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PREMISES OF PLATFORMS FAILURE IN MULTI-SIDED MARKETS
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INTRODUCTION 5
CHAPTER 1. Platform overview and specifics 7
1.1. Platform definition and typology 7
1.2. Network effects and multi-sided markets 14
1.3. Comparison between platforms and conventional value chains 17
Chapter 1 findings 20
CHAPTER 2. Platform valuation 21
2.1. Platform strategies 21
2.2. Assessment of platforms potential 23
2.3. Issues of platform valuation 24
2.4. Reasons for platforms failure 31
Chapter 2 findings 37
CHAPTER 3. Platforms failure premises determination 38
3.1. Methodology and data samples 38
3.2. Key concepts and metrics 44
3.3. Model and outcomes of quantitative analysis 46
3.4. Analysis of results 50
Chapter 3 findings 54
CONCLUSION 55
REFERENCE LIST 57
APPENDIX 62
CHAPTER 1. Platform overview and specifics 7
1.1. Platform definition and typology 7
1.2. Network effects and multi-sided markets 14
1.3. Comparison between platforms and conventional value chains 17
Chapter 1 findings 20
CHAPTER 2. Platform valuation 21
2.1. Platform strategies 21
2.2. Assessment of platforms potential 23
2.3. Issues of platform valuation 24
2.4. Reasons for platforms failure 31
Chapter 2 findings 37
CHAPTER 3. Platforms failure premises determination 38
3.1. Methodology and data samples 38
3.2. Key concepts and metrics 44
3.3. Model and outcomes of quantitative analysis 46
3.4. Analysis of results 50
Chapter 3 findings 54
CONCLUSION 55
REFERENCE LIST 57
APPENDIX 62
The world’s most valuable public companies and its first trillion-dollar businesses are built on digital platforms that bring together two or more market actors and grow through network effects. The top-ranked companies by market capitalization are Apple, Microsoft, Alphabet (Google’s parent company), and Amazon. Facebook, Alibaba, and Tencent are not far behind. As of January 2020, these seven companies represented more than $6.3 trillion in market value, and all of them are platform businesses.
Platforms are also remarkably popular among entrepreneurs and investors in private ventures. When a 2017 list of more than 200 unicorns was examined (startups with valuations of $1 billion or more), estimation showed that 60% to 70% were platform businesses. At the time, these included companies such as Ant Financial (an affiliate of Alibaba), Uber, Didi Chuxing, Xiaomi, and Airbnb.
The thorough research of the studies on platforms and their valuation has shown that the majority of the researchers focus on factors that guarantee platforms’ efficiency and the reasons to stick to this business model. However, the previous research on the evaluation of platforms is limited and there is no discussion of any hints or premises of platforms failures. The majority of such papers outline typical managerial mistakes that lead to failures of the platforms. In regard of this research gap, this paper is aiming to study the factors that can be used in platform evaluation and, thus, can reveal premises of default.
The problem is that platforms fail at an alarming rate. It seems vital for managers and investors to identify the sources of companies’ failures and, therefore, avoid the obvious mistakes. Traditional way to assess company’s value as of an investment target - are financial ratios derived from its financial statements, rating agencies reports and accounting information. Thus, promising, growing and successful companies differ from those that have high chances to default or to lose their share of the market by combination of financial ratios and metrics.
Platforms significantly differ from other types of organization: the platform business model encompasses specific features of financials, operations, contracts, communication, stakeholders, and thus, specific risks and opportunities for the business.
This paper’s aim and main goal is to determine factors that predict the failures of platform organizations.
Objectives:
1) to study and discuss the features of platform organizations that distinguish them from conventional firms
2) to study specifics of multi-sided market;
3) to discover and discuss the valuation issues concerning platforms;
4) to analyze reasons of companies’ failure that have been discussed in previous researches;
5) to define factors that can be premises of platforms failure;
6) to develop and test a predicative model with some of those metrics embedded as factors of failure prediction for platform organizations.
Subject of the research - premises of companies’ failures. Object of the research - platforms organizations that operate in multi-sided markets
The main assumption of this work is that organizations with platform business models differ significantly from conventional pipeline companies. Therefore, the valuation of such companies is different and should be based on other factors than financial metrics and market values. There is a hypothesis that a set of non-financial factors embedded into a mdoel might be a tool to predict platform’s failure. Thus, the expected results are 1) to reveal the insignificance of traditional financial approaches used to evaluate the companies’ performance and probability of collapse in case of platforms, 2) to determine the factors that make the analysis and evaluation of platforms significant and help predict default of company of that type.
The first chapter of this paper will be devoted to nature of platforms, features of multi-sided markets and distinctions between platform and pipeline types of organizations. The second chapter will contain the discussion of issue in platforms’ valuation and observation of previous researches focused on platforms’ failures. The third chapter is aiming to determine a combination of factors that can be regarded as premises of platforms’ failures via applying the multiple discriminant analysis (MDA). The managerial implication approached in this thesis is that the final list of factors embedded in the predicative model can be used as premises for companies’ failure.
Platforms are also remarkably popular among entrepreneurs and investors in private ventures. When a 2017 list of more than 200 unicorns was examined (startups with valuations of $1 billion or more), estimation showed that 60% to 70% were platform businesses. At the time, these included companies such as Ant Financial (an affiliate of Alibaba), Uber, Didi Chuxing, Xiaomi, and Airbnb.
The thorough research of the studies on platforms and their valuation has shown that the majority of the researchers focus on factors that guarantee platforms’ efficiency and the reasons to stick to this business model. However, the previous research on the evaluation of platforms is limited and there is no discussion of any hints or premises of platforms failures. The majority of such papers outline typical managerial mistakes that lead to failures of the platforms. In regard of this research gap, this paper is aiming to study the factors that can be used in platform evaluation and, thus, can reveal premises of default.
The problem is that platforms fail at an alarming rate. It seems vital for managers and investors to identify the sources of companies’ failures and, therefore, avoid the obvious mistakes. Traditional way to assess company’s value as of an investment target - are financial ratios derived from its financial statements, rating agencies reports and accounting information. Thus, promising, growing and successful companies differ from those that have high chances to default or to lose their share of the market by combination of financial ratios and metrics.
Platforms significantly differ from other types of organization: the platform business model encompasses specific features of financials, operations, contracts, communication, stakeholders, and thus, specific risks and opportunities for the business.
This paper’s aim and main goal is to determine factors that predict the failures of platform organizations.
Objectives:
1) to study and discuss the features of platform organizations that distinguish them from conventional firms
2) to study specifics of multi-sided market;
3) to discover and discuss the valuation issues concerning platforms;
4) to analyze reasons of companies’ failure that have been discussed in previous researches;
5) to define factors that can be premises of platforms failure;
6) to develop and test a predicative model with some of those metrics embedded as factors of failure prediction for platform organizations.
Subject of the research - premises of companies’ failures. Object of the research - platforms organizations that operate in multi-sided markets
The main assumption of this work is that organizations with platform business models differ significantly from conventional pipeline companies. Therefore, the valuation of such companies is different and should be based on other factors than financial metrics and market values. There is a hypothesis that a set of non-financial factors embedded into a mdoel might be a tool to predict platform’s failure. Thus, the expected results are 1) to reveal the insignificance of traditional financial approaches used to evaluate the companies’ performance and probability of collapse in case of platforms, 2) to determine the factors that make the analysis and evaluation of platforms significant and help predict default of company of that type.
The first chapter of this paper will be devoted to nature of platforms, features of multi-sided markets and distinctions between platform and pipeline types of organizations. The second chapter will contain the discussion of issue in platforms’ valuation and observation of previous researches focused on platforms’ failures. The third chapter is aiming to determine a combination of factors that can be regarded as premises of platforms’ failures via applying the multiple discriminant analysis (MDA). The managerial implication approached in this thesis is that the final list of factors embedded in the predicative model can be used as premises for companies’ failure.
As it was stated in the beginning, platforms are remarkably popular among entrepreneurs and investors in private ventures. Valuing digital platforms is challenging for two fundamental reasons. First, given the novelty of the technology, platform companies are often young and have little track record or comparable peers. Second, young platforms often generate little revenues, are unprofitable and have no significant assets on their books. The problem is that platforms fail at an alarming rate. By identifying the sources of failure, managers can avoid the obvious mistakes.
The thorough analysis of the studies on platforms and their valuation has shown that previous research on the evaluation of platforms is limited and there is no discussion of any hints or premises of platforms failures. In regard of this research gap, this paper was aiming to study the factors that can be used in platform evaluation and, thus, can reveal premises of default.
The first chapter of this paper was devoted to nature of platforms, features of multi-sided markets and distinctions between platform and pipeline types of organizations. This chapter provides definition and typology of platforms, describes its main feature - the network effects and gives a picture of multi-sided markets. The comparison between conventional companies and platforms highlights the differences in those two types of firms. There are some aspects that distinguish platforms from other companies: direct interaction among users, absence of control of these interactions, benefits of such enabling of direct interactions.
The second chapter contains the discussion of issue in platforms’ valuation and observation of previous researches focused on platforms’ failures. The challenges of valuing young companies using conventional valuation approaches are investigated in this chapter: first by examining intrinsic valuation (discounted cash flow, DCF), followed by relative valuation (multiples). Key issues associated with valuing users and subscribers are examined as well. Finally, this chapter includes the latest findings on reasons of platforms failures identified by various authors and researchers. All researches provide strategic or managerial mistakes that lead a platform to failure, those are mostly hard to quantify and, thus, measure.
The third chapter was aiming to determine a combination of factors that can be regarded as premises of platforms’ failures via applying the multiple discriminant analysis (MDA). Out of all factors discussed, four of them were quantified, gathered and embedded into the model. The factors determined as significant in the model are: local market share, peak growth rate and amount of funding gained.
The resulting model shows the direct correlation between Z-value and metrics of local market share and total funding, and indirect with peak growth rates. The zones of Z-values imply the chance of the company to fail or survive in the next 3 years after 4 successful years. The zones defined can show whether the company is in distress, unlikely to be discontinued or has moderate chance of being closed.
The outcomes of the modelling might be useful for both managers and investors. Investor may be interested in a platform that has at least 3-4 years of performance willing to place some funds in a platform. As long as traditional valuation approaches are insignificant in case of platforms, such Z-score model might be a tool for the decision making. Depending on the Z-value gathered for the target, one may define to which zone the company belongs and assess the level of risk for this company to go bankrupt or be discontinued in the nearest future. Other implication of the research outcomes might be the application of the model by managers of young platforms reaching 3,9-4 years of lifetime. This might be helpful to assess current performance of the company and its ability to survive in the next 3 years. Thus, such analysis might be helpful in defining some condition of distress and avoiding managerial mistakes that might lead to company’s failure. Even the grey zone of Z-value assigned to the company must be a sign for both manager and investor to pay attention to the performance of the company and dig deeper into the possible issues and risks within assessment and valuation.
There are some limitations of the research defined that provide field for further research. Those limitations are: size of the sample (the further research should definitely contain the wider lists of both groups of companies under analysis), factors and metrics in the model (in case of enabled option to get some private data disclosed for the researcher, other factors should be taken into consideration and embedded into the model), data availability (the possible overcoming of this constrain will enable much deeper and qualified analysis of platforms performance and, what is more important, platforms’ failure) and period of prediction (more accurate and precise prediction might be proposed in case the prediction periods are shortened, the sample is enlarged and the number of factors under consideration is increased).
The thorough analysis of the studies on platforms and their valuation has shown that previous research on the evaluation of platforms is limited and there is no discussion of any hints or premises of platforms failures. In regard of this research gap, this paper was aiming to study the factors that can be used in platform evaluation and, thus, can reveal premises of default.
The first chapter of this paper was devoted to nature of platforms, features of multi-sided markets and distinctions between platform and pipeline types of organizations. This chapter provides definition and typology of platforms, describes its main feature - the network effects and gives a picture of multi-sided markets. The comparison between conventional companies and platforms highlights the differences in those two types of firms. There are some aspects that distinguish platforms from other companies: direct interaction among users, absence of control of these interactions, benefits of such enabling of direct interactions.
The second chapter contains the discussion of issue in platforms’ valuation and observation of previous researches focused on platforms’ failures. The challenges of valuing young companies using conventional valuation approaches are investigated in this chapter: first by examining intrinsic valuation (discounted cash flow, DCF), followed by relative valuation (multiples). Key issues associated with valuing users and subscribers are examined as well. Finally, this chapter includes the latest findings on reasons of platforms failures identified by various authors and researchers. All researches provide strategic or managerial mistakes that lead a platform to failure, those are mostly hard to quantify and, thus, measure.
The third chapter was aiming to determine a combination of factors that can be regarded as premises of platforms’ failures via applying the multiple discriminant analysis (MDA). Out of all factors discussed, four of them were quantified, gathered and embedded into the model. The factors determined as significant in the model are: local market share, peak growth rate and amount of funding gained.
The resulting model shows the direct correlation between Z-value and metrics of local market share and total funding, and indirect with peak growth rates. The zones of Z-values imply the chance of the company to fail or survive in the next 3 years after 4 successful years. The zones defined can show whether the company is in distress, unlikely to be discontinued or has moderate chance of being closed.
The outcomes of the modelling might be useful for both managers and investors. Investor may be interested in a platform that has at least 3-4 years of performance willing to place some funds in a platform. As long as traditional valuation approaches are insignificant in case of platforms, such Z-score model might be a tool for the decision making. Depending on the Z-value gathered for the target, one may define to which zone the company belongs and assess the level of risk for this company to go bankrupt or be discontinued in the nearest future. Other implication of the research outcomes might be the application of the model by managers of young platforms reaching 3,9-4 years of lifetime. This might be helpful to assess current performance of the company and its ability to survive in the next 3 years. Thus, such analysis might be helpful in defining some condition of distress and avoiding managerial mistakes that might lead to company’s failure. Even the grey zone of Z-value assigned to the company must be a sign for both manager and investor to pay attention to the performance of the company and dig deeper into the possible issues and risks within assessment and valuation.
There are some limitations of the research defined that provide field for further research. Those limitations are: size of the sample (the further research should definitely contain the wider lists of both groups of companies under analysis), factors and metrics in the model (in case of enabled option to get some private data disclosed for the researcher, other factors should be taken into consideration and embedded into the model), data availability (the possible overcoming of this constrain will enable much deeper and qualified analysis of platforms performance and, what is more important, platforms’ failure) and period of prediction (more accurate and precise prediction might be proposed in case the prediction periods are shortened, the sample is enlarged and the number of factors under consideration is increased).