cyberlaxamana

by Trina Spear Imagine a high school football field.   Fall 2009.   Division Championship.   Sean Murphy, starting quarterback at Riverview High, throws the 80-yard touchdown pass to win the most exciting game of his young career.   Sean has been a very successful quarterback exploiting this exact play, the long pass.   As the clock runs up, the team hoists Sean onto their shoulders and carries him around the field.   As he struts through the halls, he feels the eyes on him and grins as people pat him on the back.   He is on top of the world – he is the star on the football team, has earned straight A’s, has recently received football scholarships to Northwestern, Wisconsin and Harvard and to top it all off, his girlfriend was just voted homecoming queen. The following month Sean finds himself in a tough position playing for the Conference Championship: down by 4 points, 45 seconds left in the game, forty yards away from the end zone.   Unlike in ...

by Alvaro Febrel More often than not, startups are ignored by big companies when seeking help. To make things worse, sometimes a startup’s business model relies on established players to succeed. “Data Aggregators” are a good example of this, as they use other companies’ data to provide new services for customers (Tripadvisor, Kayak, Cake Financial and Chegg are examples of companies that in one way or another aggregate data from different sources). The issue is, what can a data aggregator do if its sources of information don’t collaborate?  The answer: scrape! The term “Screen Scraping” is used to describe software that reads and extracts information from data that was intended for display to an end-user, as opposed to reading and extracting the same information from “non-manipulated/machine-oriented” data. Screen scraping is usually considered an inefficient way to get information, as it depends on how the end-user output is displayed. For instance, if you want to know today’s oi...

by Andrew Perlmutter Until yesterday’s class on Chegg, I thought all of the complicated dimensions of lean startup methodology revolved around getting to product-market-fit. These issues include (1) the tension between being hunch-driven and being data-driven early on, (2) having difficulty determining whether you have actually achieved product-market-fit, (3) pivoting too much (or too little), (4) identifying whether your business fits into the special set of companies that should scale prior to achieving product-market-fit, and (5) knowing when to raise additional capital.  However, I thought everything cleared up once the business reached product-market-fit. You raise money, scale the business, and become a significant business. Game. Set. Match. Of course there are several aspects of the business that still need to be sorted out once you start scaling. For example, as the class learned from a very insightful presentation given by David Skok, the business must figure out how ...