Multichannel Digital Marketing Optimizations through Big Data Analytics in the Tourism and Hospitality Industry
Abstract
:1. Introduction
1.1. Multichannel Marketing in the Digital Era
1.3. Web Analytics KPIs and Tourism Digital Brand Name
WA KPIs | Description of the WA KPIs |
---|---|
Social Media Traffic | Social media platforms such as Instagram and Facebook redirect users to the corporate website through links or advertisements. This KPI is named Social Media Traffic [104]. |
Organic Traffic | Visitors that arrive on the webpage using a search engine are referred to as “organic traffic.” [104]. |
Global Rank | The platforms’ overall traffics (organic, social, and paid traffic) and the user engagement parameters (pages/visits, average time on site) combined produce this KPI. Since a website in the first spot has a higher rating than a website in the fourteenth position, the smaller the global rank, the more famous the website [104,105]. |
Websites’ Average Time on Site | This WA KPI determines how many seconds are spent on average by users on a webpage per visit [104]. |
Websites’ Pages/Visit | This WA KPI calculates the total number of pages viewed by each user when they enter a tourism website [104]. |
Websites’ Paid Traffic | This WA KPI is generated through paid methods. For instance, Google search leads users to tourism websites when they click on an advertisement [104]. |
Websites’ Total Visits | This WA KPI measures how many users visit a website each day. This sum is computed while a cookie is used to track each visitor’s IP address [104]. |
Websites’ Bounce Rate | This WA KPI is the percentage of website users that are one-page visits, and the visitor leaving the site without reading a second webpage is known as the bounce rate [104]. |
Total Fans in Facebook/Instagram | This WA KPI refers to the total number of fans of a tourism website [106,107]. |
Total Reactions to comments in Facebook/Instagram | The number of comments made on posts that were made during the chosen time frame [108]. |
Number of Comments in Facebook/Instagram | This WA KPI illustrates the total number of comments of the tourism websites social media [106]. |
Number of Likes in Facebook/Instagram | This WA KPI presents the total number of likes of the tourism websites’ social media [106]. |
Post Interaction in Facebook/Instagram | The post interaction metric measures how frequently fans interact with a post’s content. It displays the average number of interactions per fan [109]. |
1.4. Formulation of Research Hypotheses
2. Materials and Methods
2.1. Data Extraction and Statistics
2.2. Fuzzy Cognitive Map and Agent-Based Modeling
3. Results
3.1. Statistical Analysis
3.2. Creation of Macromodel FCM and Optimization Scenarios
Creation of FCM Optimization Scenarios
3.3. Creation of the Agent-Based Model
4. Discussion
5. Conclusions
5.1. Theoretical Implications
- (a)
- Hypertargeted advertisements: It is evident that the future of digital advertising lies in the development of hypertargeted advertisements [160]. Providing the feeling of “one in a million”, hospitality marketers can deliver personalized messages to the targeted audience, based on their behavior, and receive valuable feedback. While it is true that the audience is smaller, the results of conversion rates are higher, since they improve advertising relevance. Personalized advertising has been less explored withing the HTT domain, with most research focusing on personalized services [161,162]. Lately, there has been a slight tendency for academic research to deal with personalization and advertising withing the HTT sector [163,164], a fact that indicates that personalized advertising is a promising trend within the HTT domain, with manyresearch extensions. Personalization is the future of digital advertising and the common denominator of every digital marketing activity in favor of higher conversion rates and brand awareness. The HTT sector needs to invest in hypertargeted advertisements, and not just continuously bombard consumers with over-the-top ads, with the use of market segmentation via online reviews [165].
- (b)
- User-generated content (UGC): The traditional strategy of pushing massive marketing messaged to all no longer drives clicks. The key to tourism business success lies in the development of user-friendly technology that urges users to post reviews and ratings on social media, building in this way trust and brand empowerment [166]. This is where a new opportunity arises: HTT businesses should use social media as an engagement channel for personalized advertisements. However, in order to be seen or heard on such online platforms, there is an urgent need to re-evaluate the content in the first place. The shift toward sustainable digital advertising is increasingly under the scope. If the ultimate goal of hospitality companies is generating more targeted traffic, then showcasing the company’s sustainable personality is of the essence [167]. However, it is not only the content that matters, but also the way that this brand-specific content is created and published on social media. Travelers perceive UGC as an empowerment factor that shapes their travel planning [168]. Displaying authentic user-generated content by customers and/or brand evangelists is able to influence engagement, increase conversion rates, and act as a form of modern e-word-of-mouth communication [169].
- (c)
- Artificial Intelligence (AI) in digital advertising: With an eye to deliver top-notch services, the hospitality industry needs to enhance customer experience. Top-notch services start by understanding the customer journey across all touchpoints and profiling guests. The statistical analysis demonstrates that digital branding and social media interaction are closely associated and interdependent variables. Interactions happen through “likes”, “shares”, and “posts”; however, in order to achieve higher levels of customer satisfaction, boost retention rates, and drive sales, personalizing services through automation and big data is the key to unlock business potential.The HTT providers heavily rely on AI-powered tools, such as chatbots, virtual reality, and language translators, to boost the user experience [158]. AI is revolutionizing the hotel industry since it takes personalization one step further by actually detecting patterns in data and forecasting behavioral attitudes [38].
5.2. Practical Implications
5.3. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Correlations | Social Media Traffic | SM Post Interaction | Paid Traffic |
---|---|---|---|
Social Media Traffic | 1 | ||
SM Post Interaction | −0.072 | 1 | |
Paid Traffic | 0.931 ** | −0.004 | 1 |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Constant (Social Media Traffic) | - | 0.872 | 223.821 | 0.116 |
SM Post Interaction | −0.068 | 0.126 | ||
Paid Traffic | 0.931 | <0.001 |
Correlations | Total Reactions on Posts | Number of Comments | Posts per Day |
---|---|---|---|
Total Reactions on Posts | 1 | ||
Number of Comments | 0.573 ** | 1 | |
Posts per Day | 0.471 ** | 0.784 ** | 1 |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Constant (Total Reactions on Posts) | - | 0.329 | 19.195 | <0.001 |
Number of Comments | 0.528 | 0.725 | ||
Posts per Day | 0.057 | 0.002 |
Correlations | Total Visits | Social Media Traffic | Pages per Visit |
---|---|---|---|
Total Visits | 1 | ||
Social Media Traffic | 0.889 ** | 1 | |
Pages per Visit | 0.353 ** | 0.413 | 1 |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Constant (Total Visits) | - | 0.791 | 126.897 | <0.001 |
Social Media Traffic | 0.896 | <0.001 | ||
Pages per Visits | −0.017 | 0.779 |
Correlations | Organic Traffic | Number of Fans | Social Media Traffic | |
---|---|---|---|---|
Organic traffic | 1 | |||
Number of Fans | −0.127 | 1 | ||
Social Media traffic | 0.628 ** | −0.012 | 1 | |
Total Visits | 0.859 ** | −0.097 | 0.889 ** | 1 |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Constant (Organic Traffic) | - | 0.826 | 103.200 | <0.001 |
Social Media traffic | 0.657 | <0.001 | ||
Total Visits | 0.443 | <0.001 | ||
Number of Fans | 0.006 | 0.905 |
Correlations | Global Rank | Average Time on Site | Number of Likes | Pages per Visit |
---|---|---|---|---|
Global Rank | 1 | |||
Average Time on Site | −0.483 ** | 1 | ||
Number of Likes | 0.241 * | −0.189 | 1 | |
Pages per Visit | −0.424 ** | 0.542 ** | −0.020 | 1 |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Constant (Global Rank) | - | 0.325 | 10.433 | <0.001 |
Average Time on Site | −0.345 | 0.007 | ||
Number of Likes | 0.171 | 0.107 | ||
Pages per Visit | −0.248 | 0.044 |
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Sakas, D.P.; Reklitis, D.P.; Terzi, M.C.; Vassilakis, C. Multichannel Digital Marketing Optimizations through Big Data Analytics in the Tourism and Hospitality Industry. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 1383-1408. https://doi.org/10.3390/jtaer17040070
Sakas DP, Reklitis DP, Terzi MC, Vassilakis C. Multichannel Digital Marketing Optimizations through Big Data Analytics in the Tourism and Hospitality Industry. Journal of Theoretical and Applied Electronic Commerce Research. 2022; 17(4):1383-1408. https://doi.org/10.3390/jtaer17040070
Chicago/Turabian StyleSakas, Damianos P., Dimitrios P. Reklitis, Marina C. Terzi, and Costas Vassilakis. 2022. "Multichannel Digital Marketing Optimizations through Big Data Analytics in the Tourism and Hospitality Industry" Journal of Theoretical and Applied Electronic Commerce Research 17, no. 4: 1383-1408. https://doi.org/10.3390/jtaer17040070