Product Development of Start-up Through Modeling of Customer Interaction Based on Data Mining

Morozov, Viktor; Mezentseva, Olga; Steshenko, Grigory; Proskurin, Maksym

doi:10.1007/978-3-030-61656-4_27

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is especially true of innovative projects based on startups. The issue of effective use of project management methods and tools in the implementation of innovations using IT was presented in [22,27]. For example, in [22], a description of the flexible methodology for managing innovative projects is given.…”

Section: Analysis Of Methods For Predicting Interactions In Startupsmentioning

confidence: 99%

Predicting Customer Churn Using Machine Learning in IT Startups

Morozov

Mezentseva

Kolomiiets

et al. 2021

Lecture Notes in Computational Intelligence and Decision Making

Self Cite

View full text Add to dashboard Cite

This work is devoted to the consideration of the issues of increasing the development of start-up projects with using modern methods of artificial intelligence. As a rule, such projects are based on innovations and their implementation requires registration as independent enterprises. Operating in market conditions, most IT companies are forced to develop new innovative ideas and present them in the form of startups At the same time, small and medium-sized businesses interact with many external independent potential customers. Such users of information systems may subsequently become clients of such enterprises. The growth and development of these SaaS enterprises relies heavily on the average customer, which is the topic of this article, with the goal of reducing customer churn. The direction of such research is addressed in many of the rules of thumb for SaaS metrics. In these conditions, it is important to take into account the completeness of the functional interaction of SaaS customers, the authors proposed a hypothesis on the possibility of using intelligent methods for predicting customer churn using deep learning neural networks. At the same time, the needs of the stakeholders of such projects should be taken into account, the satisfaction of which occurs when interacting with an innovative IT product. To describe the interactions, the authors consider mathematical models, and also propose modeling methods. To conduct chain training, Python functionality is used, with the processing of user activity datasets. In this paper, a section of the conclusion is proposed in which the results obtained are discussed and evaluated.

show abstract

Section: Analysis Of Methods For Predicting Interactions In Startupsmentioning

confidence: 99%

Predicting Customer Churn Using Machine Learning in IT Startups

Morozov

Mezentseva

Kolomiiets

et al. 2021

Lecture Notes in Computational Intelligence and Decision Making

Self Cite

View full text Add to dashboard Cite

show abstract

“…The United States Small Business Administration (USSBA) has introduced a startup as a business that is generally rooted in technology and has high potential for development and growth (3). In simple and experimental language, a startup is a new company that is established and marketed by one or more entrepreneurs during the presentation of a raw idea and to produce one or more unique products (4). Two-thirds of startups survive in the market for at least two years, and half of them survive for only four years (5).…”

Section: Introductionmentioning

confidence: 99%

Developing a Model for the Success of Healthcare Start-up Companies in Iran: A Systematic Review and Interviews with Experts

Majedi,

Meskarpour-Amiri,

Yaghoubi

2024

Preprint

View full text Add to dashboard Cite

Background: In the dynamic landscape of healthcare, start-ups drive transformative change. Understanding the pivotal factors influencing their success is paramount to their advancement. This study aims to present a comprehensive framework delineating the drivers of success for emerging businesses in healthcare. Methods: Employing a mixed-method approach, this research integrates expert interviews and a systematic review of pertinent literature. Extensive scientific exploration across databases, augmented by manual searches and expert surveys, identified 57 influential factors from 37 selected articles and interviews. Through expert consultations, additional factors were incorporated, culminating in an 11-dimensional classification. Finally, the position of the factors in 4 stages (before starting the business, during the activity, evaluation, control and feedback) was determined, and the final model was extracted. Results: Based on the inclusion and exclusion criteria, a total of 37 articles were reviewed. Eleven dimensions were utilized to classify the 57 factors identified in this study. The factors "choosing the appropriate marketing and advertising strategy" and "targeting how to finance the business" had the highest number of repetitions in the studies; 49% of these factors were related to the stage "before starting the business", and 33% were related to "during activity". Discussion: To ensure growth and development at various stages of business formation, founders of start-up businesses in the healthcare sector need to carefully consider the factors that impact success, particularly business financing and marketing and advertising strategies.

show abstract