[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 reports as the main source. They were included in the study because the founding teams made mistakes related to information and analytics, which contributed to their startup failure to various degrees. We analyzed the collected data using thematic analysis. [Results] Ten types of mistakes made by the 22 failed startups when dealing with information are identified. These ten types are further grouped into four categories from an analytics process perspective, including information collection, information analysis, information communication, and information usage. [Conclusions] Our findings contribute to a better understanding of how software startups are dealing with information. It provides an opportunity for software startup teams to learn from the recurring mistakes of failed startups. Interesting future research avenues include defining patterns and antipatterns in software startup analytics by studying both failed and successful startups and doing an in-depth investigation of essential metrics for software startups.