3. Escape Travel 19139507
Introduction:
The numbers are telling - Australia’s tourism industry was pegged at $107.2 billion in 2014-15, with the
segment expected to breach the $130 billion mark by 2020 [1 & 2]. Similarly, between July 2015 to June
2016, over 13.7 million Australians went on a holiday at least once, with 52.8% of these people utilizing
the services of a travel operator for their respective trip [3].
In the current world, the scope for a travel operator to make a pie from this Australia segment is
unprecedented thanks to the resources available. By utilizing analytics, Escape Travel, a part of Australia’s
largest retail travel outlet, can gain solid competitive advantageby implementing strategiesand plans
backed by data.
Critical Evaluation:
The given survey was carefully analysed, understood and evaluated, and following conclusion was
reached:
Survey –
Gaps and inadequacies:
While the survey covers most key aspects, more informative dimensions could have been
included in the questionnaire to help us gain additional insights from the data. Dimensions like
sex, occupation/profession (student, professional, business) and type of excursion (outdoors,
relaxing, historical, etc.) would have helped us to gain meaningful insights which aremore
specific, resulting in ideation of actionable plans.
Class intervals of dimensions should have been more concise, which would have helped us find
the specific target group.
Analytical findings -
The survey and its subsequent analysis using SAP Lumira and Powerpivot led us to many findings
regarding the travel preference, travel frequency and travel expenditure of customers depending
upon their location, age and income.
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Location:
Using Travel Frequency (sum)asmeasure.
Customers from South Australia travelled most frequently in a year, followed by customers from
Queensland. Customers from New South Wales tend to travel least often per the survey
conducted.
On further analysis, the preferred travel destination of customers from their respective state
was:
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Using ID (count)asa measure.
It became clear, customers from New South Wales and Western Australia had a clearpreference
for Asia Pacific as their next travel destination. Similarly, Europe was preferred by customers
from Queensland, Tasmania and Victoria. Africa scored highly as next destination for South
Australians.
Age Group:
Using Travel Frequency (sum)asmeasure.
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On the basis of the above graph, it was conclusive customers under the age of 30 travelled most
often, followed by customers between the age of 30-40. The travel frequency declined as we
moved upwards in the age-group chain.
Using ID (count)asa measure.
Customers from the age group of 30-40 and 40-50 had a clear preference for comfortable
accommodation whereas customers between the age of 50-60 and 60-70 require a luxurious
accommodation during travels.
Income:
Using Travel Frequency (sum)asmeasure.
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Customers in the income group of $70,000 – 90,000 travelled more frequently than any other
income group. They were followed by income group of $50,000 – 70,0000. Customers with
income of over $100,000 travelled the least.
Using ID (count)asa measure.
Customer’s preference for accommodation greatlyvaries among different income groups. For
customers earning below $50,000, comfort is a necessity while customers from income group of
$70,000-90,000 and above $100,000, luxury accommodation is a requirement. Customers
earning between $50,000 – 70,000 require only basic accommodation during travels.
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Recommendations:
Using ID (count)as measure and Travel offer (4 & 5)asfilter.
Previously, we found out the preferred location of customers depending upon their state. Using the
Travel offers filter enabled us to find out customers from the respective states who are very likely to plan
their next trip if a discounted offer arose. The above graph depicts various actionable insights, which are
as follows:
1. It is recommended that a special offer for a tour to Asia Pacific is made for customers from New
South Wales, Western Australia and Tasmania.
2. It is recommended that a special offer for a tour to Europe is made for customers from
Queensland and Victoria.
3. It is recommended that a special offer for a tour to Africa is specifically tailor-made for customers
from South Australia.
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Using Travel Opportunity (sum)asmeasure and Annual ($) asfilter (<$1000 for top left, $1000-5000for top right, >$5000 for bottom
centre)
The expenditure on travel varies depending upon the location of the customer. With the help of the
above visualisation, we found out customers from which state tend to spend the most on travelling and
which customers from which state tend to spend a lower value on travelling. A new measure called Travel
Opportunity is created, which is product of travel frequency and travel offer. A higher sum on travel
opportunity indicates the customer from the respective stateis a frequent traveller as well as is very
likely to respond to offers.
The recommendations based on above graphs are:
1. South Australians will travel frequently and will respond to offers if the expenditure of the travel
is on the lower side i.e below $1000 a year. As such, a tour excursion for South Australians
should be createdkeeping this insight in mind.
2. Similarly, Queenslanders travel often and are responsive to offers as long the budget doesn’t
exceed above $5000.
3. Customers from Western Australia have the most disposal income for travelling when compared
to other states. Escape Travel’s primary target group should be customers from this part of
Australia. More detailed surveys should be conducted for Western Australian, so as extract the
most from this group of customers.
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Using ID (count)asmeasure
Recommendation based on above graph:
1. Customers between the age30-40 are the most responsive to a special discounted offer,
followed by customers from 40-50 years of age. As such, plans tailored for these particular age
groups should be thought and implemented to derive maximum value.
2. Customers from the age group of 50-60, 60-70 and above 70 are fairly non-responsive to special
offers.
