Jacob Dice

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Category: Business Intelligence

Competing on Analytics and Flashcards

A recent venture finance funding that made a lot of VC news highlights was a 20M series B funding round for Quizlet. The series B round accounts for a large portion of Quizlet’s total funding, which is now approximately 32M. Essentially, Quizlet is an online flashcard service that allows users to read and test their study material and share their flashcards with other users on the platform. However, this most recent funding, along with a 12M series A, is allowing Quizlet to expand past a simple flashcard sharing platform. Essentially, Quizlet wants to develop a more advanced machine learning algorithm that tracks what users know, and what they should study repetitively so they can improve long-term retention.  Read more

Fishing for Insight

Continuing the trend of analyzing front-running machine learning and business intellige198nce applications in startup companies, this week I will be commenting on a startup named Aquabyte. This is a particularly applicable startup to BIA 6300 because they are addressing a problem on the frontier of big data: unstructured data. In the case of Aquabyte, they are working to create a machine vision algorithm that can accurately estimate the size of salmon and detect sea lice – two important tasks in fish farming.  Read more

Predictability is Key for Consumer Satisfaction

Consumers like predictable bills. It is always an unpleasant feeling when you receive an unexpected overage charge on a phone bill, or a higher than average water bill. A recent article by Business Wire highlights a company named Bidgely that capitalizes on this consumer desire. Essentially, Bidgely collects utility usage data and gives users actionable energy saving insights and alerts users when a bill is projected to be unusually high. Read more

Article Review: TechCrunch Deal Sourcing

An article TechCrunch published on December 21st highlights a new venture capital fund in Berlin, Fly Ventures, that is utilizing machine learning to deal source new companies.  In the past, I have heard of venture funds utilizing mathematical modeling to run Monte-Carlo simulations that estimate the necessary portfolio size to be profitable with a random set of investments (bigger portfolio averages return), but I have never seen a fund utilize machine learning for sourcing new investments. Essentially, they have created a machine learning algorithm that scrapes the internet (blogs, job boards, accelerators, CrunchBase, etc.) and then ranks them. Read more

The Art of Venture Capital Meshed with the Science of Business Intelligence: A Dynamic Duo

Venture capital is often lightheartedly referred to as an art, not a science. It is extremely hard to quantify the ideal investment because startups operate in emerging markets with very few concrete and calculable insights. Oftentimes this difficulty leads venture capitalists to work on gut-instinct, not data-driven insights. However, to succeed in an ever-competitive market, it is necessary for businesses to find meaningful and data-driven insights, thus giving them a competitive advantage. This intersection is where business intelligence can play a key role in venture finance.
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