[READ THIS BEFORE YOU PITCH] The Indian Bible for The Indian Investor

You have a great business idea, and you want to take it to the next level with further investment and support from people already established in the field. The logical next step would be pitching the idea and getting capital and support. What now?

Pitching your startup to investors can be a nerve-wracking experience, especially if you're not familiar with the process. Even the best product can fail if it's not pitched properly and to the right person, which is why we decided to analyze patterns and trends from Shark Tank India data to gain insights into what factors influence a successful pitch. Our research question was simple: What factors contribute to a successful pitch?

We approached this problem with a combination of linguistic and statistical methods. To start our analysis, we gathered datasets from Kaggle that contained metadata for seasons 1 and 2 of Shark Tank India. For those not familiar, Shark Tank India is a reality show wherein prospective pitchers or business starters pitch their business ideas to established people in business, called sharks.

We aim to create a bible of sorts for the Indian business owner, and how they can perfect their pitch and take it to the next level by pitching the right way, and to the right person.

The analysis of the business owner, investor and the pitch was done in 2 parts, namely Statistical Analysis and Linguistic Analysis. While statistical analysis involved studying the quantitative aspects of the business and their ideas, linguistic analysis involved studying the conversational aspects of the pitch to the investor, and identifying the common trends between successful pitches.

Statistical Analysis

We looked for correlations between various factors, such as the number of presenters, gender, industry, and final deal offered by individual sharks. We then analyzed these correlations to determine whether they had a positive or negative impact on the success of a pitch. To validate our findings, we used the p-test.

Correlation matrix

Our analysis revealed some interesting insights into the world of Shark Tank India.

We found that there were gender biases in investment, with females asking for less initially and receiving less overall, while males asked for more and tended to receive more. We also found that a shark named Peyush specifically invested in males and had a negative correlation with females. We also studied each shark to highlight the industries or age groups they tend to invest in, which could help entrepreneurs pitch to the correct investor.

When we conducted a hark-wise analysis, we also found that some sharks tend to invest in the same things, so if you want to pitch to one shark, it might be beneficial to pitch to both. For example, Vineeta and Ghazal tend to invest in the same pitches. Our analysis also showed that investors tended to favor older companies in the electric vehicle industry, while newer companies were more likely to receive investments in the beauty and fashion industry. One possible explanation for this trend is that investors seek reliability in established companies in the electric vehicle industry, while looking for trendy and innovative ideas in the beauty and fashion industry.

Linguistic Analysis

To get a deeper understanding of the linguistic and communicative aspects of a successful pitch, we obtained subtitles from YouTube and created our own dataset by splitting the subtitle files into three parts (one for each pitch) and matched them to the corresponding metadata.

Old data, just a transcript

We then manually annotated the data to determine what was spoken by the shark and what was spoken by the contestant. This dataset can now be used by various researchers for further studies in Indian entrepreneurial pitches.

New data, annotated in a
conversational manner

The team is proud to say that we are the first in the country to create such an annotated, cleaned conversational dataset for the Indian investor! While conversational annotated data exists for American business pitches, there was none for the Indian business owner, which presented a need to create such a dataset that was more relevant to the Indian business context.

We then Part of Speech (POS) tagged the datasets and analyzed factors such as the noun/verb ratio, variance, and mean of sentence lengths to determine what contributes to a successful pitch. We found that the average length of a sentence should be small, but there should be high variance. We also found that the number of nouns represent technical terms while verbs represent assertiveness. A pitch with a higher ratio of verbs to nouns tends to be more assertive and confident, leading to a higher chance of success. These linguistic analyses can be useful not just for pitching startups, but also for improving public speaking skills in general.

Word cloud with most frequent words spoken by the pitchers

Limitations

However, our study has certain limitations that need to be considered. Firstly, the episode data was in Hindi, and the subtitles had to be translated to English, which could have introduced inaccuracies in the original meaning. Secondly, the pitch data is solely based on what was spoken in the episode, which was formed using subtitles, and we do not have access to the complete pitches of the participants of Shark Tank India. Additionally, we have not accounted for any external factors that may have influenced the outcome. It is not possible to assess the demeanor of the participants, so the analysis is solely based on the translation of their words. Furthermore, the analysis does not take into account the quality or potential of the products being pitched or any other external factors. Therefore, while our findings can aid entrepreneurs in improving their pitch and help investors make more informed investment decisions, they should not be considered the sole determinant of success in the startup ecosystem.

Taking it forward

Moving forward, we can use these parameters to develop a model that can predict the success of an entrepreneurial pitch. This model can be expanded to other pitching platforms beyond Shark Tank India, providing valuable insights to entrepreneurs and investors alike. In addition, we can conduct further research to analyze the impact of non-verbal cues on the success of a pitch, such as facial expressions, tone of voice, and body language. This can be done through analyzing video footage instead of just subtitles. Additionally, we can explore the effectiveness of different types of language, such as humor or emotional appeals, on the success of a pitch. These studies can provide more comprehensive insights into the art of pitching. Finally, we can extend our linguistic analysis to other forms of public speaking, such as political speeches or presentations in business settings, to provide more practical applications for our findings. By expanding our research in these ways, we can provide entrepreneurs and investors with a more accurate and comprehensive understanding of the factors that contribute to the success of a pitch.

Ethical Review

Finally, it is important to mention the ethics involved in our study. The dataset used for the statistical analysis in this research is available on Kaggle under CC0 license, and we ensured that the data was used fairly, without any intention to harm or exploit participants. Our methods and results are transparent and accessible, which allows others to build on our work and contribute to the growth of the startup ecosystem.

Conclusions

In conclusion, our study on the factors contributing to a successful pitch in Shark Tank India revealed interesting insights about gender biases, the investment preferences of individual sharks, and the linguistic and communicative aspects of a successful pitch. We hope that our findings inspire aspiring entrepreneurs to improve their pitches and increase their chances of securing investment, and help investors identify promising pitches and make more informed investment decisions. We believe that our research can contribute to creating a more equitable and efficient startup ecosystem in India and beyond.

(L to R) Nukit, PK, BLÅHAJ, Eshika, Adith, Sankalp