Valuing young companies. A member-based approach

Tesis , 2015 , 144 Páginas , Calificación: Distinction (90)

Economía de las empresas - Otros

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.

List of Content

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

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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 as Table 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)

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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:

1) Common valuation does not accurately predict the value of a young company if it gains from a network effect.
2) The value of a company can be measured by its customer base.
3) Investment companies and start-ups will gain confidence and predict the value of such companies more accurately by using the recommended valuation method.

1.5.1 Research approach

Following the Platonist approach, three steps have been conducted. Important factors of a company’s wealth were observed and investigated, the purpose of which was to find methods for predicting the future development of young companies.

Accordingly, the first research question was answered by secondary research. Literature and investment reports were analysed with regard to company valuation; common valuation methods were examined to determine if they considered the network effect, and if they were suitable for the targeted companies.

Moreover, in conducting the second Platonist step, namely prediction, such frameworks were examined to see if they could predict the future sufficiently. Primary research, which took the form of interviews with experts, was done to connect theory and practice. As is shown in the literature review, a significant amount of research has been done in this field; however, the practice has not yet implemented the proposed methods. Furthermore, as Damodaran (2012) states, there are several different views of determining how to valuate a company, which have their reasoning and cannot be combined easily. This methodology was chosen due to the significant amount of secondary data available and the short time frame.

Similarly, to answer the second question, the literature review revealed potential frameworks that focus on the network effect. The point of departure in the review was the work of Kemper (2010) and Krafft et al. (2005). Their papers search for potential new methods for companies, depending on the customer base and, therefore, were the basis of this study. Interviews challenged the outcome of the literature research to give further insight in the practice of investment funds. The interview partners were chosen for their expertise in the investment and enterprise fields (Appendix D). The information was complex; therefore, an open interview which allows the informant to speak freely was seen as more valuable than closed questions. The first and second research questions were proposed to find general ideas which can be applied to a particular situation. According to Jonker and Pennink (2009), this process follows a deductive method and, as previously described, these methods follow the first two steps of the Platonist approach. Arguably, there was less secondary data available for answering the second question than for the first one; therefore, interviews were of high value. Still, a significant amount of research has been done to answer the second question. Thus the literature review was chosen to answer the second research question.

Finally, the last step of the Platonist approach is to control the findings. This has been done by conducting three case studies. One company valuation which incorporates the customer value was chosen and was tested in each case study. The investigated group was considered carefully. The network effect of customers is substantially different within companies, therefore, only similar companies were chosen. The chosen group included social-media-based recruiting companies. Within this category, there were approximately ten companies that operate globally, each of which has different geographic networks. LinkedIn is very successful in the US, whereas Xing has a tight network in Europe. Viadeo, furthermore, grows primarily through acquisitions, is headquartered in France and is strong in China. This provided further insight into how such networks can be acquired. Other similar companies are Facebook, BranchOut, Twitter, Microsoft and Monster (Prantl, 2014). The described approach follows a content-analytic research strategy (Kemper, 2010).

1.5.2 Data collection

Primary data was obtained through interviews. The sample consisted of people with a deep knowledge of the valuation of start-ups. The experts mostly worked within venture capital fund companies. Additionally, one start-up founder, who had successfully received an investment grant, was interviewed.

The recipients were chosen from a list of venture capital companies provided by the university’s career centre, and further research was made on LinkedIn. Companies that invested primarily in young companies were chosen. The study searched for a diversified group of participants, specialised in the investment field or entrepreneurs in the stage of searching for investments. Out of the given list, 50 companies and 60 persons were contacted, primarily by e-mail. Due to a very low response rate, Xing and LinkedIn were additionally used for contacting potential participants directly. Eight potential interview partners offered to participate in the research, but given the available time frame, only five were conducted. Table 2 shows an overview of the research sample. The aimed number of participants was between four and ten. Therefore, the number of conducted interviews was sufficient for the research.

Table 2: Interview participant sample

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Because the research questions were in a complex field, open questions were chosen to provide the opportunity for the participants to add additional information. Furthermore, probing questions were added if the answers were complex and potentially misleading in the data analysis (Saunders et al., 2011). The interviews lasted between 30 and 60 minutes.

The interview consisted of three parts. The first and the second parts focused on one of the three research questions. The first part was related to common valuation methods in use. The second part focused on the relationship between valuation and the customer base, and the last part was about growth and risk. The themes asked are listed in Appendix C. It was made clear that no confidential information should be given. The participants were assured that they would remain anonymous and that the information given would only be used for research purposes. The structure of the interview followed a semi-structured interview approach which is, according to Saunders et al. (2011), seen as a qualitative approach. In detail, they describe semi-structured or in-depth interviews for “circumstances where are either complex or open-ended and where the order and logic of questioning may to be varied” (p. 324) as preferable. The interviews were divided into themes to provide a clear train of thought. Given the open structure, the interviews differ, depending on the expertise of the participant and the flow of the interview (Saunders et al., 2011).

Because the participants offices were located in countries around the world, all interviews were conducted over Skype or telephone. This approach provided a confident atmosphere for the informants, as they were able to stay in their office (Saunders et al., 2011). The purpose of the research was made clear to the participants at the beginning. Ethics approval was granted by the Chair of the Humanities, Social and Health Sciences Research Ethics Panel at the University of Bradford on 14 July 2014.

Secondary data was primarily available through company reports and previous studies which were available through databases like ProQuest, Ebsco, Thomson Reuters, Orbis, Passport and WISO. Software that is available to students from Bradford University and the Vienna University of Business and Economics, including NVIVO, Thomson Reuters Eikon, R, and Excel, was used for compiling the data and developing the case studies.

In this research, ethical concerns, such as data collection, was handled with the best of efforts and potential ethical issues were addressed properly (Lancaster, 2005).

1.5.3 Case study methodology

Yin (2009) uses three settings for finding the optimal research method which is shown in Table 3. According to Yin, the first one, Form of Research Question, is the basis. The Research question allows one to distinguish between exploratory and explanatory research. “Where” and ”what” questions, for example, favour exploratory research, whereas “how” and “why” questions favour explanatory research, as these questions investigated concerns over a certain time frame. For the latter, Yin (2009) deems case study research most appropriate, because it investigates the environment of the case.

Table 3: Finding the optimal research method

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Source: Cosmos Corporation in (Yin, 2009 p. 8)

The second condition refers to the “control over behavioural events” (p. 11), as it distinguishes how much data is available. History research is preferable if there is no current evidence available. Because current and historical data were available, a case study research method was the best fit for this dissertation.

The last step “focuses on contemporary events” (p. 11), as it allows one to distinguish between historical- and a case study approach by determining if several channels of evidence are available. In contrast to a case study approach, in case of historical research for example “direct observation”(p. 11) might not be possible and informants might not be available anymore. For this dissertation, there was a wide range of available evidence; the author had access to historical valuation, historical share prices, current share prices, current micro- and macro-environment data for conducting a SWOT and PESTEL analysis, and interview partners. All of these factors favoured a case study approach.

Yin (2009), further, describes the case study approach as follows:

“A case study is an empirical inquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident.” (p. 18)

In conclusion, due to the form of the research question, which requires the control of behavioural events and focus on contemporary events, it has been found that a case study approach was best for assisting the first two questions and answering the third question.

1.5.4 Choosing between single and multiple case study approach

Yin (2009) describes the differences between single and multiple case studies. Each method has advantages and disadvantages. Multiple case studies have the advantage of proving a wider range of possible replication and are stronger against critiques. On the other hand, multiple case studies require more time. However, multiple case studies allow for the alteration of calculations to identify the importance of variables and enable the student to conduct different valuations within slightly different cases. Therefore, it has been decided to conduct a multiple case study approach in this paper. This decision was strengthened by Yin (2009), as he recommends doing multiple case studies because the single case design is weak in terms of reliability. Figure 1shows the overview of how Yin recommends conducting multiple case studies.

Figure 1: Multiple case study approach

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Source: COSMOS Corporation in (Yin, 2009, p. 57)

Given the range of available information, from qualitative information reports, interviews, literature as well as historic and current stock prices, valuations, and recommendations, it has been decided to conduct a mixed case study approach. This approach will enable the study to incorporate most of the given sources and strengthen the argumentation and conclusion by using quantitative and qualitative research methods within each case study. In detail, the given sources can be described as documentation, in which reports and investment recommendations are good examples. Archival records are historical valuations, derived primarily from Thomson Reuters Eikon. Direct observation will be done through conducting a current SWOT and PESTLE analysis using the latest information (Johnson et al., 2008). Finally, participant observation is similar to direct observation, but will give additional insights through calculating the most common valuation methods. This allows for the alteration of calculations and seeing differences in changing variables (Yin, 2009).

1.5.5 Data analysis

Given the range of data available for this paper in this part differences will be shown and the appropriate analysis will be chosen.

Figure 2: Defining the data type

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Source: (Saunders et al., 2011, p. 417)

According to Saunders et al. (2011), quantitative data is either categorical or numerical, whereby numerical data are seen as more precise and allow for more applications of statistical analyses. Categorical can be further divided into Descriptive (Dichotomous), if it can only be divided into states, or Descriptive (Nominal), if more states are possible but cannot be ranked; finally, it can be divided into Ordinary(Ranked) data, if the categorical data can be classified into more than two sets and can be ranked. In this paper, numerical, discrete data were used primarily within the case studies. Saunders et al. (2011) recommends which data presentation should be used. A line graph or bar chart is preferred for showing trends as well as numerical and discrete data. Also “to compare the trends for two or more variables so that conjunctions are clear” (p. 430), multiple line graphs are favoured. Such graphs have been developed for the DCF calculation as well as for visualising other value predictions of the companies within the case studies. Furthermore, it has been decided to prove the accuracy of the new model by calculating the Pearson correlation test, which is one of the most used test for analysing two datasets in terms of their correlation (Kleiber and Zeileis, 2008, Hinton, 2014)

2 Common valuation methods

Traditional investment valuation was created to predict the future income of investments. The methods applied in the 1970s, however, did not satisfy strategic decision making; therefore a theoretic- practice gap emerged. Black and Scholes aimed to solve this problem by introducing financial option pricing in 1973. Real option models were also developed for several occasions, but are still not very popular (Kemper, 2010, Meyer, 2006). Currently, there are different models in use, which, however, can be categorised into three different blocks. Discounted cash flow (DCF) models estimate future cash flow and discount them to a present value (PV). Relative valuation searches for similar pricing of equivalent assets. Option pricing builds on the assumption that the return of the investment is uncertain (Damodaran, 2012, Horster and Knauer, 2012). Figure 3 shows an extraction of suitable valuation models for this paper, as they are relevant for industries with an important customer base (Kemper, 2010).

Figure 3: Traditional investment valuation

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Source: Kemper (2010, p. 17)

Relating those traditional valuation methods to young companies, it is obvious that there is a lack of important information. Historic growth is missing, revenue is low and cash flows are often negative. According to Damodaran (2012), venture capitalists primarily use “judgement”, “historical experience” and “guesswork” to overcome the issues (p. 647), which indicates a potential gap for further research. The core valuation methods will be described in detail, and limitations as well as advantages and disadvantages will be highlighted.

2.1 Traditional valuation approach

This section will show fundamental considerations related to traditional valuations, as it is proposed by the literature, primarily by using Damodaran (2012) and Kemper (2010). Furthermore, advantages and disadvantages will be discussed.

2.1.1 Asset value approach

The Asset Value Approach is based on the assets a company owns. The aggregated assets of the organisation, so it is argued, make up the value of the company. According to Damodaran the easiest way to estimate aggregated assets is to calculate value by using the balance statement with some alterations. In addition, the potential sales income is called liquidation valuation, if all assets were sold minus liabilities. Finally, for the third method, replacement costs are estimated for all assets and show, in total, the value of the organisation (Kemper, 2010, Damodaran, 2012). Given the fact that seed companies have no assets this approach will not be investigated further.

2.1.2 Market value approach

Relative valuation, also known as the comparison approach, compares earnings, cash flows, book value, or revenues with similar companies and estimates that the overall value is similar to comparable companies (Oricchio, 2012, Damodaran, 2012). Analysts apply a wide range of multiples. Mostly the pricing of comparable assets is used, but relative valuation can be based on fundamentals as well. The industry price-earnings ratio, for example, is used to price the firm on the notion that, on average, firms are priced properly. Two other widely used multiples are the price-book value ratio and the average price-sales ratios. Other multiples are EV to EBITDA, EV to invested capital, and market value to replacement value. Putting out one example:

The Enterprise Value is calculated as following formula shows:

Source: (Investopedia, 2014a, Damodaran, 2012)

For those multiples, it is assumed that, on average, the market is reliable and only a few companies are under- or overvalued, but the market will adjust this (Damodaran, 2012).

Earnings Multiples is one of the most commonly used methods to valuate companies. This is due to its simplicity and wide range of use. One of its best known examples is the Price Earnings Ratio, which is calculated as follows (Damodaran, 2012):

PE= Market price per share / Earnings per share

Multiples are easy to use, if the market is reliable. In fact, such valuation is in need of a substantially lower amount of assumptions, as it is the case for the DCF approaches. Furthermore, it is easier to understand and, for that reason, it is more popular for clients. Furthermore, the multiples approach reflects the actual market situation more precisely, since the companies that are compared are valuated by the market at the time of valuation (Damodaran, 2012). According to Kemper (2010) the market value approach is the second most commonly used method directly after the DCF approach.

However, it is difficult to find companies that are exactly the same in terms of growth and risk. Therefore, those multiples are subjectively chosen and subjectively applied, which incurs errors (Damodaran, 2012). Moreover, in this method lies the assumption that the compared companies are valued correctly. For if the market is over-valuated or under-valuated this method would lead to an overestimation or underestimation of the companies’ value. Furthermore, future growth, differences in risk and potential cash flow are not considered. Damodaran (2012) identifies missing transparency as the potential problem. An analyst is free to choose the multiples and can, therefore, justify each valuation.

2.1.3 Discounted cash flow

According to Damodaran (2012), discounted cash flow is the foundation of several other valuations. The present value is derived from predicted future cash flows. The following equation builds the basis of this assumption.

n= Total periods of the asset

CF= Cash flow in period t

r= Discount rate

Furthermore, Damodaran (2012) describes two main approaches of the discounted cash flow models where the different models can be located. The first is the equity valuation which calculates only the value of the share of equity. The second approach reveals the value of the firm in total. DCF is one of the most often used models at this time and can easily estimate the value of a firm if future cash flows are positive and the risk can be evaluated (Damodaran, 2012).

However, limitations emerge if the cash flow becomes negative, as it is the case with firms in trouble, firms in the process of restructuring or young growing firms (start-ups). Moreover, assets which are not gaining cash flows, but are of significant value like patents, would not be reflected in the total value by only applying the DCF method. Hence, additional calculation is necessary and has to be added to the value which resulted from the DCF method (Damodaran, 2012, Festel et al., 2013). Furthermore, such models are in need of historical data, and need several assumptions to be conducted. As a whole, this makes such methods reliable for companies that have been on the market for several years and have positive cash flows. But, for the given aim of valuating young and seed companies, this approach does not seem to be satisfying. Though, as the DCF approach is seen as one of the most reliable method, it will be used to be compared with the new method in the case studies.

2.2 Real option pricing

“An option is defined as a financial contract that provides its owner with the right but not the obligation to exchange an asset or a financial contract against another at a given price at the expiration date.” (Kemper, 2010, p. 21). During the last few years it has been argued that option pricing models can be implemented into more traditional models. Particularly, patents and undeveloped reserves can be seen as real options and might better be valued as such (Damodaran, (2012). Figure 4 shows the value of the underlying Asset and the Net Payoff on Put Option as well as the Net Payoff on Call Option. The strike price is a synonym for the expiration date. Furthermore, it can be seen that the break-even point for the put option is before the strike price, and the break-even point for the call option is after the strike price.

Figure 4: Payoff diagram on call and put options

illustration not visible in this excerpt

Source: (Damodaran, 2012, p 23)

Damodaran (2012) argues that certain events can be understood as options for companies and such events increase the value of the valuated object. Mining natural resources, for example, can be delayed if the market price is not sufficiently high. Such managerial flexibilities can be seen as real options. Options can value financial assets such as stocks, bonds, and real assets, also called real options, which can value investments and real estate. Options share factors which are, according to Kemper (2010), related to “flexibility uncertainty and irreversibility” (p.21).

Option models , according to Damodaran (2012) are less reliable if the option exceeds a certain time frame. He points out that “estimation errors” (p. 25) occur if standardised models through finance markets cannot be acquired. In detail, issues derive by estimating the value of the asset and the variance. Moreover, the time frame for realising a certain project might be justifiable due to less competition as well as, the brand and other barriers, but the exact period of time can differ, which makes the valuation difficult. New competitors might enter the market earlier or later. Estimating the exact entry point incorporates potential errors (Damodaran, 2012).

2.3 Reconsiderations

It can be seen that there is a range of available traditional methods, where each method has its advantages and disadvantages, which have been highlighted in each section. Interim valuation is in need of a significant amount of historical data, which disqualifies it for seed companies and might make it unreliable for young companies. This conclusion has also been found in the interview analysis (Appendix D), where each participant explained that a DCF or similar method is not used for companies at that stage. Furthermore, it has been shown that relative valuation methods, according to literature, seem to be the second most often used valuation method after the DCF approach. In fact, it has been found from the interviews conducted (Appendix D) that those methods are also used for young companies. Most Interviewees use some kind of comparable valuation in addition to other methods. Thus, the literature shows that issues arise with finding the right peer group and, hence, also sees this method as unreliable for such young companies.

Research participants disagree with the findings of the literature. They see a comparable method as a valuable source for seeds and for young companies. Nonetheless, they argue that such methods are only secondary. Of more value is the product, the team, and other context related facts which are mostly judged by experience (Appendix D).

With regard to the network effect, it has been seen, in the literature review, that the network effect and the customer base are not incorporated in traditional models. The interviewees argued that such shortcomings are to overcome by incorporating the customer base, indirectly into a comparable method as a discount factor and into other methods in the predicted growth (Appendix D). This approach, however, cannot be seen as satisfying; therefore, further research in the network effect and customer base is seen as valuable.

3 Network effect models and Customer Valuation

In the previous chapter it has been found that there is a missing incorporation of the network effect and the Customer base in traditional methods. This chapter shows recent research which is aimed to overcome these shortcomings.

3.1 Network theory

As this paper is based on theories that relate to complex networks, this part will investigate more deeply those theories. Later it will describe models which are recommended by different authors for implementing the customer value within them and deriving the value of a company by summing up the customers’ value.

Kemper (2010) extracts the core elements of an customer network valuation. They are:

Degree, Indegree, Outdegree

Degree Distribution

Network Connectivity

Network Centrality and Structural Equivalence

Network Connectivity

Travesty, Clustering and Density

Assortative Mixing Patterns

Degree Correlations

Giant Component (p. 137, p. 138)

Kemper (2010), furthermore, describes the term small-world networks. Researchers have discovered that, on average, members of a social network are reachable to each other through six connections. Random networks, on the other hand, are described as having only random links. And, finally, scale-free networks are based on a mathematical algorithm called the Power Law, in which connections are random (Festel et al., 2013, Kemper, 2010). Since, the proposed method in this study, does not incorporate such deep network knowledge and this papers scope is restricted, the mentioned core elements will not be described further.

3.2 Literature review in regard to the Customer Valuation

Regarding the customer base, Oricchio (2012) outlines two different approaches for evaluating the customer portfolio. The first is related to the revenue and the second uses the profit as its basis. However, network based companies in particular do not have income during the early stage, which could be used for valuating the company.

Furthermore, within the marketing field customers are valuated with the customer lifetime valuation (Ehlen, 2012). This approach has been adjusted for being implemented within the valuation of businesses (Kemper, 2010, Bauer and Hammerschmidt, 2005). Furthermore Krafft et al. (2005) describes the customer equity approach and considers Customer Live time Valuation, but finally recommends a stochastic process, as this process allows for considering negative network effects. Kemper (2010) developed a software which should simulate the customer network and predict future trends. But, the software is only in the alpha version. It is thus complex and can only simulate small networks due to processor restrictions. This dissertation will focus on the value of the network. In particular, it argues that the member can be seen as a resource which generates cash flow and is a predictable source for valuating such companies.

3.3 Customer Valuation

Traditionally derived from the marketing perspective, Customer Lifetime Value was calculated as the sum of purchases a customer makes within his lifetime. It did not investigate the connection between customers and it did not value the fact that as the number of customers increases the product value also increases—as it is the case with the companies investigated in this paper (Kemper, 2010, Gneiser et al., 2012). Krafft (2005), reviews previous customer valuation within his article. He argues that the migration model developed by Jackson (1985) does not consider “cross selling and referrals” (p. 105). Moreover, Krafft et al. (2005) argues that the CLV approach does not consider external effects, and most previous methods suffer from limitations. He recommends a stochastic process which would consider positive and negative customer effects. Kemper (2010) identifies a customized DCF approach and a real option-founded methodology as considerable models for the software market. Three examples that consider the customer base will be described in detail.

3.3.1 DCF Customer Equity Model

While the classical DCF Model discounts future cash flow, the customer equity model uses the potential value of each future customer and discounts this to a present value. The following formula represents the lifetime value of each customer (Kemper, 2010, Bauer and Hammerschmidt, 2005) :

R= Revenue, C= Costs, CLV= Customer Lifetime Value, d= Discount factor

Adding customer acquisition costs and diversifying cost and revenue gives following formula (Kemper, 2010):

CLV= present lifetime profit, AC= Acquisition costs, r= Retention rate, AR= Autonomous revenues, UR= Upselling revenues, CR= Cross-selling revenues, RV= Reference value, MC= Marketing costs, SC= Sales costs, TC= Termination costs, d= discount rate, T= projection period.

As following formula describes, the total customer-based equity is calculated by discounting the sum of customers (Kemper, 2010).

CE= Customer Equity, s= customer cohort index, T= periods, d= opportunity costs of capital, k= size of cohort, r = retention rate, R= revenue per customer, C= costs This foundation can be used for calculating the Customer-Based Corporate Value as follows (Kemper, 2010, Bauer and Hammerschmidt, 2005):

CV= Corporate Value, CE= Customer Equity, t = time index, d cost of capital, FC fixed costs, InvWC= net investment in working capital, InvFC net investments in fixed capital, Tax= Tax, CV = terminal value, NA= non-operating assets, D= market value of debt Because it is similar to a traditional DCF approach, this model has comparable limitations. The limitations derive from the future prediction problem and that not enough historical data is available. Moreover, estimating a potential Customer Lifetime Value incorporates several assumptions which might be subjective and incorporate errors. Informant 1, for example, described the attempt to use the customer lifetime valuation of twitter for calculations of a seed company as “foolish,” since twitter gains significantly from the economies of scale effect, and the chances that a seed company reaches such efficiency are very low.

3.3.2 Real Option Customer Equity Model

The Real Option model, which will be used herewith to value the customer base, has the advantage that it considers positive and negative effects. The customer base is hereby described as an “inverse mean” ( Kemper, 2010 p. 91).

Within the customer equity model the cash flow of one customer and the growth rate can be calculated as follows(Kemper, 2010):

= total cash flows, = cash flow of one customer, g= growth rate

As following formula illustrates, combining the total cash flows with the total cash flow gives the expected customer base value (Kemper, 2010).

= value of all customers in state z, =total cash flows

This model builds on the Binominal Scenario Tree developed by Krafft et al. (2005). It gives valuable insights into a potential new method of company valuation. But Kemper (2010), does not provide further research; therefore, new research is needed to confirm the accuracy of the model, as well as the sensitivity of variables which makes an objective selection of variables difficult. Still, given the potential of the model, it has been decided that this research combined with the Binominal tree scenario, developed by Krafft et al. (2005) will be used within the case study analysis.

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

Krafft et al. (2005) built his model on the basis of a momentum process. is the starting point. is described as critical mass, where the number of customers either decreases or increases. If is below the number of customer will increases exponentially in the first three periods and remain on a constant level after the fast growth. If is under the number of customers would plummet and remain on a very low level. Based on a momentum process dz is used as the Wiener process where > 0 is the momentum speed (Krafft et al., 2005).

The formula considers that the critical number of customers minus the current customers at a certain time K(t) this result is multiplied by the speed factor . Furthermore the second part of the formula implements the stochastic variable dz(t) and the volatility which represents the potential fluctuation of customers. The speed given as considers, additionally, that a momentum is given (Krafft et al., 2005). These considerations are implemented within a binominal tree model. Krafft (2005)is describing his assumptions by using an easy example, where and . The model based on:

jump: k=80 /-80; speed factor:

where k represents the jump with the given fluctuation of the customers and j represents a positive or negative change. Figure 5 visualises these assumptions and organizes them into four periods (Krafft et al., 2005).

Figure 5: Binominal tree model

illustration not visible in this excerpt

Source: (Krafft et al., 2005 p. 111)

The example shows the upwards and downwards trend, it shows, that depending on the previous period, the probability increases for each direction, which represents the assumption that the speed factor in each direction increases. To clarify this, looking at period 2, it can be seen that for stage two the probability of a jump of plus 80 is 90 %, whereas in the period 0 it was only fifty percentages. The underlying formula for those probabilities is:

= Probability for the jump, a= speed factor, k= jump,= critical mass = customer in period 0, j= stage

Krafft (2005) argues, furthermore, that the growth rate can be similarly used for the cash flow decreasing rate or increasing rate. The increasing cash flow depends on the speed at which the number of customers increase and vice versa. Implementing this assumption in the same model and assuming one customer generates 1 as cash flow in the first phase gives following Figure 6:

Figure 6: Cash flows generated by all customers

illustration not visible in this excerpt

Source: (Krafft et al., 2005 p. 116)

Developing those assumptions further, the cash flow is assumed to increase with the growth rate g (Kemper, 2010):

= cash flows generated by customer, = cash flow by initial customer, j= state

Furthermore, the present value of the member base can be calculated with summing up the predicted member base multiplied with the expected cash flow.

Krafft et al. (2005) highlights that this approach overcomes some limitations of traditional valuating methods. He justifies this idea with a better relationship between the fast growth of young companies and the critical mass consideration. However, he admits that empirical evidence is necessary to prove accuracy and practicability and sees both in-depth case studies and quantitative approaches as suitable (Krafft et al., 2005).

3.4 Reconsideration

This section has shown that in terms of incorporating the customer base and the network effect, several new models were derived. Furthermore, it can be seen that some of them are promising; therefore, it has been decided to incorporate the most promising, which is the Krafft et. al (2005) model within the case study. Accordingly, the first case study will incorporate an altered version of the Krafft et al. (2005) model. If successful the results will be challenged by the second case study and if not successful another model which incorporates the DCF version, which uses the Customer Lifetime Valuation as basis will be used. However, research could not prove that these methods are more reliable than traditional methods. To clarify this, further investigation is needed. As previously mentioned, the Interview participants use different ways to overcome the disadvantages of traditional methods and are not in favour of some kind of new customer-based method. None of the participants uses such methods within their valuations. Nonetheless, when asked specifically about Customer Valuation, some did answer that Customer Lifetime Valuation can be helpful in terms of foreseeing a potential value of a product (Appendix D). Therefore, it is seen that, these methods might give further insights into the valuation of companies and assist in more accurately predicting the value of a young company, despite the fact that these methods have not yet been incorporated in the praxis.

4 Case studies

Case studies compare traditional valuation methods with a newly-developed method. To use the newly-developed method at the beginning of the case studies, there is a qualitative investigation of the market and the network for those three companies which are to be used in each case study. The traditional method that is used is the DCF, a typical flow-based method commonly used in investment branches (Damodaran, 2012). In his valuation framework, Kemper (2010) describes the following steps which he recommends for valuating a company.

Figure 7: Network effect valuation framework for Software markets

illustration not visible in this excerpt

Source: (Kemper, 2010 p. 116)

The first part investigates the business model of the targeted company. The second one analyses the environment of the industry. Kemper recommends using a PESTLE five forces analysis. The next step considers the value drivers of the company. Kemper (2010) distinguishes between the phases of the life-cycle approach as the innovation phase, the expansion phase, and the maturity phase. Furthermore, within this part of the analysis he recommends investigating the price model, the costs, and the sales figures. This level is obviously similar to an internal analysis. The outcome of the internal and external analysis will, according to Kemper (2010), become the foundation of the software market model, which is divided into scale and scope, as well as the implementation of the model. In detail, Kemper (2010) uses a customised real option model¹. The last part of the framework serves to identify its stability by challenging the variables used (Kemper, 2010). This approach has been altered for the following case studies as instead of the software market model the customer base will be investigated and the market model will be part of the internal and external analysis. Furthermore for analysing the accuracy of the new method traditional methods have been conducted and instead of the sensitivity analysis a comparable analysis, more precisely the Pearson correlation test, has been conducted for the first two case studies, namely for LinkedIn and Xing.

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

In developing a customer based model, Krafft et al. (2005) explain that internet-based companies in particular experience an exponential growth of customers in their early stage. Furthermore, on a certain level a “critical mass” will be reached where the customer growth rate slows down. On the other side, if the venture was not established successfully, a decreasing number of customers will follow an exponential curve (Krafft et al., 2005). Kemper describes the business model of social network services as textbook examples of seeing the economies of scale effect on the customer base.

By examining the key elements of the Xing AG network, Kemper (2010) concludes that the Xing AG network is best described as a small world network. He points out that more than one-sixth of the members had more than 150 contacts in 2007, and the number of edges is small. Moreover, it is argued that the high clustering coefficient, the low number of connections between random members support his hypotheses (Kemper, 2010). Figure 8 represents a typical small world network

Figure 8: Small World Network

illustration not visible in this excerpt

Source: (Siegel, 2013 p. 791)

[...]

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¹ For further information regarding a real option model please see chapter 2

• Universidad: University of Bradford (School of Management)
• Curso: Applied Management and Enterprise
• Calificación: Distinction (90)
• Autor: Bernhard Prantl (Autor)
• Año de publicación: 2015
• Páginas: 144
• No. de catálogo: V292852
• ISBN (Ebook): 9783656900177
• ISBN (Libro): 9783656900184
• Idioma: Inglés
• Etiqueta: Xing, LikedIn, Viadeo, valuation, investment, seed company, young company, start-up, customer valuation