Rocketship and Data-Driven Investing | PYMNTS.com

He said the world is inundated with data all day, every day – from mobile apps, social media, rating sites of all kinds, etc. – a flow that generates an ever-changing sea of ​​information for any investment firm.

But Ramakrishnan said that information falls into three distinct types. “There is a lot of changing data coming in daily, things like newspaper articles, job history, new executives joining, funding announcements and so on,” he told Webster.

At the other end of the spectrum is static, mostly historical, data on a company. And in between is the slowly changing data: quarterly performance results and the like.

“So there is a whole continuum of data, and not only do you need different techniques to extract the information, you also need different ways to combine these different streams to get a whole picture,” Ramakrishnan noted.

And that’s why Rocketship’s algorithm-based investment model was built. It sets up multiple coded templates in different time frames against the startup ecosystem and directs the company’s investments to early stage companies in their first investment rounds (typically the seed rounds, A and B).

The model aims to achieve the same goal of every investor at an early stage: to get on the ground floor with the next great company and shell out the funds that entrepreneurs need.

Following the data to the unexpected

Rocketship’s models are varied: some process data every day, some every few weeks, and some every few months.

Ramakrishnan said that none of these models are perfect, because perfect models do not exist. But they’re designed to learn and improve over time, filtering data into better insights and advice on where the business should look to invest.

This does not mean that the model has to make decisions on its own. Ramakrishnan said that one of the most important realities of working with mathematical modeling is that it has its limitations. Reality is full of intangibles that matter a lot to a company’s success, but are difficult to present mathematically.

“This is why we have not invested in all the companies identified by our algorithms,” Ramakrishnan explained. “As human partners, we spend a lot of time trying to understand what ‘secret sauce’ exists within the company and whether it is a sustainable and resilient element.”

But what does it means that when the data points in a certain direction, the company knows it’s the right place to start looking, even if it’s not what Rocketship expected to see.

A world of opportunity

This was the company’s experience almost immediately after launching its first fund five years ago. The plan was to do what almost all Silicon Valley investment firms were doing at the time.

Rocketship intended to start locally with all the opportunities in the valley, then along the way push into the country in general and eventually into the rest of the world. But when the company actually started running its algorithmic models, Rocketship quickly discovered that its plan was, in a word, wrong.

What the data told the company was that its backyard was the wrong place to play. The wider world was full of extraordinary companies without much regard for borders: in India, the European Union or Latin America.

Ramakrishnan said Rocketship was founded by career data scientists, all operating under a golden rule: “Never impose your strategy in conflict with what the data says.”

“The data has given us these kinds of global opportunities and we have followed suit,” he said. “We became a global investor pretty much from day one and were immediately very different from what most other investors were doing.”

Thriving during the pandemic

Ramakrishnan pointed out that the world of investing is changing all around us, but in ways that contribute to Rocketship’s strengths.

In a world where a pandemic has disrupted face-to-face meetings, everyone on Earth suddenly has to learn something Ramakrishnan said his company has been working on for the past half-decade: investing in companies whose founders you’ve never met – person.

And he added that the investment landscape is still alive in many places. For example, companies that enable cloud-shift, FinTechs that allow lending, companies specializing in employee management, and digital neobanks / banks are all areas where opportunities are exploding in response to the recently skyrocketing demand.

Democratize venture capital

Perhaps even more interesting, Ramakrishnan said, is that the investment landscape itself is starting to change as it becomes more globalized and democratized. The balance of power is shifting in ways that he believes will benefit the best and most innovative companies in the world, regardless of where they were founded.

Ramakrishnan said the next great startup could come from Silicon Valley, but it could also come from Vietnam, Nigeria, Chile or Colombia. And those companies will come to the market better able to build a track record of results without raising capital – meaning that by the time they’re talking to potential investors, “the dynamic has changed,” he noted.

Money will always be extremely important, but the world of data-driven investing of the future is much more than that, he said.

“Everyone asks investors, ‘What can you do for me?'” Ramakrishnan said. “[But] It’s not just about having the money, it’s because of our background, our data science, our data. “

“Now we have to have those reasons why you should take our money from us over anyone else offering you money,” he said. “And I think that dynamic – where there is that recognition of the value that investors play beyond just dollars – is [an] an essential part of this conversation. “