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Proceedings of FEB Zagreb 11th International Odyssey Conference on Economics and Business June 16 20, 2020 Croatia 1/2020 ISSN 2671 132X Vol 2 No 1 pp 1 886 June 2020, Zagreb Editors Jurica Šimurina U
Proceedings of FEB Zagreb 11th International Odyssey Conference on Economics and Business June 16-20, 2020 Croatia 1/2020 ISSN 2671-132X Vol.2 No.1 pp.1-886 June 2020, Zagreb Editors: Jurica Šimurina University of Zagreb, Faculty of Economics & Business, J F Kennedy square 6, 10000 Zagreb, Croatia jsimurina@efzg.hr Ivana Načinović Braje University of Zagreb, Faculty of Economics & Business, J F Kennedy square 6, 10000 Zagreb, Croatia ivana.nacinovic@efzg.hr Ivana Pavić University of Zagreb, Faculty of Economics & Business, J F Kennedy square 6, 10000 Zagreb, Croatia ipavic@efzg.hr Publisher: Faculty of Economics & Business University of Zagreb J F Kennedy square 10000 Zagreb CROATIA DOI: https://doi.org/10.22598/odyssey/2020.2 Indexed in: EconLit, ProQuest, EBSCO International Editorial Board Lovorka Galetić – chair (Faculty of Economics & Business, University of Zagreb, Croatia) Károly Balaton (University of Miskolc, Hungary) William C Gartner (University of Minnesota, USA) Aleš Groznik (Faculty of Economics, University of Ljubljana, Slovenia) Joe F Hair, Jr (University of South Alabama, USA) Ulrich Hommel (EBS Business School, Germany) Zoran Krupka (Faculty of Economics & Business, University of Zagreb, Croatia) Milan Jurše (Faculty of Economics and Business, University of Maribor, Slovenia) Tonći Lazibat (Faculty of Economics & Business, University of Zagreb, Croatia) Junsoo Lee (University of Alabama, USA) Michael J Morley (University of Limerick, Kemmy Business School, Ireland) Jurica Pavičić (Faculty of Economics & Business, University of Zagreb, Croatia) Soumitra Sharma (Juraj Dobrila University of Pula, Croatia) Robert Sonora (University of Montana, USA) Mark C Strazicich (Appalachian State University, USA) Jean-Paul Thommen (University of Zurich, Switzerland) Goran Vlašić (Faculty of Economics & Business, University of Zagreb, Croatia) Krešimir Žigić (CERGE-EI, Prague, Czech Republic) Joseph Windsperger (University of Vienna, Austria) Organizing Committee Jurica Šimurina (chair) Lovorka Galetić Marijana Ivanov Mario Spremić Božidar Jaković Ivana Načinović Braje Ivana Pavić Danijela Ferjanić Hodak List of reviewers Ana Aleksić Ana Novak Blaženka Knežević Branka Tuškan Sjauš Danijela Ferjanić Hodak Davor Labaš Domagoj Hruška Goran Vlašić Ingeborg Matečić Irena Pandža Bajs Ivan Strugar Ivana Barišić Ivana Dražić Lutilsky Ivana Načinović Braje Ivana Pavić Jasna Prester Jovana Zoroja Jurica Šimurina Lovorka Galetić Maja Klindžić Marijan Cingula Marijana Ivanov Mario Spremić Mateja Brozović Mihaela Mikić Mirjana Hladika Miroslav Mandić Nikolina Dečman Petra Barišić Rebeka Danijla Vlahov Golomejić Sanda Rašić Jelavić Sandra Horvat Tanja Komarac Vanja Krajinović Zoran Krupka University of Zagreb, Faculty of Economics and Business Beata Buchelt Cracow University of Economics, Poland Agnieszka Ignyś Poznań University of Economics and Business, Poland Jana Blštáková University of Economics in Bratislava, Slovakia Simona Šarotar Žižek University of Maribor, Faculty of Economics and Business, Slovenia Nataša Rupčić University of Rijeka, Faculty of Economics Jean-Paul Thommmen University of Zürich, Switzerland Josef Windsperger University of Vienna, Austria FROM GOOGLE ANALYTICS TO DIGITAL MARKETING OPTIMIZATION IN HOTEL INDUSTRY: PROPOSAL OF FRAMEWORK AND EMPIRICAL EVALUATION OF HOTEL INDUSTRY IN CROATIA, BOSNIA AND HERZEGOVINA AND SERBIA Kenan MAHMUTOVIĆ University of Bihać, Faculty of Economics, Pape Ivana Pavla II br 2, Bihać, Bosnia and Herzegovina kenan.mahmutovic@unbi.ba, kenan.mahmutovic@gmail.com Abstract Digital analytics is crucial in the process of benchmarking business performance and predicting future trends in the hotel industry It requires that hotel management knows new technologies and techniques and methods of digital analytics The first aim of this paper is to shed light on the role and importance of digital analytics in the hotel industry and to propose a framework for the development of a digital analytics dashboard for a hotel website This framework suggests a set of KPIs and metrics that should facilitate strategic and tactical marketing decisions in the hotel business The second objective of the paper is to examine the level of application of digital analytics (Google Analytics) in the hotel industry of Croatia, Bosnia and Herzegovina, and Serbia Through the empirical research using the content analysis method on a sample of 1006 hotel websites, the author investigates differences between countries, but also differences between hotel groups according to their categorization, and examine the significance of these differences Results confirm a significant and very strong association between the hotel location (country) and the use of Google Analytics on the hotel website However, a significant and moderate association between the hotel categorization and use of Google Analytics has been confirmed only in Croatia, but not in other countries In the conclusion, the author interprets the research findings, points out the limitations of the research, and provides recommendations for future research Keywords: digital analytics, marketing optimization, hotel industry, google analytics, KPI JEL code: M310, M15, Z330 Introduction Tourism, and in particular the hotel industry as its backbone, has become a significant catalyst for economic development in the world with an increasing share of GDP and total employment At the same time, this industry is increasingly influenced by the changing digital environment, which is full of diverse information on new consumer habits and characteristics, as well as new distribution and promotional channels Hotel websites have become the most important distribution channel, and the emergence of new channels such as online travel agencies creates new pressures on business conditions and pricing strategies Quality and timely information are essential for hotel management to understand the efficiency and effectiveness of business operations and to make good marketing decisions Some previous 828 research findings demonstrate a positive association between marketing effectiveness and business performance in terms of growth, enhanced customer satisfaction, competitive advantage, and a strong marketing orientation (Gladson-Nwokah & Gladson-Nwokah, 2015; Appiah-Adu, Fyall & Singh, 1999; Taylor, 1996) To be effective company needs adequate information available to managers (Moncarz, 1996) to conduct marketing analysis and to make effective strategic plans and implement marketing strategies (Hart & Troy, 1985; Buttle, 1986; Seaton & Bennet, 1996) In today's fast-changing digital environment, digital analytics is crucial in this process of benchmarking business performance and predicting future trends using the data collected from the hotel website It requires that hotel management knows new technologies and techniques of digital analytics, to be able to collect the needed information and to analyze them in a meaningful context Google Analytics is the most used digital analytics tool for web sites in the world Very few scientific papers and literature deal with the use of digital analytics and Google Analytics in the hotel industry This is the reason that the first objective of this paper is to shed light on the role and importance of digital analytics in the hotel industry and to propose a framework for the development of a digital analytics dashboard for a hotel website This framework suggests a set of KPIs and metrics that should facilitate strategic and tactical marketing decisions in a hotel The second objective of the paper is to examine the level of application of digital analytics (Google Analytics) in the hotel industry of Croatia, Bosnia and Herzegovina, and Serbia through empirical research on a sample of 1006 hotels, to investigate differences between countries, but also differences between hotel groups according to their categorization, and to examine the significance of these differences In the discussion, the author interprets the research findings, points out the limitations of the research, indicates the theoretical and practical implications of the paper, and provides recommendations for future research Framework development Marketing information system and digital analytics The marketing orientation as defined by Kohli and Jaworski (1990) requires adequate knowledge of consumers, competitors, as well as the overall market environment The strategic and tactical marketing decisions that the company makes regarding market segmentation, target market selection and positioning, competitive strategy, and the selection of an appropriate marketing mix and control system must be based on timely and accurate information The marketing information system (MIS) consists of people, equipment, and procedures for collecting, sorting, analyzing, evaluating, and distributing the necessary, timely, and accurate information to marketing decision-makers (Kotler et al., 2005: 337) MIS interacts with decision-makers to identify their information needs, collects information from internal sources, through the collection of marketing intelligence and the marketing research, makes data more useful through the analysis process, and then distribute information to decision-makers Today, the marketing information system is facing the challenges of the new digital era An important advantage of the digital economy, e-commerce, and digital marketing is the high degree of measurability and analysis of consumer behavior Companies can collect and process large amounts of data from the digital environment This data may come from own business sources (such as the company website or other online channels that company uses), from online marketing intelligence sources (websites and other online channels used by competitors, social networks, etc.) or through the online marketing research, and may relate to the consumers, to the competition or generally to the market environment Every click, every 829 visit, and every online activity generates a digital footprint, which is recorded on the servers, generating a valuable digital database By analyzing and mining this data, companies can obtain very useful information for decision-makers Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analysis, recommendations, optimizations, predictions, and automation (Phillips, 2013) The classic definition of MIS should be upgraded with digital analytics to support all elements of MIS Previous academic papers show a significant impact of marketing performance measurement on marketing results, financial results, and company performance (Gök, Peker & Hacioglu, 2015; O’Sullivan, Abela & Hutchinson, 2009; O’Sullivan & Butler, 2010; Ambler & Roberts, 2008; Stewart, 2009; Li, 2011; Hacioglu & Gök, 2013) Main benefits of digital analytics for digital marketing experts are website usage analysis, campaign and event tracking, digital business performance management and ad campaign optimization (Zumstein & Mohr, 2018), while data collection automation (Pauwels et al., 2009) and standardization (Russel, 2010) stand out as important features of digital analytics Despite these benefits, it is evident that digital analytics is utilized on an ad-hoc basis, the metrics data are not used for strategic purposes, and the benefits of the usage remain unclear (Järvinen & Karjaluoto, 2015) A website is a basic tool for conducting digital marketing activities Its mission is to support all other digital channels (e.g., social networks, mobile applications), as well as all activities related to the marketing mix Therefore, digital analytics as a process of collecting and analyzing data from a hotel website can help companies make both strategic and tactical marketing decisions Its task is to ensure the effective and efficient achievement of the set marketing goals, by defining and monitoring the appropriate analytical parameters and adequate and timely reporting Measuring effectiveness aims at minimizing the cost of digital marketing and maximizing return on investment in various areas, such as attracting visitors to a website, converting visitors to buyers, or encouraging customers to make repeated purchases New IT solutions have made it possible to monitor the performance, effectiveness, and profitability of individual marketing activities, and to report results through the easily understandable dashboards The new technologies not only enabled counting and calculations (number of visits, sales revenue, earnings calculation), but also adequate forecasts based on processing large amounts of data collected from several sources like social media, review data and search engine traffic Importance of the hotel industry for Croatian, Serbian, and Bosnian economy Positive economic developments with fewer visa barriers, lower transportation costs, and new tourism business models have affected the growth of the tourism industry worldwide, with 1.4 billion tourist arrivals in 2018, and hotel value sales of EUR 525 billion with an estimate of the compound annual growth rate of 4% in the next years (UNWTO, 2019; TuiGroup, 2019) At the same time, export earnings generated by tourism have grown to USD 1.7 trillion (UNWTO, 2019) making this sector a truly global force for economic growth and development With 710 million international tourist arrivals in 2018 and USD 570 billion in international tourism receipts, Europe remained the largest and most mature tourism market in the world, accounting for 51% of international tourist arrivals and 39% of tourism receipts in 2018 (UNWTO, 2019) According to the Croatian National Bank, tourism revenue in the Republic of Croatia amounted to EUR 10.1 billion in 2018, representing a 19.6% share of GDP In the same year, 830 the number of employees in the provision of food, lodging and preparation services amounted to 101,000, of which 71,000 were in legal entities, which is 5.4% of the total number of employees in legal entities of all forms of ownership (Croatian National Tourist Board, 2019) There were 731 hotels in Croatia in 2018 The most represented are 3-star (44%) and 4-star (43%) hotels, while the percentage of 5-star and 2-star hotels counts for 6% and 8% respectively In the same year, 20,436,000 overnight stays (22.8%) were spent in hotels and aparthotels, while in private rooms 43,382,000 overnight stays or 48.4% of the total nights spent Overnight stays of foreign tourists prevailed in hotel accommodation (89%) compared to overnight stays of domestic tourists (11%) (Croatian National Tourist Board, 2019) In 2018 in Bosnia and Herzegovina tourism and travel contribution to GDP counted 2.0 billion EUR, or 10.2% of GDP (Knoema, 2020) In 2019, a total of 3,371,322 overnight stays were realized in Bosnia and Herzegovina within the category of hotels and similar accommodation, of which the share of foreign tourists was 71.8% (BHAS, 2020) In 2019, there were 145 hotels and aparthotels operating in the Federation of Bosnia and Herzegovina (FMOIT, 2019), of which 77% were 4-star hotels, 15% were heritage hotels, 7.6% were 5-star hotels, and were 4-star aparthotels In the Republic of Srpska in 2020, 101 hotels are registered From the total number, 5% are 5-star hotels, 38.6% are 4-star hotels, 52.5% are 3-star hotels, 5% are 2-star hotels and one hotel is 1-star hotel (Government of RS, 2020) In December 2019 in the tourism and hospitality sector in Bosnia and Herzegovina 43,058 persons were employed, or 5.2% of the total number of employees in Bosnia and Herzegovina (BHAS, 2020) According to data from the National tourist organization of Serbia (2020), there are 250 hotels and 129 heritage hotels operating in Serbia From the total number of hotels, 2.8% are 5-star hotels, 39.7% are 4-star hotels, 36.8% are 3-star hotels, 16.8% are 2-star and 3.9% are 1-star hotels In January 2020, a total of 82,459 employees were registered in the area of accommodation and catering, or 3.9% of the total number of employees in this country In 2019, 10.1 million overnight stays were realized in Serbia, of which 60.1% were domestic tourists and 39.8% foreign tourists In 2018, tourism and travel contribution to GDP counts 2.0 billion USD (15.4% GDP) Changes in the distribution and importance of a website and digital analytics HOTREC (2018) study shows that share of direct bookings in Europe, which includes the use of offline (phone, mail, fax, walk-in reservations), and online web site contact forms (contact form on web site without of availability check, direct e-mail, real-time booking over web site) were 52% in 2017, of which 28.9% were bookings from the hotel website In the same year, the share of OTAs (online travel agencies) was 26%, while the share of global distribution systems (GDS) and social media channels in 2017 were 2.5% and 0.5% respectively Considering that HOTREC (2018) survey reported that in the last five years the number of bookings continued to grow through OTAs (from 19.7% in 2013 to 26% in 2017), which are putting increasing pressure on hotels in terms of lowering prices and meeting specific requirements, it becomes clear that hotels need to empower and use the website as a direct channel for better communication and distribution of its products and services The issue of evaluating and continuously improving the hotel website is becoming a strategic issue, and digital analytics plays a crucial role in this process Google Analytics – characteristics and capabilities The tool that is mostly used for digital analytics is Google Analytics According to W3tech (2020) company report, 35.2% of the websites use none of the traffic analysis tools, while Google Analytics is used by 55.0% of all the websites that is a traffic analysis tool market share of 84.8% The success of the tool is surely associated with the fact that it is free to use 831 Aside from figures regarding user behavior, the user's background, and interaction on the website, Google Analytics offers a precise view of all user activities Connecting Google Analytics with the hotel website, by placing GA tracking code on every page, provides insight into detailed information about visitors, their computers, geographic origin, number of visits and unique visitors, site retention, traffic paths, and bounce rates It also enables measuring of goal accomplishments such as registrations, online sales, and estimates of certain visitor demographics One Google Analytics account can be used to track more properties (for example hotel website and mobile app) For every property more "views" can be defined Views enable companies to filter data that will be used in reports For example, a hotel group that has more hotels, and one main website, can have one view that will collect all data, and one view for each hotel section of the website Or, a hotel that has a blog and news section on its website, can define one view to collect and analyze only data for blog pages, and one view for news pages This kind of structure will enable easier management and assignment of access to individual reports for individual stakeholders within the company Google Analytics reports consist of dimensions and metrics Dimensions describe the data Practically in the reports, they represent the names for the individual rows in the tables Dimensions answer the question "what/which" such as "what keyword they used" or "what city visitors came from" Metrics measure data Metrics are dimension elements that can be measured Metrics answer the question "how many" or "how long," such as "how many visits" or "how long a visitor stayed on a page." There are more than 230 metrics and dimensions that can be combined when creating specific reports within Google Analytics, and the advanced capabilities of this tool are revealed by linking companies' website databases (CRM database) to a Google Analytics database, which enables custom dimension and custom metrics analysis For example, if a visitor is browsing hotel website searching for a double room with specific requirements, this information (location, type of room, specific requirements, etc.) can be sent to Google Analytics as custom dimensions so that the company can analyze what types of visitors (their geographic, demographic, technological characteristics) are most often looking for such accommodation, and through which online channels they came to the hotel website This helps hotels better understand their customers, makes it easier for them to segment the market and to make positioning decisions, as well as to better tailor their offer and make better decisions about promotional budget allocation Also, it is important to understand that any event on the website can be tracked and also measured as a custom metric on the website For example, clicking a button to check available dates can be tracked and measured as a custom metric, which provides information to a hotel about how many potential guests were interested in the hotel Other examples of events tracked as custom metrics are newsletter signup, product details viewed, rating form submitted, contact form submitted, booking made, etc Proposal of a framework for building custom digital analytics dashboard for a hotel website In this chapter, the author suggests a framework for building a custom digital analytics dashboard for a hotel website The aim of this chapter is not to provide technical details about using Google Analytics and creating custom reports and dashboards, but to propose the framework that can be used for creating such dashboards in Google Analytics or using any other tool A key issue for many companies today is how to process a large amount of data collected and turn it into useful information for decision making (Lavalle et al., 2011) The essential question is which KPIs to monitor, which metrics to use, and how to apply the obtained 832 results in strategic and tactical decision making A practical framework with clearly defined KPIs and how they are calculated can significantly help companies to solve this problem Fáilte Ireland (2013) outlines the five categories of primary KPIs in use in the hospitality industry: accommodation, food, beverage, profitability, and liquidity, while main KPIs for accommodation category are: average room rate, bedroom occupancy rate, revenue per available room, cost per occupied room and labor cost ratio Pantelić & Nadkarni (2019) have investigated the perceived importance and relevance of key performance indicators (KPIs) in UAE hotel properties Through a detailed review of previous research, they have identified the following KPIs in the hotel industry: sales growth, market growth rate, customer online engagement, market penetration index (MPI), number of new customers acquired, total revenue, gross operating profit (GOP), F&B sales/revenue, F&B cost of sales %, revenue generated index, total operating cost, room occupancy, employee cost %, average daily rate (ADR), guest turnover ratios, revenue per available room (RevPAr), EBITDA, guest interaction quality, service outcome quality, maintaining hotel star classification and number of products and services innovated per year From a digital marketing perspective, we can define KPIs as metrics that indicate the firm's overall digital marketing performance concerning its most important digital marketing goals The KPIs are supplemented with other more granular metrics that are used to evaluate the effectiveness and efficiency of specific digital marketing activities that support the overall digital marketing performance measured by the KPIs (Chaffey & Patron, 2012) Saura, Palos-Sanchez and Cerda Suarez (2017) highlighted that the most common, and conceptually simple methods for calculating the profitability of digital marketing actions is ROI (return on investment) and CTR (Click-Through-Rate) The same authors define six basic KPIs that companies should follow and analyze with web analytics in their DM strategies: conversion rate, goals conversion rate, type of user, type of sources, keywords/traffic, keyword ranking Smith's (2014) SOSTAC® marketing planning model indicates that digital marketing implementations require strategic and tactical decisions The strategic decision level needs information related to market segmentation, positioning, online value proposition, and competitive strategy, while the tactical level needs information to help manage the elements of the marketing mix This model is a base for framework development Custom dimensions and metrics can be used to collect and analyze data that Google Analytics doesn’t automatically track (Google Support, 2020) For example, if a hotel stores the gender of signed-in users in a CRM system, it can combine this information with analytics data, to see page views by gender Or for example, while a user is making a booking request on the hotel website, information about the room type, the number of persons, the number of overnight stays and included services, as well the total value of the purchase can be sent to Google Analytics and combined in reports with all other metrics and dimensions Built on the previous elaboration, a framework presented in Table suggests that the hotel analytics dashboard should consist of two parts: strategic and tactical decisions part Through the three-step process for the creation of custom reports, the author proposes KPIs and metrics for strategic and tactical decisions The first step in the process is to define hotel business objectives The second is to translate those objectives into KPIs (key performance indicators), and the third step is to define metrics to be collected to create custom reports 833 The strategic part consists of KPIs that should help hotels understand website visitors, their characteristics and behavior, and overall company market position, but also to monitor the achievement of the set goals For every tactical marketing activity, the author suggests metrics that enable the company to benchmark the efficiency of every operation or different channels The proposed framework implies that a hotel should perform analytical measurement of transactions and analysis of purchasing activities on the website (website booking engine) This includes monitoring and analyzing transactions, including information on transaction revenue, hotel products, and services involved in transactions, conversion rates, average revenue per transaction, periods of purchases made, etc Furthermore, if the hotel is using the Google Analytics system, in addition to tracking standard dimensions and metrics, it should also track specific custom dimensions and metrics The model presented in Table suggests the inclusion of the following custom dimensions that describe guests or hotel products: type of guest (existing customer or new customer), type of accommodation, type of service package, number of persons (adults and children on the reservation), length of stay and discount coupon When it comes to custom metrics, the model suggests tracking important events on the website and measuring them through custom metrics such as completed booking, booking started, review submitted, specific offer viewed, phone number viewed, online contact form submitted Below is an explanation of the proposed KPIs and metrics that hotels should consult before making strategic and tactical decisions Strategic decisions When developing a strategy and creating a plan for its implementation, the hotel must first conduct a situation analysis to evaluate its market position, understand the market segments, the current effectiveness of the online channels, and the existing marketing mix The basic KPIs that should be analyzed at this stage is the number of sessions and the number of users who visited the hotel website, booking engine click-through rate, conversion rate, and revenue generated The dimensions by which reports should be generated are visitor locations, gender and guest type, keywords, and visitor sources Based on these dimensions, the hotel can conclude the characteristics and quality of individual market segments and their contribution to the total revenue generated Information on the most effective keywords that generate organic visits makes it easier to define or redefine online value proposition, especially when placed in context with the bookings and revenue generated through the visits from those keywords Some of the classic hotel industry KPIs that should be monitored within strategic decisions are ARR (average room rate), RevPar (revenue per available room), and OCC (average occupancy rate) Also, the framework suggests using additional calculated metrics as KPIs: WBS, WBOBR, and MPI The WBS (website booking share) should be continuously compared with the averages within the industry, but also to other sales channels to evaluate the effectiveness of a website as a direct sales channel With this indicator, it is very useful to monitor WBOBR which presents the share of bookings through the website relative to the total number of bookings online This KPI helps to evaluate website performance against other channels, as well as hotel dependency on OTAs Market penetration index (MPI) can be calculated by dividing total hotel occupancy by the total number of rooms in the local market 834 Table 1: Digital analytics framework for hotels STRATEGIC LEVEL DECISION LEVEL MARKET SEGMENTATION POSITIONING AND ONLINE VALUE PROPOSITION DEFINITION COMPETITIVE STRATEGY MARKETING MIX TACTICS PROMOTION PRODUCT PRICE DISTRIBUTION KPIs (metrics and calculated metrics): dimensions WBS (website booking share) WBOBR (website bookings share of total online bookings) MPI (market penetration index) ARR (average room rate) RevPar (revenue per available room) Users and Sessions: Visitor location, Type of guest Bounce rate: Visitor location, Type of guest, Traffic channel REV (total revenue from website bookings): Visitor location, Type of guest, Number of persons CVR (conversion rate): Visitor location, Type of Guest CVRU (conversion rate per user): Visitor location, Type of Guest ALOS (average length of stay): Visitor location, Type of Guest, Number of persons BECTR (booking engine click-through rate): Visitor location, Type of Guest, Number of persons PPS (average page views per session): traffic channel, source/medium, campaign Bounce rate: referral, traffic channel, source/medium, campaign MCPB (marketing cost per booking): referral, campaign REVMCR (revenue vs marketing cost ratio): referral, campaign CVR: referral, traffic channel, source/medium, campaign REV: room type, package, persons CVR: room type, package, persons ALOS: Visitor location, Type of Guest, Number of persons OCC (average occupancy rate) REV: coupon code CVR: coupon code REV: affiliate partner CVR: affiliate partner WBS WBOBR Example of calculation TAR = total number of rooms available WBS = website bookings / total hotel bookings WBOBR = website bookings / online bookings MPI = occupancy/number of rooms in the local market REV = SUM (website booking revenue) quantity = SUM (length of stay (days)) RR (room rate) = revenue / length of stay ARR = website booking revenue / quantity RevPar = website booking revenue / rooms available CVR = website bookings / sessions CVRU = website bookings / users ALOS = SUM (length of stay) / website bookings BECTR = booking engine sessions / website sessions PPS = pages / sessions MCPB = total marketing cost / website bookings REVMCR = website booking revenue / campaign marketing costs CVR = website bookings / sessions REV = SUM (website booking revenue) CVR = website bookings / sessions ALOS = SUM (length of stay) / website bookings OCC = rooms sold / TAR for same period REV = SUM (website booking revenue) CVR = website bookings / sessions REV = SUM (website booking revenue) CVR = website bookings / sessions WBS = website bookings / total hotel bookings WBOBR = website bookings / online bookings Source: author 835 Tactical decisions Tactical decisions are related to the elements of the marketing mix Promotion KPIs should answer the following questions: which online channels send the highest quality visitors (who stay longer on the site and have a high conversion rate, and low bounce rate), what is the cost of promotion per booking (MCPB), and how much revenue is generated from website bookings per € invested in a promotion (REVMCR) Those metrics should be analyzed per traffic channel, source/medium, referral, and campaign Answers will help the hotel decide about the most effective promotional budget allocation After the initial establishment of the analytics system, this area could be further analyzed considering that visitors usually go through several stages in the buying process from information gathering to the act of purchasing This means, that models like REAN (reach, engage, activate, nurture), or RACE (reach, act, convert, engage) should be used to separate conversion activities into a generation of leads or interactions with an online presence and then conversion to the sale (Chaffey & Patron, 2012:41) KPIs related to the product (hotel service package) that should be monitored and calculated are revenue, conversion rates, and the average length of stay Revenue and conversion rates should be analyzed per room type, package type, and the number of persons (in the reservation), and length of stay should be analyzed by visitor location, type of guest, and the number of persons The above KPIs answer the question of what types of rooms and what packages (products) are the best-selling, as well as how many persons (adults + children) usually are looking for accommodation in a particular room type These KPIs should assist the hotel management in deciding on the hotel packages and the minimum length of stay in each package The decision on the pricing strategy should be supported by information on the average occupancy rate of the hotel and the revenue generated at different price reductions Price reductions in this sense are most easily realized through discount coupons If the average occupancy rate is generally low, the hotel may consider a different pricing strategy and a general price reduction It should also analyze current OTAs offers and their pricing strategies In addition to strategic decisions, WBS and WBORB should be analyzed when considering distribution decisions If a hotel is using an affiliate program, revenue and conversion rates should be analyzed for every referral to find out which referrals are most effective An empirical study of the use of Google Analytics in the hotel industry in Croatia, Serbia, and Bosnia and Herzegovina To understand the extent to which hotels use digital analytics, the author has conducted empirical research in the hotel industry of the Republic of Croatia, Bosnia and Herzegovina, and Serbia Methodology Guided by the fact that Google Analytics is the most popular digital analytics tool in the world, this empirical research is focused on examining the existence of Google Analytics tracking code on the hotel websites For the primary data collection, quantitative content analysis is used Content analysis is a research technique that quantitatively describes the content of an aspect of communication (Milas, 2005: 500-519) In the present study, the author uses innovative automated software content analysis (web scraping) of hotel websites in Croatia, Serbia, and Bosnia and Herzegovina to determine the presence of Google Analytics code on the hotel web sites, which indirectly indicates whether or not the hotel uses 836 digital analytics Then, the results obtained were compared between the countries, as well as between different groups of hotels according to their categorization, and finally, differences were tested to determine their significance and intensity of correlation between the tested variables For web scraping, the author has developed a script for Scrapy67 - an open-source and collaborative framework for extracting the data from the websites The data collected were statistically processed by using IBM SPSS v26 software Sample profile A total of 1006 hotel websites were analyzed, of which 629 hotels in Croatia, 218 hotels in Serbia, and 159 hotels in Bosnia and Herzegovina The profile of a sample is presented in Table Table 2: Sample profile Country Total number of Number of hotels in % of the total hotels a sample population Croatia 731 629 86% Bosnia and Herzeg 251 159 63.3% Serbia 379 218 57.5% Source: author Analysis of differences between countries Table presents the results of website content analysis and the percentage of hotels that use the Google Analytics service It can be concluded that in the region, on average, 68.5% of hotels use Google Analytics, most of the hotels in Croatia (86%), while the percentage of hotels in Serbia (43.1%) and Bosnia and Herzegovina are twice smaller (34%) Table 3: The use of Google Analytics on hotel websites Use of Google Analytics NO Country YES Total Row N % Count Row N % Count Row N % Count Bosnia and Herzegovina 66.0% 105 34.0% 54 100.0% 159 Serbia 56.9% 124 43.1% 94 100.0% 218 Croatia 14.0% 88 86.0% 541 100.0% 629 Total 31.5% 317 68.5% 689 100.0% 1006 Source: author To test whether differences in the use of Google Analytics between hotels in different countries are significant the Chi-Square test is performed The null hypothesis for the test is that there is no significant association between the variable "use of Google Analytics" and variable "hotel location" (country) (p < 05) The Cramer's V is used as a measure for the strength of an association between two categorical variables in tables bigger than × tabulations Table Interpretation of Cramer's V Cramer's V Interpretation > 0.25 Very strong > 0.15 Strong > 0.10 Moderate 67 https://www.scrapy.org 837 > 0.05 Weak >0 No or very weak Source: Haldun (2018) A chi-square test of independence χ2 (2, N = 1006, p < 001; Cramer's V = 491) showed that there is a significant and very strong association between the hotel location (country) and use of Google Analytics on the hotel website Analysis of differences between hotels of different categorization In addition to the established difference in the application of Google Analytics between hotels in different countries, the author was interested in whether there was a significant difference in the application of Google Analytics between groups of hotels of different categorizations Given that different countries have different methods of hotel categorization, and that the complex administrative structure within Bosnia and Herzegovina brings differences in methodologies within individual entities (Federation of BiH and Republic of Srpska), the author decided to analyze differences between hotels of different categorizations separately for each country Table 5: Hotels categorization and the use of Google analytics in Bosnia and Herzegovina Hotel categorization heritage 2* 3* 4* 5* Google Analytics Not in use In use Total Row N % Row N % Row N % 70.6% 29.4% 100.0% 100.0% 0.0% 100.0% 60.0% 40.0% 100.0% 68.1% 31.9% 100.0% 28.6% 71.4% 100.0% Source: author Table 6: Chi-Square tests of independence (Hotel category * Use of Google Analytics) for hotels in Bosnia and Herzegovina Pearson Chi-Square Value df 6.115a p 191 Fisher's Exact Test 5.431 223 N of Valid Cases 159 a cells (40.0%) have expected count less than Source: author As in contingency table for Chi-Square test there were more than 20% of cells that have expected count less than 5, assumptions for Chi-Square test were not met (McHugh, 2013), so Fisher's Exact test has been used to determine if there are nonrandom associations between two categorical variables (hotel location and use of Google Analytics) The result presented in Table (p=.223 > 05) shows that there is no significant nonrandom association between the hotel category and the use of Google Analytics on the hotel websites in Bosnia and Herzegovina The use of Google Analytics on hotel websites in Serbia for different hotel categorizations is presented in Table Table 7: Hotels categorization and use of Google Analytics in Serbia Google Analytics Not in use In use Row N % Row N % Total Row N % 838 Hotel categorization 1* 40.0% 60.0% 100.0% 2* 70.6% 29.4% 100.0% 3* 52.0% 48.0% 100.0% 4* 58.3% 41.7% 100.0% 5* 37.5% 62.5% 100.0% Source: author Table 8: Chi-Square tests of independence (Hotel category * Use of Google Analytics) for hotels in Serbia Value df p 5.222a 268 Pearson Chi-Square Fisher's Exact Test 5.280 N of Valid Cases 252 218 a cells (40.0%) have expected count less than Source: author Similar to the previous analysis, all assumptions for the Chi-Square test were not met, so Fisher's Exact test (p=.252 > 05) was calculated, and it shows that there is no significant nonrandom association between the hotel category and use of Google Analytics on the hotel websites in Serbia Table 9: Hotels categorization and use of Google Analytics in Croatia Hotel categorization 2* 3* 4* 5* Not in use Row N % 26.2% 11.9% 15.1% 5.6% Google Analytics In use Total Row N % Row N % 73.8% 100.0% 88.1% 100.0% 84.9% 100.0% 94.4% 100.0% Source: author A chi-square test of independence χ2 (3, N = 629, p < 05; Cramer's V = 117) shows that there is a significant and moderate association between the hotel categorization and use of Google Analytics on the hotel websites in Croatia Conclusion The proposed framework, built on concepts of marketing orientation and marketing information system, should assist researchers as well as practitioners in understanding the role and importance of digital analytics for the hotel industry Also, it can help researchers in evaluating the level of adoption of digital analytics in the hotel industry It could be used as a basis for developing a scale for assessing the level of adoption of digital analytics, which could further be used as a construct in models that take into account the impact of digital analytics on hotel business performance This is one of the suggestions for future research The framework gives a clear picture to practitioners of the role of digital analytics and outlines the essential elements to consider when developing and designing a hotel website A framework could be extended to include the REAN or RACE model inside the tactical part, which would further improve the quality of the collected information, and this also could be a base for further research 839 The empirical investigation, using an innovative automated content analysis approach, revealed that there is a significant difference in the percent of hotels using Google Analytics in Croatia (86%), compared to those in Bosnia and Herzegovina (34%) and Serbia (43,1%), and that there is a very strong association between hotel location (country) and use of Google Analytics Further research should be conducted to determine factors that influence the higher rate of digital analytics deployment The author believes that it would be particularly interesting to explore the impact of higher education in the field of digital marketing and digital analytics as one of the factors, as well as the hotel management's level of knowledge in this area One of the theses that arise is that the greater importance of tourism for the economy of a country affects the education system and increases the number of higher education programs in the field of tourism and digital marketing in tourism This results in a higher level of hotel management knowledge of digital marketing and digital analytics, and higher implementation This thesis should be investigated in future researches, through the analysis of the education of managers working in hotels of different categorizations and the curricula of the faculties they have completed A significant and moderate association between the hotel categorization and use of Google Analytics has been confirmed only in Croatia, but not in other countries An assumption is that more star hotels attract higher quality managers who have practical knowledge in digital marketing but also have better resources (people, money) for planning and implementation of digital marketing activities This thesis, which should be investigated in future researches, is backed up by the results of research by Zumstein & Mohr (2018) which showed that the biggest problem in the field of digital analytics for companies is the shortage of skilled workers Some of the limitations of research may also be recommendations for future researches First, the analysis was conducted in only three countries and it would be useful to conduct new research in more countries and using larger samples Second, the use of the Google Analytics service, recorded through the presence of appropriate code on the website, tells us nothing about the extent to which this service is used for analysis Also, Google Analytics is not the only analytics service for website traffic, so these findings should be checked with additional research by using a survey method However, based on many years of experience, the author believes that this method is a good basis for getting insights on the use of digital analytics because it avoids the possibility of making mistakes by intentionally or unintentionally giving the wrong answers in surveys Despite these limitations, the author believes that this paper helps to fill some important gaps in understanding the importance of the digital analytics from hotels marketing perspective, providing additional empirical support to the relevant literature and suggesting useful 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