Information and Communication Technologies in. Tourism

Transcription

1 Information and Communication Technologies in Tourism

2 Business Intelligence and Smart Business Networks in the Tourism Industry Smart Business Networks Business Intelligence 2 ICT in Tourism

3 Business Networks and Dynamic Markets business process company Value Chain market Value Network Dynamic Markets 3 ICT in Tourism

4 Business Networks and Dynamic Markets low Dynamics high Virtual community E-auction Info brokerage Dynamic market E-mall Value chain service e-market place Value network Collaboration platform Value chain integrator Vertical integration (single company) low Investment high 4 ICT in Tourism

5 Smart Business Networks SBNs - Smart Business Networks Collection of revenue-sharing organisation units Common goal Each participant increases own value (win-win) Connection via communication network Communication via new technologies Intelligent, novel way of interaction Sustainable network What does SMART mean? Novel Unusual 5 ICT in Tourism

6 SMART destination network Horizontal (capacity utilization & collaborative offer generation) Vertical (profile matching, dynamic sourcing) Hotel Agent Agent Hotel Agent Hotel Negotiation GUI Knowledge base Inter-agent communication PMS / CRS 6 ICT in Tourism

7 Business Intelligence and Smart Business Networks in the Tourism Industry Smart Business Networks Business Intelligence 7 ICT in Tourism

8 An Interdisciplinary View Key Word Analysis (03-06, 5,500 different for 186 cities) Extracting business intelligence from UGC 20 Key words 32 cities Arrivals & Search Volume (Költringer 2009) Wolk & Wöber, Wolk & Wöber ICT in (2008): Tourism A comparative study of city travellers information copyright needs, IFITT Information Technology and Tourism, 10:

9 The Knowledge Destination Huge amounts of customer-based data in destinations unused Transaction data CRM data Tracking data Survey data Data Bases World Wide Web Navigation data Search data [keywords] UGC [ratings, blogs, e-reviews] Business Intelligence Knowledge Economic performance Marketing effectiveness Quality of visitor experience Destination Management Information System events/ attractions acco/ gastro mobility organization distri bution tourist 9 ICT in Tourism Monitor fulfilment of strategic goals e.g. Are Vision 2020: satisfied guests, stays, contacts, brand awareness Decision making support to increase degree of goal fulfilment e.g. media mix, reach, conversion, market basket, capacity forecast, cancellation

10 Knowledge destination framework architecture Destination Management Information DMIS System Knowledge application layer Data mining & OLAP Knowledge generation layer Structured data Data warehouse Data extraction (ETL) Unstructured data Explicit tourists feedback provided knowingly and intentionally - Structured data: e.g. online and offline guest surveys, ratings from web 2.0 applications, user profiles from web applications and online communities, etc. - Unstructured data: free text from E- mails and web 2.0 applications (e.g. blogs, e-comments/reviews), rich content (e.g. YouTube.com), etc. All-stakeholder encompassing de-centralized generation of knowledge strategically relevant to DMO AND suppliers through techniques of Business Intelligence Höpken, W., et al. (2011): The Knowledge Destination A Customer Information-based Destination Management 10 Information ICT in Tourism System, In: Law, R., Fuchs, M. & Ricci, F. (eds.), Information copyright IFITT and Communication Technologies in Tourism 2011, Springer, New York: Implicit tourists information traces provided unknowingly and unintentionally - Navigation data: search behaviour on web sites and online portals, community sites, etc. - Transaction data: online requests, reservation and booking data, payment, etc. - Tracking data: GPS/WLAN-based coverage of tourists spatial movements - Observation data: gathered in a laboratory context or through market observation

11 Navigation-based Navigation Patterns afterski alpina åregårdarna åregården åresjön äta bastu bergbanan bjørnen bo boende bygget club diplomat fiskecamp fjallby fjällby fjällbyn fjällbys fjällgård fjällgården fjällhotell fjällstuga fjällvärlden gästhuset holiday hotel hotell internet jope kabinbanan karta kommunikationer kvm kyrka liftkort mörviksgården oviksfjällen pistkarta radio renen röding rum rustika service ski skidliftar snasahögarna tännforsen toaletter Forecast Search-Term Analysis Pittman et al. (2010): Web Usage Mining in Tourism A Query Term Analysis and Clustering Approach. In: Gretzel et 11 ICT in Tourism al. (eds.), Information and Communication Technologies in Tourism 2010, Springer, New York,

12 Navigation-based Navigation-based User Clusters Cluster Model Attribute cluster1 Attribute cluster2 12 ICT in Tourism ownership accommodation Cluster 0: 3239 items accommodation accomm_hotel_room Cluster 1: 564 items whatson_start accomm_appartment Cluster 2: 1078 items booking acomm_booking Cluster 3: 736 items directions offers Cluster 4: 82 items activities_summer booking Total number: 5699 items restaurant_bar accomm_suite restaurant_bar_sports offers_private Attribute cluster3 Attribute cluster4 Attribute cluster5 offers pool_saunaworld panorama_pictures offers_privat pool_sauna_pool ownership_pan_pict accommodation spa media activities_summer spa_treatments accommodation booking pool_sauna_sauna accomm_appartment ownership activities pool_saunaworld accomm_package accommodation offers activities offers pool_sauna_pool Cluster Avg(session_length_minutes) Avg.(clicks_per_session) cluster_ cluster_ cluster_ cluster_ cluster_

13 Transaction-based Segment count(id) Sum (visiting days) Avg (visiting days) Sum (persons) Avg (persons) Sum (rooms) Avg (rooms) individual_domestic individual_foreign company_domestic TS guests company_foreign recurrent private Bookings / Origin Conversion Rate 13 ICT in Tourism

14 Transaction-based Cancellation by country Country Cancellation rate SWE 13,4% NOR 12,0% FIN 19,3% DNK 22,7% RUS 31,1% EST 28,7% GBR 13,6% NLD 21,6% LVA 25,7% LV 12,8% DEU 11,9% EE 13,4% Cancellation trend without local bookings 5% of customers cancellation rate 60% (days to arrival > 41 days, private, stay 7-9 days, booking logis) Target Marketing Year Cancellation rate ,6% ,5% ,6% ,1% ,6% ,7% Höpken, W., et al. (2011): The Knowledge Destination A Customer Information-based Destination Management Information System, In: Law, R., Fuchs, M. & Ricci, F. (eds.), Information and Communication Technologies in Tourism 2011, Springer, New York: ICT in Tourism

15 Feedback-based Destination attributes Valued subjective experience 15 ICT in Tourism

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