How can you predict if a new business idea is good?


Professor Pian Shu addresses one of the toughest questions in the startup world: how do you know if a new company will be successful?

by Michael Blanding

In 2008, entrepreneur Brian Chesky and his two roommates from San Francisco visited the Silicon Valley VC companies with an idea they thought was a good idea: a website and a mobile app that allowed homeowners to open their home to strangers travel, for a daily fee.

Of course we know the idea of ​​Airbnb, a $ 10 billion company with 1.5 million ads worldwide. But at the time it had to seem crazy. Liability issues alone seemed insurmountable – not to mention the likelihood that people would be willing to give their houses keys to strangers who might or might not be serial killers.

Five venture capitalists flatly rejected the project and two others did not even comment. “Investors must have thought, who would ever do that?” Said Assistant Professor Pian Shu, a member of the Harvard Business School’s Technology and Operations Management Unit. “They did not know it would be a multi-billion dollar industry.”

“BY DEFINITION, IF AN INVESTOR INVESTS, IT CHANGES THE PROBABILITY OF SUCCESS”
In a recent paper, Shu asks the fundamental question that cases like Airbnb and other previously unlikely, now successful companies (LinkedIn has also received more than 20 publications in 2003) seem to ask: how can you convey a good idea? ?

“For start-ups, especially fast-growing startups, it’s extremely difficult to predict the likelihood of success,” says Shu, who studies innovation and entrepreneurship. When it comes to something truly innovative, it’s hard to compare it to anything that happened before. This uncertainty weakens the difference between a big hit and a phenomenal flop.

Predicting the success or failure of startup is also extremely difficult to study. When venture capitalists invest in an idea that succeeds later, it is difficult to know if the idea was good in itself or if investment and mentoring made it well, a self-fulfilling prophecy.

Change the probability of success

To address this issue, Shu and her co-author Erin Scott of the National University of Singapore needed to find a framework to determine the relationship between initial assessment and future outcomes. That said, the experts rated the ideas but did not fund them or instructed them to determine their success.

“When an investor invests, that by definition changes the probability of success,” says Shu. “You have to find a framework in which to assess a start-up at an early stage, where the score is unknown to the entrepreneurs and does not influence the idea.”

New research explores a fundamental question: how can you?

predict whether a business idea will succeed or fail? © iStock
They found this frame at a location near Shu. A graduate student at the Massachusetts Institute of Technology, Shu has flirted with entrepreneurship and even applied for the MIT Venture Mentoring Service (MIT VMS) – a program that connects aspiring entrepreneurs with entrepreneurs. As part of the program, an employee writes a sales description in a consistent format and distributes it to a group of over 100 potential mentors who may be interested in the idea.

Shu and Scott realized that they had the perfect laboratory to judge the success of the ideas. By comparing the number of mentors who expressed an interest in an idea and the ultimate success of the idea, they were able to see how much the amount of interest predicted this success. At the same time, as entrepreneurs – who determine the level of mentoring they’d like to have – had no idea how many mentors had expressed their interest and that MIT had access to the same amount of resources each time, this achievement would not be a self-fulfilling prophecy become.

Ability to recognize winners
In analyzing the data with Roman Lubynsky from MIT VMS, the researchers found that mentors as a whole had a strange ability to predict the success of ideas. Compared to an average company that aroused the interest of six mentors, a company that attracted twice as much interest was 27% more likely to market (which defined Shu and his colleagues as multiple repeat sales, one among other standards of success).

However, as researchers explored the data, they found significant differences in the ability to predict the industry-specific success of the proposed idea. The interest of the experts was very predictive of success in R & D-intensive sectors such as energy, equipment, medical devices and pharmaceuticals. However, in R & D-intensive industries such as mobile applications and software, the ability to predict success was no better than chance.

This may be because it is easier to evaluate the technology that has a well-defined set of potential market needs, Shu suspects. “In an industry like drug development, it’s not like you can go from one disease to another,” she says.

While R & D-intensive companies may change the application of their underlying technology, the fact that they are based on specific “intellectual resources” makes it unlikely that these changes will be dramatic. On the other hand, “mobile apps do not require big fixed costs, so you can easily change the focus of your business.” (How Airbnb concentrated on air mattresses in the living room

Some investors think that in start-up companies, the idea does not matter as long as the quality and passion of the entrepreneur foster it; while others invest in the idea and replace the founder with a professional management team if needed. Shu speculates that in R & D-intensive companies, the original idea plays a bigger role in determining entrepreneurial success than in less R & D-intensive companies. “There is a long debate about venture capital financing to know whether to put the team on the” horse “- the idea – or the” jockey. “Our findings suggest that the response varies across industries. ”

The other interesting finding of the data was not related to the industry of the persons evaluated but to the industry of the persons responsible for the evaluation. The researchers were surprised to discover that those who worked in a particular industry were unable to predict the success of an idea in the industry than those who were not in the industry.

“We do not say the expertise is bad,” Shu adds quickly. “We just do not find any indication that industry expertise is needed to effectively assess the commercial potential of an idea.”

Of course, the mentors who valued the ideas in MIT’s VMS program were not coincidental – they had a strong entrepreneurial spirit. However, Shu argues that a large group of people with general business experience to evaluate an idea might be more effective than having a small group of people, all from a particular sector. “It runs counter to the way VC works, where there are usually a small number of partners in a group, all specialized in one area.”

For entrepreneurs, it could be more important to quickly develop a prototype for a workable minimum product and get the first responses from a much larger group of people. This applies both to entrepreneurs who are perfecting their idea of ​​a viable business and to investors who want to discover the next business in salon air mattresses, which is turning into a $ 10 billion empire.

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