Faculty of Commerce, Law and Management (ETDs)

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Subject: search.filters.subject.Big Data Analytics

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    Big Data Analytics as a Customer Retention and Acquisition Strategy within South African Retail Banking
    (University of the Witwatersrand, Johannesburg, 2024) Kharidzha, Muano; Chalomba, Nakuze
    Big Data analytics in banking can lead to superior performance, but the industry is still working to understand its relationship with organisational impact. To stay relevant, banks should invest in big data analytics, offering customer-centric experiences for retention and acquisition strategies, as threats from non-banks threaten the banking space. This study offers a novel approach to investigating how the application of BDA in the form of personalised customer engagements, product offerings, and fraud detection in the South African banking environment can be used as a customer acquisition and retention strategy. A quantitative method of study was used, where digitally active banking customers were requested to complete an online survey. The study's overall conclusion is that, thanks to the considerable impacts of the above-mentioned BDA applications on customer experience, there is a positive correlation between BDA and customer acquisition and retention
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    The impact of big data analytics on sales performance: the mediating role of customer performance
    (University of the Witswatersrand, Johannesburg, 2023) Mngomezulu, Thandolwethu Than; Mudau, Thanyani Norman
    Big Data Analytics (BDA) systems are used for gathering, storing, processing, and generating insights from large data sets. The volume, variety and veracity of the data cannot be collected and processed using traditional database engines, therefore advanced technology is used to capture this data. The outcomes from this data are used to assist stakeholders with making decisions and drive interactions on digital platforms. Organisations are making significant investment in implementing BDA, but the understanding of the impact that these systems have on sales performance (financial outcomes) and customer performance (non-financial outcomes) is limited. It is important to understand the direct and indirect impact BDA has on organisational outcomes because in cases where the impact is indirect, organisations ought to understand what capabilities are needed to mediate the desired outcomes. BDA has the potential to be used to enhance customer performance measures, such as customer satisfaction, customer retention and customer relationship management, through its use. This allows organisations to improve how their customers perceive them, the level of service they offer, customised products and experiences and keep customers making repeat purchases. This capability may translate into happier customers and increased sales performance. To address this problem, this study investigated the impact of BDA on sales performance. Furthermore, this study investigated if customer performance measures can mediate the improvement of sales performance through the adoption and use o f BDA. The data was collected using an online survey. 200 respondents were invited to participate. 126 responded to the survey and 100 responses were valid, leaving a final response rate of 50%. Descriptive statistics was used to show distribution of the data. Exploratory Factor Analysis, as well as Confirmatory Factor Analysis, were carried out to determine uni-dimensitonality. Structural Equation Modelling was then used to measure and analyse the relationships between the model concepts. This research found that the adoption of BDA does not have a direct impact on sales performance, however the adoption of BDA improves sales performance through mediating factors, i.e., customer satisfaction, customer retention, customer relationship management and market performance. This study gives a balanced view iv | P a g e of both financial (sales) and non-financial (customer) outcomes and contributes to the body of knowledge around big data. The report made several contributions. A theoretical contribution was made by extending the conceptual model of Shahbaz et al. (2020) to include customer performance measures as mediating factors. The customer performance factors include customer satisfaction, customer retention, customer relationship management and market performance. A contextual contribution was also made by conducting the study in the South African business context. Only studies about factors affecting adoption of big data analytics have been done in the South African context and not on the benefits therefore, therefore this is a new contribution in this context. In practical terms, sales and marketing professionals can get a better understanding of the customer performance measures that can be leveraged to increase sales. This can assist practitioners to achieve their key performance indicators in organisations where big data analytics have been adopted