MAXIMISING THE ECONOMIC RETURNS OF IMPROVED DATA MIMING
1.1 INTRODUCTION
The business and competitive milieu in the Financial Services Providers (FSP) environment is shifting speedily towards a global electronic arena where customers are demanding more in terms of their own innovative and specific financial products and services needs.
Efficient data mining within an organized project management (PM) team environment will ensure and enable FSP’s to convert their massive data input data into valuable information or knowledge which can not only be used to increase customer needs but also to predict possible future needs, improved marketing opportunities, increased sales and bottom line profits.
FSP’s should equip data miners better in order to re-engineer these changes and must enable decision makers to respond to these changes.
Data mining in the Financial Services environment in South Africa is totally underutilised. Although some of the best tools are available these are not used to their maximum potential. Improved data mining strategies, policies and procedures have wider economic effects and appropriate implementation will unlock economic potential for FSP’s. This thesis will propose methods and models to correct these phenomena, but with specific emphasis on Absa Financial Services (AFS).
1.2 ORIENTATION
Chapter 1 will present a sketch of what the research involved and will provide a clear understanding of the extensive intention and rationale of the study. The focus of this document is to detail the context and background for the research topic which will guide the research objective and the entire focus of the study. The chapter highlights the limitations and assumptions of the study, in order to cite the research objective.
The merits of certain theories and statements of other researchers and/or articles will be correctly tabulated against the terms of reference, the research problem and research objectives which will form the bases of this research document. This chapter concludes with the research methodology, limitations and exclusions, and a summary of all the chapters of this report.
1.3 BACKGROUND AND MOTIVATION FOR THE RESEARCH
The advent of the computer and related infrastructure changed the face of business form the 1970’s radically and rapidly. The amount of data captured has increased exponentially since then. One of the biggest problems business faces in todays extremely competitive, volatile and variable environment is the efficient sifting of this data and reproducing it in an effective and usable marketing tool.
1.3.1 HISTORY OF DATA MINING
In the mid-twentieth century, mass production techniques and mass marketing changed the competitive landscape by increasing product availability for customers. However, the purchasing process that allowed the traditional shopkeeper and customer to spend quality time getting to know each other was also fundamentally changed. Customers lost their uniqueness as they became an “account number†and shopkeepers lost track of their customers’ individual needs and behaviours as the market were overloaded with uncountable product and service options (Chen and Popovich, 2003).
Product and service companies within the Financial Service Provider (FSP) industry are becoming increasingly similar in this competitive global market. Banks and insurance companies offer similar services, products and same customer “conveniencesâ€- be it Automated Teller machines (ATM’s), Internet Banking, and special packaged products or Direct Marketing products.
1.3.2 DATABASES
Marketing efforts using improved utilisation of data mining, which is the process of extracting hidden patterns from large amounts of data in databases, can secure the competitive advantage for FSP’s.
One of the main purposes of FSP’s is to collect as much information in their customer databases about each customer as they find relevant and profitable for developing a successful marketing strategy. Access to the information is provided to all sectors of the organization involved in customer contact and to those who design products, services, or marketing programs through a process of data mining. The database and data mining are used to assist in the following:
- Innovating products and services designed to individual preferences;
- Developing individualized targeted marketing programs;
- Conducting one-on-one dialogues with a representative sample;
- Enlisting loyal customers in referral programs;
- Classifying customers by interests and profitability so as to generate special attention on those who are most likely to build additional profits;
- Devising effective marketing programs to new prospects ; and
- Providing knowledgeable customer service (Hughes, 2005).
The differentiation process through different delivery channels and the option of direct marketing efforts are complicated. Service or product companies have positioned themselves more as a communication channel through advertising and direct response marketing with the purpose of building strong corporate images and to increase profits thereby constructing relative attractiveness (Andreassen and Lindestad, 1998). This is specifically very relevant in the FSP environment, where competition between retail banks is extremely high and FSP’s are compelled to differentiate on their service predominantly through data mining activities as products no longer provide the competitive advantage.
1.3.3 FSP’S AND THEIR CUSTOMERS
The modus operandi of how FSP’s interact with their customers has altered noticeably in recent years. Sustaining business relationships between FSP’s and their customers are no longer a given fact and if FSP’s like Absa Financial Services want to survive, they cannot stagnate and wait for signs of customer dissatisfaction to surface before they react. FSP’s must be proactive and need to forecast and strategize what their existing and prospective customer’s desire.
Certain factors that are complicating data mining and customer relationship management are (Thearling, 2001):
- Data mining can increase marketing costs;
- Dense marketing cycle times are caused because of reduced customer loyalty. FSP’s continuously have to improve added customer value and they must satisfy it before competitors do;
- The market is flooded with loads of product offerings from customers who demand products that satisfy most of their needs. No inferior product offering is acceptable to customers anymore; and
- Competitors in the niche market environment are zooming in on the small, profitable market segments of FSP’s and poach this niche market away from these competitors by special offers for their specific needs.
In this global competitive environment the increase in products, customers, competition and limited time increasingly challenges FSP’s to counter changes in their business environment. It is becoming more complicated to understand customers and to adapt to continuous customer changing demands. Proper data mining management in partnership with other company departments such as marketing and finance could effectively re-engineer these thinking processes. FSP’s should overcome these factors in order to become market leaders in today’s competitive market (Thearling, 2001).
1.3.4 FSP ENVIRONMENT
Data miners should not postpone decision making and should empower managers at all levels, equipping them with accurate analyzed information and knowledge, to make decisions faster. Access to reliable, accurate, relevant and up to date customer information will improve the business marketing processes.
Certain factors that may improve the effectiveness of economic and social forces within the FSP environment are (Nemati and Barko, 2002:21):
- The massive increase in information available to customers through the Web;
- The buyer is increasingly becoming more influential than the seller; and
- The wide selection of choices of customers and the easiness in which customers can switch to different FSP’s.
Superiority in information availability is a major reason why certain FSP’s are outperforming other FSP’s (Nemati and Barko, 2002:21). Data mining enables the efficient extraction of relevant knowledge from the information in their databases to improve decision making. FSP’s are faced with the challenge to provide senior managers with efficient access to this new found knowledge.
1.3.5 DATA MINING AND THE COMPUTER
The massive multiplication of data was all triggered by the extremely fast development of computer science technology (Melab, 2001). Modern computer technology created a dynamic environment to conduct modern day busines, (Koch, 2002:17). Unfortunately, these technological capabilities were mostly applied to automate manual processes. Data mining should therefore be used to change the manner in which FSP’s generate their business in order to create and improve value and therefore maximise profits. This overload of information causes FSP’s to drown in the data that are created by computer systems rather than obtaining constructive information (Ahmed et al., 2001).
Goff (2003), after analyzing some survey questionnaires, states that managers observed some business data as “Data buried in a sea of noise!â€, “Swamped in information!†or “I’m drowning!â€
The reduced cost and ease of storing data are reasons why some FSP’s are drowning in their information. This survey also revealed that some businesses accumulate two to three times the information they had in the previous year (Goff, 2003). FSP’s should optimize these opportunities through data mining processes which convert stored data into significant information and knowledge. This is to obtain a better understanding of the business, its marketing opportunities and to empower managers to make informed and more profitable decisions.
Data mining has the capability to access and retrieve applicable data that enriches the knowledge of decision makers at all levels. The more efficiently AFS managers can access value adding data, the better their chances of establishing the perfect drivers which enable them to devise improved business strategy (Anon., 2001).
Over the past years, Absa and other FSP’s have gone to great length to capture and store maximum business related data of customers. AFS is focusing their information technology and data mining efforts towards making sense of that data.
1.3.6 FUTURE DEVELOPMENTS
Ahmed et al., (2001) expand on the above statement by stating that data mining is the new global organizational challenge and future competitiveness will depend on the ability of companies to radically and efficiently organise their raw data into intelligible and useable information. Managers cannot make informed and profitable decisions if available information cannot be accessed or utilized intelligibly.
Converting raw data into useful information or knowledge becomes the function and purpose of data miners. The traditional hierarchical organizational format will disappear and will be replaced by business re-engineering processes where data mining can play a crucial role to transform business to a more customer-oriented and knowledge based structure, i.e. a customer friendly process.
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