Chapter One — 1.1 Background to the Study

Over the past decade, the relationship between data cleaning and preprocessing practices and customer churn prediction accuracy has become a subject of considerable debate among scholars and industry practitioners alike, particularly within the context of Selected Commercial Banks in Nigeria where operating conditions differ markedly from more developed markets.

Within the context of Selected Commercial Banks in Nigeria, this relationship carries particular significance. Organizations in this setting operate under a distinct combination of economic, regulatory, and market conditions that may amplify or dampen the effect of data cleaning and preprocessing practices on customer churn prediction accuracy, making a context-specific inquiry both timely and necessary.

1.2 Statement of the Problem

Despite a growing body of literature on data cleaning and preprocessing practices, there remains limited consensus on the precise nature of its relationship with customer churn prediction accuracy, particularly within Selected Commercial Banks in Nigeria. Many organizations continue to make decisions about data cleaning and preprocessing practices without a clear, evidence-based understanding of how those decisions ultimately affect customer churn prediction accuracy. This gap between practice and empirical understanding is the central problem this study seeks to address.

1.3 Objectives of the Study

1. To examine the effect of Data Cleaning and Preprocessing Practices on customer churn prediction accuracy in Selected Commercial Banks in Nigeria.
2. To assess the extent to which data cleaning and preprocessing practices influences customer churn prediction accuracy within the study area.
3. To identify the challenges associated with data cleaning and preprocessing practices in relation to customer churn prediction accuracy.
4. To recommend strategies for optimizing data cleaning and preprocessing practices in order to improve customer churn prediction accuracy.

1.4 Research Questions

1. What is the effect of data cleaning and preprocessing practices on customer churn prediction accuracy in Selected Commercial Banks in Nigeria?
2. To what extent does data cleaning and preprocessing practices influence customer churn prediction accuracy within the study area?
3. What challenges are associated with data cleaning and preprocessing practices in relation to customer churn prediction accuracy?
4. What strategies can be adopted to optimize data cleaning and preprocessing practices in order to improve customer churn prediction accuracy?

1.5 Significance of the Study

Beyond its academic contribution to the field of data analysis, this study has practical value for management teams within Selected Commercial Banks in Nigeria seeking to understand how data cleaning and preprocessing practices translates into measurable outcomes around customer churn prediction accuracy. It is equally useful to students and future researchers looking for a localized empirical reference on this relationship.

1.6 Scope of the Study

In terms of scope, this MSc study confines itself to Selected Commercial Banks in Nigeria, focusing specifically on how data cleaning and preprocessing practices relates to customer churn prediction accuracy within that setting. Findings are interpreted within these boundaries rather than as universal claims applicable to every organization or market.