ENTER2018 eTourism Conference presentation

1. 1
How Big Data
revolutionizes decision
support in tourism
Prof. Dr. Wolfram Höpken
Hochschule Ravensburg-Weingarten
wolfram.hoepken@hs-weingarten.de
25th January 2018
2 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Facebook can predict whether you are about
to enter into a new relationship
www.break.com

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3 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Credit card companies can
predict divorce with 95%
accuracy, two years out,
based on your purchasing
decisions
www.bearron.com
4 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Predictive Analytics in Tourism
Revenue management
• Explanation of booking and
cancellation behavior
• Prediction of tourism demand
• Prediction of flight prices
(DINAMO: Yield management system
developed by American Airlines 1988)
Product optimization & sales
• Explanation of tourists’
consumption behavior
• Optimization of product bundles /
market basket analysis
• Cross selling
Customer relationship
management
• Customer segmentation
• Adaptive marketing

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5 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Integration of big data sources
Data warehouseData warehouse
ReportingReporting
OLAPOLAP
Data
mining
Data
mining
CRSCRS
ERPERP
CRMCRM
Online
platforms
Online
platforms
Web contentWeb content
• User generated content (customer feedback / opinions)
• Data on markets and competitors (e.g. changes in demand structure,
price changes)
Operative systems (OLTP) Dispositive systems (OLAP)
External data sources
6 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Integration of big data sources
Data warehouseData warehouse
ReportingReporting
OLAPOLAP
Data
mining
Data
mining
CRSCRS
ERPERP
CRMCRM
Online
platforms
Online
platforms
Web contentWeb content
• Economic data (e.g. GDP, employment data in sending
countries)
• Weather data (historic weather data and weather forecasts)
Environment
data
Environment
data
Operative systems (OLTP) Dispositive systems (OLAP)
External data sources

4. 4
7 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Integration of big data sources
Data warehouseData warehouse
ReportingReporting
OLAPOLAP
Data
mining
Data
mining
CRSCRS
ERPERP
CRMCRM
Online
platforms
Online
platforms
Web ContentWeb Content
• Interactions with local infrastructure (light, air conditioning,
minibar, stereo equipment, TV, telephone, etc. e.g. in hotel room)
Environment
data
Environment
data
Local
infrastructure
Local
infrastructure
Operative systems (OLTP) Dispositive systems (OLAP)
External data sources Interactions with environment
8 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Integration of big data sources
Data warehouseData warehouse
ReportingReporting
OLAPOLAP
Data
mining
Data
mining
CRSCRS
ERPERP
CRMCRM
Online
platforms
Online
platforms
Web contentWeb content
• Location tracking (GPS-based)
• Reaching POIs (QR code/RFID/NFC-based)
Environment
data
Environment
data
Local
infrastructure
Local
infrastructure
Movement
profiles
Movement
profiles
Operative systems (OLTP) Dispositive systems (OLAP)
External data sources Interactions with environment

5. 5
9 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Integration of big data sources
Data warehouseData warehouse
Operative systems (OLTP)
ReportingReporting
OLAPOLAP
Data
mining
Data
mining
Dispositive systems (OLAP)
CRSCRS
ERPERP
CRMCRM
Online
platforms
Online
platforms
External data sources
Web contentWeb content
Environment
data
Environment
data
Local
infrastructure
Local
infrastructure
Interactions with environment
Movement
profiles
Movement
profiles
Typical characteristics of big data sources
• Often unstructured (web content)
• Very large data volumes
• External
10 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Revenue Management
Prediction of demand based on google search volume
Tourist arrivals
arrivals
googlesearchvolume

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11 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Revenue Management
Prediction of demand based on google search volume
Prediction of tourist arrivals at different forecasting horizons
Including
Google Trends
data
significantly
increases
prediction
performance
12 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Revenue Management
Identification of relevant search terms and most significant time lag
3 to 2 month before arrival -> search for activities in Sweden
One month before arrival -> more precise queries, searching specifically for Are

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13 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Revenue Management
Prediction of demand based on big data sources
Tourist arrivals and google online traffic
Tourist arrivals and jet fuel price
Predicting tourist arrivals based on
past arrivals and big data
Used data sources: google online traffic, jet fuel
price, GDP of sending countries, price level of
destination & alternative destinations
14 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Revenue Management
Prediction of demand based on big data sources
 Including big data sources significantly increases prediction performance
MAE (mean average
error) over all sending
countries is reduced
from 620 to 432,
thus, by 30%

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15 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Optimization of real product
Analysis of customer feedback (sentiment analysis)
 Extraction of customer feedback from review platforms
 Detection of topic
 What are customers talking about
(e.g. hotel room, reception, staff, …)
 Detection of subjectivity
 Is the statement objective or subjective
 Detection of sentiment
 Is the statement positive or negative
Method Accuracy
Topic detection
SVM (with POS tagging) 72.36%
Naïve Bayes
(with POS tagging)
49.72%
k-NN (with k = 8) 57.08%
Dictionary-based 71.28%
Subjectivity detection
SVM 65.50%
Naïve Bayes 60.67%
k-NN (with k = 5) 55.50%
Dictionary-based 82.63%
Sentiment detection
SVM (with bigrams) 76.80%
Naïve Bayes (with trigrams) 69.80%
k-NN (with k = 8) 69.60%
Dictionary-based 71.28%
16 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Optimization of real product
Analysis of customer feedback (sentiment analysis)
Detailed analysis of customer feedback
(positive/negative statements)

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17 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Optimization of real product
Analysis customer feedback (sentiment analysis)
Benchmarking along product topics
18 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Optimization of real product
Dynamic topic detection
 Identification of (fine-grained) topics mentioned in customer feedback
(without the need to predefine topics)
Approach Accuracy
Identification of frequent words
(nouns only)
82.86%
Keyword Clustering
(nouns only, sentences-based,
k=80)
88.45%
LSI - Latent Semantic Indexing
(nouns only, sentences-based,
k=80)
85.46%
NER – Named Entity Recognition
(Naïve Bayes, 2 words +/- as
context)
75.17%
Fine-grained topics
with keywords
Predefined high-level
topics
restaurant
service
staff
center
city
halmstad
station
train
walk
hotel
parking
dinner
food
food &
beverage
staff location
breakfast
place
beach
hotel
location

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19 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Optimization of real product
Topic detection by picture classification
Detecting topics like lunch, dinner,
reservation, service, atmosphere,
etc.
Deep learning by neural networks
20 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Marketing & sales
Movement patterns extracted from flickr
Clustering of
flickr foto uploads
(by DBSCAN)
146,958 photo
uploads from 11,289
users for the period
2005-2015

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21 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Marketing & sales
Movement patterns extracted from flickr
Association rules
Rule Sup % Conf % Lift
1, 3 → 8 1 53.3 2.97
1
3
8
22 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Marketing & sales
Movement patterns extracted from flickr
More fine-grained
clustering for city
center
(by k-means)

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23 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Marketing & sales
Movement patterns extracted from flickr
Association rules
Rule Sup % Conf % Lift
1,2 → 3 1.6 100 7.86
1
2
3
24 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Marketing & sales
Movement patterns extracted from flickr
Sequential patterns
Frequent Sequence Sup %
<Max-Joseph-Platz>
<Odeonsplatz>
1.6
Frequent Sequence Sup %
<Frauenkirche>
<Hofbräuhaus>
1.3
Frequent Sequence Sup %
<Frauenkirche>
<Heilig-Geist-
Kirche>
1.3

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25 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Marketing & sales
Movement patterns extracted from flickr
# Frequent sequence Supp.
1 〈0, 142, 10〉 0.010
2 〈0, 64, 10〉 0.010
3 〈152, 0, 64〉 0.010
4 〈206, 190, 178〉 0.010
5 〈0, 142, 64, 10〉 0.008
6 〈0, 10, 126, 42〉 0.006
7 〈0, 142, 10, 126〉 0.006
8 〈118, 0, 64, 187〉 0.006
9 〈152, 0, 64, 75〉 0.006
10 〈152, 142, 64, 10〉 0.008
11 〈190, 178, 171, 169〉 0.006
12 〈75, 16, 178, 190〉 0.006
13 〈16, 0, 142, 64, 10〉 0.006
14 〈178, 0, 142, 64, 10〉 0.006
Frequent sequences based on k-Means clustering
Combining association rules and frequent sequences
26 Prof. Dr. Wolfram HöpkenHow Big Data revolutionizes decision support in tourism
Conclusion
 Current trends
 Tourists leave traces during nearly all touristic activities
 Booking/consumption behavior, information need, preferences,
movement patterns, feedback, etc.
Big Data
 Today all this information can technically be gathered and
analysed
Improvement of decision support
Adaptation/optimization of operative processes and
personalization of customer interactions (Operational BI)
 Challenge: Evaluation of feasability
 Do the available data sources deliver the required knowledge and
can the intended decision support or customer benefit be realized?