Skoltech researchers used Google Tendencies’ Massive Knowledge ensuing from human interactions with the Web to develop a brand new methodology – a software and a knowledge supply – for analyzing and researching the expansion of startups. A paper reporting these vital findings was published within the expertise administration journal Technological Forecasting and Social Change.
Startups and high-growth technology-based ventures they rework into are considered the important thing drivers of financial improvement, innovation, and job creation on the nationwide and world degree. Nonetheless, regardless of their essential significance for the financial system and excessive curiosity from researchers and policy-makers, startups show development patterns which might be troublesome to research. These fragile, early-stage personal companies, which can rapidly scale up, shouldn’t have time, curiosity, or obligation to share a lot knowledge about what they achieved, when, or how. Thus, to exterior observers, startups seem like “black packing containers” whose progress can hardly be assessed resulting from a scarcity of goal data.
Maksim Malyy, a PhD pupil from the Skoltech Middle for Entrepreneurship and Innovation (CEI), has been intrigued by this drawback since he labored in a startup accelerator in St. Petersburg earlier than becoming a member of Skoltech. Wanting into theoretical and sensible elements of the issue for the final three years, Maksim, his supervisor, professor Zeljko Tekic, and Skoltech assistant professor Tatiana Podladchikova got here up with worthwhile insights on how you can cope with the information shortage drawback in learning startups. A few of their findings had been printed within the paper.
Maksim explains why this analysis is so vital: We show that web-search site visitors data, notably Google Tendencies knowledge, can function a worthwhile supply of high-quality knowledge for analyzing the development of startups growth-oriented technology-based new ventures they evolve into. We analyzed a big and transparently chosen set of US-based firms. We confirmed the existence of a powerful correlation between the curves primarily based on Google searches by firm title and people depicting valuations achieved by means of a collection of funding rounds.
In accordance with the authors, this correlation permits utilizing Google Tendencies knowledge as a proxy measure of development as a substitute of private and infrequently out there measures like gross sales, worker, and market share development. Google Tendencies knowledge, that are public, straightforward to gather, and out there for nearly any firm since its inception, may help construct extra correct and even real-time data-driven development paths for startups. With these evolution curves, one may revisit some outdated solutions, ask new questions, and develop extra strong ideas, theories, and predictions.
Maksim believes that this research has robust implications for start-up analysis: Our findings counsel that for startups, particularly thriving unicorns or B2C digital platforms, the proposed method could turn out to be an equal of an X-ray scan, providing an inexpensive, straightforward, and non-invasive method to perceive the workings of a technology-based new enterprise.
By means of remark, professor Tekic and professor Podladchikova cite a report by one of many reviewers: “I feel this paper will stand the check of time and be helpful for a few years to come back. It really is an interesting research.”