The A to Z of Convolutional Neural Networks
418 days ago
Take your first steps towards Chatbot
418 days ago
Top 20 programming language of the year 2018.
Python is not a language for everyone. Why?
10 best Online Courses for Data Science
Big Changes Big Data is Bringing To The Airline Industry.
- How much you have historically paid for a ticket (what you're willing to pay)
- When prices have been higher then you typically purchase for; when you drop out of the sales funnel and abandon a purchase (what you're not willing to pay)
- How many times you research a flight (if any) before making a purchase and how far through the booking process you get each time (from other data they can guess when you're most likely to continue to the payment screen)
- Where your cursor (if PC) hovers during the search process (does it gravitate towards first class pricing or the insurance info-graphic on the left?)
- The colours of specific action points on the page people like you most respond to (everyone is different and has attractions to different colours. Are you colour-blind? Airline can figure this out from it's own data and display appropriate alternatives that are customized to you)
- The average amount that people like you pay for the same flight (also factoring in time/day, seasons, flight loads, aircraft type etc..)
- If there is a specific event on in your destination city related to your line of work and the likelihood you need to be there on a specific day/time
- Are you obsessed with specific seats? (If your favourite seats are taken and you're less likely to fly on that flight - this can be factored into the equation to move the price needle up and down)
- The likelihood of you booking the fare on the airlines own website based on all this, past and similar data on people that match your micro-demo graphical profile
- How motivated you (and your family/friends) are to reach the next level of status/retain your current status
- Share of wallet/loyalty that airline has over your spend on that specific route (readily available information for airlines)
- Your typical payment method and data on other users of this payment method (e.g.: Tracking the first 8 digits of your credit card number tell the airline what bank and type of credit card you are using for transactions. It's also possible to figure out who is paying for your flights)
- If you have a family, how big it is and how you value time spent with them (easy to obtain from airlines own data sources when overlaid with outside metrics)
- How often you have been upgraded for free, used miles/certs to upgrade and calculating if you're likely to go for an upgrade or pay the extra based on this and many more above factors.
- Third party cookie tracking/data sharing to map where your browsing behaviour has been in the 100's of pages leading up to the purchase (e.g.: did you come from your company webpage prior to this search? Historical data could indicate this is a work trip, work is paying, you have no/budget restrictions therefore higher price options can be displayed. Other people from your company in Cxx positions typically pay/book XX and YY)
- Have you recently engaged with a specific brand through online advertising? (What did it look like, was there a call to action and your metrics compared to others who also interacted)
- Is your boss on any of these flights? (How long since your last promotion, are you trying to impress your boss? Will you receive an upgrade before they do? Do you like your boss and/or do people like you like their bosses? Maybe this flight isn't for them‚?¶)
- Your existing frequent flyer points balance, how you typically redeem for flights and if you've recently searched for an award flight to a destination you need more miles to make a redemption for (+ 1 for less likely to use miles on upgrades, likely to be okay with booking higher class fare if existing bookings wouldn't make this requirement)
- A close friend or relative has recently passed away and you're searching for a flight to the funeral (although most airlines have compassion fares - this data can be available in some regions)
- Your history of purchasing miles, when and how you redeem these miles (on the home carrier? in which cabin? How far in advance is the booking? Do you make changes? Is it during school holidays? Business or leisure destination? Are you travelling with others? What do the others frequent flyer accounts look like - how will it weight this passengers score on this flight search? Who and how are taxes paid for? )
- Have you ever checked the insurance box (even for a second to test it), hovered over a ‚??more info' graphic, or even purchased insurance during the check out process
- How many other flights does this passenger have booked in the coming days/weeks/months, with the home or partner airline, what class of travel, who travelling with, who paid etc (this can play a large part in determining if the current booking is biz/leisure and helps the airline map out what it thinks your work to vacation ratio looks like)
- Have you recently obtained a new credit card, what type and do we have any record of miles posting to the account from this card type? How often do you engage with these cards?
- If you've not recently had any bonus miles from credit card applications - you might be offered a specific deal during the check out process to apply for a card which the airline thinks you don't yet have in your wallet
- Do you have any confirmed hotel reservations in the arrival city? (are you already committed to a flight purchase? Watch the price creep up across the board!)