DATA

REVOLUTION

How Big Data Will Change the Way of Doing Business

Michael Toedt (Editor.)

1st Edition

Data Revolution
Michael Toedt
Copyright: © 2014 Michael Toedt
published by: epubli GmbH, Berlin
www.epubli.de
ISBN 978-3-7375-1689-1

Limit of Liability/Disclaimer of Warranty: This book is designed to provide information and motivation to our readers. The publisher does not intend to render any type of psychological, legal, or any other kind of professional advice. The content of each article is the sole expression and opinion of the author, and not necessarily that of the publisher. No warranties or guarantees are given by the publisher’s choice to include any of the content in this volume. Neither the publisher nor the individual author(s) shall be liable for any physical, psychological, emotional, financial, or commercial damages, including, but not limited to, special, incidental, consequential or other damages. Our views and rights are the same: You are responsible for your own choices, actions, and results.

Contents

Introduction

Part A: Data Revolution – Michael Toedt

1. Just A Hype?

2. Big Data Stories

2. 1. The Google Flu Map

2. 2. Selling Without Owning

2. 3. Pregnant Without Knowing

2. 4. $750.000 for No Reason

3. Things You Should Know about Big Data

3. 1. The Definition of Big Data

3. 2. The ROI of Big Data

3. 3. Loyalty Programs – A Good Way to Collect Data

3. 4. Where Data Comes from – The Customer Journey

4. Big Data in the Hotel Industry

4. 1. The Reality of Hotel IT

4. 2. The Big Data Warehouse - The Heart of Big Data

5. Big Data – the Next Step in the Evolution of Marketing

5. 1. Data Security and Data Protection

5. 2. Big Data Marketing

5. 3. Communication: Content and Timing

5. 4. Communication Frequency and the Boomerang Effect

5. 4. 1. Response-Functions to Measure the Result of Marketing

5. 4. 2. How Consumers React To CRM Activities – Marketing Becomes Measureable

5. 5. The Value of Communication Channels

5. 6. The Change of Market-Research and Decision-Making

5. 7. The Social Media Pitfall

6. Changes in the Organizational Structure

7. Conclusion

Part B: Expert Views

7. Good or bad hotel customer from a sales perpective?

7. 1. Introduction

7. 2. Segmentation

7. 3. Codes

7. 4. USALI: structure of accounts

7. 5. Conclusion

8. Turn „Big Data“ Into Big Business

8. 1. Intro

8. 2. The Circle of Trust

8. 2. 1. Get Them Talking More: How to Increase Review Volume via Surveys

8. 2. 2. Making Sense of It All & Bringing Calm to the Chaos: Online Reputation Monitoring

8. 2. 3. Shout It From The Rooftops: Marketing A Hotel’s Success

8. 3. Big Brother Is Watching

8. 4. The “Hospitality Gene”

8. 4. 1. Hospitality Gene: The Beast

8. 4. 2. Hospitality Gene: The Blessing

8. 4. 3. Hospitality Gene: The Payoff

9. „Big Data“ What does Data Protection Law say?

9. 1. Introduction

9. 1. 1. A Brief Overview of Data Protection Law and „Big Data“

9. 1. 2. A Glance at the EU Provisions

9. 2. Which Law applies to Hotels in different Countries?

9. 3. The Prohibition Principle and the Separation of a Process into individual Steps

9. 4. Important

9. 5. What are the Solution Strategies?

9. 5. 1. Solution 1, Consent:

9. 5. 2. Solution 2: Waiving Personal Data

9. 6. Deleting Data & „Big Data“: an insoluble Problem?

9. 7. How does this relate to the Deletion Principle?

9. 8. What may happen in Case of Violations?

9. 9. But who is liable?

9. 10. Conclusion

10. Who cannot handle Data, successfully kills his business

10. 1. Survival of the Fittest

10. 2. Relevance matters

10. 3. Data know how as corporate asset

Index

About Michael Toedt

About Toedt, Dr. Selk & Coll.

Further Publications

Literature

Introduction

Data is everywhere. There is no way to stop, deny or avoid it. Big Data is the next economic revolution. After the triumphant success of the Internet, the capabilities of managers to use the endless and steadily increasing amount of data will split companies into those who know what they do, and those who just guess. There will only be winners or losers. Managers need to understand that we are on the verge of a new economic era. The more they listen and learn, the higher the chance to win the data race.

Why is Big Data so important? Big Data can help companies to increase their revenues, improve their profit margins, reduce risks and cut costs. Intelligent use of data supports customer acquisition, allows for higher prices, minimizes the risk of unprofitable investments, improves profit margins, supports the direct distribution channels, and helps to bypass expansive third-party vendors. It also generates customer loyalty and eventually helps to reduce technology, administration and payroll costs. There are manifold reasons to take a deeper look into Big Data.

Customer Data is the Salt of Our Modern Times

In medieval times, merchants in the Mediterranean accumulated extreme wealth by trading in spices such as salt. In the very near future, the prosperity of a company strongly correlates with its ability to manage and use customer data. Data can be used to enhance existing and to create new products and services (co-creation), to market the right services at the right time through the right channels, and to provide a personalized service at the different points of interaction and sale.

Huge volumes of data are generated through the increasing use of social media and smart phones, as well as the continuous digitalization. The growth is exponential. This phenomenon is referred to as Big Data.

Experts agree that the Big Data phenomenon will have a significant impact on the structure of organizations and the way companies will work and interact with their customers. Many managers and organizations are still not aware of this fact, or fail to implement the changes, which are necessary to leverage Big Data. The lack of competence and knowledge and the lack of sufficient IT skills within the senior management prevent most companies from generating value from the available assets. The data is usually collected in data silos, which sit in the different departments and are not connected at all. The reason lies in the past decade, IT decisions were mainly driven by operations and were not based on an overall consumer oriented strategy.

One central goal of Big Data is to provide added value to the customer. Thus, it seems logical that the marketing department takes the lead in Big Data initiatives. This, however, requires profound analytical and technology skills on the management level, as well as additional competencies and responsibilities. The quality of marketing within a Big Data environment goes beyond appealing design. It is characterized by a real-time content delivery process, which provides offers and information to the recipients based on their individual needs.

Increase of profitability by up to 100%

Big Data can help especially hotels to push direct distribution, which results in higher profit margins. Therefor, Hotels should start using Big Data in order to counter-act the loss of business to costly third-party vendors. Big Data can help to secure the competitive advantage and to increase profitability by up to 100%. The biggest obstacles are the lack of expertise within the senior management and the reluctance to implement the necessary technical, organizational, structural and HR changes.

This book provides an evaluation of the significance of Big Data and targets managers on a senior level and everyone who is interested in this topic. Many examples help to show the current situation and to explain the changes, which are necessary to deal with the challenges associated with the current revolution of doing business – the data revolution.

Part A: Data Revolution – Michael Toedt

1. Just A Hype?

“The Internet is among the few things humans have created, but do not truly understand. It is intangible and constantly changing, growing and getting more complex with each passing second. It is a source of tremendous good and potentially dreadful evil, we are just beginning to witness on the world stage.”

Eric Schmidt, CEO of Google, wrote this in his book “The New Digital Age - Reshaping the Future of People, Nations and Business”.

By replacing the term “Internet” with “Big Data” it can be described where we are right now:

Big Data is among the few things humans have created, but do not truly understand. It is at intangible and constantly changing, growing, and getting more complex with each passing second. It is a source of tremendous good and potentially dreadful evil, we are just beginning to witness on the world stage.¹

When we talk about Big Data, we talk about the steadily increasing volume of data and the opportunities, which comes with it. Critics say that Big Data is not a new topic, but only a new and trendy marketing term created by software and consulting companies. The press is full of articles and everyone talks about it. Especially since Edward Snowden Big Data has become a topic with a negative connotation. Snowden used to work for Booz Allen Hamilton, a consulting firm hired by the US National Security Agency (NSA). In June 2013, he disclosed thousands of classified documents that he acquired as an insider working for the NSA. The release of confidential NSA material was the most significant leak in US history.² The basis for Snowden’s release was the fact that after 9/11 the NSA had started to collect data from almost everywhere. The data volumes and sources as well as the possibilities made people and governments all over the world feel insecure, and have resulted in worldwide protests against the NSA and the US government.

Since Snowden has been in the news over months Big Data has become the talk of the town. Hearing about the practices of intelligence services around the world, people started to question what companies can do with data and how this may change our lives in the future. Big Data has become a buzzword and many people talk about it without knowing much about it.

Figure 1: Google Searches on „Big Data“³

Companies have always collected data, but a lot has changed including the pace with which data is generated and collected, and the vast number of sources where data can be pulled from. Our life has become digital and without even knowing we leave our digital footprints everywhere we go. These facts have led to an exponential growth of data in the past years. The basis of Big Data is the amount of data available and the possibilities that come with it.

The technological achievements of the last decades have built the foundation for the current revolution. The development of powerful database management systems and network capabilities, decreasing costs for hardware, software and storage, the triumphant success of the Internet and its evolution through social media are responsible for the current changes. In addition to Facebook and other social media platforms, mainly the launch of the iPhone in 2007 has led to the explosion of data. The Steve Jobs and Apple achieved to revolutionariz the mobile phone market. From a more or less unemotional device with no additional functions, the mobile phone has turned into a status symbol with multi-media functions providing Internet access everywhere and at any time. This connectivity enables to collect and to use data.

Worldwide, about 1. 8 billion mobile phones have been sold in 2013, 53% of which, and growing, were Smart Phones.⁴ Today, even the youngest in our society - 80% of the ten to thirteen year olds and still 33% of the six to nine year olds - possess a mobile phone. It has become normal to grow up with a smart phone and a computer. No wonder that 82% of the kids under 13 are computer-savvy and almost 60% use the Internet on a daily basis.⁵

The triumphant success of smart phones led to the creation of billions of mobile applications, which cater to the users’ needs and provide for entertainment for all situations. The small programs are just a fingertip away and are available for almost all aspects of life. The side effect is, however, that they track and store everything the user does. Many apps are free, but actually, the user pays a price, just in a different currency. The currency is data and the price is the amount of data the app collects. The provider uses the data to learn about the user, a knowledge he will try to capitalize on in the future.

In 2012, Facebook took over Instagram a photo sharing and social media community with 30 million users at the time. The purchase price was about $1 billion,⁶ a ridiculous amount for a company with only 13 employees. In addition to the unique functionalities of Instagram, the motivation of Facebook for the take-over was probably a different one and most likely had something to do with the data Instagram collects with each transaction by a community member. With each picture taken the GPS coordinates and further data including date, time, and the name and type of the device with which the photo was taken, are collected. The GPS tracking tool shows on a map where the picture was taken, which is a nice and helpful feature for users.

Where this can lead to shows the following example. In summer 2014, during the Ukraine crises, however, the Russian government was at a loss to explain the following. Alexander Sotkin, a Russian soldier, took several selfies and posted them on Instragram. Against the firm assureances of Russian politicians that no Russian soldiers supported the separatists in the East-Ukraine, the map view on Instragram clearly showed that the pictures were taken on Ukraine territory.

Figure 2: Russion Soldiers in the East Ukraine reveiled by Instragram⁷

This example shows that some features have the potential of being more than just a technical gimmick. The GPS coordinates of pictures taken can be used for instance to gain a deeper insight into the travel habits and movement patterns of the users. This knowledge is extremely valuable for targeted communication and marketing activities. Marketing managers are willing to pay a fortune for this data, as it enables them to offer their products and services at the right time and at the right place to a relevant micro-segemented audience. The collection of data brings them closer to fulfilling this ultimate goal of marketing.

Big Data – a game changer

Is Big Data just a hype? This question is raised in almost every panel discuission. The clear answer is no. Big Data is a revolution, which comes with the massive collection of data in all life situations. The numbers are impressive and every manager should realize that a big change is happening. The question every manager needs to answer sooner or later is how he can drive value from Big Data, and what it means for him, his skills, his job, and his life?

Figure 3: What happens today?⁸

The figure above indicates what is happening around us on a single day in July 2014. Almost 3 billion internet users were online, 95 billion emails were sent, 1. 6 million blogs were written and 292 million Tweets were sent. This, however, just reflects a very small portion of the overall data volume.

Every year, the volume of digital data increases by 35-50%. Companies process about 1,000 times more data than a decade ago.⁹ Every two days as much digital data content as from the beginning of civilization until 2003 is created.¹⁰ According to a McKinsey study, companies with over 1,000 employees store on average 235 terabytes of data¹¹. One terabyte equals 1,024 gigabyte. Since bytes are abstract to many people, the following numbers put it into the right perspective. In 2000, about 25% of all information worldwide was available digitally; this number had soared to almost 98% in 2013. ¹²

The Volume of Data is Increasing by 35-50% Per Year

In January 2014, Facebook, one of the main social media players, counted approx. 27 million users in Germany, compared to approx. 5 million four years earlier. ¹³ Overall, Facebook had 1. 23 billion users worldwide with a daily activity ratio of 62%.¹⁴ These users create about 2. 5 billion pieces of content, 2. 7 billion “Likes”, and 300 million pictures every day.¹⁵ And these are only the numbers for one single source!

90% of All Data has been Generated in the Past Two Years

It is hard to believe but 90% of all data available worldwide has been generated in the past two years. By the end of 2020, the available data will have increased by the factor 50, compared to 2009. ¹⁶ Looking at these numbers one thing is for sure: the current development of Big Data cannot be stopped and Big Data is not just hype.

Figure 4: Exponential Growth of Data Volume¹⁷

We are still in the early stages of the Big Data Revolution and companies and managers still have some time to prepare themselves. This will be completely different in five years from now. Consumers will have the choice and there will be a natural clearing of the market.

Existing global players will disappear and new ones will take over. Like Kodak denied the role of digital photo´s may companies will loose the change process. Big Data will act like a catalysator for managers who are not willing to adapt the necessary changes.

2. Big Data Stories

The following true stories will help to get a glimpse of what data is able to deliver and will lift any doubts that we already live in a digital Big Data world. Schoenberger and Cukier wrote in their NY Times Bestseller, “Big Data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more.“¹⁸

2. 1. The Google Flu Map

Google is the dominant search engine. Data is their core business. A good example for what Big Data is able to deliver is the so-called Google Flu Map. By analyzing search queries Google realized that they are able to predict where a flu breakout will occur. Based on the number of search queries Google is able to calculate the flu activity worldwide almost in real-time.

Data analysts at Google compared query counts with traditional flu surveillance systems and found that certain search queries are popular exactly during the flu season. By counting how often these search queries occur, Google can estimate the flu frequency in different countries and regions around the world.

Figure 5: Goolge Flue Map¹⁹

Governments, phycicians, and pharmaceutical companies can react much faster on a flu breakout by using the Google Flu Map. Traditional flu surveillance systems look at the past and are therefore always behind the current situation.

2. 2. Selling Without Owning

Walmart is the largest retail company worldwide. Its annual revenue of approx. $450 billion is higher than the GDP of four-fifths of the world’s countries. The idea of Walmart was to record each single product through a system called Retail Link. With this tool, the suppliers of Walmart were able to monitor each sales transaction. Based on this knowledge Walmart forced the suppliers to take care of the stockage, i.e. they were able to outsourc the maintenance of the stockage for certain products. In that case, the supplier owns the product until the point-of-sale. This brought the financial risks down to a minimum.²⁰ The smart usage of data is one of the reasons for Walmart’s success.

2. 3. Pregnant Without Knowing

The following is a true story from outside the hotel industry and shows the power of Big Data. “Target” is a US reseller for all kinds of commodity consumer goods such as baby supplies, furniture, electronics, toys, etc. Like other companies, Target faces the challenge that customers usually only come to the store when they need a certain item they associate with Target. In order to increase revenues the customers had to be convinced that Target is the only store they need. This is hard to achieve through classical advertising, as shopping habits are engrained and difficult to change. Therefore, Target started to analyze the available customer data (everything is stored under a so-called guest ID which enables the link between the customer and the purchased products) in order to create individualized marketing campaigns. The project started in 2002. Revenues had skyrocketed from US$ 44 billion in 2002 to US$ 67 billion in 2012. Of course, not all of the success can be attributed to the direct marketing activities using Big Data, but Target is definitely doing something extremely right.²¹

Analysts at Target found out that women who purchase about 25 different products are potentially pregnant. This led to the creation of a pregnancy prediction model.

Figure 6: Traget - A “Big Data“ Success Story

“Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought a cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There’s, say, an 87 percent chance that she’s pregnant and that her delivery date is sometime in late August. What’s more, because of the data attached to her Guest ID number, Target knows how to trigger Jenny’s habits. They know that if she receives a coupon via Email, it will most likely cue her to buy online. They know that if she receives an ad in the mail on Friday, she frequently uses it on a weekend trip to the store. And they know that if they reward her with a printed receipt that entitles her to a free cup of Starbucks coffee, she’ll use it when she comes back again.”²² This is quite scary and the following true story a result of it:

“A man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation. “My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”²³

As a result Target has shifted to a more subtle marketing approach. Promotions for products pregnant women would never buy are now added to the mailings. This makes the baby ads look more random.²⁴

2. 4. $750.000 for No Reason

In order to drive value from the available data, managers have to be able to ask the right questions, but also to understand the limitations. Analysts can provide beautiful charts, which suggest a highly valuable conclusiveness. Managers have to be aware that not the beauty of a chart should drive the decision, but the quality of the data and the analysis behind.

Like online stores, most hotels nowadays send so-called post-stay messages asking the guests for feedback. In many cases, the used tools collect data anonymously. When reviewing the reports provided by the web-based questionnaire tool, a hotelier saw the need to enhance his spa area and thus budgeted $750. 000 for an upgrade. He did this although the spa had been rated as one of the best spas in the country. During the annual strategy meeting an external data analyst questioned that budget decision. He tooks the anonymous data and linked the results to the individual guest profiles. Structured data has a much higher value and quality than unstructured data. It is curcial to link the guest profile to the feedback results, in order to generate sustainable results and to be able to draw the correct conclusions. This “obvious” requirement is, however, almost unknown and most managers lack the skills to question the delivered reports. In most companies there is literally a big wall between the customer profile and the questionnaire results.

Figure 7: A Wall between the Guest Profiles and Questionnaire Results prevent a Qualitative Analysis

With this in mind, the data analyst repeated the analysis after having linked the guest profile with the questionnaire results. He even went a step further and allocated the results to the different customer segments of the hotel and their overall contribution to the company’s financial success. By doing that, the analyst was able to show that 90% of the customers were happy with the spa area. The core target group of the hotel, the “Double-Income-No-Kids” segment, saw no need for an upgrade. The result was falsified by 10%, results from guests with kids, a customer segement the hotelier did not want to target anyways.

This example shows how easy it can be to drive exceptional value from data. Compared to Google, Wallmart or Target this story shows the importance of asking the right questions and undertanding that Big Data is no rocket-science.

3. Things You Should Know about Big Data

3. 1. The Definition of Big Data

McKinsey, one of the leading strategy-consulting firms, describes Big Data as a new type of corporate asset, which will cut across all business units. It will function like a powerful brand does and thus represent a key basis for competition.²⁵ Companies using Big Data will be able to increase their profit margins by up to 60%.²⁶ The impact on industries like the hospitality, which depend on a close relationship between seller and consumer and on direct marketing, can be even higher.

We have entered the era of Big Data. But what precisely is Big Data? The following definition is a universal interpretation of the topic:

Big Data is the Beneficial Usage of Data to Support the Positive Economic Development of an Organization

The success of Big Data depends on the competence of the people and not just on the collection of data. Big Data is about transforming data into valuable and usable information. The concept behind is very simple: it is about collecting, cleaning, analyzing, and acting within a living and learning environment.

Figure 8: Big Data Concept

The topic sounds and actually is quite simple from a helicopter perspective, but is getting more complex the deeper you go. The challenges are manifold and start with the collection of data. Today, most companies have a broad landscape of software applications in place. As Big Data was never in the minds of the decision makers, the systems are not linked to each other. So the first challenge is to bring everything together that is potentially valuable.

The next step is to clean the data. Otherwise, the results may be flawed. In particular, data quality management is a key element of a Big Data initiative. Unfortunately, it is disregarded in many projects. Bad data quality results in questionable results and wrong decisions, which sometimes have a disasterous outcome. About 80% of all data is flawed. This explains why comprehensive data cleaning processes and data entry standards have to be put in place. The immense amount of data coming from various sources, in either very short intervals, or real-time can no longer handled by manual data cleaning processes. There are specialized software systems, which ensure that the incoming data is usable. The success of Big Data CRM clearly correlates with the quality of the used data. It is critical that decision makers are aware of any potential lack in data quality in order to avoid the risk of being deluded by the nice graphs and charts. It sounds boring, but data quality is a key element of Big Data and therefore a topic for the senior management.

After the data has been collected and cleaned, the analysis can start. State-of-the-art software tools deliver deep insights and reveal correlations between variables, which nobody thought would exist. But managers should not rely blindly on the numbers provided by the data analysts. Researchers, for example, established a clear correlation of r = 0. 7 between the birth rate in some countries and the population of storks. The “r” stands for the Pearson correlation coefficient, which ranges between -1. 00 and +1. 00. The further away the correlation is from zero, the stronger the relationship. A minus represents a negative, and a plus a positive correlation between the variables.²⁷ The reason for this irrelevant correlation is that both variables in fact correlate with the economic development of a country. The education level of women increases with increasing economic power. This results in a decreasing birth rate. At the same time, economic power entails higher environmental pollution, which in the end has an impact on the population of storks.²⁸ This example makes it clear that managers should always question analytical results and use them wisely. In addition to their statistical capabilities, they should apply their professional expertise and market knowledge, as well as common sense. The good old gut feeling will become more and more irrelevant in decision-making processes. Correctly analyzed data provides the perfect basis for sustainable decisions. Big Data can thus help to significantly reduce the error rate of managers, to save money and to support a healthy economic development.

The final step after having analyzed the data is acting. Big Data can be used to improve current products and services. Based on the learnings from data analyses, even new products and services can be created. Feedback from online questionnaires and social media platforms or blogs can help to build a community of developers and consumers in order to co-create new things from the first idea until its final release. The danger of unprofitable investments decreases the more data is available to measure the real needs of the targeted consumer group. Big Data not only transforms the product and service offering, but also the marketing communication from standardized mass marketing, which pushes homogenous messages through the different marketing channels, to a highly personalized and thus valuable experience for the receiver. Big Data marketing negates the existence or the creation of target groups each customer is an individual target. Micro-segementation is the buzzword in modern marketing. Big Data not only changes marketing, but also the service level of a company at the different points-of-sale. Everything about the customer is available everywhere. Technologies such as iBeacon, Google glass or RFID will and already have changed the way companies are doing business.

The 5 V’s of Big Data

The characteristics of the Big Data concept include collecting, cleaning, analyzing and acting. At the beginning of the Big Data era, the three “V’s” - Volume, Velocity and Variety – were used to explain the topic.²⁹ Volume stands for the huge amount of data, which builds the basis for Big Data. Velocity stands for the requirement to process as much data as possible in nearly real-time. Variety describes the huge number of different data sources, which IT has to unite in one Big Data warehouse. But in order to describe the overall concept of Big Data with all its characteristics something is missing.

Figure 9: The Five V's of „Big Data“

The missing aspects and additional two V’s are Veracity and Value. Veracity summarizes the requirement of a database with clean data in order to get a clean result. Data quality management is a central element of a Big Data strategy and therefore an integral characteristic. The last V stands for Value. Data may be structured or unstructured. The more structured the various data sources, the better they can be directly linked together. It is clear that such a structured data set is much more valuable than unconnected and unstructured data. Not all generated data is usable. Only about 23% of the available data is usable, if the data was tagged.³⁰ Tagging means that data records are linked between several sources. Tagging enables, for example, to follow the recipient of an e-mail. If the receiver clicks a link in the newsletter, the sender can follow him on the company website and track his surfing habits. In this case, the company knows where and when the customer leaves the website and can use this knowledge to react adequately, e.g. to send automated follow-up e-mails.

Looking at the definition of Big Data, its concept and the five V’s it becomes clear that Big Data is not hype or a selling strategy of software and consulting firms to push a new topic. Big Data is indeed a revolution, which has already started and has the power to fundamentally change the way we do business.

3. 2. The ROI of Big Data

According to McKinsey, one of the leading think tanks and strategy-consulting firms, companies using Big Data are able to increase their profit margins by up to 60%.³¹ The effect on the hotel industry is even higher with a Return on Investment (ROI) of nearly 100%. But how can Big Data have such a significant impact on the profitability of an organization? What are the drivers for Big Data success?

The main goal is to optimize the profitability of a company. The following aspects are the reasons for such a bright future:

Improvement of decision-making processes

Reduction of distribution costs

Worthwhile media allocation

Increase in productivity

Decreasing administrative costs

ROI Through Improved Decision-Making

Managers can significantly reduce the risk of taking wrong decisions by using data in a smart way. Especially for strategic decision-making processes, the value of a qualitative Big Data foundation is almost priceless. Not only can managers improve existing products and services based on intelligent analysis, can also develop new offerings. The story of the hotelier who stopped a $750. 000 investment by simply questioning the result of an unconnected data analysis is a good example for the effects Big Data can have on the profitability of an organization.

ROI Through Reduction of Distribution Costs

Increasing distribution costs have a considerable negative impact on the profit margings of many B2C companies. Therefore, targeted and data-driven Customer Relationship Marketing (CRM) is required to achieve or maintain profitability. In the hospitality industry, for example, a huge number of bookings come through so-called Online Travel Agents (OTAs). Platforms such as Expedia or HRS charge commissions of up to 30% per booking. The average commission rate a hotel pays for a booking through a third-party or indirect distribution channel is about 15%.³² The share of indirect bookings varies with the type of hotel and its location. On average, 40% of the bookings are indirect bookings. If the occupancy rate does not go up despite having an OTA in the sales mix, the shift from a direct to an indirect booking will have a direct impact on the profit margins. The revenue share of OTAs has increased by 290% between 2008 and 2013. ³³ This results in total customer acquisition costs between 15% and 20%.³⁴ According to a study published in 2014 by the Hospitality Assets Managers Association the total customer acquisition costs increased by 23% and third party commissions by 34% between 2009 and 2012. ³⁵

Using Big Data cannot prevent companies from the effects of a changing distribution landscape or consumer habits, but provides valuable support in marketing and direct communication. Big Data gives a 360° view of the customer. The heart of the communication strategy is the centralized customer profile, which includes all transactions, preferences, habits, etc. With this information at hand each communication and content be personalized. Most companies still use standardized communication. This, however, no longer fulfills the needs of the customers. By using Big Data each customer gets the right information at the right time through the right channel. Continuous communication is a key variable for loyalty³⁶ and loyal customers tend to use direct channels. Therefore, Big Data marketing is so beneficial and so important for the ROI calculation.

ROI Through Worthwhile Media Allocation

Even though many experts try to convince marketing managers that post mail is an outdated channel for delivering marketing messages and that communication via electronic channels is an adequate replacement for the old-fashioned snail mail, a lot of companies still send mass mailings by post to their customers. And they are right. Each communication channel has its own characteristics and touches a different sense of the receiver in a certain way. The chance that a consumer perceives a marketing message transmitted by post mail is much higher than it is for a posting on Facebook or an e-mail.³⁷ Therefore, post mail still has its place in a modern communication mix. The problem is, however, that marketing managers tend to send too many messages. And the target groups are generally too big and too many customers receive mailings through that costly channel. Value-driven data analysis can help to reduce the size of a post-mail target group by 40-50% and generate the same results. For small companies with limited marketing budgets the optimization of the media allocation can have a positive impact on the profitability and the ROI of a Big Data intitiative.

ROI Through Enhanced Productivity

Big Data means bringing all relevant data sources together in one central data warehouse. Many processes only work with a centralized solution. This also improves the productivity of the marketing and ra. One of the key areas within Big Data is data analysis. The user has to create reports, establish correlations, or cluster target groups for marketing actions. In a heterogenous IT environment, where various systems operate side-by-side, but unconnected, it is almost impossible for the user to do the required work in an acceptable timeframe and quality. Take the sending of an e-mail newsletter, for example. Usually, the marketing department calls IT and requests data about a certain target group to be pulled from the operational system. A back and forth starts between the two departments to define the right target group. After the data has been pulled from the legacy system, IT sends an Excel list to marketing. Then a manual review process starts and other files such as e-mail subscribers are added. After that, marketing manually imports the data into the E-mail Management System (EMS) and then finally sends the newsletter. All state-of-the-art EMS providers offer beautiful charts and reports around KPI’s like Opening Rate, Bounce Rate or Click Rate, but the number, which is really interesting, namely the revenue generated, is impossible to measure. The reason that the systems are not connected. Therefore, managers are used to getting shallow reports. They still believe that marketing is unmeasureable and that it is fine to not know who booked when, what, and why. But the use of Big Data Marketing makes marketing measureable. This is a clear paradigm shift in the way we look at marketing.

ROI Through Lower Costs

Big Data has a clear positive impact on the cost structure of a company. One of the central parts of Big Data is a customer-oriented IT strategy and enviroment, where all relevant systems work together and feed the central data warehouse. This not only improves productivity and cuts the maintenance costs, but also helps to reduce software and hardware costs. A Big Data strategy requires to review each software application and to question how it supports the ultimate goal of a customer-centric IT solution. This process usually leads to the reduction of the number of software providers a company works with. This consolidation process helps to reduce the costs for software licenses and hardware, and the need for interfaces and maintainance work.

3. 3. Loyalty Programs – A Good Way to Collect Data

For some industries is quite complicated to use Big Data, as not have enough customer data is available. Most retail businesses or restaurants, for example, cater to an anonymous clientele. Even loyal customers cannot be identified. The only way to change this is to introduce a loyalty program, where customers have to register and identify themselves during the check-in or checkout process. In 2000, the total number of memberships in loyalty programs in the US was 973 million. 12 years later, in 2012, the different program providers managed over 2. 647 billion memberships.³⁸ This equals an increase of 270%!

Memberships Increased by 270%

Reward programs follow the give-and-take principle. The consumer shares his contact information and his profile is connected to the ongoing transactions. This gives companies a deep insight into the habits of the consumer, which can be used to send promotions, which are targeted and tailored to the individual needs of the customers. This can help to stimulate additional sales. It is obvious why loyalty programs and direct marketing actions are so en vogue. Mass marketing channels like TV or radio have lost their influence over time. It has become much more efficient to address the consumer directly via post mail, e-mail, or other electronic channels than to spend marketing budgets on uncontrollable and unmeasurable channels.

As described, the principle of a loyalty program is to give and take. This means that there are costs involved for the provider including the costs for running the program, i.e. maintenance and payroll, and costs for the offered rewards. Discounts usually range between 2% and 20%. This has to be taken into consideration to evaluate the profitability of a loyalty program. The number of participants or their satisfaction level should not be the key indicators to evaluate the performance. Only factors that drive the financial outcome, i.e. the change in the buying pattern, the buying frequency and the average order value, show the success of a loyalty program.

Industries, where the customer is identifiable, show an increase in memberships in loyalty programs by 73% between 2006 and 2012. ³⁹ Hospitality is one of these industries, which has access to enormous amounts of data. Hotels hold valuable customer data including basic personal information, preferred meals, interests, booking patterns, and preferences. This information has been collected throughout the entire customer journey.⁴⁰ Thus, hotels possess highly valuable transactional and behavioral data, especially as the data is structured. This means that the different sources can be connected to a guest profile, which provides a perfect basis for Big Data. Therefore, hospitality is one of the industries, which can largely benefit from the explosion of data.

3. 4. Where Data Comes from – The Customer Journey

The customer journey is the key to get a clear overview of the different data sources and the links between them. Leading OTAs like Booking.com and Expedia are good examples of how to leverage data. They all follow the “Amazon Principle”. Many years ago, the idea of Jeff Bezos, the founder of Amazon, was to recommend books to the customers based on their individual preferences, a knowledge generated from their transactions and surfing habits. The result – Amazon has become the world’s largest online retail store.⁴¹ The success of Online Travel Agents is based on the same idea. They use the available data in a smart way to stay in touch with their customers and to stimulate additional sales.

The customer journey helps to get a clear picture of what is available and where. Organizations have to understand that the customer experience begins prior to the time of sale, even prior to the search, and goes beyond the purchase and the actual travel.⁴² It also includes the reflection period.⁴³ Google, the dominant search engine, divides travel into the following five stages: dreaming, planning, booking, experiencing, and sharing.⁴⁴

Figure 10: Google’s Five Stages of Travel⁴⁵

For each of the stages Google provides tools and applications to make sure that the users do not leave the Google Empire. This approach to the customer journey makes sense and explains the expansion and diversification strategy of Google. For the hospitality industry, however, it is not precise enough. Therefore, it is more appropriate to describe the journey of hotel customers with the Nine Stages of Travel.

Before cheap electronic media channels were available, the customer journey was described in eight stages. CRM was not a fix component of the customer journey, as CRM initiatvies were mostly limited to valuable customers. Based on a value-driven data model, such as a RFM analysis, CRM activities targeted only a small part of the available customer base. It was too expensive to communicate to all customers. With the triumphant success of electronic communication, which contrary to traditional channels like post mail is almost free-of-charge, to the selection and limitation to certain customer groups is no longer required. Smart media allocation is, of course, still necessary, but the exclusion of huge parts of the customer base has become history in the era of Big Data. Therefore, the customer journey now consists of nine stages.

Figure 11: The Nine-Stages-of-Travel

The Nine Stages of Travel is a perfect model to show the different areas of hospitality where data is generated. It helps to understand the interrelations between the different stages, which customers pass during their journey.

The Nine Stages of the Customer Journey

INSPIRATION

The journey starts with the inspiration phase, in which intrinsic or extrinsic processes generate the need for a new travel experience. Inspiration can come from a movie, a newspaper article, an advertisement, or a chat with a friend. This phase is very difficult for marketers to influence. Public relations as well as on- and offline advertisements are the main drivers for inspiration. In addition, the different social media channels are part of this phase. 83% of the Facebook users enjoy browsing through the vacation pictures of friends and family members, even if they do not intend to go away. 52% say that they start dreaming about a vacation, even if there is no trip on the horizon.⁴⁶ Therefore, it is smart to encourage or even reward customers for postings on the different social media platforms. Ongoing communication is required to inspire existing customers. In times of communication overload, companies have to intensify communication with their customers in order to create remembering effects. The fight against oblivion is one of the key objectives of CRM. In addition to factors such as the company’s image, the customer’s expectations, satisfaction level and complaints, communication plays a major role in the creation of loyalty. With the help of a Big Data analysis, it was for the first time possible to evaluate the real value of communication within CRM. The result was that communication was responsible for about 10% of all recurring bookings.⁴⁷

SEARCH FOR INFORMATION

Phase 2 starts when the consumer is inspired and feels the need for a new travel experience. This phase can be described by the so-called “white car phenomenon”. If you intend to buy a new white BMW you begin to see more white BMWs on the road than before. This is the same for travel-related information. In phase 2 the consumer consciously and subconsciously searches for information which is relevant for his trip. He searches the web, is receptive to marketing messages and absorbs more information than before. Marketing messages, which had not been heard before, are suddenly absorbed and perceived. This is the reason why the right timing for communication is so important to ensure success. Hotel reservation systems store if and when a booking is made. By linking the historic booking patterns with new bookings, hotel marketing managers are able to identify when the customer is open for travel-related marketing messages. The outcome is extremely positive, if this is put into a daily-automated e-mail routine. E-mails open give access to another source of data. But data is not only generated through e-mails. Consumers leave traces everywhere they move in the digital space. The challenge is to track and link them together. Take the company website as an example. The stored log-files have a high potential. A lot of insight can be gathered by knowing who visits the website, which pages he clicks and where he leaves the page. The visited pages are a clear indicator for the consumer’s interests and preferences. The exit page helps to rate the customer’s needs and if a follow-up action is recommendable. Hotels.com track the search patterns of every visitor. If a user searches for offers in New York, but does not book on the page, a follow-up e-mail with further information and offers on New York is sent out. This is, however, only possible if the visitor has logged-in to the website and a member of the loyalty program, i.e. the consumer has identified himself. This shows how OTAs work and how important it is to convince website visitors to identify themselves. The reason why OTAs are so successful is that most hotel companies are far behind when it comes to technology and many hotel managers lack the required knowledge. In order to avoid losing more market share to indirect booking sources, hotel managers have to open up to the possibilities available and acquire the required skills.

THE BOOKING PROCESS

Phase 3 covers the booking process, starting with the request, followed by the offer and ending with the booking confirmation. This includes direct bookings through the hotel’s proprietary website as well as indirect bookings through third-party sources. Many channels make a huge effort to attract customres in order to secure a piece of the travel cake. Data is being collected along the way. The soaring distribution costs and commissions for third-party channels force hotel managers to push the direct channels. CRM and the hotel’s own website are integral parts of a direct distribution strategy. In order to attract customers is important to optimize the hotel website for search engines (SEO – Search Engine Optimization) and adword campaings (SEM – Search Engine Marketing). Main focus has to be on increasing the conversion rate. Tools like Google Analytics help managers to track these numbers. One of the key performance indicators (KPI) is “Unique Visits”. It represents the total amount of customers visiting the website. Together with the bookings generated through the integrated booking engine (IBE) a ratio can be calculated. Conversion rates ususally range between 1% and 2%. An important tool to push conversion is testimonials. Almost all hotels use widgets or sites to show guest comments, which verify the sales messages and confirm the visitor in his choice. There are many tools available, which collect guest feedback and enable to promote the results on the own website.

Figure 12: Starwood Guest Feedback⁴⁸

In order to drive conversion the guest statements should build trust and in order to build trust the visitor has to believe that the published feedback is correct and real. It is recommendable to use widgets from well-known evaluation platforms instead of unknown providers. Some managers fear that the promotion of external evaluation platforms like Tripadvisor may encourage the visitor to leave the website or book with a competitor. This is usually not the case. The use of a known widget helps to increase the conversion rate and is controllable through web analytics.⁴⁹

Once the visitor has made a booking and has turned into a customer, the standard process is that a confirmation e-mail is sent automatically by the booking system. Where room rates are quite moderate, managers tend to sell additional services in the confirmation e-mail. Even big chains started doing that. But does this make sense? In general, it is recommendable to keep the e-mail free from up-selling messages around the core product, the room. However, cross-selling of other products or services like airport transfer makes sense. Krugman’s theory of psychological reactance can be used to explain this. If the booker of a hotel room does not know the property, it is counter-productive to offer him a higher room category, as this might create a feeling of uncertainty, which again might trigger behavioral effects to regain a positive feeling towards the journey. This could even result in the cancellation of the trip and a switch to a competitor.⁵⁰ Therefore, managers should avoid pushing too hard.

PRE-TRAVEL

Phase 4 is the pre-arrangement phase in which the booker prepares himself for the trip. During this phase, he is very susceptible to marketing messages. Targeted pre-stay communication can be used to transmit information and for up- and cross selling, as the room rate is no longer in the focus. About 70% of pre-stay messages are opened and read by the receivers. The reason for such a high opening rate is that the perception level for messages from the hotel booked gets higher the closer the arrival date, as the customer is already preparing for the trip and thus more receptive to relevant messages. The hotelier should take advantage of this fact and use it as an opportunity to deliver relevant marketing messages. Customers who received a pre-stay message and know more about the products and the offerings tend to spend more than uninformed guests, i.e. the share of wallet on-site is much higher. By sending pre-stay messages, hotels can increase the potential of incremental revenue and the satisfaction level of the guests. Guest are less disappointed, if they know before that it is required to pre-book a spa treatment or a table at the restaurant. Customers who know what they want will ensure that they book prior to their arrival. In addition, a good pre-stay message including information on upcoming events, possible activities, or shopping tips, will create a pleasant feeling and anticipation for the trip. Business travelers are also receptive to the relevant information. Therefore, hotels with a clear focus on business customers should provide this service.

THE ARRIVAL

Phases 5 covers the actual stay at the hotel. Technology should provide the necessary information at the point-of-sale and the different service points to ensure that the hotel personnel recognize the guest and provide extraordinary service. Especially during the stay, guest recognition is a central service aspect and differentiating factor. The more personal information about the guest is available, the better the service. Managers should ensure that all processes to provide service excellence are in place and controlled. Some companies have started to use Google Glass, RFID key cards or similar devices to identify guests without asking for their name. It is, however, important to make sure that the customer does not feel fully transparent, even if this is the case in the Big Data era. In a perfect world, the front desk staff should be able to see the guest’s preferences, interests, value and any problems, which might have occurred during previous stays. This is, however, only possible if the hotel integrates the results of the online questionnaires and the complaint management in the PMS⁵¹, which is rarely the case.

In particular, the check-in process is undergoing many changes. More and more hotels use terminals, key cards or mobile phones. However, it is important to ensure that the target group appreciates such impersonal service and that technology does not harm the relationship between hotel and customer. At the end of the day, the staff always makes the difference. Even the smartest technology or coolest design cannot replace the human factor.

THE STAY

Phase 6 covers the actual stay. Modern technology enables to keep in touch and communicate with the guests during the stay. Messages can be transmitted at all times via the hotel App, e-mail, SMS or social media channels. Relevant content includes events, appointments, restaurant bookings, etc. It is important, however, to avoid a communication overload. Ideally, the customer is able to set his communication preferences. In addition to sales and service aspects, on-site communication also helps with the complaint management. If the guest is offered a tool to make a complaint during the stay, management can react and solve the problem immediately. This results in guest satisfaction and increased loyalty, as the guest has no reason to complain afterwards. On-site complaint management reduces the risk of negative guest feedback on social media channels and evaluation platforms. Therefore, intelligent real-time feedback is one brick in the strategy to achieve a high ranking on evaluation platforms and to improve customer acquisition. Furthermore, a real-time feedback system helps to get a real picture of the guest satisfaction, as only 1 out of 10 people make a complaint and many complaints never reach the management level. Therefore, companies should encourage their customers to give feedback.

Although QR (Quick Response) codes are still not very common in many countries, they can help to create deeper insights about the own product. The customer can scan the code with a QR code reader installed on his smart phone. After scanning the code, the web browser opens and a short questionnaire appears. After hitting the