When travelers return from vacations, they are often reminded of something other than collecting
their baggage and booking a taxi. That something is an empty fridge. However, a European airline
has its customers covered. It allows passengers arriving at The Hague airport to pick up a pre-booked
welcome home meal box.1 The airline is conducting this experiment in partnership with a Dutch
supermarket chain. This is one example of the numerous ways in which airlines are diversifying their
revenue sources and creating newer opportunities to increase ancillary revenues.
Ancillary revenue is becoming increasingly important for airline profitability. Hence, airlines are
betting heavily on predictive analytics, digital, Artificial Intelligence (AI), Machine Learning (ML),
Natural Language Processing (NLP) and Internet of Things (IoT) to help them win the ancillary game.
For instance, a European airline plans to invest Euro 200 Million in digital and data development by
2020.2 A leading U.S. airline’s investment in big data technology now enables the real-time analysis
of more than 150 variables about customers to predict individual future actions.3
Let’s take a look at some of the key areas where analytics is enabling airlines to increase
Customer preferences continuously evolve with
time and are also influenced by external factors.
Many industries have been using analytics to
predict future trends to determine products
and services. For example, beverage companies
often use predictive tools to determine which
flavors of chips might scale up in the future.
Airlines can similarly leverage such predictions
while planning the in-flight menu, which can be
based on data such as weather, sector and
For example, a leading U.K.-based airline started
serving branded sandwiches on board based on
findings that customers would be willing to pay
for food from a known brand.4
Another example is the in-flight sale of
duty-free products which has been on the
decline. An obvious response for airlines is to
discontinue the sales when it stops being
viable. However, analytics is helping airlines
predict shopping trends and create alternatives
such as e-commerce platforms for duty-free
products, which the customers can access
using the in-flight wi-fi service.
Behavioral profiling can help airlines decide on
appropriate bundling of ancillary products.
A family traveling with children for a holiday is
more likely to buy excess baggage than a solo
traveler. A mother with a toddler, flying coach,
is more likely to pay for extra legroom and
priority baggage. Similarly, a business traveler
may choose to pay for in-flight wi-fi services.
Behavioral profiling also enables dynamic
pricing of ancillaries. A traveler who has
historically always booked based on the
lowest price will have a lower propensity to
pay more for comforts, as compared to another
customer who has opted for business class
on many occasions.
Personalized marketing5 has made deep
inroads in industries such as retail. For airlines,
until not too long ago, offerings centered
mostly on flight seats and a few add-ons
primarily because the distribution channel
relied on Global Distribution Systems (GDSs)
and travel agents. Today, however, airlines are
adding ancillary revenue streams every year
and customers are increasingly booking directly
using online channels by making comparisons
in real time.
Analytics can help airlines design innovative
ancillary products and services in areas where
untapped demand exists. For example, an Asian
airline offers customers an ‘empty seat’ option
so that they can purchase one or two
unoccupied seats next to theirs on the day of
travel.6 Sleeper sofa7 is another option wherein
a row of coach seats can be converted into
a single, horizontal sleeping space using
detachable headrests and mattresses.
Customers can indicate their interest in this
ancillary at the time of purchase and are given
an option to book on the day of travel based
on the seat inventory. In all these examples,
analytics is helping airlines innovate ancillary
products across products and services.
According to one study,8 the ancillary share of
revenue for leading airlines was found to be
greater than 27 percent for market leaders and
merely 5 percent for laggards. Clearly, there is
untapped potential for airlines to leverage.
While airlines collect vast amount of data, they
face significant challenges in bringing the data
together in a way that will drive ancillary
revenues to the next level. As airlines gradually
evolve the ancillary revenue streams, there are
typical gaps that I have observed which can be
addressed by analytics in combination with
technologies such as AI, ML and IoT.
Here are some key challenges and gaps in
leveraging the full potential of ancillary revenue.
1. Lack of Systems to Integrate Data
Estimates suggest9 that the annual data
generation by modern aircraft will touch 98
billion gigabytes by 2026. However, airlines lack
the systems to aggregate all the data required
to predict customer behavior. Information
across data streams such as GDSs, Customer
Relationship Management (CRM) and booking
portals do not always synchronize with each
other. Without the right data infrastructure,
airlines cannot convert all the data they access
2. Inability to Make Predictions Across
Without the ability to aggregate varied data
sources, airlines cannot generate the insights
required to make the right ancillary
recommendations to customers. Even where
recommendations are made, they tend to be
unidimensional and simplistic. For example, in a usual scenario, a passenger booking a
ticket is offered ancillaries such as seats with
extra legroom and meals. However, what
if the customer could be offered a coupon
to buy something at a store after completing
the security check? Airlines are missing such
opportunities to increase ancillary revenue
because of data not coming together in
ways that factor in interaction effects and
3. Lack of Differentiated Ancillary
I have also observed gaps in the merchandising
strategies being used by airlines at present.
Ancillary bundled offerings do not offer
adequate flexibility to customers. The emphasis
is still on pull-driven sales when the customer
enquires at the call center or counter. Despite
personalization being a stated priority for
airlines, ancillary sales are still tilted towards
a one-size-fit-all approach.
Airlines are also personalizing
recommendations10 based on customers’ past
ancillary purchases. Recommendations based
on the itinerary are better predictors of ancillary
purchase as compared to historical data and
persona-based predictions. The former allows
for predictions to be made even if there are low
transaction volumes or if the customer is new.
Personalization based on relevance to itinerary
is real-time and actionable.
Without building the right capabilities to
leverage the data they collect and power
AI-enabled predictions, airlines will find it
difficult to answer questions such as:
Who should ancillaries be offered to?
Which channels should be used?
How to optimize the ancillary bundle?
What is the right bundle pricing?
Retailer Model for Airlines
In the past few years, the business model for
airlines has been evolving to the extent where
it has become similar to the retail industry.
In this context, analytics certainly takes
centerstage and airlines will need to focus
on building this capability.
A crucial success factor will be in finding service
partners who not only understand the industry
dynamics but have the right analytics and
technology capabilities to join the dots of large
volumes of data and generate insights that
actually drive revenues.
Building the data infrastructure will also be a
necessary precursor to leveraging emerging
technologies such as AI and ML. For example,
if a customer purchases a ticket two months
in advance, the option of buying a particular
ancillary product such as excess baggage
remains open till the day of the journey.
Advanced predictive models can help airlines
predict the likelihood of a passenger buying
excess baggage at the time of booking itself.
The airline can accordingly decide to promote
its excess baggage offer to passengers with
high propensity to buy and accelerate the
sale of ancillary.
Analytics clearly offers airlines the opportunity
to win in this new business context. However,
it will be neither easy nor linear. Focusing on
building analytics capability by finding the right
partner and investing in the appropriate mix of
technologies will be the new name of the game.