Help flight search get started

 

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Upon logging in, in case you have purchased multiple datasets from ForwardKeys, you can access them by hovering over the dataset menu on the upper left corner of your screen:

Each dataset may have several modules to be used, and certain modules may have different sections to be used.

ForwardKeys is fed daily with searches performed through Skyscanner, a travel metasearch engine and fare aggregator website. A metasearch engine is a type of search engine that collects the results from other search engine databases. The Skyscanner site is available in over 30 languages and is used by 60 million people each month. It concatenates results from online travel agencies (OTAs) and airline websites, including low cost carriers, and it returns a more comprehensive output to the client. Consequently, clients can make better decisions according to their preferences.

Before booking a flight, travellers search to find the best offers for themselves in regards to itinerary, timing and fare. The aviation industry actors need to understand the real intentions of the traveller beyond the constraints imposed by supply limitations to detect potential markets and measure the quality of the current services. In other words, to understand the relationship between what the customers were asking for and what they finally acquired.

Therefore, finding out where and when travellers want to travel, where they are located and how much they are willing to spend after picking an offer are essential metrics for network planning strategies, route development, revenue management and to improve the impact of any marketing/targeting action.

Flight search data helps to carry this out by analysing Searches and Flight Picks.

• A search contains the details of origin, destination, travel dates and number of passengers the traveller has provided in the search parameters. This reveals the true intention of the traveller regardless of any service limitation.
• Flight Picks contains the details of the offer actually chosen by the traveller. These details include airline information and fares. Each search should lead to a “Flight Picks” in the best scenario but in reality, the number of searches far exceeds the number of Flight Picks due to several reasons such as a lack of non-stop routes, expensive fares and/or inappropriate departure/arrival times.

In addition, these searches are enriched with geolocation data that provide estimations of the real-world geographical location of the device (pc, tablet, smartphone…) used by the traveller.

 

Decide your analysis needs

 

ForwardKeys has developed two new modules: Travel Willingness and Catchment Area.

• Travel Willingness module allows analyses on the most searched O&D pairs, identify which of them remain unserved and evaluate the potential of opening new services thanks to frequency and fares analyses. Here, you can discover the link between search dates and travel dates and focus on a specific area of residence to drive your marketing actions to the right audience. These analyses are based on Searches, some of which converted Flight Picks.
  
• Catchment Area module helps the analyst to understand how the attractiveness of an airport and their competitors fluctuates for the travellers living nearby, who are expected to travel primarily from the selected airport. This attractiveness can be analysed by route, destination, airline and fare. These analyses are based on Flight Picks and they focus on the first stage of the trips where the point of interest is the first departing airport.

 

This module analyses Searches data in terms of origin and destination. Also known as the first step in the booking process of a traveller.

Select “Travel Willingness” module in the modules selector, just below the dataset menu.

Depending on the goal of your analysis, you can focus on either analysing when the travellers search or when they want to travel. Select “By Search Date” or “By Travel Date” in the sections area at the top.

 

You will see the key filters to perform your analysis in the Basic level:

POI-1: A filter and a segmentation. It refers to either an origin or a destination of the trip; the real definition will depend on the directional filter. The user must choose at least one location here to run an analysis. Multiple selection is allowed and you can select cities, countries and/or subcontinents. In other words, you can select your city of interest and/or competitor cities here. The segmentation to visualise this is called O&D.

POI-2: A filter and a segmentation. It is either an origin or a destination of the trip; the real definition depends on the directional filter. This filter can be left in blank for global analyses related to the location(s) selected in POI-1. The segmentation to visualise this is called O&D.

Directional: A selector which sets the direction between POI-1 and POI-2. It allows the user to focus on searches from POI-2 as origin to POI-1 as destination (POI-2 → POI-1), from POI-1 as origin to POI-2 as destination (POI-1 → POI-2) or considering both POI-1 and POI-2 as origin and destination (non-directional).

Service: A filter and a segmentation. Each O&D pair has been classified as operated or non operated by crossing them with capacity. We classify an O&D pair as non operated (unserved) when there are no a non-stop services connecting the two cities. We take the search date and we check if there were flights scheduled that year. It could be that a flight is operated during the summer season. When a potential traveller looks for the flight during winter time (whether it be searching or travelling) the flight appears as unserved. You can work around this, by selecting a time period longer than one year.

Dates selector: Used to select the period of interest. The application will make sure that the dates selection is in line with the time granularity selection.

By selecting the standard mode, you will see some additional filters that could help you to perform queries focusing on travellers living in a specific area, classify the routes by haul length or see the number of paxes that were added in the search. In addition, you can change how the origins and destinations are displayed; the default selection is by cities unless you have modified it.

KPIs available in the results are as follow:

Searches (Pax): Number of passengers specified in the search. For one search with a selection of 5 passengers, we will see 5 pax here.

Shares: They are calculated over the Searches (Pax) figure.

 

Which are my top 15 unserved routes?

Let’s see which are the most searched unserved O&D pairs for Valencia airport in 2018 year to date.

Select Valencia in POI-1, Non-directional and Non-operated, and choose the time period of your interest.

Click on Analysis button and go to segmented by O&D.

Use “O&D” filter at the top of your screenscreen to focus on a specific O&D pair and use segmented by filter to look at the location of the searchers who are interested in this O&D pair.

 

Catchment Area module analyses Flight Picks data.

After accessing Flight Searches data, select “Catchment Area” module in the modules selector, just below the dataset menu.

Depending on the goal of your analysis, you can focus on either analysing when the travellers search or when they want to travel. Select “By Search Date” or “By Travel Date” in the sections area at the top.

 

You will see the key filters to perform your analysis in the Basic level:

POI-1/Origin: Filter and segmentation. It refers to the first departing location of the trip. The user must choose at least one location to run an analysis. Multiple selection is allowed and you can select airports, cities, countries and/or subcontinents. In other words, you can select your airport of interest and/or competitor airports here.

Segment destination(s): Filter and segmentation. It is the first flight destination after departing from the origin airport. It does not take into consideration the following steps the traveller might make. This is for route analysis.

True destination(s): Filter and segmentation. It is the first destination where the traveller stays more than 24 hours (more than 1 day, end of trip and return home). If the traveller stays more than 24 hours in the segment destination, this and the true destination will be the same. This is used for O&D analysis in the Catchment Area module.

Pax Residence Area: Filter and segmentation. It is used to narrow the area where the travellers search, and to focus on a market of your interest, such as travellers living nearby your airport. Granularity options include airports, cities (with an airport), countries and/or subcontinents.

Area extension: A filter which is used to define a radius in km around your selected residence area. It complements the pax residence filter.

Show pax residence as: Defines how the pax residence area results will be displayed. Granularity options include towns, which are defined as cities without an airport.

Time granularity: Defines how  the results will be aggregated in a time scale. Daily, Weekly, Monthly, Quarterly, and Triannual options are available.

Dates selector: Used to select the period of interest. The application will make sure that the dates selection is in line with the time granularity selection.

By selecting the standard mode, you will see some additional filters that could help you to perform queries focusing on specific departure times from the trip origin, airlines operating the first flight of the trip, haul length and pax per booking. In addition, you could change how the origins and destinations are displayed; the default selection is by airports unless you have modified it.

KPIs available in the results are as follow:

Flight Picks (Pax): Number of passengers that have elected a trip after a search.

Shares: They are calculated over the Flight Picks (Pax) figure.

Average Fares (USD): Price in US dollars of the full trip including all flights.

 

You will see the key filters to perform your analysis in the Basic level:

POI-1/Origin: Filter and segmentation. It refers to the first departing location of the trip. The user must choose at least one location to run an analysis. Multiple selection is allowed and you can select airports, cities, countries and/or subcontinents. In other words, you can select your airport of interest and/or competitor airports here.

Segment destination(s): Filter and segmentation. It is the first flight destination after departing from the origin airport. It does not take into consideration the following steps the traveller might make. This is for route analysis.

True destination(s): Filter and segmentation. It is the first destination where the traveller stays more than 24 hours (more than 1 day, end of trip and return home). If the traveller stays more than 24 hours in the segment destination, this and the true destination will be the same. This is used for O&D analysis in the Catchment Area module.

Pax Residence Area: Filter and segmentation. It is used to narrow the area where the travellers search, and to focus on a market of your interest, such as travellers living nearby your airport. Granularity options include airports, cities (with an airport), countries and/or subcontinents.

Area extension: A filter which is used to define a radius in km around your selected residence area. It complements the pax residence filter.

Show pax residence as: Defines how the pax residence area results will be displayed. Granularity options include towns, which are defined as cities without an airport.

Time granularity: Defines how  the results will be aggregated in a time scale. Daily, Weekly, Monthly, Quarterly, and Triannual options are available.

Dates selector: Used to select the period of interest. The application will make sure that the dates selection is in line with the time granularity selection.

By selecting the standard mode, you will see some additional filters that could help you to perform queries focusing on specific departure times from the trip origin, airlines operating the first flight of the trip, haul length and pax per booking. In addition, you could change how the origins and destinations are displayed; the default selection is by airports unless you have modified it.

KPIs available in the results are as follow:

Flight Picks (Pax): Number of passengers that have elected a trip after a search.

Shares: They are calculated over the Flight Picks (Pax) figure.

Average Fares (USD): Price in US dollars of the full trip including all flights.

 

Typical question to solve in this case: how many travellers, who are living in the defined catchment area of my airport, depart from other airports instead of departing from my airport?

Let’s carry out a leakage analysis for Valencia (VLC) against Madrid (MAD) and Barcelona (BCN).

Choose your airport and the competing airports to compare with. We are going to choose Valencia (VLC), Madrid (MAD), and Barcelona (BCN) in the Main trip origin(s) filter.

Since we want to analyse the number of travellers that are living within 100km from Valencia Airport and how many of them depart from Madrid or Barcelona.

We choose Valencia in the Pax Residence Area filter and we define a radius of 100km.

We are going to focus on searches made in 2017.

Click on the Analysis button.

Once you have the results on your screen, go to the “Segmented by” selector at the top and choose “Trip origins”.

53.2% of the travellers searching in the Valencian area (<100km) picked an offer starting their trip at Valencia airport, whilst 25.1% chose Madrid and 21.6% chose Barcelona. Therefore, Valencia airport only got about half of their potential passengers who lived within the defined catchment area of within 100km of Valencia airport.

There are several reasons that can explain this behaviour such as the lack of non-stop routes to the travellers’ desired destinations, more expensive fares, and more suitable departing times from other airports.

To find them, you can use the “segmented by” filter at the top of your screen to check different segmentations or run new queries using “categorisation by” filter in the Analysis filter group.

 

Typical question to solve in this case: how many travellers, who are living in the defined catchment area of my airport  and want to travel to a true vacation destination, are leaking from my airport to other airports?

Let’s make an leakage analysis for Valencia (VLC) against Madrid (MAD) and Barcelona (BCN) with a focus on one true destination: Bangkok.

• True destination is defined as 1. the first destination where the traveller stays more than 24 hours; or, 2. the end airport of one-way tickets.
• Segment destination is the first flight destination after departing from the origin airport. A segment destination can be either a true destination or a stopover.

Choose Bangkok in the true destination filter and click on the analysis button.

We can see that the leakage for travellers flying to Bangkok is 76%, much higher than the general leakage.

Looking at prices (on the left side of the table), departing from Madrid and Barcelona is between 50-70$ cheaper than Valencia, which, depending on the number of passenger in the same booking, could lead to big savings.

By analysing pax per booking, we can see that almost 1 out of 2 are couples and, therefore, they can save between 100-150$. Something to consider.

There are other variables to consider such as departure times and hub airports. If you choose segmented by Departure times (or Segment destinations), you can select each origin one by one in the Trip origins selector to see the main differences and get more insights per specific route more in depth.

From this example, we can see that travellers are taking advantage of the multiple frequencies from Madrid to Dubai, Doha, London and Delhi, reducing travel and connection times, instead of a few frequencies to Moscow, Istanbul and Zurich from Valencia.

A deeper analysis with the help of other modules and datasets will help you to have a more comprehensive study.