Booking flights is the key to SocialAir operations. Aggregating multiple parties into efficient, high-utilization flights is absolutely essential to the long-term success of the business. As such, a great deal of time and thought have gone into developing this aspect of the business.
Given that SocialAir is a based on a unique business model, it follows that the core booking engine will be unique as well. In fact, the SocialAir booking engine is based on three different yet integrated aggregation mechanisms: 1) match scoring, 2) social booking, and 3) heuristic aggregation algorithm.
Match Scoring
Match scoring is the first step in coordinating flights. A repeating process checks each trip against each other trip and compares the departure airports, arrival airports, and times for each flights. Matches and close matches on each of these axes are scored and factored together to arrive at a set match score between any two Member trips. So when a Member specifies any given trip he has requested, the system simply generates a list of the other Members’ trip requests that are the most closely matched to there.
This mechanism has already been developed and tested as a prototype.
Social Booking
The Match Score process will present Members with the closest matches to their own travel plans. (See Trip Board) Chances are, of course, they will not be perfect matches. So we need a mechanism for closing the gaps in Members’ desired times and locations. By providing a user-friendly social booking tool through the Members Portal, we allow the members to work out these differences themselves rather than relying on an algorithm to do the job.
Flexibility Settings
A key to the social booking tool is the flexibility setting describe in Operations » Members’ Portal » Profile Management. This setting allows Members to indicate how much flexibility they have on either side of their desired departure and arrival time and in the distance they are willing to travel to alternate airports. Using this information the Social Booking engine can identify areas of overlap between different Members’ flexibility ranges.
For example, if two Members want to leave the same airport, one at 9am and the other at 5pm, and the first Member has four hours of flexibility after his desired (as late as 1pm) time and the second Member has five hours of flexibility before his desired time (as early as noon), the system can suggest that time between noon and 1pm as a compromise departure time.
Again, while the social booking tool can present Members with options, it is up to the members to agree to the compromise deal. So in the above example, a Member could be looking at their trip request and see a note saying “Would you be willing to depart at noon instead of 9am?”. By clicking on the “Yes” button, the Member effectively syncs their plans up with other Members.
As covered in Business Model » Rewards Program, Members are incentivized to be flexible in making travel arrangements in order to increase occurrences of fully-booked flights. The wider Members are willing to set their flexibility settings, the more options for compromise the Social Booking engine will provide. It is then up to the members to fine-tune the coordination.
Polls
A simple polling tool will also allow Members to work out specific logistical questions. For example, Members could post a poll to vote on what movie to watch in flight, or whether to detour 15 minutes to fly over the Grand Canyon.
Heuristic Aggregation Algorithm
In order to achieve maximum efficiency, it will sometimes be necessary to combine trips into multi-leg flights. For example, if one member is flying from New York to LA and another member wants to fly from Denver to LA on the same day, it makes sense for the jet to touch down and pick up the second passenger. Identifying these more complex aggregation opportunities is beyond the ability of the simple Match Score process. Nor can Members be expected to notice those types of patterns.
To identify these opportunities we will employee a Heuristic Aggregation Algorithm originally developed by Chip Garner for the Jumpjet system. This system is designed to evaluate all permutations of trip combinations based on parameters such as highest number of legs in a flight. The results from this algorithm will generally involve breaking a flight up into multiple legs. These options can then be presented to the Members just as the other options are presented above.
Expectation Management
Between the three trip aggregation mechanisms described above, we will be able to find the most convenient trips possible for our Members. Of course, as discussed on Operations » Volume the closeness of the matches between Members’ requests (which is directly related to convenience) is primarily a function of the size of the Membership. The more Members, the more matches.
In the beginning stages (specifically Founder and Charter) it will be important to manage Members’ expectations so they don’t expect flights exactly when and where they want them at that stage. At this stage Members should be taking a longer, more strategic view of the business. By the time we have reached the Lift Stage Members can start to have some expectations about convenience, but they should also be prepared to be somewhat flexible.
Only after the Cruise Stage when membership has risen to 10,000 across the U.S should Members begin to expect frequent enough flights that the convenience approaches that of private charter or commercial airlines.