What bait do Sharks bite?
Decoding the popular show “Shark Tank” with data
Shark Tank is an American business reality show that has since gone on to be adapted in multiple countries. The concept is simple: entrepreneurs walk in to present a business pitch for their product to 5 panelists (called sharks in the show) and hope to get them to invest in their venture.
Getting experienced business people to invest in your company is a daunting task for many, not knowing what kind of bait the sharks would bite. But if you stare at something long enough, patterns begin to emerge. With that in mind, let’s try to break down some data on what business pitches have worked on the sharks, and see how data can help us understand hidden meanings in unassuming places.
So are we hunting for Sharks?
As cool as that sounds, no. The purpose of this article is to show how understanding data can help us understand the world around us. In this blog we will try to analyse data and answer a few questions:
- What categories do well on Shark Tank? Is there a product category that leaves the rest in the dust?
- What influence does the investment value asked on the show have on the company’s success?
- What can be said about the company’s eventual success after securing funding? Are they assured success and others guaranteed failure?
Well, let’s jump into the ocean of data and find out!
About the data
This data has been taken from the Shark Tank Companies dataset uploaded to data.world 5 years ago. It is a comprehensive dataset containing data on the companies, their ideas, evaluation and more. If you would like to explore the data further, do check it out!
What do the Sharks like?
So the million dollar question: What companies do sharks usually like? Now in a topic such as this, there are a lot of variables into play, including how well the pitch is actually received by the panelists. That does not however, stop data scientists like us from trying to find some patterns that emerge from years of data. On that note, let’s dive into some analysis:
The first graph is a comparison between the different categories and corresponding success rates in Shark Tank. As we can clearly see in the graph, Storage and Cleaning Products have had the highest chances of securing a funding on the show. The second graph relates the total products pitched on the show against the same categories. Clearly, this gives a completely new dimension to the analysis. Storage and Cleaning Products has had lesser number of products pitched as compared to others, and we observe that Specialty Food has had more products and an acceptable success rate on the show (around 55%). This would seemingly imply that though a significantly large amount of pitches were related to Specialty Foods, the sharks had no troubles partnering with over half the companies!
Isn’t that beautiful? A large dataset with seemingly unrelated numbers, when plotted in an orderly fashion, suddenly answers questions that we did not even know could be answered. This arbitrary distribution of data suddenly appears to hold the key to finding patterns in this seemingly aleatory show!
Let’s keep going :)
The above graph relates the categories and the average amount of money entrepreneurs have asked for on the show as investment into the company. We can clearly see major industries like Entertainment, Automotive and Electronics claiming the top seats in investment asked for. But does that really reflect on the projects that actually got investments? Let’s find out!
This is where things get really interesting. The graph on the left shows the categories compared with the median investment asked for, and a deal was brokered between the venture and the sharks. The other graph on the right shows a similar comparison, however when no deal was struck. These bar charts exhibit something very interesting. We actually do see the three industries we discussed earlier take the top spots in the accepted deals graph. But what about the other industries? Taking Outdoor Recreation for example: when the asking value was around $100k, investors seemed to be interested in the venture. But increase the value to $300k, suddenly the prospects dwindled. Perhaps the sharks deemed this category as a whole to lie on a lower end of the spectrum in terms of revenue generated? We cannot know the thought process behind it from simply this data. But therein lies the beauty of data analysis: without knowing anything in detail about the topic, a person can form informed opinions and analyze trends that were not visible otherwise.
Interesting right? Let’s check out the biggest category of this dataset: Entertainment. Clearly, the sharks have valued the high investment projects in this industry over the lower valued ones. Once again, one can speculate that the investors see this industry to have a “high risk, high reward” sort of nature, and are willing to enter the industry for the higher returns. Once again, we are able to have these speculations, not knowing anything about the industry, nor the investors, and simply due to the fact that we were able to plot the data we had and analyze the trends that stood out.
Now that I have roped you into Data Analysis, let me conclude this by deriving another cool inference from the data we have available.
If we visit all the websites of all companies listed in this dataset, we find that some of them are not available anymore. Of course there may be a situation where the website was moved to a new domain or the company was taken over; but for our simplicity and purpose, I will assume that all shut down websites have shut down due to the company shutting down.
Now, out of the 338 companies that have their websites still active, 251 of them (74.2%) are ones that received funding from Shark Tank. Similarly if we consider all of the companies that have received funding on the show: out of the 251 companies to receive funding, 182 companies (72.5%) companies are still booming.
Speaking in probabilities: if a company is funded, the probability of their venture succeeding is 73%. On the other hand, the probability of a company not managing to secure a funding, yet remaining in business is 63%. Though companies do expand their businesses against all odds, the funded ventures have clearly shown a slight edge over the others.
Meaning companies that receive funding have shown a higher likeliness to remain in business. This was to be expected intuitively and has been made evident by the data we have worked with.
How do I hunt Sharks?
Now I am not here to talk about how you can secure the highest chances of getting an investment on Shark Tank. What I can tell you instead, is that in a short span of 5–10 mins, I have been able to go from knowing nothing about what sort of companies attract investors, to being able to make logical assumptions that are reflected in the real world. And I could have done it with any field, given a dataset. There is a certain elegance to Data Science. From simple facts to hidden patterns: you can discover many things as you learn to play around with data. When used by businesses, data can help them understand and predict shortcomings and come up with solutions effectively. It is a powerful resource waiting to be utilized and with the right touch, there is no limit to what you can do.
