How data analytics helps in making strategic investment decisions across geographies

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How are you using data and algorithms for making investment decisions ?

Sailesh Ramakrishnan, Partner, Rocketship.vc

Rocketship.vc has a unique approach to investment that sets it apart from its peers. Our team uses machine learning and data science to identify and invest in startups around the world. Our smart algorithm tracks companies globally using a series of metrics to indicate the probability that a startup will be successful. We look at metrics such as proven market demand, scalability, traction, customer success, as well as data from sources like social media and Crunchbase to get a better understanding of where the company fits into the ecosystem. Once our models point us to an opportunity, our team of data scientists will evaluate further and make the call if we will pursue and reach out to the startup.

Please share with us information about your tech startup database. Also, how are you spotting early global trends in this space?

Being the first investor to spot an emerging industry or startup can be hard to do. However, our comprehensive database gives us an advantage to spot trends globally, even where we don’t have a local network. We are also all data scientists by trade so we are comfortable evaluating technology and having a sense for its impact potential from the start. This hybrid approach of utilising both data science and human evaluation allows us to spot early global trends.

Which tech segments in India have seen the maximum investment from you post the Covid crisis?

The pandemic has brought increased focus on two areas in India: Healthcare and Education. We see major opportunities here from online vernacular education to telemedicine, online pharmacies to support and mental health. Indian tech companies play a significant role in molding these trends. Our plan is to continue to focus on how India can overcome some of these challenges as the next generation of global category leading companies are built there.

Has data analysis been a driving factor for these investments?

Yes, we definitely follow our data to find really promising companies and new markets to invest in.

Your perspective of data investment in VC funding. How will this impact fundings for Indian tech startups?

We have had conversations with other VCs and they ask us how Rocketship.vc is writing checks during a time like this, where it is almost impossible to travel and meet founders face-to-face. Even before the pandemic, we have always made investments by relying on Zoom, Skype and WhatsApp meetings with founders, and it enabled us to continue operating and investing in the present scenario without breaking a stride. We’re proud of our investment model which removes a lot of the bias around gender, race and personal founder networks and instead focuses on the merits of the startup and the problems they solve. We envision that more VCs will follow suit and leverage data to make investment decisions.

In which sectors do you foresee Indian tech startups making many pioneering achievements?

Amid the pandemic the Indian tech ecosystem has made great advancements in pioneering services and products. B2B has been grabbing a lot of our attention. We see a large scope for significant changes in the B2B space as consumer innovations make deep inroads into businesses creating software infrastructure from accounting to logistics to supply chains, that will allow them to reduce costs and become globally competitive.

Your investment plans for this market in the near future.

While we invest in category defining outliers in all verticals, we are currently seeing great companies being built in cloud-based collaboration and B2B tech. Both areas have been spiking and coming on our screen more than other sectors. While we cannot share specific companies yet, those industries are grabbing our attention and are sectors we are excited about.

If you have an interesting article / experience / case study to share, please get in touch with us at [email protected]

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