Nps Net Promoter Score

NPS Net Promoter Score ______________________________________________ Research assignment for Hotel unknown: Study to the Net Promoter Score, and the application process within the company ______________________________________________ Thesis 19 July 2010 ______________________________________________ Presented for the Rotterdam Business School, A faculty of Hogeschool Rotterdam PREFACE This report is a graduation assignment written and composed in assignment of Hotel unknown and the Rotterdam Business School, faculty of the Hogeschool Rotterdam.

I would like to thank Pieter Bas van de Burg and Mirjam Veenstra for giving me the opportunity to conduct my graduation research at your organization. Also Kristian Nielsen for his guidance and supervision throughout the whole research process and Eric Waterman for providing me with the literature that enabled me to create a design for the research. Melano Winter EXECUTIVE SUMMARY TABLE OF CONTENT Chapter 1 INTRODUCTION 1. 1 Introduction case company 6 1. 2 Case study7 1. 3 Problem definition8 1. 4 Thesis objectives8 1. 5 Research questions8 1. 6 Chapter’s summary9 Chapter 2 LITERATURE REVIEW 2. The SERVQUAL-model10 2. 2 The Net Promoter Score12 2. 3 Cronbach’s ? 13 2. 4Student’s T-test14 2. 5 Regression analysis15 Chapter 3 RESEARCH METHODOLOGY 3. 1 Introduction16 3. 2 Research design16 3. 3 Data collection and research questions 16 3. 4 Measures taken for creditable results17 3. 5 Table of research steps18 3. 6 Chapter’s summary18 Chapter 4 RESEARCH RESULTS: EXPLORATORY STUDY 4. 1 Introduction19 4. 2 RQ1: How is Hotel unknown service quality framework defined? 19 4. 2. 1 GAP-analysis20 4. 2. 2 RATER-analysis21 4. 2. 3 Conclusive summary 22 . 3 RQ2: What are the pros and cons for Hotel unknown when using the NPS as a measurement tool? 23 4. 3. 1 Background: Rent-a-Car Case23 4. 3. 2 Principles of the NPS 25 4. 3. 3 Claims of refutations26 4. 3. 4 Conclusive summary28 Chapter 5 RESEARCH RESULTS: EXPLANATORY STUDY 5. 1 Introduction29 5. 2 Selection of useful data29 5. 2. 1 Cronbach’s ( analysis30 5. 2. 2 Conclusive summary 30 5. 3 RQ 3: Which factors have a significant influence on the NPS of Hotel unknown? 31 5. 3. 1 NPS City – NPS Coast & Water – NPS Green31 5. 3. 2NPS Promoters – NPS Detractors33 5. . 3 NPS new customers – NPS known customers39 Chapter 6 CONCLUSIONS AND RECOMMENDATIONS 6. 1 Conclusions exploratory study40 6. 2 Conclusions explanatory study41 6. 3 Recommendations42 6. 4 Final conclusion 43 CHAPTER 7 BIBLOGRAPHY AND APPENDICES 7. 1 Bibliography43 7. 2 Appendix A Statistical table44 7. 3 Appendix B Interviews45 7. 4 Appendix C Customer survey48 CHAPTER 1 | INTRODUCTION 1. 1 CASE COMPANY Hotel unknown is a Dutch hostel chain founded in 1929, providing hospitality services with 29 accommodations in the Netherlands. It is the only hostel chain in the Netherlands.

Hotel unknown is part of Hostelling International, the biggest hostel chain in the world with over 5,000 hostels in 80 countries. Hotel unknown is market leader in the Dutch group- and budget accommodations with annually almost 900,000 stay in 29 hostels. The hostels are located throughout the Netherlands and are divided over three destinations: Cities – 7 hostels, located in the heart of Amsterdam, Rotterdam, The Hague, Haarlem and Maastricht. Nature – 10 hostels, situated in or near green surroundings. Coast and Water – 12 hostels, situated at the coast of the Netherlands or at recreational waters.

The Hotel unknown hostels are for everyone: young and old, families or groups, from all over the world. The hostels provide comfortable rooms or beds without superfluously luxury in an informal atmosphere against an affordable price. All hostels are facilitated with a restaurant, bar and provide Internet facilities. In 2007 Hotel unknown received as the first European hotel- or hostel chain the European ECO-label, which is a certificate that states that Hotel unknown as an organization conducts an environmental-friendly company policy. Internal organization

Hotel unknown has a line-staff organization wherein each member within the organization, from General Director to hostel staff, has one superior. Board of Supervisors – The Board of Supervisors consist of members who are or were active in businesses or hold a governmental function. In agreement with the General Director they supervise the activities of Hotel unknown. General Director – The General Director has the daily control over Hotel unknown. She develops the strategic course and directs the chiefs of staff and the Operations manager

Operations Management – The Operations manager directs the region managers who direct their hostel managers within their destination (seen above). Hostel managers – The hostel managers are responsible for the daily activities within the hostel. They operate independently within the agreed constraints of the organisation. They justify their actions to their region managers. Staff departments – There are several staff departments that support and advise the hostels in several areas. The staff departments are: Marketing & Sales, Human Resource Management, Financial Administration, ICT, Building affairs and Marketing. pic] 1. 2 CASE STUDY As the only hostel chain in the Netherlands and one of the leading European hospitality chains in the field of innovative, sustainable and environmental-considerate management, Hotel unknown has a strong market position in the budget accommodation market. The organization is continuously looking for opportunities to increase their market share. In order to do this, Hotel unknown has defined a strategy that intensifies the emphasis on the customers needs. The focus is placed on increasing the service level within hostels, improvement of the product and more attention to detail.

With the market changing continuously also the needs of customers change. It is important for Hotel unknown to identify these changes in needs in an early stage to be a step ahead of their competitors and maintain and improve their market position. To identify the needs of their customers, Hotel unknown has developed a customer survey where they provide the possibility to guests who visited one of their hostels to share their experiences they enjoyed and give feedback to the organization for eventual improvements.

The guests rated the organization’s overall performance in 2009 with a 7. 7, with a satisfaction level of 80% (source: “Plan of Results Hotel unknown 2010”, December 2009). The rating of Hotel unknown’s overall performance in recent years has always swung around between the 7 and 8, and it became difficult for management to find the crucial points for improvement derived from the customer survey. There for they decided to look for measurement tools that can help them identify the crucial elements that need improvement.

In 2009 Hotel unknown started to report the Net Promoter Score (NPS), a score that measures customer loyalty, which is defined in the likeliness of guests making recommendations to family, friends or colleagues. The NPS requires one ultimate question being asked: “Would you recommend us to family, friends or colleagues? ” Guests are able to answer the question by giving a number on the scale of 0 to 10. Results have shown that guests that give a high NPS (the Promoters) are loyal, willing to spend more and make free mouth-to-mouth publicity.

Guests who give a low NPS are not likely to come back (no loyalty, the Criticasters) and require higher service costs. Hotel unknown wants to use the NPS as a tool to turn costumers’ feedback into specific points of improvements. Each hostel within the Hotel unknown chain will get defined objectives linked to the NPS. In order to do this, it is important to know for Hotel unknown if there are specific elements within their service that can lead to a higher NPS. 1. 3 PROBLEM DEFINITION

As mentioned in the Case Study, Hotel unknown started to use the NPS to measure customer loyalty and the likeliness of guests making recommendations to third parties. A high NPS will lead to company growth while a low NPS will limit the company’s potential and endanger its position in the market. In 2009 the organization scored a NPS of 28. 4, which is calculated by deducting the score of the Detractors (13. 5%) from the Promoters (41. 9%). Hotel unknown wants to increase the NPS by minimizing the percentage of Detractors, and increase the group of Promoters.

The NPS and the meaning of the numbers will be explained in detail in Chapter 2. In order to increase its NPS, Hotel unknown needs to know what is important for customers to turn them into promoters. Therefore it is necessary to find out which elements in Hotel unknown’s hospitality service are the most important for a guest to make recommendations to others. 1. 4 THESIS OBJECTIVES The objective for writing this thesis is to give recommendations that help set out a new strategy that helps meet Hotel unknown determined goals, with the focus on identifying coherent relations between the satisfaction level of customers and the NPS.

To give recommendations it is important to identify certain trends in guests’ behaviour that help highlight which elements in the service influence a guest’s decision to make a positive or negative recommendations. When these relations are identified, the next step is to give recommendations on how the strategy can be formulated. In order to do this it is necessary to take Hotel unknown’s current strategy and its structure into consideration. 1. 5 RESEARCH QUESTIONS In order to meet the predetermined objectives the following research questions are formulated: 1 How is Hotel unknown service quality framework defined? What are the pros and cons for Hotel unknown when using the NPS as a measurement tool? 3 Which factors have a significant influence on the NPS of Hotel unknown? 1. 6 CHAPTER SUMMARY Hotel unknown is the Netherlands’ only hostel chain and has 29 hostels in the Netherlands. Hotel unknown is member of Hostelling International, the biggest hostel chain in the world with over 5,000 hostels in 80 countries. The hostels are found throughout the country and are distinguished by three types of location where they can be found: in the city, at or near the water (recreational waters or the sea) and in green surroundings.

Hotel unknown profile themselves as quality budget accommodation providers and strives to provide their guests an experience that meet or exceed their expectations. In order to do this, they constantly ask feedback from their guests through the use of a customer satisfaction survey. Hotel unknown has successfully developed a measurement tool and has a relatively high response rate and the overall opinion of the guests is positive, but the organization realizes that it is difficult to find points of improvement within the service-quality framework when the average numbers of the survey are the same each year.

They are striving to attract more customers through the use of their own guests that should make mouth-to-mouth recommendations to their family, friends or colleagues. They have implemented a new management tool called the Net Promoter Score. It measures customer loyalty and with this technique it should be able to make a distinction between satisfied customers and satisfied customers who could be valuable to the company. What is interesting to know for Hotel unknown is which elements within their service-quality framework contribute to their guest’s decision to make a mouth-to-mouth recommendation.

When this is known it enables Hotel unknown to focus their strategy on improving those elements and stimulate their guests to make recommendations to third parties. CHAPTER 2 LITERATURE REVIEW This chapter discusses several literature and theories that will be used for the analysis of the customer survey data. First Parasuraman’s SERVQUAL-model is discussed, which we will use for comparison with the service quality framework of Hotel unknown. Furthermore the Net Promoter Score is more closely explained.

An overview is provided of the necessity of measuring customer loyalty and the additional value for the company. The Cronbach’s ? analysis is being used in the customer survey data-matrix to determine which items (questions) in the survey provide us with reliable information for the final analysis (the regression analysis), and if to what extent the items measure the same concept. The Regression analysis is being used to analyze the filtered (necessary) data. It will help determine whether there is a relation between elements in the service of Hotel unknown and customer loyalty. . 1 SERVQUAL-MODEL The SERVQUAL-model stands for Service and Quality and is a marketing model that was developed in 1985 by Parasuraman, Zeithaml, and Berry to measure consumer behaviour by giving a reliable and valid rating for the services of an organisation. The same model is also used to diagnose shortcomings in the services sector. This model has five dimensions with all having their own influence (van Iwaarden et al. 2003): Reliability – The ability to perform the promised service dependably and accurately.

Assurance – Knowledge and courtesy of employees and their ability to inspire trust and confidence. Tangibility – The appearance of physical facilities, such as: equipment, and personnel. Empathy – Caring and individualized attention that the firm provides to its customers Responsiveness – The willingness to help customers and provide prompt service. The SERVQUAL model compares the difference between expectations around the quality of the service and the actual perception of customers; the difference is being formulated into a GAP-analysis.

The GAP-analysis identifies seven* gaps: 1 | Customers’ expectations vs. management perceptions: as a result of the lack of a marketing research orientation, inadequate upward communication and too many layers of management. 2 | Management perceptions vs. service quality specifications: as a result of inadequate commitment to service quality, a perception of unfeasibility, inadequate task standardization and an absence of goal setting. 3 | Service quality specifications vs. ervice delivery: as a result of role ambiguity and conflict, poor employee-job fit and poor technology-job fit, inappropriate supervisory control systems, lack of perceived control and lack of teamwork. 4 | Service delivery vs. external communication: as a result of inadequate horizontal communications and propensity to over-promise. 5 | Discrepancy between customer expectations and their perceptions of the delivered service: as a result of the influences exerted from the customer side and the shortfalls (gaps) on the part of the service provider.

In this case, customer expectations are influenced by the extent of personal needs, word of mouth recommendation and past service experiences. 6 | Discrepancy between customer expectations and employees’ perceptions: as a result of the differences in the understanding of customer expectations by front-line service providers. 7 | Discrepancy between employee’s perceptions and management perceptions: as a result of the differences in the understanding of customer expectations between managers and service providers. * The model is an extension of Parasuraman et al. 1985), by ASI Quality Systems, 1992; Curry, 1999; Luk and Layton, 2002. [pic] Model of service quality gaps (Parasuraman et al. , 1985; Curry, 1999; Luk and Layton, 2002) The analysis can be used as a diagnosis to identify shortcomings in the service of a company. For this research we will have a look at Hotel unknown’s SERVQUAL formulation and link it to the use of the NPS, which is discussed in the next paragraph. 2. 2 THE NET PROMOTER SCORE Introducing the Net Promoter Score, by Frederick F. Reichheld. The Net Promoter Score is a management tool that measures the loyalty of customers towards an organization.

It is an addition to the traditional customer satisfaction research. Fred Reichheld, founder of loyalty practice Fred Reichheld, Bain & Company, developed the Net Promoter Score in 2003. He discovered the method at Enterprise Rent-A-Car. He developed a more commonly applicable method and published his findings in his book The Ultimate Question. The greatest benefit that the NPS gives is a simplified overview of a companies’ customer base, the distinction between type of customers and a tool to support autonomous growth.

The aim of the method is to identify the loyal customers and stimulate customer loyalty, since loyal customers help create sustainable growth. The method evolves around one “ultimate” question that is asked to customers and which will help determine whether they are loyal customers or not. That question is: To which extent are you willing to recommend this company/product/service to your family, friends or colleagues? Customers are asked to give a score on a scale of 0-10.

With 0 being certainly unlikely that he or she will make recommendations to third parties and 10 being so enthusiastic, that he or she will recommend the company, product or service to others. Reichheld’s developed method distinguishes the respondents into three groups: 9-10 Promoters 7-8Passives 0-6Detractors The group of Promoters are the most important group for a company: they are the loyal, satisfied and enthusiastic customers. Loyal customers buy more products, companies have longer lasting relationships with loyal customers and loyal customers are more likely to bring new customers to the company.

The passive customers are also satisfied but not enthusiastic or loyal; they can easily be lured away by competitors through promotions etc. Detractors are customers who aren’t satisfied and have a bad relationship with a company; they can create bad publicity and could be harmful for a company on a long-term. The NPS is calculated as followed: (Promoters – (Detractors= NPS With (Promoters being the total percentage of Promoters and (Detractors the total percentage of Detractors. In a perfect situation, a company would score a NPS of 100%.

In that case a company would only have Promoters, customers who are enthusiastic about their products or services and would create enormous growth for the company by mouth-to-mouth recommendations or free online publicity (through personal web pages, forums etc. ). In the worst case scenario a company would have a NPS of -100%, where a company would only have criticasters, customers who aren’t satisfied with the delivered product and service. In 2009, Hotel unknown scored a NPS of 28,4%. The following table shows how their NPS was calculated.   |Detractors |Passives |Promoters | |NPS |0 |1 | |Preliminary stage |Several reservation methods |Empathy – Clear and appropriate | | |Supply of information (brochures, |communication to understand the | | |confirmation) |customer’s needs and situation in order | | |Signposting to the Hotel unknown hostels |to create a sense of Reliability | |Arrival |Feeling of being welcome |Assurance and Responsiveness – The | | |Friendliness and efficiency of staff at |competency to deliver the promised | | |reception |service, and the willingness to provide | | | |help and prompt service when problems | | | |occur. |Stay |Design of physical tangibles |Same as above, including Reliability | | |Atmosphere |(which should be established at this | | |Hygiene |stage) and Tangibles, where the quality | | |Quality of Food and Beverage |of physical facilities, equipment and | | |Friendliness and efficiency of staff |employees is appropriate. | 4. 2. 3 Conclusive summary The assessment that links Hotel unknown’s service-quality framework to Parasuraman’s SERVQUAL-model confirms that the organization has a well-developed framework and successfully identified the most important elements that are necessary to meet the expectations of their customers. The grade (7. ) customers gave the organization in 2009 and the level of satisfaction (80%) confirms that. In order to take the organization’s service to a higher level, Hotel unknown strives to look for a tool that can help contribute in doing that. The implementation of the Net Promoter Score should help the organization identify what improvements need to be made so that customers are happier about the organizations performance and are more likely to recommend the organization to third parties. The following research question deals with the thought behind the NPS. What the merits of implementation are and which (positive or negative) factors should be taken into consideration when using the NPS.

The research result start with a business case similar to the situation of Hotel unknown, and assesses how it has developed a tool that helped identify and improve the service and performance level of the company. It stands for the initial development for the Net Promoter Score, which is assessed later on. 4. 3 What are the pros and cons for Hotel unknown when using the NPS as a measurement tool? Frederick F. Reichheld’s critically acclaimed Harvard Business Review article “The One Number You Need to Grow” from the beginning of the new millennium has made the Net Promoter Score one of the most embraced parameters in the customer satisfaction sector.

In his book “The ultimate question” (2006, Harvard Business School Publishing Corporation) he discusses his developed concept of the link between customer satisfaction and customer loyalty (retention) and company growth achieved through profitability. The NPS is popular among business executives and many large companies like General Electric, Allianz and American Express have adopted this approach in their service-quality framework. However, despite its acclaim there are also academic and market research sphere who criticizes the functioning of the NPS and the claims Reichheld is making in his book. This assessment serves the purpose of providing an overview of possible opportunities and difficulties when implementing the NPS as a management instrument in the service-quality framework of Hotel unknown. 4. 3. 1 Background: Rent-a-Car Case

The development of the Net Promoter Score originated from the mid 1990’s when US car rental company “Enterprise Rent-a-Car” held a meeting in 1994 to present the annual achievements of the previous year. The company achieved major successes in that year by surpassing its greatest competitor Hertz and becoming the biggest car rental company in the US. However, the enthusiasm of all members of the meeting was tempered when Jack Taylor, the founder and President of the Board discovered that the scores of the customer satisfaction level had stalled. For years had Taylor worked to set up a company that provided the best quality of service in his industry, and was astounded to find out that the level of satisfaction was scaled lower than one of their competitors.

Although the company was making profit and growing at a steady pace, Taylor realized that his company would face severe problems if the level of satisfaction would not improve. To deal with the problem, Taylor and his team worked on a measurement tool that enabled them to accurately map and measure the level of satisfaction and introduced the “Enterprise Service Quality index” or the ESQi. The ESQi was developed around one main question that would measure the level of service quality provided: “How satisfied are you in general with the car you have rented at Enterprise? ” The answers were categorized in five variables from very satisfied to very unsatisfied.

By the aforementioned question the variables were calculated in percentages, these percentages represent the ESQi. The introduction of the ESQi within Rent-a-Car soon encountered resistance from managers and problems. Overall the results were moderate: 86 percent of the respondents were at least “reasonable satisfied” while only 60 percent rated the company with the highest score to indicate that they were fully satisfied. Moreover, the scores of different regions were highly diverged. While at one moment a region scored 80% on the highest rating, the other month they barely made 50%. And one of the largest and most profitable regions only scored 54%, which at some times was the lowest score of the whole enterprise.

Not surprisingly the managers criticized the research method, and pointed out that the ESQi did not take the size of the subsidiaries into consideration, and claimed that different subsidiaries in different regions might create different expectations. The accumulations of poor results lead to managers questioning the relation between level of satisfaction and financial results. Taylor and his team continued their research with the critics from their managers in mind and found out that the size of the subsidiary and region did not influence the level of satisfaction, they found good and bad performers in each category or region within the organization. Ultimately they introduced three modifications to the strategy that should make the difference in using the ESQi.

At first they recognized that the experiences of customers are at a local level. So instead of only collect the data per region, the subsidiaries individually had to collect and report the data. (At the time the company had around 1,800 subsidiaries, in 2006 there were more than 6,000. ) It was also necessary that the numbers were reported on a monthly basis; the quarterly statements did not provide up-to-date information that the company timely could use to investigate and improve the quality service framework. There was also quota of at least 25 respondents each month for each subsidiary to ensure proper sampling. Finally, managers wanted to be ensured that a higher ESQi would lead to beneficiaries for the company.

Therefore the relation between items on the questionnaire and behavioural patterns such as continued purchasing and recommendations was examined. They found out that 86% of the customers who were fully satisfied manifest certain behaviour. For the customers who gave the company the highest rating, it was thirteen times more likely that they would return to the company than customers who filled in a lower score. Almost 90% of the positive recommendations came from customers who gave Rent-a-Car the highest score. The outcome of the research led to the conclusion that the highest scores immediately manifest growth and profit. Measures for taking ESQi serious

Although the new measures created more awareness among managers, more is needed to fully embrace ESQi and take the concept seriously. The first step is to incorporate the scores from the ESQi to recognition by the organization. At Rent-a-Car the ESQi score is linked to the prestigious President’s Award, a price for members within the organization who had a special contribution to the company. Later on Rent-a-Car introduced conditions for promotion: No employee could make promotion if their subsidiary or region did not score an ESQi above average. It was the first sign that top-management was serious about the ESQi. The second signal was the new structure of the monthly reports: the emphasis is placed on the importance of the ESQi.

The regions and subsidiaries were ranked based on the ESQi and the net profit they generated, in this way region-managers could see in a blink of an eye how their performance is corresponding to others. Managers with low scores needed to justify the score and provide a plan with measures taken to improve the ESQi score. The third procedure was extensive communication about the ESQi. The ESQi became a topic of discussion at almost every meeting that where Taylor gave speeches, this in order to stress out the importance of the concept. These measures ensured that customer satisfaction and ESQi in particular an integrated element of Rent-a-Car’s culture.

Gradually the average ESQi scores of the subsidiaries improved: in 1994 the average score was 64 and in 1998 it climbed to 72. In 2002 the average score was 77. Also the gap between the lowest and highest ESQi score converged: while the margin in 1994 was 28, eight years later it was reduced to 12. The ESQi was developed to realize two objectives: To generate more respondents with high scores and reduce neutral or negative scores. Which is similar in terms of using the Net Promoter Score method: Increase the group of Promoters and reduce the group of Detractors. The most important difference lies in that exact same field: Rent-a-Car does not make use of the term Detractors neither does it use the lower scores in the ESQi.

ESQi is only based on the highest scores, while the NPS pays attention to the highest as well as the lowest scores. Also the rating scale differs: while the ESQi uses a five point scale with items named “very satisfied and very unsatisfied”, the NPS uses a scale from 0 to 10. Reichheld believes it’s important to take that extra step to ensure you pay attention to both parties, he also claims that the NPS and the growth rates show a higher relation than only the Promoters and the growth rate. 4. 3. 2 Principles of the NPS The development of the NPS originates from the idea that customer satisfaction and more specific customer loyalty is the key for any rganization that strives sustainable company growth. F. Reichheld believes that sustainable growth can only be created through healthy profits. A lot of companies do not understand or don’t make the distinction between healthy and unhealthy profits. Unhealthy profits are achieved through gaining revenues at the expenses of the relation with the customer. Every time a customer feels misled, inappropriately treated or enforced, the profits retrieved from the customers are unhealthy. Examples of unhealthy profits are revenues gained from hidden costs, complex price schedules, exorbitant administration costs and high allowances for e. g. late payments.

Unhealthy profits are creating damage to a company on a long term because they produce Detractors. Detractors are people who feel poorly treated by a company. It could lead to less purchasing and even customer loss. Detractors are invisible on companies’ annual accounts but could possible cause more damage than anyone could imagine. Customers who feel poorly treated will look for ways to get even. If it’s not complaining to the company they will turn to family, friends, colleagues, the media or even politicians to share their experiences. Such bad mouth-to-mouth communication could cause severe damage to companies and with the help of online social network platforms it could go a lot faster than one might think.

The alternative for unhealthy profits is healthy profits. A company generates healthy when customers are enthusiastic about the delivered product or service, such customers are more likely to make return purchases or visits and will make positive recommendations to others. In fact, they become part of your marketing department since they promote your company; they become your Promoters. Key in this process is that a relationship based on loyalty is established between the customer and company. This relationship was also the outcome of a research of Reichheld’s company and Bain & Company, who did research on the relation between loyalty and growth.

They collected data that indicated that an increase in customers of 5 percent could lead to a growth profit of 25 to 100 percent. They noticed that the income of loyalty leaders (companies with the highest customer loyalty) could increase their average income twice as fast as their competitors. The following paragraph is a recapitulation of what has been assessed in the literature review (chapter 2. 2) and it is equated to what the organization already knows. The ultimate question Reichheld has formulated the ultimate question to make a distinction between healthy and unhealthy profits: To which extent are you willing to recommend this company/product/service to your family, friends or colleagues?

The number that is produced in that way is known as the Net Promoter Score. Customers are asked to give a score on a scale of 0-10. With 0 being certainly unlikely that he or she will make recommendations to third parties and 10 being so enthusiastic, that he or she will recommend the company, product or service to others. Reichheld’s developed method distinguishes the respondents into three groups: 9-10 Promoters 7-8Passives 0-6Detractors The NPS is calculated as followed: (Promoters – (Detractors= NPS With (Promoters being the total percentage of Promoters and (Detractors the total percentage of Detractors. In a perfect situation, a company would score a NPS of 100%.

In that case a company would only have Promoters, customers who are enthusiastic about their products or services and would create enormous growth for the company by mouth-to-mouth recommendations or free online publicity (through personal web pages, forums etc. ) In the worst case scenario a company would have a NPS of -100%, where a company would only have criticasters, customers who aren’t satisfied with the delivered product and service. The following figure shows the NPS of well-known companies with the most efficient growth-machines. |Harley-Davidson |81% | |Costco |79% | |Amazon. om |73% | |eBay |71% | |Apple |66% | |Cisco |57% | |FedEx |56% | |Southwest Airlines |51% | |American Express |50% | |Dell |50% | |Adobe |48% | “Top NPS companies”. Source: F. Reichheld (2006) The Ultimate Question: Driving Good Profits and True Growths, Harvard Business School Publishing Corporation. According to Reichheld, the average enterprise has a NPS between 5 and 10%, which means that the amount of Promoters is barely larger than the amount of Detractors.

A lot of enterprises and even whole sectors (such as the power supply sector) have a negative NPS. That means that every day they produce more Detractors than Criticasters. This explains why these companies fail to produce sustainable profits and growth. Reichheld unerringly stresses out the importance of customer loyalty and how these loyal customers could bring beneficiaries to the company. And it is indubitable that the list presented in his book consists of successful enterprises that have created sustainable growth are prime examples within their sector, since their NPS are impressing. However, many are questioning the relation Reichheld makes between customer loyalty and company growth and how the NPS is set up and calculated.

The following overview contains criticisms, remarks and marginal comments that have been placed by experts, writers of business articles and managers who are pertained to the NPS. 4. 3. 3 Claims of refutations The most important claim Reichheld makes in his book is that greater customer loyalty leads to better financial performance, since loyal customers are more likely to spend more and they are the group that cost the organization the least in terms of customer retention. It is a consensus in the marketing world that loyal customers are important and vital for a company. However, many scientists criticize the Reichheld’s claim that a higher NPS, as in a higher degree of customer loyalty, leads to a better financial performance.

At ING they did not find a direct relation between the NPS and financial performance. (Lang & Vogelaar, 2008) “There are simply too many other factors that influence the financial performance of an organization. ” Moreover, even Reichheld mentions between the lines that the NPS is not the best indicator for each company. Each company should test by the use of research results and behavioural patterns if this actually is the case. This is basically the main reason for this research. But there are several characteristics of the NPS that makes it extremely difficult to identify certain patterns. Several noteworthy ones are: Aggregation and transformation of the scores

The way in which the NPS is calculated (into percentages and the deduction of the Detractors from the Promoters) makes it critically difficult to create an analysis that indicates points for improvements. The way in which the NPS is set up makes the proportion between Promoters and Detractors indifferent; it doesn’t matter if you have 60% Promoters and 30% Detractors or only 35% Promoters and 5% detractors, in both cases the NPS is 30. 11-point scale Unlike many marketing researches, the NPS uses a scale that runs from 0 (= extremely unlikely) to 10 (extremely likely). Reichheld intensely stresses out the importance of the 11-points scale in his book because the NPS should not be regarded as a report grade.

No indication is found that the use of a 10-point scale, which is common for reports, and an 11-point scale makes a significant difference, especially not in the way the scale is being used at the NPS. Weighting There is no weighting at the NPS; no distinction made between Detractors who give a 0 and Detractors who give a 6. Moreover, a Detractor weighs as heavily as a Promoter. It also raises the question why the NPS does not simply make use of a 3-point scale. Calculation of the NPS In some cases, the way in which Hotel unknown measures and calculates the NPS it can happen that the score does not give a representative view of the level of customer loyalty. For example: Hotel unknown Doorwerth is a favourite destination for groups who practice sports or play music.

They have many groups who return to the hostel on an annual basis, because they are satisfied with the provided facilities, atmosphere and the provided service. But due to their customer base they don’t get a respectable amount of feedback; they received 100 respondents on the customer survey while that amount represents a group of 18,000 guests. This means that when the operator (the one that made that booking for a group of 200 guests) fills in the customer survey, only his or her opinion is taken into consideration and is being used for measuring the NPS. On the other side, a couple spend a weekend at Hotel unknown at the same time as a large group; this had a large influence on the experience of the guest and consequently gave a low NPS when they filled in the customer survey.

This gives a false position to the groups since only one Promoter is being counted while the voice of one Detractors weighs just as heavily. Other points of criticism are the assumptions that are being made by the NPS-method. – The fact that respondents state in their survey that they would be likely to make a recommendation does, in many cases, not have to lead to a recommendation. – The NPS combines the lowest scores (0-4) with neutral or average scores (5-6), while it is assumable that there are significant differences between these Detractors. – Even the Passives (7-8) are sometimes undeserved separated from the Promoters (9-10), while the loyalty level of both groups could be the same.

A clarification for this could be the type of culture within countries. If you for example look in the United States, when a satisfied customer is asked to rate the organisation on a scale of 1-10 he is more likely to give a 9 or a 10, while in Great Britain it is an 8 or a 9. In the Netherlands people have the tendency to rate around an 8 when they are satisfied; this could be because of the down-to-earth mentality of people within the Netherlands. – Low recommendation scores do not automatically have to lead to customer loss. Factors that could play a role in customer’s consideration are ability to choose between competitors and their own abilities in terms of financial means. If a customer for example is not able to travel further to buy a certain product elsewhere, or does not have the financial means to pay more for a product, it is likely that the unsatisfied customer stays with the company when the provided product or service is a necessity). – The NPS requires a different interpretation for each sector. Each sector has a different average NPS. For example, consumer banks have an average NPS of -5, supermarkets have an average of 1 and power supply companies score on average a NPS of -50. 4. 3. 4 Conclusive summary It is a common understanding that customer experiences need to be measured and that it could help creating a better performance for companies, so it makes sense to measure the level of likeliness to make recommendations.

But the NPS-formula should not be the only indication for the success of a company, because a relation between the height of the NPS and financial development could not be established. It is possible that at a certain moment in time a statistical relation could be determined, but there are too many other factors that could play a role in the level of financial performance. CHAPTER 5 RESEARCH RESULTS: EXPLANATORY STUDY This chapter is an outline of statistical analyses of the data from the customer satisfaction survey. As an introduction, the statistical facts of the customer survey are given and the requirements for useful data are mentioned in order to filter these whether through assumptions or through the Cronbach’s ( analysis.

Each analysis or test is preceded by an explanation of the analysis – where necessary containing a hypothesis – and is ended by a short conclusion. The elaboration of the conclusions can be found in Chapter 6. 5. 1 INTRODUCTION The organization’s SERVQUAL-model as assessed in the previous research question is used for analyzing the data that is retrieved from the customer satisfaction survey. Several analyzes are being conducted in order to establish relationships between variables or identify significant factors that might influence the organization’s performance. In order to conduct an analysis with useful data, a selection is made based on assumed requirements. 5. 2 SELECTION OF USEFUL DATA

Amplixs provided the retrieved data from the customer surveys for this analysis. In 2009 there were 32,583 surveys returned, 8,527 of them were useful for the data analyses (the rest of the returned surveys did not had a NPS) in this research. In order to make a selection of relevant elements, we use the RATER-analysis from 4. 2. 2 as the basis for the analysis. |STAGE |ELEMENTS |RATER | |Preliminary stage |Several reservation methods |Empathy – Clear and appropriate | | |Supply of information (brochures, communication to understand the | | |confirmation) |customer’s needs and situation in order | | |Signposting to the Hotel unknown hostels |to create a sense of Reliability | |Arrival |Feeling of being welcome |Assurance and Responsiveness – The | | |Friendliness and efficiency of staff at |competency to deliver the promised | | |reception |service, and the willingness to provide | | | |help and prompt service when problems | | | |occur. | |Stay |Design of physical tangibles |Same as above, including Reliability | | |Atmosphere |(which should be established at this | | |Hygiene |stage) and Tangibles, where the quality | | |Quality of Food and Beverage |of physical facilities, equipment and | | |Friendliness and efficiency of staff |employees is appropriate. |

In agreement with the organization it was concluded that the preliminary stage (reservation method and information supply) would have little or no influence on the level of customer loyalty, which means that all elements belonging to the preliminary stage are left out of the analysis. This means that the elements belonging to the stages “Arrival” and “Stay” are used for the analysis: • Feeling of being welcome • Friendliness and efficiency of staff • Design of physical tangibles • Atmosphere • Hygiene • Quality of Food and Beverage 5. 2. 1 Cronbach’s ( analysis Due to the large amount of variables, it is necessary to conduct a Cronbach’s ? analysis prior to the other tests to determine whether all variables in the datasheet show a strong cohesion in the customer survey.

The concept that is measured in the survey is customer loyalty, and whether there is a relation between customer loyalty and several elements in the service of Hotel unknown. The outcome of the Cronbach’s ( analysis is shown below. Assurance and responsiveness |Reliability Statistics | | Cronbach’s Alpha |N of Items | |,913 |12 | |Case Processing Summary | | |N |% | |Cases |Valid |3732 |43,8 | | |Excludeda |4795 |56,2 | | |Total |8527 |100,0 | |a. Listwise deletion based on all variables in the | |procedure. |

Tangibles (physical facilities) |Case Processing Summary | | |N |% | |Cases |Valid |5676 |66,6 | | |Excludeda |2851 |33,4 | | |Total |8527 |100,0 | |a. Listwise deletion based on all variables in the | |procedure. | |Reliability Statistics | |Cronbach’s Alpha |N of Items | |,921 |13 | Both tests result in an ? higher than 0. 70, which means that the survey is reliable and measures the concept correctly.

There is no need to remove elements (questions) from the survey for further research. 5. 3 RQ3: Which factors have a significant influence on the NPS of Hotel unknown? This research question deals with how the NPS can be implemented as a performance tool in Hotel unknown’s service-quality framework. Based on the strategic or organizational design, several elements are tested against or with each other to determine if there are specific factors need to be taken into consideration that could influence the NPS. This could stretches from geographic factors, such as the location of the hostel, to the type of customers and the (intended) decisions they make. 5. 3. NPS City – NPS Coast & Water – NPS Green Hotel unknown has divided its hostels into three destinations that are distinguishable by the location where they can be found: in cities, at the coast or near recreational waters or in green surroundings. Because Hotel unknown defines this as a product differentiation it is interesting to find out whether a distinction in destination needs to be made when establishing relationships between variables. The analysis consists of a statistical test to determine whether the differences in NPS between the destinations are coincidence or whether the difference is large enough to call it statistically significant.

Based on what we want to test, the following hypothesis is formulated: H0 = (city = (water =(green H1 = H0 is incorrect The following table shows the NPS the three destinations have scored in 2009. |Destination |NPS | |City |27. 0 | |Green |24. 0 | |Water |31. 7 | The objective of this test is to determine whether these three scores differ statistically significant from each other. In order to do this the student T-test* is used. The student T-test allows us to determine the scores from two sample sizes. Since we have three samples it means that the test needs to be conducted three times: City vs. Green, City vs. Water and Green vs. Water.

All sample sizes are independent, which means that the choice of the respondents in one sample does not influence the composition of the other sample. The first problem we encounter is that the NPS is an index; it is the deduction of two percentages retrieved from calculations of the collected data. Because of the characteristics of the NPS it is not possible to use the appropriate techniques (such as Laspeyres and Paasche) to determine a certain relation. The alternative to make sampling possible is to use the average NPS score that has been given by the guests, the sample size of each destination and the (estimated) standard deviation. ____________________________________________________________ ____________________________________________________________ ____ While compiling this report and after conducting the test I discovered that the analysis of variance would have been a better technique to determine the results. In consultation with the Thesis supervisor it came to agreement that the use of the T-test is continued. The variance of analysis shows a result similar to the one that has been found with the T-test. In the appendix the outcome of the conducted analysis of variance with the help of Excel is outline and will support my findings while conducting the T-test. Using the data spreadsheet provided by Amplixs, the following figures were collected. | |CITY |GREEN |WATER | |Average NPS |7. 941 |7. 56 |8. 104 | |Sample size |3285 |1670 |3572 | |Estimated standard deviation |2. 077 |1. 751 |1. 823 | Test City vs. Green The first test that is being undertaken is City vs. Green, following the aforementioned test procedure. Specified: | |CITY |GREEN | |( |7. 941 |7. 56 | |n |3285 |1670 | |sx |2. 077 |1. 751 | The first step is determining (, through the pooled variance formula. S2F = (3285-1)( 2. 0772 + (1670-1) ( 1. 7512 = 14166. 54 + 5117. 81 = 19284. 35/4953 = 3. 89 3285-1+1670-1 4953 S2F =3. 89 Varcitygreen = S2F/ncity + S2F/ngreen = S2F (1/ ncity + 1/ ngreen) Varcitygreen = S2F(ncity+ ngreen/(ncity( ngreen) Varcitygreen = 3. 89(3285+1670/3285*1670) Varcitygreen = 3. 89(4955/5485950) Varcitygreen = 0. 00351 ( = ( Varcitygreen = 0. 0593

The confidence limits are calculated as followed: Specified: ( = 0 (0-hypothesis) z = 1. 96 ( = 0. 0593 cr = ( + z( cl = ( – z( c = o + 1. 96( 0. 0593 = 0. 116 Which gives confidence interval Z = {( | ( < -0. 116 or ( > + -0. 116} The observed difference (city – (green = 7. 941 – 7. 956 = -0. 015 (or 0. 015) does not lie outside the confidence interval, which means that it cannot be stated that the determined NPS of City and Green show a significant difference. Following the same procedure, the differences of the other two variables are determined: CITY vs. WATERGREEN vs. WATER Z = {( | ( < -0. 092 or ( > + 0. 092}Z = {( | ( < -1. 05 or ( > + 1. 05} city – (water = 7. 941 – 8. 104 = -0. 163 (green – (water = 7. 956 – 8. 104 = -0. 148 In both cases we find that the difference are outside the confidence interval, which means that we can say with 95% confidence that there is a significant difference between the NPS of Water and coast hostels and the other hostels (City and Green). 5. 3. 2 NPS Promoters – NPS Detractors This analysis deals with the relation between elements in the service of Hotel unknown, and the NPS. The objective of this analysis is to determine the significance of certain elements on the height of the NPS, and which elements particular contribute to the height of the NPS.

Following the structure of the customer survey, the following variables are formulated. In order to determine which elements have a significant influence on the NPS, it is necessary to formulate a model with one dependent and one (or more) independent variables. Due to the great amount of (independent) variables, which are derived from the customer survey, the regression analysis will be split up in several analyses with categorized variables. The categories are outlined below. The dependent variable Y is the NPS, since it depends on the outcome of variable(s) X, this is applied on all analyses. Y = NPS The independent variables are categorized as followed: General |Assurance and |Tangibles design, and |Tangibles (food and | | |Responsiveness |hygiene |beverage) | |Price/quality |Feeling of being welcome|Public areas |Quality breakfast | |Customer expectations |Friendliness of staff |Bedrooms |Price/quality Lunch | | |Efficiency of staff |Bathrooms |Price/quality packed | | | |Restaurant |lunch | | | |Bar |Price/quality dinner | Analysis 1: General This analysis is to determine the relation between customer’s overall opinion and the height of the NPS. The overall opinion is based on the organization meeting their customer’s expectations. The following variables are formulated: Y = NPS X1 = Price quality = Q213_2 X2 = Customer expectations = Q213_3 Outcome of the analysis through SPSS: [pic] [pic] Explanation In the Model Summary, the R-value stands for the correlation coefficient and the R Square for the determination-coefficient.

These coefficients give an impression of the regression line, in other words: to which extent a straight line would suit a scatter diagram. The value of R lies between -1 and 1. When R=0, there is no linear relation. When R is negative, there is a decreasing linear relation. When it is positive, the relation is increasing (see figure). The closer the value of R is to -1 or 1, the better the quality of the linear model, the better the prediction of the NPS. The R Square (determination coefficient) can be interpreted as a percentage and stands for the extent in which the variation of variables justify the height of the NPS. In the case of the first analysis doest 61. % of the variation in the customer’s expectation explains the height of the NPS. The R Square is significant when it has a value of more than 50%. The variables X need to be significant factors to be accurate indicators to predict variable Y. Variables X are considered to be significant when ? < 0. 05. The value of ? can be found in the Coefficients table at “Sig. ”. Both variables X are in the first analysis significant (value =/? 0. 000), which means that both are good predictors for variable Y. We use the regression formula to determine the regression line that could help predict the value of Y, the NPS: Y = (+ (X1 + (X2 + ( With ( standing for the constant value which is 0. 599 for the first analysis.

The value of ( for the independent variables can be found under the unstandardized coefficients B. That gives 0. 303 for X1 and 0. 666 for X2. For the simplicity we ignore the std. error ((). NPS = Y = 0. 599 + 0. 303 X1+ 0. 66X2 This means that the NPS can be estimated through the scores customers give for the price quality and their expectations. In other words, when a customer rates the price quality proportion of the organization with a 7 and the extent in which the organization meets their expectations with an 8. There is a likeliness of 61. 9% that these two factors determine the NPS, which in this case means that the NPS would be an 8 (0. 599 + 0. 303 • 7 + 0. 66 • 8 = 0. 99 + 2. 121 + 5. 28 = 8). It also means that 100- 61. 9 = 38. 1% is explained by other factors. Note that the ( of X2 is larger than X1, which means that factor X2 has more influence on the height of the NPS than X1. Analysis 2: Assurance and Responsiveness Y = NPS X1 = Feeling of being welcome = Q1738_2 X2 = Friendliness of staff at reception = Q1738_3 X3 = Efficiency of staff at reception = Q1738_4 X4 = Friendliness of staff at restaurant = Q210_2 X5 = Efficiency of staff at restaurant = Q210_3 X6 = Friendliness of staff at bar = Q210_4 X7 = Efficiency of staff at bar = Q210_5 X8 = Friendliness of cleaning staff = Q210_6 [pic] [pic] Only 38. % of the variation of variables Xn determines the height of the NPS. This amount is not significant, which means that there are other factors that play a bigger role in the height of the NPS. The following variables indicate of significant importance (Sig. = < 0. 05) in this analysis, the variables are ranked in importance by looking at the height of (: ( = B X1 = Feeling of being welcome = Q1738_20. 207 X5 = Efficiency of staff at restaurant = Q210_30. 175 X3 = Efficiency of staff at reception = Q1738_40. 147 X8 = Friendliness of cleaning staff = Q210_60. 145 This means that customers primarily take these factors into consideration when giving the organization an NPS.

Because the determination coefficient was insignificant (only 38. 1%), we dismiss the assumption that the formula that would be created through the help of the aforementioned variables would give us a good indication of the height of the NPS. However, it is to be assumed that these factor do play a role in the decision to rate the overall performance of the organization (as seen in the first analysis), which means that these variables are related to other variables. (This is why not all variables could be put into one analysis, since it would show a correlation between variables that is too high). Analyse 3 Tangibles (design and hygiene) Y = NPS Design of X1 = Public areas = Q203_2 X2 = Bedrooms = Q203_3

X3 = Bathrooms = Q203_4 X4 = Restaurant = Q203_5 X5 = Bar = Q203_6 Hygiene of X6 = Public areas = Q205_2 X7 = Bedrooms = Q205_3 X8 = Bathrooms = Q205_4 X9 = Restaurant = Q205_5 X10 = Bar = Q205_6 [pic] [pic] 47. 8% of the variation of variables Xn determines the height of the NPS. This amount is nearly significant, though other factors play a bigger role in the height of the NPS. The following variables indicate of significant importance (Sig. = < 0. 05) in this analysis (again, ranked in importance): X2 = Design Bedrooms = Q203_3 X4 = Design Restaurant = Q203_5 X5 = Design Bar = Q203_6 X1 = Design Public areas = Q203_2 X7 = Hygiene Bedrooms = Q205_3

X9 = Hygiene Restaurant = Q205_5 X3 = Design Bathrooms = Q203_4 X6 = Hygiene Public areas = Q205_2 X8 = Hygiene Bathrooms = Q205_4 Overall it means that there is no significant element within the physical facilities where the customer pays primarily attention. They expect that the design of all facilities meet their expectations and prefer clean bedrooms. Analysis 4: Food and Beverage Y = NPS X1 = Quality breakfast = Q1768_0 X2 = Price/quality Lunch = Q268_0 X3 = Price/quality packed lunch = Q271_0 X4 = Price/quality dinner = Q274_0 [pic] [pic] The following variables indicate of significant importance (Sig. = < 0. 05) in this analysis: X1 = Quality breakfast = Q1768_0

X2 = Price/quality Lunch = Q268_0 44% of the variation of variables Xn determines the height of the NPS. This amount is nearly significant, though other factors play a bigger role in the height of the NPS. The quality of the breakfast is considered to be the most important factor of the likeliness to give a high NPS. 5. 3. 3 NPS new customers – NPS known customers The purpose of this test is to determine whether customers who have visited Hotel unknown before are more likely to make recommendations to others than first-time visitors. If this is the case, the group of new customers deserves extra attention and it might requires further study into the ehavioral pattern of new customers, in order to identify how to turn new customers into loyal customers. The test for this analysis follows the same procedure as the destinations T-test (5. 3. 1). To follow the same strategy the following figures are collected: Sample size Type of customer NPS The customers are divided into three groups: First-time visitors Familiar visitors (Returning visitors at Hotel unknown, but first-time visitors of the concerning hostel) Returning visitors Based on what we want to test, the following hypothesis is formulated: H0 = (FS = (Familiar =(Returning H1 = H0 is incorrect Considering the aforementioned criteria the following data is collected and calculated: |First-time |Familiar |Returning | |Sample size |3876 |2862 |1581 | |Average NPS |7. 938 |7. 895 |8. 411 | |Estimated standard deviation |2. 0526 |1. 881 |1. 549 | The T-test procedure is identical has the conducted test from 5. 3. 1, therefore only the outcome of the T-test is outlined below. First-time vs. ReturningFirst-time vs. Familiar Z = {( | ( < -0. 112 or ( > + 0. 112}Z = {( | ( < -0. 48 or ( > + 0. 048} (FS – (Returning = 7. 938 – 8. 411 = -0. 473 (FS – (Familiar = 7. 938 – 7. 895 = -0. 043 Familiar vs. Returning Z = {( | ( < -0. 109 or ( > + 0. 109} (Familiar – (Returning = 7. 895 – 8. 411 = -0. 516 The values of the first and third test lie outside the confidence interval, which means that we can say with 95% confidence that there is a difference in the likeliness of

November 27, 2017