Analysis of results

The centroids of the model are -1,997 and 2,179. The mid-point is 0,091.

-1,997 0,091 2,179

Classification: there are 3 possible zones for the predictive Z score in this model similarly to the ones the Altman’s model introduces:

Z < - 1,997 Zone of failure -1,997 < Z < 2,179 Grey zone

Z > 2,179 Safety zone

The zones of Z-values imply the chance of the company to fail or survive in the next 3 years after 4 successful years. A Z-score that is lower than -1,997 means that the company is in distress and with a high probability of going bankrupt. On the other hand, a score of 2,179 and above means that the company is in a safe zone and is unlikely to file for bankruptcy. A score of between -1,997 and 2,179 means that the company is in a grey area and with a moderate chance of being discontinued or gone bankrupt.

Table 14. Classification of results

Failure 0 1 Total

Number 0 18 2 20

1 0 20 20

% 0 90,0 10,0 100,0

1 ,0 100,0 100,0

95% of the original grouped observations are classified correctly. The model has classified all of the discontinued companies correctly. The mistakes appeared in the group of companies that kept performing at least till the end of 7^th year. Wrong classification appeared only in 2 cases.

The first significant factor in the model is peak growth rate gained by the end of 4^th year.

There is indirect relationship between this factor and Z-value. This might be due to the fact, that the peak growth rates gained by the platforms that have failed in 5^th-7^th years are extremely higher than of those that have been still active till the end of year 7. The main reason for this might be inability of companies to cope with such sharp growth and maintain their product and service on sufficient level.

Local market share is the next factor. In case the platform is described as an entity that is fueled with network effects, companies are highly dependent on the critical mass of the users.

Therefore, the competitive position seems to be highly significant. There is direct relationship between the Z value and the local market share – the higher is the share of the company by the end of the 4th year, the more chances to survive the company has in the next 3 years, meaning the increase in the value of this metric decreases the risk of being closed in the predicted period.

Amount of total funding is the next factor in the model. It also has the direct relationship with the Z value. It seems obvious that sufficiency of funding is the vital factor for survivorship of a platform, moreover, the funding should be balanced, as there are cases in practice when young companies were overloaded with funds and could not make it. But the overall correlation is the following – the higher are the funding company raises during the first 3 years, the more chances it has to survive in the nest 4 years.

Global market share appeared to be insignificant for the model. This might be due to specifics of the sample and data availability – some of the companies are more active and popular in their local markets and do not expand their operations.

The zones of Z-values imply the chance of the company to fail or survive in the next 3 years after 4 successful years. A Z-score that is lower than -1,997 means that the company is in distress and with a high probability of going bankrupt. On the other hand, a score of 2,179 and above means that the company is in a safe zone and is unlikely to file for bankruptcy. A score of between -1,997 and 2,179 means that the company is in a grey area and with a moderate chance of being discontinued or gone bankrupt.

The relative distribution of companies in the sample according to the model among those zones is the following: 60% of the sample were in the grey zone by the end of the 4^th year of performance meaning they had a moderate chance of being discontinued; 20% were in the safe zone being equal to 20% in the zone of distress. If comparing two groups of companies:

discontinued during 5^th-7^th years of their performance and those that were active up to the end of their 7^th year of performance, the number and share of companies in the grey zone were equal:

60% of each group (12 companies). The number of firms from the discontinued sample that were determined to the failure zone was 8, and the number of firms from the ‘successful’ group

determined to the safe zone was also 8. It has to be highlighted one more time that the groups were of equal size.

The model has classified all of the discontinued companies correctly. The mistakes appeared in the group of companies that kept performing at least till the end of 7^th year. Wrong classification appeared only in 2 cases. 95% of the original grouped observations are classified correctly. Those mistakes might have appeared due to lack of data on some parameters of these two companies. Thus, the assumption is that with the fully available data the model would have shown the fully correct classification of initial sample.

Managerial implications

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 a result of analysis conducted on the gathered sample of companies, the model based on 3 factors of company’s performance was proposed. Applying Z-scores of the factors under analysis, this model provides Z-values for every case and the zones of those scores: distress, grey and safe. Investor may be interested in active company 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 this line of 3,9 years of lifetime (as this period was defined as average lifespan of failed platforms). 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.

Definitely, the results of this research have limitations and, thus, create a field for further research. The main limiting construct of the quantitative part of this research is the sample and companies under analysis: due to the fact that the lists of companies were gathered manually, the chance of slightly different results on other groups of companies can not be rejected. Thus, the further research should definitely contain the wider lists of both groups of companies under analysis. As an option, some categorization of companies in those two groups might be applied:

by revenues, number of users/subscribers, regions and presence of any specific operations in

companies’ activities. The next limitation is created by choice of factors. This research focus on most obvious and available ones. Those, that can be gathered and analyses by checking data from open sources. Of course, 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. Those factors might be the structure of capital of the company, presence of unpaid debts, the sources of funding raised (private equity or venture capital) and other metrics available for internal use only in most cases (changes in DAU (daily active users) and MAU (monthly active users), CLV (customer lifetime value), CAC (customer acquisition cost), CMRR (committed monthly recurring revenues), NDR (net dollar retention), MRR (monthly recurring revenue) and ARR (annual recurring revenue), IRR (internal rate of return), CoC (cash on cash return)). As it seems clear from the previous comments on limitations, the main issue encountered during research process was the data availability. Data on platforms is very limited and even if present can not be described as full and representative. One of the reasons is that most of platforms are private companies, the other one is that they are prevalently young and have not a lot of historical data on their books.

This was a significant constraint in sample creation and factors’ metrics gathering. Thus, the possible overcoming of this constrain will enable much deeper and qualified analysis of platforms performance and, what is more important, platforms’ failures. The last but not the least limitation of current research is the prediction period chosen. The period of minimum lifespan of the company and further survivorship was determined by the average values defined by previous researches. May be more accurate and precise prediction might be proposed in case the prediction periods are shortened, the sample is enlarged and the number of factors considered is increased.

Still with regard of the constraints encountered during the research process, the results seem to be sufficient and significant, therefore, might be applied in practice.