Analytics Mistakes that Derail Software Startups

Rafiq, Usman; Melegati, Jorge; Khanna, Dron; Guerra, Eduardo

doi:10.1145/3463274.3463305

Cited by 16 publications

(5 citation statements)

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“…The study [17] also reported a few other analytics-related mistakes, e.g. "avoiding to collect further information", "poor team communication" and "mishandling information".…”

Section: Discussionmentioning

confidence: 96%

“…In recent work, Rafiq et. al [17] studied how software startups deal with the information to make decisions from an analytics perspective. They reported ten types of recurrent analytics mistakes that might derail software startups.…”

Section: Related Workmentioning

confidence: 99%

“…There is still uncertainty, however, how software startups understand and apply analytics throughout the product as well as the business development. The role of analytics in startups is unknown despite the widespread use of analytics in other businesses [2,17]. Therefore, the current study seeks to address this gap.…”

Section: Introductionmentioning

confidence: 99%

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Towards Understanding Analytics in Software Startups

Rafiq

2022

Preprint

Self Cite

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Analytics plays a crucial role in the data-informed decision-making processes of modern businesses. Unlike established software companies, software startups are not seen utilizing the potential of analytics even though a startup process should be primarily datadriven. There has been little understanding in the literature about analytics for software startups. This study set out to address the knowledge gap by exploring how analytics is understood in the context of software startups. To this end, we collected the qualitative data of three analytics platforms that are mostly used by startups from multiple sources. We covered platform documentation as well as experience reports of the software startups using these platforms. The data was analyzed using content analysis techniques. Four high-level concepts were identified that encapsulate the real understanding of software startups on analytics, including instrumentation of analytics, experimentation, diagnostic analysis, and getting insights. The first concept describes how startups set up analytics and the latter three illustrate the usage scenarios of analytics. This study is the first step toward understanding analytics in the software startup context. The identified concepts can guide further investigation of analytics in this context. It also provides some insights for software startups to set up analytics for data-informed decisions. Given the limitation of the data used in the study, the immediate next step is to ground as well as validate the acquired understanding using the primary data, by directly interacting with software startups CCS CONCEPTS• software and its engineering → Software creation and management.

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“…The study [17] also reported a few other analytics-related mistakes, e.g. "avoiding to collect further information", "poor team communication" and "mishandling information".…”

Section: Discussionmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Towards Understanding Analytics in Software Startups

Rafiq

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some of the factors are namely competitors, team, marketing strategy, product fit, etc. The authors of this study [21] have collected data of 22 different failed startups and analyzed them. Upon analyzing the data, they have identified the mistakes that the failed companies have made.…”

Section: Related Workmentioning

confidence: 99%

Present Situation of Tech Startups in Bangladesh: A Case Study

Saha¹,

Raihan²,

Anjum³

et al. 2022

IJEM

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In contemporary times startup is a very popular and growing entity, especially for the software industry as the technology field continues to grow. However, the failure rate of software startups is extremely high, and many reasons are behind this failure. There are many different variables behind the success or failure of a company. The purpose of this study is to mainly identify the possible reasons behind why the startups can't reach the promise they are presumed to reach. To identify the reasons, company and employees of the company data were collected and analyzed using sentiment analysis and statistical correlation. Based on the analysis, some constructive suggestions were proposed accordingly. The discussion will enable the company employees and employers to bring out a transparent relation to ensure productive and satisfactory work environment. All the collected data for the study and the codes that were used to analyses the findings are attached below [28].

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“…In Australia, jobs created by startups accounted for almost all the 1.6 million jobs created in the economy between 2003 and 2014 [6]. However, startups also have a very high rate of failure, with some estimates placing this number between 70 and 90% [7][8][9]. A study conducted by CB Insights [10] revealed that among startups that manage to stay afloat for over a year and go on to acquire some level of venture backing, 70% of startups in the technology field failed within a span of 20 months after raising their first round of financing.…”

Section: Introductionmentioning

confidence: 99%

The Blitz Canvas: A Business Model Innovation Framework for Software Startups

Shanbhag

Pardede

2022

Systems

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Software startups are temporary organizations created with the purpose of bringing a profitable business idea to life. In the initial stages, the commercial viability of any product concept is yet to be proven and until the startup can generate revenue, resources are always in short supply. To this end, this research proposes a process-oriented, competition-aware, metric-driven business model development and innovation framework. The proposed framework is designed to aid this process, by supporting the creation and validation of the business model. A web-based tool is created to demonstrate the working of the proposed model and validation is performed using survey data collected from the usage experience of participants. The data is used to evaluate the research questions and the ability of the proposed framework to overcome the shortcomings of the business model canvas. The results showed that the tool (and by extension, the framework) made the task of business model creation a quick and easy process, while at the same time covering all the required areas to create a holistic business model. The framework contributes to startup success by creating a structured approach to business development, helping to visualize the avenues for product differentiation and planning growth.

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Analytics Mistakes that Derail Software Startups

Cited by 16 publications

References 16 publications

Towards Understanding Analytics in Software Startups

Towards Understanding Analytics in Software Startups

Present Situation of Tech Startups in Bangladesh: A Case Study

The Blitz Canvas: A Business Model Innovation Framework for Software Startups

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