Digital Marketing: Tool to Increase Hotel Awareness

Digital Marketing
Digital Marketing

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Digital Marketing: Tool to Increase Hotel Awareness

Digital marketing is the process by which products, services or brands are advertised through one or more forms of electronic media. Unlike the old-style of marketing, this process consists of methods and strategies that allow an organization to evaluate marketing schemes and understand the approaches that work and what is not in real time. Digital marketers carefully observe how long and how often prospective customers visit the site.

The internet is the avenue that is closely related to digital marketing. However, digital marketing also involves the use of electronic billboards, digital television, radio channels, and mobile apps. Digital Marketing is important because most consumers spent more than half of the day online and they often use social media as a source of social interaction, entertainment, news, and shopping (, n.d.).

            Digital Marketing has some advantages. To begin with, it is a global platform – a website allows the business to find new target markets across the globe and be able to trade with other people even without seeing them. Second, it tracks and measures results – through the use of online metric tools, determining the number of buyers and consumers who visit the online website is easy. Therefore, the result or the statistics that can be drawn from the metric tools can be a useful measure to determine whether the company is reaching its targets and objectives.

Finally, it creates openness and personalisation. Social marketing and networking can help a company to build good relationships with their customers. Furthermore, digital marketing could rapidly increase the social awareness of a particular brand could (, 2016).

            Digital Marketing has been the primary tool of all industries – including hotels in the hospitality industry. The industry saw positive growth in 2015. With the continued progress, hotels are expected to use technology to increase the awareness of the market. The rise of millennial travelers as the dominant consumers in hotels has a growing interest in the usage of mobile devices and applications to look for more personalized hotel guest services (Hospitality Net, 2015). Therefore, it is important that Mobile Search is present in hotels.

The trend in Digital Marketing is the Mobile search. Research studies have established that three out of five individuals use mobile devices to explore and look for information, and 80% of local hunts are converted to purchases. It is worth noting that hotels have a website which is optimized for mobile search to enhance mobile traffic and ensure a full-bodied content that can be shared on social media. Also, sites should contain directory listings, maps, and local citations to strengthen the hotel’s local presence.

Engaging the customer through the content requires exciting local attraction, activities, visuals, and trending topics. Through the use of digital marketing tools which determine the time spent on a visited web page, hotels can identify the type of contents that should be enhanced to encourage customers to visit the website again. By doing this, a potential customer could buy products or services offered by the hotels (, 2015).

            Understanding the behavior of travelers would help to develop the digital marketing strategy of the hotel. The industry should realize that people are more interested in a business that knows their needs, provides a personalized and relevant communication and that which provides tailored preferences depending on their necessities. Therefore, it is essential that the company or hotel should update the content of their website based on the needs of the potential customers or travelers. This will help to attract more clients; thus, it will lead to more profit for the company (, n.d.).

            Hotels in the hospitality Industry should remember that brands will not exist without consumers patronizing on it. For this reason, the purpose of digital marketing is to build and increase brand awareness especially in remote hotels.  The goal of the digital content marketing is to establish the hotel’s brand as attractive, valuable and trusted. The reason why digital marketing requires fresh and innovative content is to make sure that the brand message will not be overlooked.

It is necessary because nowadays high competition is very intense in the hotel industry. The high competition in the hotel industry is healthy because many search engines give rewards to those businesses that update the content of their sites with a higher ranking while silently punishing the stagnant websites with low organic rankings.

            With this in mind, hotels can improve their websites in three simple ways. First, hotels should identify the interest of the travel enthusiasts’ and potential customers. Second, provide information that is relevant to their needs. Finally, provide them clients with an edge that will have value to them.

            Though hotels can use the digital content marketing to showcase the new services and products, honesty and accuracy about the brand should be the primary core value. Digital content marketing allows the hotel to come up with a creative explanation on how they can address the needs of their customers. The content in websites should also indicate how the industry is established and the popularity of the business in the marketplace (Parker, 2016).

Regression Analysis

            Regression analysis is a mathematical way of sorting out which variable has the most impact. It answers the questions of which variable matter most and how these variables interact with each other (Gallo, 2015). Regression analysis is used when we want to predict a continuous dependent variable from independent variables. After the collection of data (variables), all information will be relayed using a chart (Abrams, 2007).

            The fundamental and the most common predictive analysis used is the Linear Regression analysis. The formula defines the basic form in one dependent and one independent variable:

y = β0 + β1x

Where y is the estimated dependent, β0 is constant; β1 is the regression coefficients, and x is the independent variable.

            Regression analysis is often used in the casual analysis, the forecast of effect, and the trend of the forecast. To begin with, the casual analysis is used to determine the effect of the dependent variable to the independent variable. Second, forecast effects help the company to recognize the weight of result to the dependent variable change if there will be a change in one or more independent variables. Finally, forecasting the trend helps to point out the approximations where a typical question could be the price of X months from now (Statistics Solutions, 2013).

            On the contrary, when conducting a hypothesis test, one is likely to come across two types of possible errors namely; Type I and Type II errors. The risks of these errors are determined by the level of significance and the power of the test. Therefore, it is important to identify which type of mistakes has more severe consequences.Type I error occurs when the null hypothesis is correct, and it has been rejected.

The common mistake for this kind of error is when it is confused with statistical significance and practical significance. One should be cautious that a large sample size is more likely to detect a small difference. Hence, it is essential to consider practical importance when the sample size is large (, 2011).  Conversely, when the null hypothesis is not correct, and it is not rejected it is often referred to as Type II error (, 2016).

Assumptions of Regression

            Some statisticians believed that when a statistical process is not normally distributed, there is a mistake with the process, or the process itself is out of control. A chart can be used to determine when the process is non-normal so that statisticians can make corrections and return it to normality. Many procedures do not follow the normal distributions. Some of the examples include cycle time, customer waiting time, shrinkage et cetera (, 2016). In line with this, content validity is concerned with sample-population representativeness such as knowledge and skills (Yu, 2012).

Linear regressions require at least two variables of metric scale. The rule of regression analysis is that it has to have at least twenty cases per independent variables in the analysis. Regression has four assumptions. First, it should be linear such that there should be a relationship between the dependent and independent variables. The use of scatter plot can be used to test this assumption. Second, all variables have to be multivariate normal as required in the regression analysis.

This assumption can be tested through the utilization of a fitted normal curve or a Q-Q plot and a histogram. Third, the analysis agrees that multicollinearity is not in the data. It happens when no dependence occurs among independent variables. Finally, auto correlation is expected to be less in the data of regression analysis. The auto correlation exists when there is no reliance on the residuals (Statistics Solutions, 2016).

The following will provide different formula with the corresponding variance of returns:

Beta value is a measure of how strong one stock responds to the systematic instability of the whole market. 1 Beta occurs when the stock reacts to the uncertainty of the market with the market on average.


On the other hand, correlation coefficient should be between -1 and 1. Where -1 means that the market and the stock move in the opposite directions. On the other hand, 0 means that the market and the stock do not have a relationship with the movement. Lastly, 1 implies that the market moves along with the stock.


            Both will tell the strength of the linear relationship between Xi and Yi. However, they always provide distinct information. The correlation provides a restricted measure that can be understood independently of the scale of the two variables. On the other hand, Beta gives a useful quantity construed as the predicted change in the expected value of Yi for a particular value of Xi (, 2012)

            The R-squared is a measure used to identify how to close the data to the fitted regression line. R-squared is always between 0 and 100%. It is defined as the variable response variation’s percentage explained in a linear model.

            The zero percent (0%) happen when there is none of the variability of the response data is around its mean. On the other hand, the 100% pertains when all its variability of the response data is around its mean. However, there is a question often asked on what should be the good value for R-squared or how big does R-squared is required for the regression model to be valid and reliable. It would be advisable to look at adjusted R-squared rather than R-squared (, n.d.).

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Based on the result of the data from SPSS, R-squared is .145 or 14.5%. The adjusted R-squared is .139 or 13.9%, is just almost the same percentage of the R-squared pertaining to the mean square of regression which is 13.352. Since the R-squared is relatively low; the model proposed is not possible or fitted based on the data provided.

            Moreover, the probability of examining the observed results when the question is valid from the null hypothesis is called the calculated probability or the P-value. The P is defined as rejecting the null hypothesis when it is true (, 2016). The one-sided P-value is only used when an unexpected direction from a significant change makes no relevance to the study. To connect with the hypothesis, the P-value is used to specify a probability which is adopted to calculate after a particular study, and the level of significance pertains to a pre-chosen likelihood.


The increase of hotel awareness in relation to the data presented varies on the result of each of every variable in the calculation. The Pearson correlation of Y2_spc_brecal shows a positive 1.000 correlation. Moreover, the Pearson Correlation of X2_spc_person shows a positive .381 correlation.  These values indicate that the variables in the data move in tandem since both are positive. As one variable increases, other variable increases, and vice versa.  The table below shows the correlations between the variables.

The model summary of the result emphasized that R-squared is .145, or a 14.5%, and the adjusted R-squared is .139 or 13.9%. The result indicates that the model is not fitted because R-squared and adjusted R-squared are both low. The Standard Error of the Estimate is an estimate whether the prediction is accurate or not. In regression line, when the standard error of the estimate is small, the prediction tends to be accurate. Based on the result, it shows that the Standard Error of the Estimate is .737 or 73.7% which is obviously high. It means that the prediction is not accurate.

Marketing Hypotheses Analysis

There are several hypothesis that can be drawn in marketing but the most common and essential hypothesis is that digital advertising can directly enhance brand recall and recognition. The two (brand recall and brand recognition) are an entirely different thing. Though, there would be no brand recall without brand recognition. Remembering and recognizing a brand plays a critical role in attracting more buyers to stay firm in the brand of a certain product and buy them continually.

There are ways to study if a purchaser will select a product because of brand recall or brand recognition. For an instance, a brand recognition may occur when a customer watches movies or televisions where the advertisement of a product is placed. When a buyer knows the presence of a product through the online ads, the internet, or television, he/she will be aware of the product and subconsciously her/his brain will look for that brand and instantly decide to buy the product. Hence, digital marketing helps increase the recognition of a particular product/brand.

Another hypothesis could be brand recall can be improved through personalization in digital marketing. In a simple definition, personalization is the delivery of messages or experiences to a consumer based on the info about a particular person. Some terms that could describe digital personalization are data management platform (DMP). It is a database that saves consumer info which can be shared with other media like the website, email channel, applications and dynamic creative optimization (DCO), the technology that is utilized to modify sending messages from DMP data (Diamond, 2015). 

There is two new development in digital personalization. The first pertains to the customers. It is said that consumers have changed as well as their demands. A brand gathers customer’s data with the conjecture that the info will be applied to add worth or value to a consumer’s experience. The anticipation of pertinent and personalized brand collaborations will progress as businesses keep on making more investments human capital and customer-focused technology.

The second development is the technology needed for integrating personalization in digital marketing stratagems which are more accessible to product trademarks. Currently, marketers have the tools to gather info wherein they can precisely reach the clients and prospects. It helps increase the effectivity and efficiency of their campaigns through digital marketing (Diamond, 2015).

In relation to the hotel industry, the hypotheses discussed are relevant in a sense that customers always rely based on the experience of other people. For example, if a traveler wants to stay in a particular place, he/she will browse the internet to see the different hotels and its reviews. He/she will read the experiences of the other people who stayed in the hotel. If the rating of a hotel is high or has excellent reviews, the probability of a potential customer to stay in that hotel is very high. Also, recognition and recall of a hotel’s name are crucial in the preferences of the client to stay in a hotel.

Table 3. Hypothesis and Its Correlation

Hypothesis 1: Digital Advertising can directly enhance brand recall and recognitionBased on the SPSS result there is a significant  correlation between digital advertising and brand recall of because its regression result which is .416 is above 0.05 level.
Hypothesis 2: Engagement in digital marketing with interactive media programming and value content can increase brand recognition and brand recall.   
Hypothesis 3: Positive and neutral value added UGC is valuable in affecting brand recall in digital marketing. 
Hypothesis 4: Brand recall can be improved through personalization in digital marketing 
Hypothesis 4: Brand recall can be improved through personalization in digital marketing 
Hypothesis 5: Brand awareness can be increased by influence in digital marketing 
Hypothesis 6: Digital advertising can increase brand recall. 
Hypothesis 7: Digital personalization can increase brand recall. 
Hypothesis 8: Digital influence can increase brand recall. Hypothesis 9: Digital user-generated-content can increase brand recall 
Hypothesis 10: Digital engagement can increase brand recall. 


It is evident that Digital Marketing and Social media are having a substantial impact on how customers behave. With the use of technology, hotels can take advantage of the growing online spending and Smartphone technologies. Through the use of social media and mobile marketing strategies, hotels can increase the awareness of brands in the hospitality industry since prospective customers spend most of the time using different social media tools.

Travel enthusiasts and other potential customers will be provided with direct reservations using the booking software hotels continue to embrace the idea of digital marketing.Through the use of regression analysis, hotels in the hospitality industry would be able to determine the behaviors of the travelers. Moreover, this will allow hotels to identify how they can provide full-bodied information to engage customers and lead them through finalizing their purchase.


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Gallo, A. (2015). A Refresher on Regression Analysis. [online] Harvard Business Review. Available at: [Accessed 26 May 2016].

Hospitality Net. (2015). Hospitality Net – The Global Hotel Industry and Trends for 2016. [online] Available at: [Accessed 26 May 2016]. (2016). Are You Sure Your Data Is Normal?. [online] Available at: [Accessed 26 May 2016]. (2011). Type I and II Errors. [online] Available at: [Accessed 26 May 2016]. (2016). The benefits of digital marketing. [online] Available at: [Accessed 26 May 2016].

Parker, A. (2016). Building Brand Awareness Through Digital Content Marketing | G/O Digital Marketing. [online] Available at: [Accessed 27 May 2016]. (n.d.). What’s a good value for R-squared?. [online] Available at: [Accessed 26 May 2016]. (n.d.). Digital Marketing: What is it?. [online] Available at: [Accessed 26 May 2016]. (2016). What are type I and type II errors? – Minitab. [online] Available at: [Accessed 26 May 2016].

Statistics Solutions. (2013). What is Linear Regression? – Statistics Solutions. [online] Available at: [Accessed 26 May 2016].

Statistics Solutions. (2016). Assumptions of Linear Regression – Statistics Solutions. [online] Available at: [Accessed 26 May 2016]. (2012). How does the correlation coefficient differ from regression slope?. [online] Available at: [Accessed 26 May 2016]. (2016). P Values. [online] Available at: [Accessed 26 May 2016].

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Yu, C. (2012). Assessment: Reliability and validity. [online] Available at: [Accessed 26 May 2016].

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