline planning. The addition of Diio Signals and the recently available Diio Schedule Snapshot provides airlines with complete end-to-end planning and analysis from looking back at schedules by publication date all the way through providing rich data sets for forecasting.