Introduction

Some form of analytics is known since the beginning of times. Its first usage was noted thousands of years ago where prehistoric tribespeople would place marks into wooden sticks and bones to keep track of trading activities. They would then compare these sticks to carry out rudimental calculations, enabling them to predict how long their food supplies would last (Cooper, 2012). Analytics has been used throughout centuries in various ways. However, lately due to rapid development of technology analytics has reshaped the face of business.

A tremendous amount of content generated by consumers by social media just as by any other means continues to grow and impact the hospitality industry

 (Dumbill, 2013). The job of so-called business analytics is to analyses external as well as internal content, pick out useful information in such a way that it will help the business to make more informed decisions (Tetlock, Saar-Tsechansky and Macskassy, 2007).

This proposal will examine how Big data, and analytics has developed in past centuries as well as an attempt will be made to gain insight of how crucial analytics is for businesses of 21st century in relation to the guess experience. This piece of work will start with Aims and Objectives of the proposal followed by Literature Review which will focus on gaining insight of constantly changing face of analytics and its influence on the world of business. With this done, the proposal will move onwards to talk about the methodology of the project as well as ethical aspects of the study will be considered.

Aims

The aim of this dissertation is to asses the influence of analytics on hospitality Industry, especially the hotels.

Objectives

1.  To Identify the key benefits analytics might bring to ‘The Guess Experience’ In relation to AccorHotels Brand

2.   To Evaluate whether Big Data gathered by AccorHotels is influential enough to improve the business model.

3.  To Measure the role that data analytics has played in the success of AccorHotels brand.

4. To Test if customer satisfaction can be influenced if a business knows more about a customer in advance.

Literature Review

Business analytics starts with a ‘dataset’ which is a simple collection of information.  In this instance, it can be anything from information about people, location to complex calculations (Bartlett, 2013). These datasets are stored in a Database which must be stored somewhere once it grows too big to be stored on a local computer (Bartlett, 2013). From this point onwards newly, developed technologies are used such as ‘computer clouds’ which is a hardware that stores all the data and allows remote access when needed and ‘Data warehousing’ which Is a collection of a variety of databases ready for analysis to pick out information that matter (Negash,2004).With Rapid development of business analytics and access to nearly unlimited amounts of data, data warehouses have grown so big with data sets being so large and complex that a new term was introduced to describe them, known as: Big data,a term which is gaining a lot of popularity due to its rapid growth and importance. Data within the big data is so big that software systems are hardly able to process these data sets at all (Isson and Harriott, 2013).  Isson and Harriott (2013, p. 61) also mentions term little data which according to them is everything else that is not labelled as big data. These types of data describe smaller datasets which help businesses to keep track of their customers and their buying habits or whatever else might be of interest for a given business type. In simple words, Analytics is a process of finding useful information within large date sets that are very likely to have a lot of data which is not going to be used. (Stubbs, 2011).

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Analytics, business analytics and business intelligence are the three terms in business literature that are often related to one another. Analytics itself, can be definite as a process which involved the use of various techniques such as visualization graphs, data mining and programming to explore, visualize and discover trends and patterns which help to make informed decisions for a business (Stubbs, 2011). An easy example of the use of analytics in a hospitality industry would be knowing preferences of regular hotel guests. For example: Preference of a high floor quiet room, or a room near a gym etc.

There are different types of analytics and these need to be organized to understand their uses. The Institute of Operations Research and Management Sciences suggest for grouping these types of analytics into three types presented below:

Table 1.1 Types of Analytics

Types of Analytics

Definition

Descriptive

Predictive

Prescriptive

The use of simple statistical methods that describe the dataset. For example: An age bar chart which visualises the age of hotel guests based on the day of the week.

An application of statistical software which identifies predictive variables and then uses them to build advanced models which are likely to identify terns and relationships amongst the data

An application of decision models to make the best use of allocable resources. For example: A small business might have limited advertising budget to target customers. Linear programming models can be used to optimally allocate the budget to various advertising media.

(Reference)

Table 1.2 represents the purpose and mythologies for each type of analytic mentioned above. Table 1.2 also presents the difference between analytics and business analytics. Whilst the first one is strictly focused on generating useful information from large data sets, business analytics goes a step further and leverages analytics to create a competitive advantage over other businesses using the data. (reference)

1.2 Analytics and its purpose

Types of Analytics

Purpose

Examples of Methodologies

Descriptive

Predictive

Prescriptive

To identify trends in high volume databases.

To build models which will predict and identify coming tendencies

To allocate available resources in best possible way but also to make prepare for coming tendencies.

Descriptive statistics such as mean, median and mode. As well as, measures such as standard deviation.

Statistical methods such as multiple regression and ANOVA.

Operations research methodologies

(reference)

Business Intelligence is a collection of various procedures and technologies which picks out and transforms relevant data into something useful for organizations. Whilst some say that business intelligence is a broad subject that included analytics and business analytics (Negash, 2004). Others say that it mainly focuses on collecting, storing and exploring businesses for information useful to decision-making and planning (Negash, 2004). BI seeks to answer questions such as what is happening at the moment and where, but it also goes further and helps to decide what actions should be undertaken based on prior experience. Business Analytics (BA) on the other hand, can answer question such as why something is happening and what will happen next.  Summarizing the general idea behind types of analysis BA includes reporting results just like BI but it seeks further to explain why these results have occurred.

1.3 The Characteristics of different types of analytics

Characteristics

Analytics

Business Analytics

Business Intelligence

Uses descriptive analytics as a main source of analysis

Uses predictive analytics as a main source of analytics

Uses prescriptive analytics as a main source of analysis

Mixture of all types of analytics

Business oriented

Focuses on sustaining the data

Requires focus of improving business value and performance

Yes

Yes

Yes

No

Maybe

No

No

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

No (Only historically)

No (Only Historically)

No

Yes

Yes

No

(reference)

All the data types mentioned above are used in every industry including hospitality, each of the types has uses in different situations. Especially in 2019 where Social Media has experienced a rapid growth. In 2004 Facebook was at the top of social networks with over 1 million users, by 2011 its population was being compared to that of a country and today its population is counted in billions. With other social medias just as close. All the status updates, pictures and video added by people on their social media becomes public information, including their demographics, likes and dislikes and so on. This information can then be analyzed in many ways. For example, social media data can be analyzed to reveal the proportion of social media users that enjoy a meal at a time of the day depending on location. This type of data could be used for any business in hospitality sector. For example, an international restaurant might offer different menus in different regions based on these findings. (Whitler, 2018). The colossal growth of social media and content generated by consumers has played a major role in the development of analytics as without content there would be nothing to analyze.

Studying data from social media and other content that is user generated has attracted a lot of attention in the field of analytics for the value that it has. For example, research has demonstrated that online reviews are used to predict product quality (Oh, 1999). Furthermore, it has been found that online news postings have enough of linguistic content that it can effectively predict a firms earnings and stock returns (Bae, 2016).Recently, Ghose and Ipeirotis has used consumer generated content and reviewer characteristics to calculate the usefulness the economic impact of online hotel product reviews. Furthermore, Abraha’s et al has developed a technique to detect vehicle defects using end user discussion forums. Moreover, it has been proved that product development systems can be developed based on mining of consumer generated content in combination with other external data (Paksoy, Özceylan and Weber, 2012).

Kimberly Whitler from Forbes magazine has recently interviewed Wes Nichols the former CEO of Market Share. During the interview Kimberly has asked Wes Nichols to describe the changes that introduction of analytics has brought to businesses. According to Wes Nichols the biggest change has occurred in analytics being a ‘nice to have’ a decade ago to be a ‘must have’ in todays market. Whilst years ago, firms have used analytics in a way which was tangential whilst now it’s a firm’s core strategy for further development (Lewis, 1985). Whilst before, analytics didn’t really matter now businesses that don’t develop a competency in analytics are more than likely to lose their market share to those firms that do. According to statistics (Whitler, 2018). There is currently more devices connected to internet than there are users which is creating enormous amount of data available just waiting to be harnessed by firms, and the ones that do it first will gain the most.

In Hospitality industry businesses use analytics in a variety of situations. In hotel industry, predictive analytics helps to create an individual image of a customer which can help a hotel to tail its services to meet the needs of the individual. Providing person specific services helps to improve guess experience as well as business revenue at the same time (Leavitt, 2013).

According to the research, predictive analytics can be used in any area of hotel operations. Analytics is useful in any art of the business with perishable inventory but also where there is a difference between when the purchase is made and consumed which describes a wide range of services such as restaurants, rooms, spas, conference rooms and so on (Gray and Pauwels, 2016).

Large Hotel brands such as AccorHotels which operates in over 100 countries, with more than 4,200 hotels, over 250,000 employees and is operating in 26 different segments of hotel market gathers so much information on daily bases, that when used correctly can give a huge competitive advantage. For example, if these is a guess in one of the hotels in Germany with very specific requirements such as specific type of pillow, when visiting a different hotel later, this type of information can be used to prepare the room in advance according to the persons needs making it beneficial for both the customer and the company. Nevertheless, such a hotel brands can use predictive analytics to promote a specific range of additional services depending on various factors such as time of the year, the type of concert that is being held nearby and many more (Gray and Pauwels, 2016). Hotels that utilize the data and have the knowledge of knowing who will visit hotel in certain moment in time and have knowledge of their demographics, likes and interest can effectively predict how to promote their additional services suitably as well as adjust the price to match the customer type.

With all the benefits that analytics bring to businesses also comes the difficulties, one of the biggest challenges that business in hospitality industry face are silos. The Silo mentality is a mindset where individual departments do not wish to share information with others in the same company (Forsten-Astikainen and Heilmann, 2014). An example of this on a large scale would be one of the hotels in the AccorBrand not sharing information with another hotel of the same brand for any reason which might work in the benefit of the hotel itself but against a common interest of the brand. As mentioned above, the hospitality sector industry has access to large amount of data. However, historically there has been a lack of co-ordination amongst hotels within the same group chain as they are more likely to compete against each other rather than help one another.

Methodology

Data collection

As the project is still on the way, methods of data collection are still likely to change. However, various data collection methods are considered. For example, one of the very likely scenarios is the use of Expedia.com which was chosen because it is one of the largest online travel agencies with more than 16 Million monthly visitors. Furthermore, expedia.com is a good source for review analysis as a user is obligated to make at least one transaction through the website to be allowed to write the review which heavily eliminates bias data. Others form of analysis will include interviews conducted with the management team of various hotels as well as first-hand experiences of an employees. Besides, this study will have access to some of the internal data from certain hotels which has already been granted.

Method of analysis

As part of Data analysis will include raw data in form of text, several steps will have to be included in the analysis. These would be: preprocessing, of the data, classification of the data, statistical analysis, as well data visualization and conducting interviews. The first two steps of analysis mentioned above are crucial to establish the validity of the data, as uncleaned data would possibly give incorrect results of the analysis itself. Additional methods of preventing corruption of the data will include misspelling correction. The aim of identification will be to classify guess experience related words as well as non-guess.

Limitations

One of the limitations of this study might be the complexity of the guest experience, as everyone has different values and believes which could have an affect on individual expectations of a guess. Furthermore, as the study will involve external sources which in this case will be guest view as well as internal data from the managers perspective it has been proven that there is a difference among what managers believe to be important and what guests believe is important in selection and evaluation of accommodation. (Reference 5)

Ethics

All the data used in this dissertation, will be used from sources which are accessible for public. Furthermore, the participants of interviews will take part completely voluntary, individuals will be informed about possibility to eave or withdraw from the interview at any time, if an individual will wish he will remain anonymous for the rest of the project. Furthermore, nobody will be harmed during the analysis as well as no private data will be released without permission.

References

Cooper, A. (2012). Analytics Series. 1st ed. Bolton: The University of Bolton.

Tetlock, P., Saar-Tsechansky, M. and Macskassy, S. (2007). More than Words: Quantifying Language to Measure Firms’ Fundamentals. SSRN Electronic Journal.

Dumbill, E. (2013). A Revolution That Will Transform How We Live, Work, and Think: An Interview with the Authors of Big Data. Big Data, 1(2), pp.73-77.Dumbill, E. (2013). A Revolution That Will Transform How We Live, Work, and Think: An Interview with the Authors of Big Data. Big Data,

Bartlett, R. (2013) A Practitioner’s Guide to Business Analytics . McGraw-Hill, New York, NY.

Negash, S. (2004) “Business Intelligence.” Communications of the Association of Information Systems

Isson, J.P., Harriott, J.S. (2013) Win with Advanced Business Analytics . John Wiley & Sons, Hoboken, NJ.

Stubbs, E. (2011) The Value of Business Analytics . John Wiley & Sons, Hoboken, NJ.

Whitler, K. (2018). Are You Ready For The New Data And Analytics World Order?. [online] Forbes.com. Available at: https://www.forbes.com/sites/kimberlywhitler/2017/08/17/are-you-ready-for-the-new-data-and-analytics-world-order/#41582c02418c [Accessed 15 May 2019].

Oh, H. (1999). Service quality, customer satisfaction, and customer value: A holistic perspective. International Journal of Hospitality Management, 18(1), pp.67-82.

Bae, S. (2016). An examination of motivation, satisfaction, attachment, and loyalty using structural equation modeling. International Journal of Tourism and Hospitality Research, 30(4).

Ghose, A. and Ipeirotis, P. (2011). Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics. IEEE Transactions on Knowledge and Data Engineering.

Lewis, R. (1985). Predicting Hotel Choice. Cornell Hotel and Restaurant Administration Quarterly.

Abrahams, A., Jiao, J., Wang, G. and Fan, W. (2012). Vehicle defect discovery from social media. Decision Support Systems.

Paksoy, T., Özceylan, E. and Weber, G. (2012). Profit oriented supply chain network optimization. Central European Journal of Operations Research, 21(2).

Leavitt, N. (2013). Bringing big analytics to the masses. Computer, 46(1).

Gray, K. and Pauwels, K. (2016). Data, Analytics and Decisions: How thinking like a scientist can help you make better decisions. Research World, 2016(56).

Forsten-Astikainen, R. and Heilmann, P. (2014). Breaking Down Organizational Silos – Competences and Courage. Academy of Management Proceedings, 2014(1).