Three years ago, a team of researchers, entrepreneurs and data geeks set out on an ambitious mission: To put the world’s technology startups under the microscope in an effort to better understand why some succeed and why 90 percent eventually go the way of the dinosaur.
Fast forward to today, and The Startup Genome (as it’s now called) has analyzed data from over 100,000 startups around the globe and has conducted hundreds of in-depth, qualitative interviews with entrepreneurs and investors. The results provide an exciting look into not only what characteristics and qualities make for a successful formula, but how different startup ecosystems stack up with each other.
The team behind the project has also begun to leverage its unique data sets to create a benchmarking tool to enable entrepreneurs to evaluate their progress compared to their peers and help them make more informed product and business decisions. This month, after more than a year of testing and tweaking, the team finally released Compass into the wild.
Compared to prior iterations, Compass founder and CEO Bjoern Herrmann (who is also one of the co-founders of The Startup Genome Project) tells us, the now fully-baked startup benchmarking tool offers automated data collection from a host of tools and services popular among SMBs, including services like Salesforce.com, MailChimp, Google Analytics, Mixpanel, PayPal, Quickbooks and Stripe.
Using data derived from the sources, Compass then funnels your startup’s business metrics into its revamped dashboard, allowing them to view company benchmarks across a range of categories, configure an alert system to stay up to date on company revenue, churn rate, user growth rate, acquisition costs and so on, while offering visualizations of that data in correlation charts, graphs and via tailored, supplemental analysis.
But the real key to the new Compass product, Herrmann says, its the new dynamic system its team developed to generate benchmarks based on large data sets. Up until today, most benchmarking solutions have relied on 50-year-old methodologies to collect and analyze data, so, the team has instead developed a methodology designed specifically for Big Data analysis.
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