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Proceedings of the International Conference on Business Management & Information Systems, 2015

Innovative Mobile Marketing Strategies for Tourism Sector using Segmentation for Targeted and Effective Marketing Programs Tasnim M. Taufique Hossain and Mohammad Sakif Amin Department of Marketing and INB, North South University, Dhaka, Bangladesh E-mail: [email protected], [email protected]

ABSTRACT In recent times consumers are increasingly adopting advanced mobile phones and portable devices. Mobile marketing is gradually becoming an important communication tool for different companies. Businesses related to tourism (SMEs) can also utilize mobile marketing to effectively reach their potential target customers. This paper utilizes an exploratory analysis to segment the tourists conferring to their mobile usage behavior. Different people in the city of Dhaka, Bangladesh and also a few respondents from countries such as Singapore, United Kingdom and Australia participated in this research. This paper utilized quantitative research in terms of “Cluster Analysis” to segment respondents according to their usage of different smart phone features and frequency of use. Four segments: Mobirati, Medievalists, Entertained Socials and Productivists were identified through statistical analysis. Mobiratis showcased extensive use of all smart phone features, though their phone call frequency is lower. Medievalists on the other hand, are detached from all smartphone features; they rely on phone calls only. Entertained socials use all the basic smart phone apps but rely on the entertaining apps such as music and gaming more. Productivists are similar to entertained socials but their reliance is on productivity and business apps more. These groups were further profiled according to their demographic characteristics such as age, occupation, gender and income. Finally, the respondents were asked series of question regarding smart phone usage in a tourism setting. Statistical analysis in terms of ANOVA was used to determine which factors varied from one segment to another, hence, providing deeper insights regarding the segments’ behavior in a tourism setting. This paper will provide valuable insights to the marketing managers of different tourism services about the demographic and behavioral characteristics of different tourist segments. It will also provide recommendations about the appropriate marketing strategies that can be utilized towards each group. This will enable tourism managers to understand their customers better and to reach its targeted customers in a reliable and less costly manner using mobile marketing strategies effectively.

Keywords: Mobile Marketing, Tourism Marketing, Segmentation, Cluster Analysis, Mobile Apps

Introduction Smartphone technology has revolutionized internet usage, information adoption and communication throughout the world. The increasing use of smartphones is making mobile application as the fastest growing media outlet in consumer history (Khalaf, 2014). For tourism, mobile apps play a significant role as well. Travel apps are the 7th most downloaded apps category according to Mickaiel (2011). Consumers are increasingly using mobile apps to search, book and pay for different tourism services. Mobile apps are becoming influential for tourism decision making (Kennedy-Eden & Gretzel, 2012). Because of its growing popularity managers of tourism businesses should utilize this opportunity and develop firm understanding of the mobile marketing landscape. Due to this phenomenon Kennedy-

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Eden and Gretzel (2012) argued that, from a theoretical perspective, it is important to classify mobile apps according to mobile usage dimensions. This paper aims to segment consumers according their mobile usage behavior and relates the segments behavior with mobile marketing adoption in tourism.

Literature Review Mobile Marketing and Segmentation Mobile marketing and mobile advertisement are becoming very popular now days. As technologies such as 3G and 4G mobile broadband are being introduced, the use of mobile apps and websites are increasing rapidly. Mobile advertisements are being further facilitated through technologies such as Bluetooth, Wi-Fi, Hotspots and LBMs (Shin, Choo, & Beom, 2011; Taylor & Lee, 2008). Due to portability, location specificity and untethered capabilities mobile marketing is creating great opportunities for different types of businesses (Bolton & Saxena-Iyer, 2009; Deighton & Kornfeld, 2008; Shankar & Balasubramanian, 2009). Segmentation has been argued as highly beneficial for marketing strategic decision by different authors (Claycamp& Massy, 1968; Smith, 1995; Wedel & Kamakura, 2012). The mobile marketing sphere is getting complex day by day. Kim and Lee (2015) argued that businesses should look for more targeted approach to increase effectiveness of their promotional campaigns. Developing segments and understanding the specific characteristics for each segment is very important for targeted mobile marketing communications (Barutçu, 2007; Grant & O’Donohoe, 2007).

Segmentation Approaches for Mobile Marketing Intention towards mobile marketing adoption can vary within different segments. Demographic characteristics such as age, gender, occupation education culture etc. can significantly influence mobile marketing adoption. Few scholars have focused on demographic influence on mobile marketing. Research found younger and more educated consumers are more prone towards mobile marketing adoption (Barutçu, 2007; Grant & O’Donohoe, 2007). Different lifestyle choices and psychographic characteristics have been utilized as well as the basis of mobile marketing segmentation. Shankar, Venkatesh, Hofacker, and Naik (2010)has defined three consumer segments i.e. the Millennials, the Road Warriors, and the Concerned Parents according to their lifestyle patterns. Zhu, Wang, Yan, and Wu (2009) used consumer lifestyle choices and mobile expenditure as the basis of market segmentation. Persaud and Azhar (2012) argued that rather than demographic characteristics, behavioral segmentation are more important while exploring mobile marketing acceptance. Goneos-Malka, Strasheim, and Grobler (2014) have proposed four mobile marketing segments by analyzing the usage of specific mobile features by customers. The four segments: Connectors, Conventionalists, Technoisseurs and Mobilarti. Connectors where further profiled according to different attitudinal and behavioral variables. Sohn and Kim (2008) used caller ID to define three segments: utilization of paid for services, frequent use of low-cost or free services and indistinct usage pattern. Kim and Lee (2015) used Q-methodology to segment customers according to different behavioral questions and proposed four segments “The Business partner”, “The Skillful enthusiast”, “The New experience seeker”, and “The Close buddy”. Head and Ziolkowski (2010) proposed two segments: high users of text messages and segment which considers text message as important but rated internet and email as more important features.

Innovative Mobile Marketing Strategies for Tourism Sector using Segmentation...

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Mobile Marketing in Tourism and Segmentation It is becoming crucial for tourism destinations and especially for different suppliers and tourism service providers in a tourism destination, to find more targeted and effective methods of marketing communication strategies to increase destination competitiveness (Govers, Go, & Kumar, 2007). In such a scenario mobile marketing is providing better opportunities for tourism service providers. Lee and Mills (2010) have indicated mobile services as new and exciting option for tourism marketing. Eriksson, Wöer, Frew, and Hitz (2002) argued that while other businesses are taking mobile marketing as an added value only, for tourism it would become into an integral part. Kumar and Zahn (2003) argued that mobile marketing would reduce the cost for tourist and result in better tourism experience. Through mobile marketing tourists can access important information from any location (Yan & Lihua, 2005). Although mobile marketing possesses great potential for tourism marketing, not a lot of researches have been done in its regards. Oliveira and Martins (2009) revealed a lack of m-commerce and tourism integration. In addition to that no researchers have been conducted in terms of segmentation and mobile marketing in tourism. Hence, this research provides a great opportunity to understand segment criteria for mobile marketing in tourism and provide managers of tourism services with valuable information regarding effective mobile marketing strategies.

Methodology Cluster Analysis After thorough literature review segmentation criteria were developed. According to several literatures and websites articles 18 smartphone activities were used for segmentation measures. Respondents were asked to indicate how often they utilize those smartphone features. A 7-point likert scale starting from “Not at All” to “Always” were developed. No specific time such as “once a week” or “once a day” were used. It is because users perception of utilizing a particular smartphone feature or apps might differ. Making one call everyday might seem low in contrast to using calendar once everyday. Hence, the judgment of frequency was made according to each respondent’s perception. “Cluster Analysis” is an appropriate tool to analyze whether smartphone usage behavior differs within different groups of user or not. Cluster analysis is used to find structures in a set of items where several groups are created which are homogeneous within, but separate from one another (Everitt, 2009; Wedel & Kamakura, 2012). A K-Means cluster analysis was used in SPSS 22 and 4 clusters/ segments: Mobirati, Medievalists, Entertained Socials and Productivists were identified. Demographic and some behavioral characteristics of each cluster/segments are described in the results section.

ANOVA In addition to smartphone features 17 items from several researches were developed to analyze mobile marketing adoption in tourism sector. Once again respondents scored each item in a 7-point scale based on their perception of importance for each item. ANOVA was used to analyze how each of the 17 items was related to each of the 4 clusters. Items where the segments’ means differed were identified and the analysis is presented in the results section. A total of 110 university going students were approached for filling out the questionnaire. They were asked to circulate the questionnaire to their friends and family members as well. A restriction was placed where only smartphone users and people who have travelled abroad for tourism were selected.

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Due to this number of respondents were not as high. A total of 250 questionnaires were distributed and after collecting and coding the results 125 valid responses were retained.

Results Clusters/Segments Profiles Four segments were identified through cluster analysis: They are termed as Mobirati (29% of the overall sample), Medievalists (10% of the overall sample), Entertained Socials (32% of the overall sample) and Productivists (29% of the overall sample). Table 1 shows the mean score of each group in comparison with the overall sample. Apart from making calls and sending texts all the other features of smartphone usage are statistically different among groups as significance is less than .05. Table 1: Mean Score of Segments According to Mobile Usage Behavior

Smartphone Features

Making Calls Sending Texts Browsing Internet IM apps, i.e. WhatsApp, Viber, etc. Video Chat App, i.e. Skype, Facetime, etc. Social Media Apps, i.e. Facebook, Twitter, etc. News and Reviews Apps, i.e. BBC, CNet, IGN, etc. Navigation App, i.e. Google Maps, Apple Maps, etc. Reading e-Books, i.e. Kindle, iBooks, etc. Watching Videos, i.e. YouTube, Vimeo, etc. Taking Pictures Taking Videos Playing Mobile Games Listening to Music Learning Apps, i.e. Cooking, Photography, etc. Productivity Apps, i.e. Calendar, Notes, Reminders, etc. Business Apps, i.e. Docs, Spread Sheet, Presentation, etc. e-Commerce Apps, i.e. eBay, Amazon, OLX

Clusters/Segments Produc- Mobirati Medieva- Enter- Actual ANOVA tivists lists tained Mean SignifiSocials cance

6.56 6.44 6.61 6.25 5.11 6.50

6.03 6.42 6.75 6.78 6.36 6.78

6.69 5.54 4.54 3.92 2.46 3.00

6.50 6.48 6.60 6.60 5.25 6.28

6.40 6.35 6.43 6.27 5.24 6.14

.266 .056 .000 .000 .000 .000

5.12

5.25

2.69

3.35

4.33

.000

5.03

5.47

3.62

4.08

4.70

.000

4.72 5.71 5.94 4.72 4.14 4.65 2.97

5.06 6.39 6.78 6.53 6.36 6.67 5.53

2.00 2.62 4.85 3.69 2.15 3.77 1.69

2.55 5.18 6.33 5.18 4.35 5.80 3.38

3.84 5.41 6.19 5.28 4.64 5.52 3.71

.000 .000 .000 .000 .000 .000 .000

5.56

6.00

4.31

5.15

5.43

.002

5.14

5.46

3.00

2.20

4.06

.000

4.06

5.14

1.54

1.95

3.43

.000

Utilizing crosstab analysis with chi square test, no difference were found among the groups regarding gender, occupation, tourism budget and expenditure behind internet. However, age, hours spent behind internet, average number of apps usage and mobile usage expertise level are different for each clusters as significance level is less than .05.

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Table 2: Demographic Characteristics of Segments Pearson Chi-Square

   

Value 6.116a 19.531a 35.916a 16.170a 25.422a 23.255a 26.498a 28.680a

Clusters * Gender Clusters * Occupation Clusters * Age Clusters * Budget Clusters * Hours Spent Behind Mobile Clusters * Internet Budget Clusters * Number of Apps Clusters * Mobile Usage Expertise

df 3 12 15 18 12 21 12 9

Asymp. Sig. (2-sided) 0.106 0.076 0.002 0.581 0.013 0.331 0.009 0.001

Table 3 shows the difference between each segment. Significant characteristics for each segment have been indicated thorough bold letters. Further explanation of each group is described afterwards. Table 3: Segment Characteristics According to Different Factors Mobile Usage Expertise

Number of Apps

Hours Spent Behind Mobile

Age

Still a novice user An average user An advanced user An expert 1-3 4-6 7-10 11-20 More than 20 Less than 1 hour 1-2 hours 3-4 hours 5-6 hours More than 6 hours less than 18 18-24 25-32 33-38 39-45 More than 45

Productivists 5.6% 61.1% 19.4% 13.9% 8.3% 36.1% 27.8% 25.0% 2.8% 8.3% 19.4% 30.6% 33.3% 8.3% 2.8% 41.7% 27.8% 5.6% 16.7% 5.6%

Mobirati 8.3% 33.3% 47.2% 11.1% 5.6% 38.9% 30.6% 19.4% 5.6% 11.1% 16.7% 16.7% 25.0% 30.6% 2.8% 58.3% 25.0% 11.1% 0.0% 2.8%

Medievalists 30.8% 53.8% 15.4% 0.0% 46.2% 23.1% 15.4% 15.4% 0.0% 30.8% 38.5% 23.1% 0.0% 7.7% 0.0% 23.1% 15.4% 0.0% 30.8% 30.8%

Entertained Socials 0.0% 55.0% 20.0% 25.0% 7.5% 27.5% 52.5% 10.0% 2.5% 5.0% 30.0% 27.5% 10.0% 27.5% 2.5% 45.0% 40.0% 5.0% 0.0% 7.5%

Mobirati Mobirati are a group of people who projects extensive use of mobile application. Apart from making calls this group utilizes all the other smartphone features significantly more that the rest of the segments as seen in Table 1. 58% of this group belongs to the age of 18 to 24. Almost 55% of this group uses smartphone more than 5 hours a day and they feel they are advanced user of smartphone devices. However, interestingly they use less number of apps regularly compared to the entertained socials. Almost 70% of this group uses 4-10 apps regularly compared to 80% of Entertained Socials.

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Medievalists Medievalists are a group of people who are devoid of using smartphones though they own one. They use their smartphones only for making calls. As Table 1 suggests they do not use any other smartphone feature. This group of people is older than the rest of the groups and 30% of this group belongs to the age of 39 to 45. They spend less than an hour behind phones and utilize only a few apps regularly. They consider themselves novice or an average user of smartphones.

Productivists Productivists are mainly users of more business and productive applications such as calendar, notes, word processors, presentation tools etc. They tend to collect information from YouTube, learning apps, eBooks and navigation apps. They use less IM apps than entertained socials but have more than average social media usage tendency. They spend less time behind mobile phones compared to Entertained Socials and Mobiratis. Almost 60% of this group use smartphones for 4 to 6 hours daily. This group mainly considers themselves as average users of smartphone devices.

Entertained Socials The last group “Entertained Socials” is the primary users of different entertainment and connectivity apps such as music, taking pictures, using IM apps, using video chats more than overall average. Although their mobile gaming is less than the overall mean, it is still higher that the Medievalists and Productivists, indicating the frequent use of that kind of apps. Entertained Socials use highest number of apps regularly. Almost 80% of this group uses 4 to 10 apps regularly. A portion (27%) of this group utilizes mobile phone more than 6 hours a day indicating the highest time spent by any group behind mobile phones. 45% of this group belongs to the age group of 18-24 and 40% from 25-32.

Segments’ Behavior in Terms of Mobile Marketing in Tourism The four segments where further profiled according to their behavior regarding mobile marketing in a tourism setting. 17 questions regarding their intention to adopt mobile marketing in tourism were asked. Segments projected different behavior in 7 items as significance level is less than .05. For rest of the items no difference in behavior is observed within segments. Table 4 shows the ANOVA analysis of all the items according to different segments. Table 4: ANOVA Analysis of Items Related to Mobile Marketing Adoption in Tourism   1. Obtain information 2. RecieveText Ad 3. Respond to Ads 4. Mobile Apps Convenient 5. Maps Convenient 6. Improve Tourism Experience 7. Save Money 8. Increase Cost 9. Will Annoy

F 2.116 3.983 1.134 1.110 2.560 2.965 2.685 .433 .195

Sig. .102 .010 .338 .348 .058 .035 .050 .730 .899

  10. Complicated 11. Not a good option 12. Reliable 13. More Convenient then Others 14. In Touch With Family 15. Share my Experience 16. Provide me safety 17. In touch with Business

F 1.407 1.797 3.486 1.276 .870 3.145 5.400 1.640

Sig. .244 .151 .018 .286 .459 .028 .002 .184

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Innovative Mobile Marketing Strategies for Tourism Sector using Segmentation...

According to the ANOVA analysis the difference among the groups (significance