In its current iteration, a quarterly release cycle provides loose structure to the initiative. Innovation never happens neatly, so the cycle expands and contracts to accommodate.
There’s really something to be said about having a home for a company’s dedicated product teams to showcase the latest intrigue. While commercialized products and services are a company’s bread-and-butter, these early-stage concepts are the next wave of commerce.
It’s key to emphasize with internal teams that innovation comes from everyone. There’s no set line, no fixed department, and certainly no rules as to who can and who can’t innovate. Innovation is a process that comes from a strategy of encouraging smart people to work on interesting problems. And it’s a process that might result in small features or sea-shifting big new ideas. By taking the pressure down a notch, we can move through the heads-down work required to deliver.
Innovation is a process that comes from a strategy of encouraging smart people to work on interesting problems.
In many instances, innovation certainly has become hackneyed and trite. But it’s less about the buzz and more about the feeling. That feeling of excitement that comes from something a bit different. That feeling of anticipation about what this embryo can become.
The understanding that all of this — the excitement, the emotions, the time commitment — might not quite pan out. And, in the end, that’s OK. If large companies want to innovate, there must be a tolerance for experimentation. With the resources of a larger company supporting this process, there can truly be some significant innovations that move from the iterative to the evolutionary. It’s in that spirit that we share our previous quarter’s projects from the Innovation Hub.
The Hotel Beaconator
Hotels are perfect environments for flow maximization. Hotels can learn how guests are moving around a property, leading to thoughtful enhancements to the layout. Bottlenecks of both space and time can be identified, while promotions can be more targeted. As data comes in, valuable business intelligence emerges. While personalized offers could target a specific device — say a user who has the hotel’s loyalty app installed — it could also simply suggest placement of new on-property signage, or even help inform the best use of renovation budget.
To learn more about the Hotel Beaconator, the full coverage lives here.
Machine learning is the process that applications learn from past behavior. This learning promises a “magical” search experience, automatically taking personal preferences into account. This preference-driven shopping framework is a true personalization in travel, helping suppliers more efficiently match trip itineraries to travelers.
One example of this emerging practice in travel is a concept called Preference-Driven Airline Shopping. The concept is driven by a display algorithm for preference-driven shopping, which takes traveler preferences into account when displaying search results. Rajeev Bellubbi, Sabre’s senior principal scientist, wanted the application to focus on personalization beyond the standard flight search filters, which have their own inherent limitations.
The preference-driven algorithm uses existing profiles — say general preferences of business travelers — alongside the machine learning that evolves a traveler profile based on what was shopped before. Based on this learning, preferences update automatically in the background. This continuous improvement increases conversion by targeting the right itinerary to the right customer.
To explore Preference-Driven Shopping, read the full coverage here.
Hotel Split Search
Demand for hotel rooms is never as consistent as hoteliers might like: large conferences and special events often stress inventory to the max. Guests don’t all arrive and depart on the same day during these peak times, which can leave some rooms unfilled. Guests cannot book an entire stay due to demand and the hotel ends up losing the guest altogether.
But what if a guest could be enticed to fill that inventory — without visible discounting on a last-minute stay channel? Or what if a guest wants to be near multiple points of interest during a city stay (such as a conference hotel and then one closer to downtown) and is willing to move hotels for proximity?
A new tool called Hotel Split Search offers a solution to these hotelier challenges. Released through Sabre’s Innovation Hub, the app delivers a new way to search for hotel stays. Rather than simply blocking out any hotels without inventory for the full stay, the app allows different combinations of hotels to be selected during the entire length of stay. The app introduces a different way to shop and view hotels, expanding options and expanding flexibility — especially during peak demand times.
For more on Hotel Split Search, browse the full coverage.
Travel inspiration is often a mixed bag. It’s not always simple and straightforward to explore potential trips without a fixed destination or specific date. Thankfully, Big Data is coming into the fold, powering new interfaces that offer alternative ways to search for travel — including simple ways to book travel according to elite-qualifying miles earned. Given that most frequent flier programs have moved to revenue-based models, there’s an increasing incentive to manage status by booking the most affordable elite-qualifying miles. Basically, working to maintain status at the lowest possible price-per-mile flown.
These flexible tools make it easier to find trips around a specific theme (say a beach trip), a set budget range, or even around earning a certain number of elite-qualifying miles at a maximum price-per-mile. Escape is a web app that centers the travel search experience on exactly these sorts of “soft qualities.”
The app (explore it here) tailors the user experience by deploying Sabre APIs into two search views: Flexible and Inspiration. Flexible is a fairly standard search between an origin and destination with flexible travel dates. Inspiration is a broader search from only the departure airport, showing all available flights from a specific city. This allows one to see potential travel experiences across the world.
Escape to the full coverage here.
Operational Cost Model
The Operational Cost Model, or OCM, is a step deeper into the world of predictive analytics. The system improves the quality of parameters used by flight operations systems to calculate the true cost of delays. In order to calculate how much a potential disruption would cost to an airline, the sophisticated algorithm pulls in real values from real-world operations.
To support this holistic view and initiate precise decision making all the way from flight operations to airline management, a sophisticated operational cost model could provide a place for (and significantly improve the quality of) all cost parameters for flight operations center systems.
On-the-spot data and analysis are required for efficient flight operations. OCM provides clear and actionable decision support to operations controllers for managing delays. The data is also delivered in a visual way, providing a clear visualization of the delay-cost forecast. So operations controllers can quickly understand the impact of delays on costs coming from passenger connections, speedup opportunities, knock-on delays, airport curfews, missing maintenance windows, and other factors to help the airline operate on a lowest-cost basis. When predictive analytics layers on top of massive amounts of data, it’s possible to incrementally improve decision making capabilities over time through machine learning.
For a deeper look at Operational Cost Model, view the concept here.
This article can be originally found on Sabre’s Insights Blog.
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