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Behaviour of virtual visitor based on eShop and DMO websites: A comparative

study by means of data mining techniques

Fidel Rebón, Gloria Ocáriz, Jon Argandoña, Jon Kepa Gerrikagoitia and Aurkene Alzua-Sorzabal

CICtourGUNEDonostia - San Sebastián, Spain

fidelrebon@tourgune.org

http://www.tourgune.org

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1. Introduction2. Related Work3. Methodology4. Results and Discussion5. Conclusion and future work

Agenda

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Introduction

• Internet is providing a global channel for communicating, informing and purchasing and therefore e-Marketing and e-Commerce activities must be adapted to it.

• With the increasing diffusion of the Internet, DMOs need to understand the web user’s behaviors in order to adapt their online marketing strategies to the prospective customers’ preferences and requirements and by doing so, having an impact on the traveler decision making process and engagement (Wang & Fesenmaier, 2006; Xiang & Gretzel, 2010).

• Most of the DMOs are including products and experiences for sale on their web portals, providing the final consumer contact with private operators that want to have presence on the DMO portal, i.e Spain.info, Turismo de Tenerife, Barcelona Turisme, I Amsterdam, Visit London,…

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Introduction

•Destination Web Monitor (DWM) is a defined as “a system to measure, analyse, and model the behaviour of visitors in different virtual areas in which a destination is promoted and with the objective of providing benchmarking ratios that facilitate strategic surveillance and intelligent marketing policies” (Alzua-Sorzabal, Gerrikagoitia, & Rebon, 2014:6).

•The primary objective of a eShop is merely commercial and the DMO website is rather informative and persuasive.

•This research performs a comparative study between the virtual user behaviour of an e-shop and a DMO web portal in order to know whether the behaviour of both websites’ users.

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Related Work

•Web personalization through clustering techniques has been dealt in some research by using Web Usage Mining in order to discover patterns that are useful to categorize users with similar interests (Castellano & Torsello, 2009; Zhang, Xu, & Zhou, 2005).

•Relations between the e-shop and the informational website’ users’ behaviour has been mentioned in some studies (Lee, Qu, & Kim, 2007). This research declares a positive relation between the consumer intention to search online information in depth and the execution of online purchases.

•Other studies focus on discovering user s interest on an e-commerce site using clickstream data and these value the variables of navigation: category visiting path, browsing frequency and relative length of access time (Chen & Su, 2013).

•However, clustering has not been performed to understand the differences between users of different types of websites. Therefore, it is pertinent to conduct a research that analyses the behaviours of different websites in order to understand differences for better engagement.

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Methodology

3.1. Assumptions

3.2. Criteria for variable selection

3.3. Consolidated process for obtaining variables

3.4. Cluster selection software

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Methodology

3.1. Assumptions

• The frequency of a user visiting a web page and the time that a user spends on a web page is positive relevant to his/her interest.

• Every user has preference characteristics when he/she visits a website every time.

• The sequence of the web pages visited by the user is related to his/her interest.

• Users with similar interest should have similar browsing patterns (Chen & Su, 2013).

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Methodology

3.2. Criteria for variable selection:

The selected variables of the virtual user behavior on a website have been: connection time, number of visits at a given time of connection, time length of connection and number of pages/views or actions taken

Other variables:

•Location: highly depend on the international projection•Devices or browser: depend on technology and the platform for which the portal is developed•Type of traffic: closely linked to marketing campaigns carried out on social networks•Bounce rate number: directly consequence of visits were distorted

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Methodology

3.3. Consolidated process for obtaining variables:

ETL process ETL process

WEB USAGE MINING

CLUSTERING

Destination Web Monitor: web behaviour measurement and analysis framework

Data collection period:

01/01/2014 - 01/04/2014

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Methodology

3.4. Clustering model and software selection:

• The statistical R language has been chosen to apply the cluster model algorithm. This language provides proficient features in portability, computational efficiency and memory management (Ihaka, 2009).

• R supports several kinds of clustering techniques.

• R can be used together with some IDE (Integrated Development Environment) like Rstudio.

• The selected clustering technique is K-means and the group selection method is Pseudo-F value (Calinski & Harabasz, 1974)

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Result and Discussion

Pseudo-F value (Calinski & Harabasz, 1974) suggests 19 cluster partition to K-means clustering.

The analysis is based on ratios per visit:

•Page Ratio: viewed pages•Time Ratio: Time spent on the site

The observations are characterised by an underlying behaviour rather than by website's volume observations

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Result and Discussion

The contingency table provided by the model for each of the clusters given the origin website:

• There is a clear separation of the origins in the clusters

• 4 of the 19 clusters account for 66% of the observations

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Result and Discussion

DMO eShop

• DMO visitors are highly concentrated in 4

categories.

• 3 of the clusters do not contain DMO

observations at all. DMO users and e-shop behave

different from each other.

• page ratio: 6,6 views

• time ratio: 6,2 mins

•The e-shops are more dispersed over the 19 clusters

•60% of the group in 12 of the 19 clusters. This

suggests that there is a wider variety of users in an e-

shop.

•page ratio: 11,1 views

•time ratio: 8,1 mins

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Result and Discussion

• DMO users (triangle shaped) tend to visit more pages than the e-shop users and have a balanced time usage per visited page

• e-Shop users (cross shaped) show less balanced time usage behaviors

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Conclusion and future work

• The visitors’ behaviour in both platforms is different.

• This implies that appropriate e-marketing policies should be developed targeting each of the detected behaviors differentiating the website.

• The number of users of an e-Shop does not experiment a pronounced renovation, so the audience is more loyal than the one in a DMO

• Future works should look for using the detected users’ Typologies and categorizing them depending on the behaviour of the user and the type of web the user is navigating on.

• With this knowledge the web owners would find out to whom, to what, to how and to when to refer to the visitor, ergo, to know the “consumer decision journey” and strengthen their engagement.