| This is terrible research. The results are meaningless. At best, it's a case of confirmation bias. The most highly-differentiated startups don't raise anything and no one ever hears about them. Crunchbase doesn't capture the majority of these, so the data used here isn't representative. Then they use NLP to determine how different the products are. Those of us who get pitched by SaaS vendors all the time know that two companies can do the exact same thing and have totally different product descriptions. Salesforce started talking about IoT for a while, and they still bill themselves more as an ERP when most people still use them as a CRM. There's also the issue of those who pivoted. Most startups pivot, and the author admits to ignoring pivots altogether! > "2. Found the websites of all these companies, in the year each startup was launched (using WaybackMachine, a historical archive of web pages)." > "3. Scraped the text on these websites and ran them through a natural language processing (NLP) machine learning algorithm (doc2vec)." > "4. Measured how different the startups’ value propositions were to those of competitors (e.g. focused on a niche product feature vs others that talked about price)." |