Machine Learning and Customer Satisfaction Report (Assessment)

One way to retain clients is by concentrating on their happiness through customer satisfaction. One crucial factor to consider is that the business has been experiencing consumer turnover for the last three years. Therefore, it may be better to utilize a combination of various machine learning tools to ensure that the decision-making process considers multiple aspects of the clients’ preferences. First, clustering can provide customers’ behavioral knowledge, reveal patterns of groups to offer personalized service recommendations, and identify best practices for operations (Lee and Shin, 2019; Briker et al., 2019). Second, classification can be applied to predict how consumers will act in the future and can be useful in optimizing the organization’s procedures (Lee and Shin, 2019; Briker et al., 2019). Third, association rules can offer suggestions based on establishing patterns and relations between clients and product properties (Briker et al., 2019). Consequently, to ensure that the company makes thorough decisions, it should invest resources in all three mentioned machine learning tools to advance operations, monitor current customer behavior, and make forecasts.

To effectively use the k-Nearest Neighbour algorithm (k-NN) and create useful managerial insights, one must understand the strengths and drawbacks of k-NN. Accordingly, the first step in utilizing k-NN should be the identification of its purposes. For instance, k-NN has trouble with big datasets and incomplete information (Triguero et al., 2018). Consequently, the next step is determining how to approach k-NN’s weaknesses, such as concentrating on data processing strategies (Triguero et al., 2018). Upon analyzing k-NN’s shortages and finding ways to handle them, organizations should identify steps for utilizing k-NN in practice, such as which data to load or how to address classification (Harrison, 2018). Accordingly, the successful use of k-NN is based on managing its drawbacks and adequately operating the algorithm.

Reference List

Briker, V., Farrow, R., Trevino, W., and Allen, B. (2019) ‘Identifying customer churn in after-market operations using machine learning algorithms’, SMU Data Science Review, 2(3), pp. 1-22.

Harrison, O. (2018) Machine learning basics with the k-nearest neighbors algorithm. Web.

Lee, I. and Shin, Y. J. (2019) ‘Machine learning for enterprises: applications, algorithm selection, and challenges’, Business Horizons, 63(2), pp. 157-170. Web.

Triguero, I., García‐Gil, D., Maillo, J., Luengo, J., García, S., & Herrera, F. (2018) ‘Transforming big data into smart data: an insight on the use of the k‐nearest neighbors algorithm to obtain quality data’, Wiley Interdisciplinary Reviews, 9(2), pp. 1- 24. Web.