South African Financial Industry

Non-traditional participants continue to aggressively explore revenue opportunities offered by the South African banking industry. The increased market threats, highly competitive and continually changing environment has led to Strategy& (2017) to refer to it as "a market place with no boundaries". Figure 1 below, shows how the South African financial industry has progressed into the current state from just over a decade ago.

The environment is characterised by intense competition, globalisation, heightened customer mobility and demand and deregulation (Bedeley, 2014; The Banking Association South Africa, 2014). Advancements of digital solutions has been the key enabler for the non-traditional participants to re-shape their value proposition and endeavour into the banking market. This has resulted in empowered global customers (Somal, 2017). To combat the increasing threats and outperform the new entrants, traditional banks continue to seek out digital transformational strategies (Bedeley, 2014; Somal, 2017; Strategy&, 2017).

To South African traditional banks, the more threatening challenge is customer retention over attraction (Bedeley, 2014). They acquire vast customer data from the large market share evident in the second paragraph. This data continues to emerge at an alarming rate, due to increased market shared, increased customer base and innovation (Bedeley, 2014).

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The South African Banks have the opportunity to develop more customer centric strategies to respond to the wealth of data at hand. The key lies with translating this customer data into insight to enhance relationships with existing customers. Referred to, in the modern age, as data analysis. Data collection and analysis need to be a crucial part of business strategy (Somal, 2017). Data analysis depicts what has changed, and how to respond to it (McKinsey&Company, 2018; SAS, 2018).

This implies the data capture, storage, processing and analysing strategies must make full use of the technologies available to take up the challenges born from the data surge (Bedeley, 2014). Harvesting data and looking for patterns and anomalies to provide insight lead to better business decision making and outcomes.

This is not limited to, but includes, reduces operational costs, business risk analysis, reduced business uncertainty, consumer behavioural predictions, and guide smarter strategies to optimise current offerings or develop new ones (Bedeley, 2014; EY,2017; Stringfellow, 2014). Collecting and analysing customer data is not a new trend, the challenge is storing vast amounts of data, but, new technologies have relieved that liability (Forrester, 2018; TDWI, 2011). Organisations that adopt data analysis surpass their competition by 5% in productivity, and 6% in profitability (EY, 2017; Stringfellow, 2014).

According to EY (2017), by 2020, each human being will generate 1.7 megabytes of new information per second. And, in the past two years, human beings have generated more information in the history of humankind. Effective internal and external knowledge management grants organisations the agility to detect opportunities and threats (e.g., reacting to new products or services of competitors); grasping possible opportunities (e.g., expanding into new markets), and staying afloat in a market whilst possessing competitive advantage (e.g., digital strategies to deliver efficient products or services) (Côrte-Real, Oliveira & Ruivo, 2017; Bedeley, 2014; EY, 2017; McKinsey&Company, 2018).

Understanding the South African Market

This section aims to put into perspective the current market that South African banks serve. In 2017, 80.1% of South Africans lived in formal dwellings, 16.5% in informal dwellings, and 5.5% in traditional dwellings (StatsSA, 2017). According to a report by Standard Bank (as cited by BusinessTech, 2014), the poorest of the households in South Africa account for 62.3%, with members who earn a combined income of R7, 167 per month.

Middle class households, earning from R86, 001 to R1.48 million per annum, account for 26.4%. Affluent households account for 0.4%, with an income of more than R2.36 million per year. The bank notes, only 5.5% of households possibly have the capability to save each month; Furthermore, the affluent households have a 65% saving capability each month of their income after-tax.

The poor households contribute 11.2% to the country's income, the middle class contribute a total of 64.6%, whilst the affluent contributes 22.6%. This report highlights the severity of inequality in South Africa, whilst on the flip side highlights growth in the middle class (BusinessTech, 2016).

Businesslive (2017) states, in the fourth quarter of 2016, there were 24.31-million credit consumers in South Africa, which is 8 million more than the employed South African population; Moreover, two out of every five credit-active consumers have an impaired record, which is 40% of the 24.31-million credit-active consumers; However, other debt including loan sharks debt were not included. In 2015, the World Bank report, declared South Africans as the world's largest debtors (Businesslive, 2017).?

Costumer Satisfaction Index for the South African Banking Industry

In the fourth quarter of 2017, Consulta released a Customer Satisfaction Index (SAcsi) for the South African banking industry. This satisfaction index is based on brands exceeding or falling short of customer expectations, and the respondents' idea of the ideal product to achieve an overall result out of 100 (Consulta, 2017).

The report reveals the degree of satisfaction of South Africans with their banks. Survey participants included 13,099 bank customers across various segments selected at randomly (Consulta, 2017). The table below shows the year-on-year SAcsi scores for the South African traditional banks from the year 2017 till 2017.

Absa shows a decline in the past three years, dropping from 74.8 in 2014 to 73.3 in 2017, resulting in obtaining the bottom position among the banks included in the benchmark. Standard bank previously held the last position, but, made a recovery with a substantial 3.3% increase in 2017 from the previous year.

Nedbank suffered a 0.9% in 2017 from the previous year, 2016 77%, obtaining a 76.3% moving it to below the industry average of 77%. FNB obtained a 0.4% decrease in 2017 from the previous year, 2016 81.3%, but remains above the industry average. Capitec customers have remained the most satisfied for past five consecutive years. The 2017 Capitec score was 85.3%, 8.3% above the overall industry average.

Gap Withing the South African Banking Industry Regarding Data Analysis

Banks are only using a portion of the customer data that is available to them to generate insight to optimise current offerings. The reasons for the low insight is silos and organisational structures, skills and talent gaps, data privacy, regulatory and legal framework or ethical issues and high costs associated to data analysis strategies (Somal, 2017).

This prevents them from responding to changing customer needs; hence, leads to missed revenue opportunities. By prioritising data analysis to a key component to daily decision making, South African Banks can be equipped to integrate data from the different sources and develop solutions to better serve their customers, which will deliver noteworthy benefits (Strategy;, 2017).

The outcome of this section suggests that with data analysis South African banks can provide business value by facilitating the acquisition of supply chain and marketing knowledge (Côrte-Real et al., 2017). That translate to the right person offered the right product on the right device at the right time (Bedeley, 2014). Since banks have so much data available with the necessary analysing tools, they have a 50% chance of retaining a customer that is about to leave (Somal, 2017).

Section three forms the fundamentals of the investigation of this paper. The section begins with the use of insight harnessed from customer data analysis to enhance customer experience by reviewing closely related literature; and then grants much needed detail on the data required for this resolution; how it will be collected from diverse sources to build better models and gain more actionable insights; improved to generate the right results and avoid making incorrect conclusions; and analysed for better decision making. The section concludes by presenting some key challenges and benefits of data analysis.

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