WHITE PAPER Business Intelligence for Airlines and Flight Distribution Sample NDC Charts
Table of Contents 1. Introduction 3 1.1 Deriving Business Intelligence 3 2. IT Operations: Request Volumes By Type 4 3. IT Operations: Response Times By NDC Request Type 5 4. IT Operations: Search Times by Origin-Destination 6 5. Demand: Top 10 Flight Destinations/Dates from Toronto 7 6. Availability: Top 10 No Availability Searches by Origin/Destination 8 7. Management Ratios: Revenue Per Search via Scatter Chart 9 8. Pricing: Per Pax Trends for Top Routes 10 9. Customer Insights: Look Ahead and Duration by Predicted Market Segment 11 10. Intelligence with a Difference 12 Page 2
1. Introduction For online travel organisations, their inbound and outbound network traffic is an exacting record of their opportunities, successes and failures, with their customers experience measured by the timeliness, availability and relevance of the product and services being offered. The adoption of XML based search and booking services, either as part of an IATA NDC program or simply the introduction of B2B services as part of a merchandising/ancillary revenue strategy opens up the prospect of tapping to readily available search and booking traffic as an unparalleled source of IT management and business intelligence. Triometric s Web Services Analyzer provides the capability to surface the wealth of information that is contained in the flow of transactions up and down the network. The transactions and their content are captured, reverse engineered and analysed in real-time to produce a comprehensive set of business intelligence KPIs and detailed operational metrics. This whitepaper presents a brief introduction to the typical business intelligence available through Web Services Analyzer and includes a collection sample charts. 1.1 Deriving Business Intelligence The data content of online transactions is stored in an XML format. Online travel organisations typically have their own suite of custom XML-based web services which define the search, price and availability, booking and other services. The data that accompanies requests is rich in content and defines exactly what is being demanded. Likewise responses typically contain a list of available products or confirmation of a booking. Web Services Analyzer examines each requestresponse combination, applies a set of rules to extract business relevant data as KPIs and stores them in a database. Overall millions of transactions for a given client are typically analysed each day to build a comprehensive picture of the search patterns and how the business has responded to these demands. Whilst Web Services Analyzer supports the capability to examine individual transactions, the real power of the analysis relies on the ability to aggregate and summarise the data. Looking at sets of transactions and then breaking these down by dimensions in the data provides the route from raw big data to generating little data that represents information that is actionable. For example, taking a week s worth of search transactions for slicing and dicing using requesting agent, destination and check-in-date or other dimensions, then sorting it by transaction volume provides a prioritised view of what a particular agent is interested in. In addition to aggregated data it is also highly desirable to include a suite of calculated measurements that aid interpretation of the data. Perhaps the most obvious index derived from the raw travel data is the look-to-book ratio. The capability to treat these indices in the same manner as the aggregated data means that it is possible to slice and dice them using the same dimensions as other data which is extremely powerful. Continuing with look-to-book as the example, it becomes easy to generate reports showing conversion rates by agent and/or by destination by check-in-date. Page 3
Overall, the actual data available for analysis and reporting is ultimately dependent on the data content of the original XML. Triometric works with their clients to understand not only which KPIs will best serve their business processes but also the derived indices discussed above. For any given client, it is likely that around powerful 30-50 KPIs will be identified plus potentially as many again derived measurements in the form of averages, percentiles, ratios and other calculations. The remainder of this whitepaper showcases a small selection of KPIs and derived measurements to demonstrate how appropriate combinations can deliver very significant insights for the business. 2. IT Operations: Request Volumes by Type This initial chart simply shows the volume of request serviced by the system for the top NDC requests including ticket issues. In practice there are a lot more requests types within the NDC standard and even the small traffic sample used to build this chart originally contained over twenty different request types with progressively lower demand. Even this simple chart provides a lot of feedback on how searches convert into PNRs and PNRs proceed to ticket issues in other words a high level booking funnel. IT Operations would most likely want to plot these numbers as line charts over time to track any major dips in any part of the funnel. Request Type Requests % of Requests (%) FareSearchRQ 50,497 92.30 FlightPriceRQ 2,998 5.48 FareRulesRQ 381 0.70 PNRCreateRQ 165 0.30 PNRRetrieveRQ 134 0.24 PNRChangeRQ 130 0.24 TicketIssueRQ 119 0.22 Page 4
3. IT Operations: Response Times by NDC Request Type This chart and corresponding table presents the round trip response times as seen by the requesting Agent for various NDC request types ranging from making basic price and availability searches, through allocating a PNR to issuing a ticket. The round trip time is readily divided into server and network time. Whilst the requestor experiences the total round trip time, only the server time tends to be within the control of the organisation operating the search & book platform. This is simple example of starting to slice and dice data to make it more actionable. The 90th percentile column, which is the time taken to process 90% of requests, is shown to reflect the natural spread in response times. In this example the spread looks quite reasonable but equally it might have shown a very high 90th percentile response time indicating some transactions are taking an adversely long time. The next level of action might be for example to drill into the FareSearchRQ requests to see if different Origin-Destination(s) show dramatically different response times again with a view to understanding if any of the more popular routes were offering adverse response times and therefore revenues. Request Type Network Time Avg (secs) Server Time Avg (secs) Total Response Time Avg (secs) Total Response Time 90th %ile sec FareSearchRQ 0.219 5.107 5.326 8.333 182 FlightPriceRQ 0.157 1.233 1.390 2.485 12 PNRChangeRQ 0.269 0.450 0.719 0.719 1 PNRCreateRQ 0.179 2.245 2.424 2.424 1 TicketIssueRQ 0.268 5.164 5.432 5.432 1 Requests (hits) Page 5
4. IT Operations: Search Times by Origin-Destination This chart and table provides a more detailed breakdown of FareSearchRQ response times when considering the top consumer requested Origin- Destination cities. The idea is to step back from using average response times for all searches. Each Origin-Destination is likely to involve different carriers, inter lines or may just be a richer route with more options that need to be explored by the pricing engine before it responds. The 90th percentile column, which represents the time in which 90% of all searches were serviced, provides a further means of stepping back from relying on averages for what could be time critical In the above sample. Toronto-New York, the third most popular route is noticeably slower than other routes at 11.6 seconds, so it could be subjected to time outs and is likely a source of missed revenue. Origin City Destination City Network Time Avg (secs) Server Time Avg (secs) Total Response Avg (secs) Total Response 90th %ile (secs) Toronto Las Vegas 0.244 5.214 5.459 8.645 Toronto Orlando 0.227 5.373 5.601 7.153 Toronto New York 0.243 8.518 8.760 11.612 Toronto Fort Lauderdale 0.233 4.363 4.595 7.186 Montreal Fort Lauderdale 0.237 3.767 4.005 5.418 Toronto Miami 0.237 6.007 6.245 7.260 Toronto Montego Bay 0.270 5.050 5.320 6.852 Toronto Cancun 0.257 5.286 5.543 7.138 Toronto Vancouver 0.234 5.899 6.133 7.428 Toronto Tampa 0.250 5.922 6.173 7.419 Toronto Varadero 0.197 2.967 3.165 3.948 Toronto Kingston 0.235 4.640 4.875 7.013 Page 6
5. Demand: Top 10 Flight Destinations/Dates from Toronto The most popular routes are not necessarily the most searched products since seasonal trends or events at a destination can dramatically skew demand. This report shows search demand for the top destinations and departure date. The report was filtered using Toronto as the originating destination. The chart provides a graphical representation of the routes based on GPS coordinates of the airports concerned. The size of the aircraft reflects the importance of the route. Destination Airport Destination City Departure Date Searches Norman Manley Intl Kingston 12 Dec 2014 62 Owen Roberts Intl Georgetown 15 Mar 2015 54 Mc Carran Intl Las Vegas 10 Oct 2014 53 Mc Carran Intl Las Vegas 16 Oct 2014 45 Juan Gualberto Gomez Intl Varadero 22 Dec 2014 45 Mc Carran Intl Las Vegas 09 Oct 2014 44 Orlando Intl Orlando 14 Mar 2015 39 Playa De Oro Intl Manzanillo 06 Jan 2015 37 Mc Carran Intl Las Vegas 17 Oct 2014 35 Orlando Intl Orlando 03 Oct 2014 34 Mc Carran Intl Las Vegas 23 Oct 2014 34 Fort Lauderdale Hollywood Intl Fort Lauderdale 16 Oct 2014 34 Page 7
6. Availability: Top 10 No Availability Searches by Origin/Destination This chart shows the number of requests that have resulted in no availability responses by City and Destination Airport. These flight searches are against key routes which are not being fulfilled and represent significant lost revenue opportunities. Whilst it could be that a requesting agent is generating route requests that are simply not on offer, it is usually the case that there is a major revenue leak due to either insufficient capacity allocation being made available, a mapping problem in the local system or, if there are third parties involved that the supplier has failed to load their inventory correctly. In nearly all cases, the opportunity is to resolve the problem and restore the availability. In the case that an agent is generating unexpected route searches, the opportunity is to report it so that they no longer load the booking engine and delay other revenue generating searches. Origin City Destination Airport Destination City Requests (hits) Toronto Mc Carran Intl Las Vegas 1,789 Toronto Fort Lauderdale Hollywood Intl Fort Lauderdale 1,356 Toronto Orlando Intl Orlando 1,118 Montreal Fort Lauderdale Hollywood Intl Fort Lauderdale 996 Toronto Miami Intl Miami 980 Toronto Sangster Intl Montego Bay 880 Toronto All Airports New York 790 Toronto Cancun Intl Cancun 765 Toronto Vancouver Intl Vancouver 628 Toronto Tampa Intl Tampa 584 Page 8
7. Management Ratios: Revenue Per Search via Scatter Chart Look to Book is a very useful management ratio adopted by many online retailers. The challenge is that it doesn t provide any form of monetary ($) feedback so products which have a low look to book ratio (sell well) might only generate a small amount of revenue because they aren t actually searched or purchased very often! Revenue per Search is a better theoretical concept which simply equates the revenue generated by a product (bookings) to the number of searches required to generate that revenue. A monetary look to book as it were. Using the scatter chart we can actually take this concept a level further and show the outliers at the same time. In our chart, any origin destination pairs that appear top left are real winners, any route appearing bottom right is expensive to sell. Not surprisingly, the vast majority are bottom left which means they generate solid revenues our cash cows. The highlighted outlying point is Toronto (YYZ)- Fort Lauderdale (FLL) which shows a relatively high number of searches but only a modest revenue. Origin Destination Revenue (price) Searches (hits) $/Search YYZ MBJ 6,571.66 879 7.48 YYZ YEG 6,003.40 318 18.88 YYZ YVR 5,444.42 666 8.17 YYZ POP 4,497.40 220 20.44 YLW YYC 4,088.64 76 53.80 YYC YYZ 3,134.08 413 7.59 YYZ NAS 3,021.14 197 15.34 YYZ FFL 229.33 1108 0.21 Page 9
8. Pricing: Per Pax Trends for Top Routes This report tracks the offer prices at the per search and per passenger levels, being made for the most searched origin-destinations. The number of passengers is also presented since some routes are predominately more popular with couples than say single or family groups. The report goes even further by comparing the offer pricing between two time periods to provide upward or downward price trend feedback. The biggest faller amongst these top routes is Toronto-LA (last line in table) which shows quite a dramatic slowdown in demand even though the price is unchanged. It could be that a competitor is running a special offer on this route or an event that had been pushing up demand has finished. Either way, it should be investigated. Origin City Number of Pax Destination City Price Per Pax ($) Searches Total Price ($) % change Period 1 Period 2 Period 1 Period 2 Period 1 Period 2 Toronto 2 Las Vegas 661.61 673.79 405 281 1,323.22 1,347.59-1.81 Toronto 1 Montego Bay 649.38 717.88 241 143 649.38 717.88-9.54 Toronto 1 Orlando 585.91 543.26 196 142 585.91 543.26 7.85 Toronto 1 Las Vegas 672.14 684.44 179 143 672.14 684.44-1.80 Toronto 1 Ft Lauderdale Toronto 2 Ft Lauderdale 618.73 660.31 178 154 618.73 660.31-6.30 549.19 616.50 154 116 1,098.38 1,233.00-10.92 Toronto 1 New York 429.85 425.73 150 110 429.85 425.73 0.97 Vancouver 1 Toronto 781.61 785.97 141 107 781.61 785.97-0.55 Toronto 2 Orlando 540.36 553.41 133 123 1,080.72 1,106.83-2.36 Toronto 1 Vancouver 814.66 812.54 130 121 814.66 812.54 0.26 Montreal 1 Ft Lauderdale 665.72 682.74 124 111 665.72 682.74-2.49 Toronto 1 Los Angeles 935.66 1,104.47 124 75 935.66 1,104.47-15.28 Page 10
9. Customer Insights: Look Ahead and Duration by Predicted Market Segment Even though booking engines further down the travel supply chain have less chance of identifying consumers explicitly, this chart shows how search request parameters can be used to apply a demographic segmentation to the traffic. Most XML search requests contain the number of adults, children and infants along with number of seats, departure dates/day, duration of stay and other information. Using a customised heuristic, searches can be predictively allocated to market segments for use as part of an analysis process. This particular data set reflects a very strong single and couples leisure traveller market with relatively low business sector searches being performed. It is interesting to see how their travel planning and stay length varies. This isn t a complete surprise since the original data was based on a cost centric carrier s traffic. The values presented here may be simply high level segment aggregates but it would be very interesting to drill down to look at other factors such as destination and price sensitivity. All of this information can be extremely valuable to market teams planning and formulating the timing and content of marketing campaigns. Market Segment Searches (hits) Look Ahead Avg (days) Duration Avg (days) Leisure Single 1,066 53 11 Leisure Two 745 75 10 Business Single 267 34 1 Leisure Group 252 86 10 Business Two 131 43 2 Leisure Family 101 88 8 Business Group 15 67 2 Page 11
10. Intelligence with a Difference Processing transactional data efficiently into meaningful insights is a major differentiator of the Web Services Analyzer product. Triometric believes that delivering insights in a near real time basis provides organisations with the opportunity to take actions and drive or save otherwise lost revenue. This is very different from the historical reporting offered by most business intelligence systems. If you would like to understand more about Triometric s Web Services Analyzer or how it could be applied to your business, please feel free to contact Triometric at info@triometric.net or visit Triometric s website at www.triometric.net Page 12
WHITE PAPER Business Intelligence for Airlines and Flight Distribution Sample NDC Charts