ENTER2019

1. Daily Deals in the
Mediterranean Region: a Data
Analytical Approach
Luka Tomat a
, Miha Bratec a
, Katarzyna Minor b
and Marko Budler a
a
University of Ljubljana, Slovenia
b
Cardiff Metropolitan University, Wales, United Kingdom

2. The problem
• The rise of e-sales in hotels to include third party channels such as
OTA and daily deals
• Daily deals combine:
• advertising benefits for merchants
• substantial discounts for customers
• Utilisation of the power of social media to propagate the offers
• Daily deal model is a popular booking medium amongst the industry
and the customers
• Yet, daily deals are still a fairly unexplored phenomenon
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3. Focus of the study
• To address current basic assumptions about daily deals:
• 1) DD are used off-season, and by struggling destinations;
• 2) DDs are used by low quality hotels, or hotels with poor online presence;
• 3) DD are used as one-off promotion.
• Based on English versions of ‘niche’ DD websites and TripAdvisor
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4. Methodology
• Web analytics based on observations - January to July 2018
• Three main Europe-wide, similar characteristic, travel-focused DDs websites;
• 13,963 daily deals offered by 3,525 hotels
• TripAdvisor quality scores of the participating hotels
• Individual attributes mapped using Web parsing in JAVA script
• destination, longitude, latitude, offer start date, offer end date, offer title,
hotel name, and hotel address
• Data collated in SQL database and processed in MS Access, analysed
applying descriptive techniques.
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5. Findings 1
• Contrary to research to date daily deals are offered mainly by the
hotels in destinations, where the tourist demand is higher
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6. Findings 2
• Hotels using the DD websites have relatively high TripAdvisor scores,
thus DD usage is not likely to be linked to hotel quality.
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7. Findings 3
• Three levels of engagement with daily deal websites: single (one
promotion), sporadic (minimum of two and maximum of six
promotions), and frequent (one to three times a month)
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8. Future research
• 1) DD seasonal and destination usage
• Further investigations into regional DD usage and distribution
• 2) DDs’ effect upon TripAdvisor reviews
• Two sets of year on year data, where both DD and quality scores are observed
• 3) DD usage patterns
• Further detailed cluster analysis of DD variables, e.g. availability of offer vs time of the
year, the length of the booking window
• 4) DD geographical qualities
• e.g. deepness of discount vs frequency of deals, location, quality scores etc.
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9. Thank You!
Contact: miha.bratec@ef.uni-lj.si; kminor@cardiffmet.ac.uk
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10. Thank You!
Contact: miha.bratec@ef.uni-lj.si; kminor@cardiffmet.ac.uk
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