1. Differentiating a rural tourism
website based on user´s travel
motivations
Juho Pesonen
Centre for Tourism Studies,
University of Eastern Finland
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2. Agenda
• Introduction
– Problem
– Previous research
– Research questions
– Contribution
• Data and methods
• Results
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3. Introduction
• Importance of market segmentation research
in tourism
– Plethora of studies and literature reviews
– Different ways of doing segmentation research
– Marketing management perspective
• Gap between segmentation theory and
practice (Dibb & Simkin 2009)
• Segmentation important in website design
(Perdue 2001)
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4. Earlier research
• Both data driven and common sense
segmentation in tourism
• Focus on finding segments
• What it means to segment tourists in online
context?
– Automatic data mining and analysis
• Measuring performance of segmentation
results
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5. Research questions
• RQ1: What has been the impact of the Internet
and digital on the application and role of
segmentation in tourism?
• RQ2: What kind of rural tourist segments can be
found among rural tourists that use Internet to
plan or book their holiday?
• RQ3: How reliable and valid motivation
segmentation results are?
• RQ4: How can a website be differentiated to serve
different segments and how effective the
differentiation is?
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6. Contribution
• Bridging the gap between segmentation
theory and practice.
• Measuring segmentation effectiveness.
• Measuring segmentation quality.
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7. Data and methods
• Two surveys on the largest Finnish rural
tourism website www.lomarengas.fi
– Summer 2009, spring-summer 2011
• 727 and 1776 valid filled questionnaires
• Segmentation based on travel motivations
– Large number of validating questions to
examine difference between segments
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8. Data and methods
• Cluster analysis
• Discriminant analysis
• Comparative analysis
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9. The results
• Slight increase in segmentation studies and
ICT in 21st century
– Internet is just another information source
among others.
– No performance measures using new tools such
as website log files.
• Four segments: Home region tourists,
Wellbeing tourists, Family travellers, Social
travellers
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10. The results
• Segments exist but for example k-means
cluster analysis fails to predict segment
sizes.
• Very difficult to differentiate a
accommodation search engine for different
segments.
– Performance unknown at this point.
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11. Thank you!
• Juho.pesonen@uef.fi
• Questions / comments?
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