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[Context] Software startups are engines of innovation and economy, yet building software startups is challenging and subject to a high failure rate. They need to act and respond fast in highly uncertain business environments. To do so, they need to identify crucial and actionable information that supports them in making correct decisions and reduce uncertainty. So far, the software startup literature focused predominantly on what information to measure from a metrics perspective. Thus, there is a lack of research investigating how to deal with information from an analytics perspective.

[Objective] The current study aims at understanding how software startups are dealing with crucial information that could lead to meaningful actions. The overall research question that guides the study is: what analytics mistakes do software startups make?

[Method] We investigated 22 failed software startups using their post-mortem …