Predicting customer behavior in a B2B market with machine learning
Aalto, Patrik (2024)
2024
Tietojohtamisen DI-ohjelma - Master's Programme in Information and Knowledge Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2024-10-10
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202409178774
https://urn.fi/URN:NBN:fi:tuni-202409178774
Tiivistelmä
In today’s data-driven business environment, understanding customer behavior is crucial for maintaining competitive advantage, particularly in B2B markets where long-term customer relationships are essential. The objective of this thesis is to develop a prediction model to predict which customers are more likely to engage in long-term service agreements and to identify the characteristics of these customers. The research utilizes a case study, where the focus is on a case company that provides cloud services and aims to deepen its understanding of customer behavior and long-term relationships to enhance its decision-making, sales processes, and customer engagement.
To address the business problem, the thesis employs both descriptive and predictive analytics techniques. Firstly, descriptive analytics is used to analyze historical data about customers, uncovering patterns and trends that characterize long-term customer engagements. This analysis provides a foundational understanding of the customer base and informs the development of the predictive model. Secondly, predictive analytics is applied to identify the case company’s clients who are likely to enter into long-term service agreements based on the characteristics and behavior patterns of the clients.
The research follows the CRISP-DM model, ensuring a structured approach from data understanding and preparation to modeling and evaluation. This model helps in building a prediction model tailored to the case company's needs.
The key findings from the study reveal that certain client characteristics, such as project history, duration of the relationship, and service acquisitions, are strong predictors of long-term relationship potential. The predictive model demonstrates relatively well prediction capability in identifying clients likely to engage in long-term agreements, providing the case company with valuable insights into understanding and nurturing these relationships.
The implications of these findings can be seen as significant for the case company's sales and marketing operations. By leveraging the predictive model, the case company can more effectively allocate resources, reduce marketing costs, and increase revenue predictability. Furthermore, this data-driven approach can enhance customer relationship management, nurturing stronger client loyalty and satisfaction. This case study not only offers a practical tool for the case company but also contributes to the broader field of business analytics by illustrating the application of both descriptive and predictive models in real-world business context.
To address the business problem, the thesis employs both descriptive and predictive analytics techniques. Firstly, descriptive analytics is used to analyze historical data about customers, uncovering patterns and trends that characterize long-term customer engagements. This analysis provides a foundational understanding of the customer base and informs the development of the predictive model. Secondly, predictive analytics is applied to identify the case company’s clients who are likely to enter into long-term service agreements based on the characteristics and behavior patterns of the clients.
The research follows the CRISP-DM model, ensuring a structured approach from data understanding and preparation to modeling and evaluation. This model helps in building a prediction model tailored to the case company's needs.
The key findings from the study reveal that certain client characteristics, such as project history, duration of the relationship, and service acquisitions, are strong predictors of long-term relationship potential. The predictive model demonstrates relatively well prediction capability in identifying clients likely to engage in long-term agreements, providing the case company with valuable insights into understanding and nurturing these relationships.
The implications of these findings can be seen as significant for the case company's sales and marketing operations. By leveraging the predictive model, the case company can more effectively allocate resources, reduce marketing costs, and increase revenue predictability. Furthermore, this data-driven approach can enhance customer relationship management, nurturing stronger client loyalty and satisfaction. This case study not only offers a practical tool for the case company but also contributes to the broader field of business analytics by illustrating the application of both descriptive and predictive models in real-world business context.