Valuing young companies. A member-based approach E-Book

Bernhard Prantl

• Herausgeber: GRIN Verlag
• Sprache: Englisch

Veröffentlichungsjahr: 2015

Beschreibung

Diploma Thesis from the year 2015 in the subject Business economics - Miscellaneous, grade: Distinction (90), University of Bradford (School of Management), course: Applied Management and Enterprise, language: English, abstract: Purpose: This study sought to enhance the process of valuing young companies with a high potential for growth, by considering the link between the member base and the market value of the company. Outcomes were supposed to be an increase in predictive potential concerning young companies and their value as investments. A potential integration of more accurate methods would lead to a significant rise in profits for investment companies. Moreover, the resulting increase in trust in risky projects through better understanding of their value would also increase the number of new innovations. Hence, more funding would be available due to decreasing investment risk. Methodology: Following the Platonist philosophy proposed by Lomas (2011), the study incorporated three steps. First, an intensive investigation revealed factors which have an impact on the value of companies, and evaluated traditional approaches. The second step was to predict the potential of the new methods based on the member base of the organisation. Finally, the last step was deployed in a mixed case study approach following the recommendations of Yin (2009), where these predictions were challenged. In particular, LinkedIn, Xing and Viadeo were chosen to challenge the proposed method based on the research of Krafft et al. (2005) and Kemper (2010). Findings: The literature review was able to reveal several gaps in traditional methods, particularly when it comes to valuing young companies. Additionally, primary research – more precisely, qualitative interviews – revealed that traditional calculations are, at best, used as secondary sources, when it comes to the value of a young company. Accuracy was revealed by the interviews to be acceptable given the high potential for profit. But, considering the low success rate of 30% to 50%, a high potential for more accurate prediction was revealed. The model was successfully deployed in the case studies, where qualitative and quantitative data was used to determine the value of each company under consideration for several different time periods. The direct comparison of traditional valuation methods with the new proposed method revealed the high potential of the member-based method. It has been established that the new model can considerably increase the accuracy of the valuation and assist in predicting member base growth.

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ABSTRACT

Purpose: This study sought to enhance the process of valuing young companies with a high potential for growth, by considering the link between the member base and the market value of the company. Outcomes were supposed to be an increase in predictive potential concerning young companies and their value as investments. A potential integration of more accurate methods would lead to a significant rise in profits for investment companies. Moreover, the resulting increase in trust in risky projects through better understanding of their value would also increase the number of new innovations. Hence, more funding would be available due to decreasing investment risk.

Methodology: Following the Platonist philosophy proposed by Lomas (2011), the study incorporated three steps. First, an intensive investigation revealed factors which have an impact on the value of companies, and evaluated traditional approaches. The second step was to predict the potential of the new methods based on the member base of the organisation. Finally, the last step was deployed in a mixed case study approach following the recommendations of Yin (2009), where these predictions were challenged. In particular, LinkedIn, Xing and Viadeo were chosen to challenge the proposed method based on the research of Krafft et al. (2005) and Kemper (2010).

Findings: The literature review was able to reveal several gaps in traditional methods, particularly when it comes to valuing young companies. Additionally, primary research – more precisely, qualitative interviews – revealed that traditional calculations are, at best, used as secondary sources, when it comes to the value of a young company. Accuracy was revealed by the interviews to be acceptable given the high potential for profit. But, considering the low success rate of 30% to 50%, a high potential for more accurate prediction was revealed. The model was successfully deployed in the case studies, where qualitative and quantitative data was used to determine the value of each company under consideration for several different time periods. The direct comparison of traditional valuation methods with the new proposed method revealed the high potential of the member-based method. It has been established that the new model can considerably increase the accuracy of the valuation and assist in predicting member base growth.

Keywords/Phrases:

Xing, LikedIn, Viadeo, valuation, investment, seed company, young company, start-up, customer valuation, member prediction, network effect

VALUATING YOUNG COMPANIES, A MEMBER-BASE APPROACH

by

Bernhard Reinhold Prantl

2014

ACKNOWLEDGEMENT

This project would not have been written without the support of a few people along the way. Therefore, I would like to express my profound gratitude to the following people:

My supervisor, Mr Andrew Coutts, for his guidance and monitoring throughout this study. He was able to motivate me to dig deeper into the subject matter and helped me overcome challenges in critical parts of the study.

My family, particularly my parents, my sister and my grandma, for their support and patience. Without their support, studying abroad would not have been possible.

The participants of the interviews, who are in top positions in a very stressful environment, but still found the time to participate. This gave me valuable insights into the praxis of investment in young and seed companies and helped significantly in conducting this study.

List ofContent

List of figures

List of tables

List of abbreviations

1 Introduction

1.1 Course of analysis

1.2 Research motivation

1.3 Research aims and objective

1.4 Possible application

1.5 Methodology

1.5.1 Research approach

1.5.2 Data collection

1.5.3 Case study methodology

1.5.4 Choosing between single and multiple case study approach

1.5.5 Data analysis

2 Common valuation methods

2.1 Traditional valuation approach

2.1.1 Asset value approach

2.1.2 Market value approach

2.1.3 Discounted cash flow

2.2 Real option pricing

2.3 Reconsiderations

3 Network effect models and Customer Valuation

3.1 Network theory

3.2 Literature review in regard to the Customer Valuation

3.3 Customer Valuation

3.3.1 DCF Customer Equity Model

3.3.2 Real Option Customer Equity Model

3.3.3 Binominal scenario tree technique developed by Krafft et al. (2005)

3.4 Reconsideration

4 Case studies

4.1 Investigation into the network effect of social media-based recruiting companies

4.2 LinkedIn case study

4.2.1 LinkedIn – introduction and core products

4.2.2 LinkedIn – PESTLE analysis

4.2.3 LinkedIn - SWOT analysis

4.2.4 LinkedIn – financial statement

4.2.5 LinkedIn – historical stock price

4.2.6 LinkedIn – traditional valuation

4.2.7 LinkedIn – intrinsic valuation

4.2.8 LinkedIn – relative valuation

4.2.9 LinkedIn – investigation into the customer base

4.2.10 Members, page visits and activity

4.2.11 Network effect (Small World)

4.2.12 Network geographically and further considerations

4.2.13 The new model Krafft et al. (2005) - member valuation approach

4.2.14 Comparison of the different methods

4.2.15 Outcome limitations and reconsiderations

4.3 Xing case study

4.3.1 Xing – introduction and core products

4.3.2 Xing – PESTLE analysis

4.3.3 Xing - SWOT analysis

4.3.4 Xing – financial statement

4.3.5 Xing – historical stock price

4.3.6 Xing – traditional valuation

4.3.7 Xing – intrinsic valuation

4.3.8 Xing – relative valuation

4.3.9 Xing – investigation into the customer base

4.3.10 Members, page visits and activity

4.3.11 Network effect (Small World)

4.3.12 The Krafft et al. (2005) model

4.3.13 Comparison of the different methods

4.3.14 Outcome limitations and reconsiderations

4.4 Viadeo case study

4.4.1 Viadeo – introduction and core products

4.4.2 Viadeo – PESTLE analysis

4.4.3 Viadeo – SWOT analysis

4.4.4 Viadeo – traditional valuation

4.4.5 Viadeo – multiple valuation

4.4.6 Viadeo – investigation into the customer base

4.4.7 Members, page visits and activity

4.4.8 The Krafft et al. (2005) model

4.4.9 Comparison of the different methods

4.4.10 Sensitivity analysis

4.4.11 Outcome limitations and reconsiderations

4.5 Cross-case study reconsiderations

5 Findings

6 Conclusions

Appendix A Research Proposal

Appendix B First contact e-mail

Appendix C Interview protocol

Appendix D Interview analysis

Appendix E DCF calculation

Appendix F Krafft et al. (2005) valuation

Appendix G Statistical analysis

Appendix H Glossary

9 References

List of figures

Figure 1: Multiple case study approach

Figure 2: Defining the data type

Figure 3: Traditional investment valuation

Figure 4: Payoff diagram on call and put options

Figure 5: Binominal tree model

Figure 6: Cash flows generated by all customers

Figure 7: Network effect valuation framework for Software markets

Figure 8: Small World Network

Figure 9: Income stream in million dollars

Figure 10: Historical share price of LinkedIn in dollars

Figure 11: DCF calculation compared with historical enterprise valuation

Figure 12: Comparison between value of equity per share (DCF) and historical stock price of LinkedIn

Figure 13: Starmine relative valuation model

Figure 14: LinkedIn historical valuation P/Book

Figure 15: LinkedIn member and unique visitor growth in million

Figure 16: LinkedIn registered members globally

Figure 17: Expected customer base calculation Krafft et al (2005) first quarter 2011 (year 1)

Figure 18: Comparison of member- based approach and historical market price

Figure 19: DCF and Kraft et al. (2005) comparison with historical market capitalisation

Figure 20: DCF and historical market capitalisation comparison

Figure 21: Starmine relative valuation model

Figure 22: Xing AG - Historical valuation

Figure 23: Xing member growth

Figure 24: Number of Xing members by region

Figure 25: Predicted member base plus company valuation for quarter 1 2014

Figure 26: Predicted member base plus company valuation for quarter 1 2014 RoW

Figure 27: DCF and Krafft et al. (2005) comparison

Figure 28: Legal structure of Viadeo

Figure 29: Evolution of the number of registered members worldwide

Figure 30: Member growth in thousands

Figure 31: Unique visitors

Figure 32: Predicting company value for France in March 2014

Figure 33: Predicted company value for China in March 2014

Figure 34: Predicted company value for RoW in March 2014

Figure 35: Sensitivity visualisation in thousands

Figure 36: Traditional Investment Valuation

Figure 37: Interview word frequency, top 50

Figure 38: Interview Nodes summary

Figure 39: Volatility calculation

Figure 40: Probability of alpha distribution and alpha result

Figure 41: Kraft et al. (2005) member growth calculation

Figure 42: Kraft et al. (2005) member prediction result for y1

Figure 43: LinkedIn Kraft et al. company valuation example

List of tables

Table 1: Course of analysis

Table 2: Interview participant sample

Table 3: Finding the optimal research method

Table 4 : Key personal of LinkedIn

Table 5: PESTLE analysis for LinkedIn

Table 6: SWOT analysis for LinkedIn

Table 7: LinkedIn Key financial figures

Table 8: Discounted cash flow calculations LinkedIn

Table 9: Key ratios of LinkedIn

Table 10: Relative valuation of LinkedIn with peer group

Table 11: Direct comparison of LinkedIn and Xing

Table 12: Company valuation results compared with historical market capitalisation

Table 13: Xing income by segment in euros

Table 14: Xing key employees

Table 15: PESTLE analasys

Table 16: SWOT analysis

Table 17: Xing key financial results

Table 18: DCF results from 2006 to 2013

Table 19: Xing - Key metrics

Table 20: Peer group comparison

Table 21: Direct comparison with LinkedIn

Table 22: Key financial factors of Viadeo and Tianji

Table 23: Acquisitions of Viadeo from 2007 to 2013

Table 24: Key personal

Table 25: Viadeo - PESTLE analysis

Table 26: Multiple calculations

Table 27: LinkedIn DCV calculation for Quarter 01 in 2014

Table 28: LinkedIn DCF results for each quarter

Table 29: Xing DCF calculation example

Table 30: Xing DCF results for each quarter

Table 31: Kraft et al. (2005) and DCF inputs for R

List of abbreviations

1 Introduction

Facebook’s recent acquisition of WhatsApp valuates it at $19 billion. By means, one customer is worth $50 (Rushe, 2014, Kemper, 2010). Common valuation techniques, as for example the Shareholder Value Added calculation, however, valuate WhatsApp substantially lower. The same is true for other valuation methods. Facebook was valuated at $38 per share at its IPO in May 2012. But, the shares plummeted shortly afterwards. In fact, three months later the share price was down by nearly 50% (Yahoo, 2014). These are just two recent examples of questionable valuations on the market.

According to Damodaran (2012), it is not evident if the market is efficient and companies are valuated correctly. He points out that some patterns can be found in stock prices and price-to book and price-to-earnings ratios seem to be long-run indicators. Damodaran’s research found that investors could not gain from these findings. He justifies this with transaction costs and issues with executing theory in praxis and the characteristic of studies analysing the long term. He argues that investments that are short term bear higher uncertainties due to fluctuations. Furthermore, it is argued that investment managers seem to change their strategy, which lowers the chance of harvesting a return in the long run (Damodaran, 2012). Quoting Warren Buffet: “It’s extremely difficult to value social- networking-site companies” (Thakur, 2014). This and several other cases show that a common valuation of enterprises needs to be adopted for companies with substantial gains from the network effect of customers. The Network effect can be described as the value added for one customer by the increasing number of users (Liebowitz and Margolis, 1994).

1.1 Course of analysis

This paper is constructed asTable 1 describes. The Introduction comprises the identification of the research gap and the motivation for studying in this field. Then, the aims and the objective will be described and the beneficiaries of the research will be detailed. After, the methodology will be explained in detail. The Introduction finishes with the course of analysis where each part of the paper will be mentioned briefly. Chapter Two focuses on traditional methods of young companies and highlights their advantages and disadvantages. Chapter Three investigates the network effect of customers and illustrates three potential valuation methods. Chapter Four focuses on the specifics of the network of social media recruitment companies, and three companies will illustrate the efficiency of the chosen method in comparison to traditional valuation methods. Chapter Five summarizes the findings and compares the differences between the case studies. Finally, Chapter Six concludes the research and gives an outlook for further research.

Table 1: Course of analysis

Adopted by author from Kemper (2010, p. 11)

1.2 Research motivation

Given the huge amounts of money lost because of wrong calculations and forfeiting, which the study aims to counteract, valuating young companies more precisely, will have a number of benefits. These include increasing potential investments in innovations that increase long-term economic growth. The study will furthermore add to existing research on this subject, particularly with the emerging links between the user base and the value of the company. Furthermore, the study will serve as a foundation for further empirical investigations, as the Krafft et al. (2005) model will be developed to an extent where additional quantitative research will be possible.

1.3 Research aims and objective

The objective of this study is to find a suitable valuation method for a certain group of young companies that gain significantly from the network effect and show the potential of the method, by developing three case studies, which will use quantitative and qualitative data to prepare the Krafft et al. (2005) model and deploy an alternative valuation for those companies. The resulting method will serve as a potential foundation for negotiations within investment companies and start-up companies. According to Lomas (2011), the investigated problems are related to prediction. The investor has to foresee future returns on investments and planning and, therefore, an understanding of the network effect will help in predicting future trends.

1.4 Possible application

This study seeks to find a more reliable valuation method for a certain group of young companies. In particular, the focus is on young companies operating in software markets with high growth, where the customer base plays an important role. Moreover, as the study is part of the MSc Applied Management Programme, it will focus on the applied aspect of valuation and follow the corporate perspective of an investor, with a particular emphasis on the network effect on customers. The research is primarily targeted towards investors, but can assist start-ups, by valuing their businesses, and researchers, for further development of customer-based valuations.

1.5 Methodology

Lomas (2011) describes two different views of reality based on their inventors. The Platonist philosophy is built on the assumption that research is determined to find what already exists. Lomas (2011), explains this view using the example of the Manhattan Project during World War II. This project was initiated to find a powerful weapon, known today as the atomic bomb. The research approach lead by Dr. J. Robert Oppenheimer was based on the Platonist philosophy, as it was an attempt to find what scientists, namely, Albert Einstein and Leo Szilard, believed existed and had yet to be discovered (Lomas, 2011).

Aristotelians, on the other hand, believe that everything can be invented. Aristotle believed that “what makes things as they are” (Moss, 1987, p. 71) can be investigated using four causes known as Organon. The first one, the “Material cause,” describes the purpose of the invention by its material. The “Formal cause” forms the object by using patterns or forms. The third cause describes the process of change. And the final cause answers “Why it is made” (Lomas, 2011, p. 9). These four causes are made more visible with an example. Related to a Greek God statue: The material cause refers to the material of which the statue is made, in this case stone or marble. The formal cause refers to the blueprint which the artist is following. The Efficient cause is explained as the artist who pledges the statue. His capabilities influence the shape of the statue. And, the final cause is seen as the reason why the statue is made, the final purpose of the statue. The final cause is, for example, as being presented in an atrium (Lomas, 2011).

This dissertation follows the Platonist philosophy, as it is believed that the connection between the customer base and the success of a company already exists and only has to be discovered.

Lomas (2011) further distinguishes between four groups of problems: Problems of observation, prediction, planning and business theory. The first range of these problems refers to an unknown area, where the purpose of research is to discover the functionality of certain circumstances. Problems of prediction focuses on the research that seeks to find ways to forecast future achievements for making right decisions. One related question might be, for example: Will this company succeed in this market? Problems of Planning, furthermore, are related to questions which investigate into the increase of efficiency of changes. And finally, problems of business theory examines how theory can be applied, and how precisely theory can be seen in reality (Lomas, 2011).

The research questions are:

1) Do common valuation methods accurately predict the value of a young company with substantial gains from a network effect?

2) Can the relationship between customers and the customer base assist the accuracy of the value predictions?

3) How can these findings be used for creating a better method of valuating such companies?

Hypotheses are: