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.
Table of Contents
1 Introduction
1.2 Course of analysis
1.3 Research motivation
1.4 Research aims and objective
1.5 Possible application
1.6 Methodology
1.6.1 Research approach
1.6.2 Data collection
1.6.3 Case study methodology
1.6.4 Choosing between single and multiple case study approach
1.6.5 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
Objectives and Research Themes
This dissertation aims to improve the valuation process for young, high-growth companies that benefit significantly from network effects. By analyzing the relationship between an organization's customer base and its market value, the study seeks to develop a more accurate predictive model for investment purposes, thereby reducing investment risk and fostering innovation.
- Evaluation of traditional company valuation methods and their limitations for start-ups.
- Investigation of network effects and their impact on customer-based valuation.
- Application of the Krafft et al. (2005) model through comparative case studies.
- Analysis of LinkedIn, Xing, and Viadeo to test member-based valuation accuracy.
- Comparison of quantitative models with historical market performance.
Excerpt from the Book
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).
Summary of Chapters
1 Introduction: This chapter identifies the research gap regarding the valuation of young companies and establishes the motivation, objectives, and methodological approach of the study.
2 Common valuation methods: This section reviews traditional investment valuation techniques, highlighting their limitations for early-stage companies, and discusses the theory-practice gap.
3 Network effect models and Customer Valuation: This chapter explores network theory and examines specific customer-based valuation models, including the DCF Customer Equity and the Krafft et al. (2005) model.
4 Case studies: This chapter applies the proposed member-based valuation method to LinkedIn, Xing, and Viadeo, comparing the results against traditional metrics and historical stock performance.
5 Findings: This chapter synthesizes the research results, confirming that traditional methods are insufficient for young, network-dependent companies and demonstrating the higher accuracy of the member-based approach.
6 Conclusions: The final chapter summarizes the research, acknowledges limitations, and recommends future work in the field of member-based company valuation.
Keywords
Xing, LinkedIn, Viadeo, valuation, investment, seed company, young company, start-up, customer valuation, member prediction, network effect
Frequently Asked Questions
What is the core focus of this research?
The research focuses on improving the valuation of young, high-growth companies, particularly those in the social media and software sector, by integrating the network effect and customer base into the valuation process.
Which sectors are analyzed in this dissertation?
The dissertation primarily examines social media-based recruiting companies, using LinkedIn, Xing, and Viadeo as primary case studies.
What is the main objective of the study?
The objective is to find a more reliable valuation method for young companies that gain significant value from their customer network, which current traditional methods fail to capture accurately.
Which research methods are employed?
The study uses a mixed-method approach, combining a literature review, expert interviews, and three detailed case studies utilizing quantitative and qualitative data.
What does the main body of the work cover?
The main body covers a critique of traditional valuation methods, an exploration of network theories, and the application of the Krafft et al. (2005) model to evaluate how member growth correlates with company value.
What are the primary keywords characterizing this work?
Key terms include valuation, start-up, network effect, customer lifetime value, and the specific companies LinkedIn, Xing, and Viadeo.
How does the Krafft et al. (2005) model differ from traditional methods?
Unlike traditional methods that rely heavily on historical revenue or cash flow data—which are often lacking in start-ups—this model focuses on customer growth and network effects as a basis for valuation.
What were the major conclusions regarding the accuracy of the proposed model?
The case studies indicated that the member-based valuation model showed a higher correlation to historical market prices than traditional DCF models, suggesting it is a more accurate tool for this specific category of companies.
- Citation du texte
- Bernhard Prantl (Auteur), 2015, Valuing young companies. A member-based approach, Munich, GRIN Verlag, https://www.grin.com/document/292852
