Chapter One — 1.1 Background to the Study

Over the past decade, the relationship between big data analytics adoption and customer churn prediction accuracy has become a subject of considerable debate among scholars and industry practitioners alike, particularly within the context of Selected Listed Manufacturing Firms in Nigeria where operating conditions differ markedly from more developed markets.

Selected Listed Manufacturing Firms in Nigeria presents a useful setting for examining this relationship precisely because the conditions there — structural, regulatory, and behavioural — differ from those typically assumed in the broader literature, most of which draws on evidence from more developed economies.

1.2 Statement of the Problem

While big data analytics adoption is widely discussed in policy and industry circles, empirical evidence on its actual effect on customer churn prediction accuracy within Selected Listed Manufacturing Firms in Nigeria remains sparse and, in places, contradictory. This lack of localized, rigorous evidence makes it difficult for decision-makers to know with confidence whether current approaches to big data analytics adoption are helping or hindering customer churn prediction accuracy — a gap this study sets out to close.

1.3 Objectives of the Study

1. To examine the effect of Big Data Analytics Adoption on customer churn prediction accuracy in Selected Listed Manufacturing Firms in Nigeria.
2. To assess the extent to which big data analytics adoption influences customer churn prediction accuracy within the study area.
3. To identify the challenges associated with big data analytics adoption in relation to customer churn prediction accuracy.
4. To recommend strategies for optimizing big data analytics adoption in order to improve customer churn prediction accuracy.

1.4 Research Questions

1. What is the effect of big data analytics adoption on customer churn prediction accuracy in Selected Listed Manufacturing Firms in Nigeria?
2. To what extent does big data analytics adoption influence customer churn prediction accuracy within the study area?
3. What challenges are associated with big data analytics adoption in relation to customer churn prediction accuracy?
4. What strategies can be adopted to optimize big data analytics adoption 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 Listed Manufacturing Firms in Nigeria seeking to understand how big data analytics adoption 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 study confines itself to Selected Listed Manufacturing Firms in Nigeria, focusing specifically on how big data analytics adoption 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.