Changing Face of Customer Intelligence in the Airline Industry - Moving from hindsight to insight to foresight
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Whitepaper
Changing Face of Customer
Intelligence in the Airline Industry
Moving from hindsight to insight to foresight
Executive Summary
Traditionally, airlines have been known to be a flight centric entity with lot of emphasis on operations aimed at
transporting passengers from one place to another. Little did they realize that the transition from a flight centric
unit, with minimal or no customer intelligence, to a customer focused airlines will decide their fate. It is to be noted
that airlines collect lot of data for historical, archiving and compliance purpose. However, there needs to be
considerable effort towards obtaining the customer intelligence from the huge data repository. The instance of
time at which this intelligence is mined by airlines defines the culture which prevails there; from being a backward
looking culture of hindsight to a forward looking culture of foresight. Airlines need to take a leap from the
traditional CRM and BI systems, which provide understanding about the past & current state of business, to next
gen systems deployed for predictive analytics of potential customer behavior.
Airline’s ability to obtain customer intelligence relies heavily on enabling the integrated view of customer which
encompasses various touch points and multiple data sources that may or may not reside within the airlines
ecosystem. It includes customer profiles, transaction details, complaints, social behavior- likes & dislikes,
psychographic data, techno graphic data etc. Airlines would need sophisticated Big Data ecosystem to make
sense of such structured & unstructured data sets characterized by large volume, high velocity and great variety.
Once this intelligence is in place, airlines can utilize it to perform behavioral merchandizing, recommend products
& services, personalize their offering for every customer in a better way, up-sell & cross-sell and develop new
revenue stream altogether. Personalization can be as simple as having a different look and feel for different
customers at home page of airlines website .It can also help in offering targeted ancillaries which is perceived to
be of higher value by a particular traveler. At the airports, it can help in providing customer service aimed at
converting the otherwise disgruntled customer into a happy & satisfied customer. Airlines have a lot to learn from
the retail industry which is at a much higher point in the maturity curve as far as mining customer intelligence and
offering personalized service is concerned.
Airlines need to build a complete journey of the customer which is based on both operational as well as emotional
aspects. The airlines of the future will be defined by the ability to obtain & process this huge mine of gold
“Customer Intelligence” at a speed superseding the pace at which customer behavior and business environment
might change. However, it is easier said than done. Creating an all-encompassing source of information is
complicated as well as resource intensive. It is worth noting that creating multi-structured data analysis
ecosystem is easier for new players where there is no existing capability at all. For full service existing carriers,
the big data ecosystem will have to co-exist with prevailing “legacy” technology architectures which is quite
challenging. The scarcity of resources skilled in data science and predictive model building makes it even more
difficult.
© InterGlobe Technologies Ltd. 2014: www.igt.in
Why CRM system alone will not help airlines in obtaining
customer intelligence?
Traditional CRM systems are vital for airlines in order to streamline the customer interaction which otherwise
remains in silos & isolation. Fig1 shown below depicts how CRM can enable centralized customer interaction
between various functions/departments of an airline.
Scattered Customer Interaction CRM Enabled Centralized Customer Interaction
Campaign Team
Campaign Team
Reservations
Reservations E-Commerce
E-Commerce
Social Media Support
Customer Customer Social Media
Flight Support
Flight Operations Operations
Call Centre
Call Centre
Fig 1: CRM – Vital for centralizing interaction in airlines
It is important to note that CRM can help in addressing some of Airline’s problem areas as mentioned below:
• Fragmented view of customer because of scattered interaction across various touch points during the
customer journey
• Sales staff spending lot of time on administration activities instead of interacting with customer and
finding out their pain points
• Service and support staff lacking consistency and thereby causing conflict in customers mind, inadequate
information availability to serve the customer
• Marketing team not able to achieve good ROI because of inaccurate segmentation & targeting
However, CRM alone can’t be a solution to the problem of lack of insight about the customer and their potential
behavior. Airlines need to make sure that advance analytics ecosystem is used in conjunction with CRM systems
to know their customer better. For e.g. CRM can only tell how the frequent flier customers have behaved with
respect to point accrual and redemption pattern but it can’t tell the probable non frequent flier customers who will
enroll into airlines loyalty program in future. It requires a statistical analysis technique to predict the potential trend
and customer behavioral pattern. This is nothing but predictive analytics. This analytics technique studies the
characteristics of the passengers who exhibit same behavior on various attributes and uses that to find out the
potential passengers who can show similar behavior based on the correlation between their attributes. It helps in
making correlations, isolating patterns and tracking trends to serve up the type of information to allow the airlines
tailor the customer experience for improved engagement and better profits.
Also, it is important to note that the airlines should first use analytics to demonstrate the ROI of CRM before
venturing any further to adopt customer focused strategies and systems. Otherwise, it will be hard for them to
justify the investments incurred to enable such system in first place.
© InterGlobe Technologies Ltd. 2013: www.igt.in
Types of data available for mining customer intelligence
In today’s world new data sources have been created (shown in Fig 2) by virtue of digitization and advancements
in Mobile technology. Some of these are structured in nature and some are highly unstructured which can be only
analyzed by deploying big data appliances. Analyzing this data with the help of data mining and predictive analytics
techniques will give actionable insights into customer behavior.
Different types of data which airlines can use are as follows:
• Enterprise Data – This includes the data sets which are owned by the enterprise like PNR, customer profiles,
Loyalty data, past purchases, past call center interactions etc. These are structured in nature.
• Non-Enterprise Data – These data sets are not owned by enterprise and can be obtained by public sources or
through secondary research. It includes location based data, demographic data, socio-economic data and
psychographic data. These are again structured in nature.
• Social Data – This includes the data train generated by the passenger social behavior and includes likes and
comments on Facebook, You Tube and other social channels. These are unstructured in nature.
• Machine Data – It includes data related to smart phones, sensors, website logs, NFC data etc. These are
unstructured in nature
Enterprise Data
• Customer profiles
• CRM Data
• Loyalty Data
• Past purchases
• Call center data
Non-enterprise Data
• Location Data
• Demographic Data
• Socio-economic data
• Psycho -graphic data
Social Data
• Blogs, discussion forums
• Reviews & Ratings
• Likes & Comments
• Sharing on FB, Twitter,
YouTube, LinkedIn etc.
Machine Data
• GPS Data
• NFC Data
• Smart phones, sensors
• Machine logs
Fig 2: Nature of data available for mining
© InterGlobe Technologies Ltd. 2014: www.igt.inAnalytics Maturity Curve in Airlines Industry
Currently airlines are at low maturity level in terms of obtaining customer intelligence. Airlines industry may be
positioned in Gartner’s analytics maturity model as shown in fig 3. At best, airlines are able to exploit descriptive
and diagnostic analytics for understanding more about the health of the business. However, they are slowly and
steadily graduating towards embracing predictive & prescriptive analytics ecosystem for getting actionable
insights. The pace at which this adoption takes place will decide the future outlook and customer focus of the
airlines.
Some of the existing work which has been done in predictive analysis and text mining space for airlines / airports
are as follows:
• Sydney Airport case study - A study was performed to predict the passenger queue size and possible delay in
immigration in order to plan the staffing levels at the airport. It was also studied that how would the queue size
and delay change if the staffing levels were changed.
• American Airlines case study
• Customer churn modelling – This predictive model identifies high valued customer whose recent
travel has substantially decreased and they are then given treatments to come back to the American
airlines. The model helps in finding out the characteristics of the customers that leave AA and go to
other airlines. A decision tree was then built with the objective of gaining insights on the factors that
lead to customer churn.
• Aircraft Maintenance wait time estimates – As soon as a plan is taken for maintenance activities, its
entry and exit from the maintenance facility is recorded. However, what is not known currently is the
time required for parts or mechanics to be available. Text mining was used for gaining insights from
unstructured comments that are logged every two hours combined with timestamp information. Text
mining allowed useful patterns to be unlocked for further analysis that could not be done previously.
Hindsight Insight Foresight
How the seats should
be priced at various
intervals to ensure
optimum occupancy?
Who are the Prescriptive
customers that Analytics
can be potential
Which factors FFP customers? How can we
contributed to make it
Predictive
Value
decline in load factor happen ?
Which are the Top for last FY ? Analytics
10 OD pair based
on the last quarters Diagnostics What will happen ?
revenue? Analytics
Descriptive Why did it happen ?
Analytics
What happened ?
Fig 3: Analytics maturity curve in Airlines Industry
© InterGlobe Technologies Ltd. 2014: www.igt.inFew possible use cases for predictive analytics in Airlines
Industry
• Predicting customer response to a campaign – Marketing teams in airlines end up spending lot of
resources on sending mass campaigns to target segment based on the marketing lists which they have
created on various criteria. However, they can’t afford to segment the customer based on what behavior
customers have shown in the past. It needs to be real time. It becomes all the more necessary to predict
the customer response to a particular campaign so that they could be targeted with the right campaign
which ultimately leads to cost saving. Sending right offer to customer will help in making customer stick to
the brand.
• Predicting the passenger enrolment in loyalty program – With the help of predictive analytics, airlines can
find out the potential customers who are likely to enroll into their loyalty program based on their behavior
like frequency of travel, distance travelled etc. Airlines can then have a proper targeting mechanism in
place ensuring the customers enrolment into the loyalty program which has become a revenue stream for
airlines these days. Another case can be to predict the customers who are likely to defect or unsubscribe
from the loyalty program. Airlines must ensure that the best offers are kept reserved for this set of
customers.
• Prediction of Passenger Air Demand - The fundamental task of airline planning process is to create a
schedule that maximizes profit while satisfying all business and operational restrictions. Correct demand
forecasting is vital for the successful performance of an airline. Predictive analytics can be used to build a
demand forecasting model for air transportation industry in passenger segment. The factors influencing the
air demand can be both geo-economic (like population, GDP, employment) as well as airline specific
parameters (ticket price, promotions etc.). This analysis will enable an airline make better decision
pertaining to introducing new flights on a specific sector. Identifying long term demand will help airline in
fleet & network planning and short term demand is crucial for success of revenue management function.
• Predicting passenger No-Shows – A frequent practice in the airline industry is to overbook flights to make
up for losses caused by absent passengers (also called no-shows). Predictive analytics can help in
understanding the effects of various flight and passengers characteristics on No Show rate. Even modest
improvements in no-show prediction will lead to optimum yield which can translate to millions of dollars in
annual revenue.
© InterGlobe Technologies Ltd. 2014: www.igt.inThe ultimate goal for Airlines – Having a 360 degree view of
the customer
Customer Profile
• Demographics • Passenger type
• C -SAT score • Preferences
Devices/Channels Used Airport behavior
• Smartphones /PC • iOS /Android • Shopping interest • Kiosk usage
• Mobility adoption • Website/OTA • Lounge preferences • Activities
Revenue Contributed Social Behavior
• Total & per trip • Ancillary revenue • Activity Index • Followers
revenue • Friends • Influence
Loyalty Info Ancillary Purchased
• Loyalty status • Miles accrued • Ala Carte • Time of purchase
• Brand affiliation • Miles redemption • Commission based • Bundling
Travel Pattern Cost to serve
• Travel frequency • Alone/Family travel • No Shows • Change Requests
• Frequent destinations • Cancellations • Complaints
In-flight behavior Competitive consideration
• Meal preferences • Wi -Fi usage • Competitor airlines • Consumption
• Entertainment viewing membership info pattern
Data trails
• Browsing behavior / Machine logs / GPS data
Fig 4: 360 Degree view of the airlines customer
Fig 4 above shows the 360 degree view of the customer (let’s say Mr. X) which an airline wants to have. The
end objective is to enable the scenario where airlines could exactly know the behavior of Mr. X at various touch
pints as described below:
• Mr. X is a business traveler from UK
• He travels very frequently on London- Amsterdam sector
• Mr. X has created reservations 50 times in the past with the average ticket size of 300 dollar per trip.
• Mr. X has called the contact center 5 times for various service requests like cancellations & modifications.
• On his previous travel with the airline, his luggage was delayed.
• Mr. X has been spreading positive sentiments on various social media channels like Twitter & Facebook.
• He has enrolled in top category of airlines loyalty program and has accrued 5000 miles out of which he has
already redeemed 2000 miles.
• He uses android device for creating a reservation through the airline app.
• He always uses the airlines lounge for a quick relaxation at the airport.
• He likes non-veg meal and needs access to Wi-Fi while on board.
• He always books a priority boarding pass and extra leg space seat while purchasing an airline seat.
• He spends an average of 10 mines when he visits the airline website and spends maximum time at new
product launches tab.
© InterGlobe Technologies Ltd. 2014: www.igt.inHow can airline utilize behavioral analytics of the customer
After all encompassing view of the customer is enabled, airlines can then leverage it for taking various actions. It is
still important to track the relevant KPIs and metrics but that may not be the differentiating factor when it comes to
being a favorite airline for the customer. It surely needs to factor in the customer experience part which can be
improved by leveraging the analytics. Some of the possible actions which airlines might take are as follows:
• Send all the campaigns which are related to London-Amsterdam sector to Mr. X and the campaign should be
optimized for viewing in android devices.
• As Mr. X is a business traveler, the focus should be on providing all time connectivity and hence asking the
customer to buy a Wi-Fi coupon while he is booking on the website may be a good proposition. Also, the
focus should be on on-time performance rather than price.
• At check in desk, the executive might apologize for the delayed baggage during the last trip which Mr. X took
with the airlines.
• Since the customer always buys priority boarding pass and extra leg space whenever he travels, the airlines
might want to bundle these two ancillary products together and offer at a discounted price.
• Offer rewards in lieu of the brand advocacy which the customer is generating on social forums.
References:
• http://timoelliott.com/blog/2013/02/gartnerbi-emea-2013-part-1-analytics-moves-to-the-core.html
• Enabling Pro-Active Decisions via Predictive Analytics - John-Paul Clarke
• http://www.textanalyticsnews.com/west2012/materials/slides/Judy-Pastor.pdf
• http://venturebeat.com/2006/12/10/aggregate-knowledge-raises-5m-from-kleiner-on-a-roll/
• http://www-05.ibm.com/innovation/nl/pdf/highlights/integration/crm_airline.pdf
• http://spotfire.tibco.com/blog/?p=12660
About the Author
Jaybind Kumar Jha
Jaybind Kumar Jha is a Senior Business Analyst with InterGlobe Technologies. He specializes in
implementing CRM initiatives for travel industries customers. He has worked extensively on Siebel
CRM & MS Dynamics CRM. Jaybind has completed his MBA from NMIMS Mumbai. He can be
reached at Jaybind.jha@igt.in.
© InterGlobe Technologies Ltd. 2013: www.igt.inInterGlobe Technologies (IGT) is a leading BPO & IT Services provider committed to
delivering innovation and business excellence across the entire spectrum of the travel,
transportation and hospitality domain.
The company offers integrated IT-BPO services comprising of Application Development
and Maintenance, Contact Center Services, Back Office Services, Consulting Services
and Solution Frameworks to the travel industry worldwide.
InterGlobe Technologies Pvt. Ltd.
InfoTech Centre, 2nd Floor
14/2, Old Delhi-Gurgaon Road
Dundahera, Gurgaon – 122016
Haryana, India
T +91 (0)124 458 7000
F +91 (0)124 458 7198
www.igt.in
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