This paper seeks to assess the several ways that customer relationship management aids organisations in gaining a competitive edge in a given market environment and in the implementation of other strategic organisational objectives related to a company. Customer relationship management is a crucial organisational and marketing approach which translates into improved marketing strategies. The role that customer relationship management underscores in the realisation of an organisation’s endeavours to meet its strategic core business goals makes research on the CRM critical to gaining invaluable insights which when optimised can be pivotal to an organisation’s success.
In the recent past, customer relationship management has arisen as a favoured marketing strategy which basically involves understanding the customers’ preferences and adapting them to the organisation’s overall marketing strategy. The Customer relationship management approach engages the management of an organisation to create, nurture and develop on relationships with all their customers as a means with which to retain old customers while at the same time attracting new customers. Therefore, an organisation’s ability to pinpoint and attract more customers leads to the eventual retention and articulate identification of strategic customers. Thus, customer relationship management can be considered as a continuously evolving course of action which ultimately deviates from previously upheld sentiments with regards to the traditional business strategies which focus on internalised decision making. Customer relationship management as a business strategy requires organisations to constructively interact with customers while at the same time making informed investment decisions on people, cutting edge technology and up to date business processes.
In today’s’ ever changing market environment which is increasingly becoming more and more competitive the customer has become a crucial asset and retaining customers has developed into quite a challenge for organisations. This is a challenge that organisations have to go through to retain a competitive edge in an ever evolving market environment. This has led most innovative organisations to adopt and further more invest in customer relationship management in an effort to better come to terms with what the customers expect from an organisation’s products or services and thus be able to formulate organisational strategies which can translate customer behaviour and react accordingly to the customer preferences.
It has therefore become a common marketing strategy that organisations maintain databases for customer oriented services to act as massive repositories of potentially invaluable data and knowledge which can be used and as such go a long way in determining the success of an organisation as a result of enhanced customer service. Over the past few years, data mining has been highlighted as a initiative which has contributed positively to customer relationship management such that it provides a basis for determining valuable customer preferences patterns which are not accessible to customers.
To maintain a competitive edge in an ever dynamic market environment, organisations seek to invest in the latest customer relationship management software which emphasizes on data mining. However, it is worthy to note that the implementation and subsequent effectiveness of acquired software is dependent on the efficiency of critical success factors assessed prior to software installation. This has played a critical role in the success of organisations has been the importance of incorporating critical success factors.
The objective of this research proposal is to outline the principle factors which should be comprehensively considered in order to realise success in the implementation of the customer relationship management approach with an emphasis on data mining techniques. It is evident from this statement that proper and adequate planning should ideally incorporate considerations arrived at through the proper assessment of critical success factors. This translates as to whether effectiveness and efficient implementation of customer relationship management can succeed through data mining. This paper is outlined such the second section highlights related research, the third section delves into the customer relationship management approach, the forth section concentrates on critical success factors necessary for successful customer relationship management implementation applying data mining techniques and the final section is a conclusion on the need for organisations to encourage the analysis of critical success factors prior to advancing in customer relationship management software applying data mining techniques.
Organisations around the world have embraced customer relationship management and the subsequent application of data mining. Data mining with regards to CRM and factors that have been pivotal to the success of the CRM approach and data important in the development of marketing strategies that enhance customer satisfaction with a view of customer loyalty are the basis for this paper.
As far back as 1998, data mining had been reviewed as is evident from Saarenvirta (1998). In 2007, Da Silva and Rahimi studied a model that revolved round the critical success factors for effective and efficient data mining in CRM application strategies. In 1997, Flynn and Rice appropriately discussed in a case study the need for sustenance of the analysis of critical success factors in Information Technology forecasting in relation to the requirement unique to each an every organisation for a successful CRM strategy In 2001, Chang critically established the application of different CRM outline in organisation based on the size of operations, i.e. for both large scale business entities to medium size business entities. In 2004, Wong et. Al, made the effort t examine the efficiency and effective of intelligent data mining techniques for accurate CRM analysis of customer preferences.
In 1999, Berson and others sought to expound on the construction of information technology based data mining techniques for the enhancement of he CRM approach in customer retention In 2001, Mukhopadhyay and Nath underscored the importance of periodic measurements to determine the effectiveness of the CRM approach in a number of organisations and they were subsequently competent to propose a CRM efficiency rating model.
In 2001,Edwards and other authors critically expounded on the application of data mining in a customer relationship management strategy and the ways with which the approach and technique improve on timely decision making processes.
Customer Relationship Management
CRM has been referred to as an organisational process with the sole purpose of attracting and more so retaining customers via the enhancement of interaction experience during service delivery. CRM greatly aid organisations such that it can be able to put in place strategies that can enhance retention of customers by ensuring customer loyalty in relation to its line of business. As the market environment evolves, the variety of customers also changes as does their purchasing behaviour and preferences for the services offered or goods produced by an organisation. The changes in customer preferences and an organisation’s timely response to these changes as observed have the propensity to bring about an almost immediate effect relative to an organisation’s performance through customer retention. CRM also helps organisations in decision making processes that are important in the formulation of strategies which can enhance the customer loyalty in future interactions with the organisation.
It is worthy to note that not all customers can be considered by an organisation as important with regards to their lifetime worth. This brings about the need for organisations to segment customers according to their underlying strategic importance to the organisation. CRM can therefore be considered as the acquiring, analysis and contribution to helpful information about customers and with customers. CRM therefore is critical in determining how an organisation can apportion its investments to improve customer relations so as to build an and maintain a customer base that leads to robust competitive edge in the market as well as maximising on the returns on investment. Therefore, organisations have the motivation to observe what make a particular customer segment come in or may be migrate and thus an organisation is able to implement effective means with which to retain lucrative customer segments by analysing data on customer behaviour and preferences. CRM can therefore be considered to be a rather general term used by organisations for the management of business relations with regards to customers with the sole aim of improving customer relations and enhancing customer loyalty.
In principle, for the customer relationship management approach to be sufficiently effective, the collection, analysis and sharing information and knowledge with the customer as a client for the speedy, punctual and appropriate service to the customer. CRM offers organisations with an all inclusive perspective of the customer’s experience. This begins with IT based CRM software applications whose main purpose is to capture customer preferences and behaviourisms and the analysis of such data to better understand on how best to improve on customer relations with a view of strengthening an organisations competitiveness thus maximizing on returns on investments. It is therefore paramount for an organisation to relate to customers as very crucial resources for any organisation and as such should be proactively engaged to maintain there presence through conclusive CRM strategies.
CRM can therefore be construed to imply that retailers can better understand what their customers prefer thus ensuring returns on investments. CRM embraces technology to incorporate operational strategies to the sole purpose of creating and sustaining long term relations and interactions with their customers.
Data mining is critical to every aspect of a business’ operations. However it is more prevalent in service delivery organisations. Data mining can be defined as the minor dig up of implicit, of information that was earlier on unknown which turns out to be prospectively vital information form the collected data sets. It is a process that involves the extraction of customer information from extensive data sets which is enabled through the application of algorithms and statistical methods and database management and administration systems. Data mining incorporates classification techniques for predicting changes in customer behaviour, grouping customers with similar preferences and associations through the application of genetic algorithms and a system of neural networking. All these are crucial in deciphering unknown data patterns in a data set. Such data is crucial for an organisation’s decision making process when formulating operational and marketing strategies with the aim of improving on customer royalty.
Critical success factors
In a given business environment there are crucial factors that determine the success of an organisation and as such vary from organisation to organisation depending on the operating environment. Critical success factors can be interpreted as those factors which an organisation has to focus and optimise on to realise successful business oriented strategies. They can therefore be considered to be the limited areas that provide an organisation with fitting outcomes for departments within an organisation. The ability of an organisation to pinpoint critical success factors enhances the efficiency and effectiveness with which systems are put in place. Failure to incorporate critical success factor results in less than projected outcomes.
Rapidbi (2008) described four basic critical success factors as specific features prevailing in a given industry; strategic business goals in an effort to maintain a competitive edge in a given business environment; critical success factors determined by economic changes and technological know how and lastly those that are associated with internalised organisational requirements and necessary changes. These four critical success factor are measurable and do vary with time and generally include product or service quality; workforce attitudes and temperaments; manufacturing dexterity and brand awareness.
Critical success factors for enhanced CRM implementation though the use of data mining.
These vary depending on an organisation operating environment. The customer centred approach actively engages customer information collection and analysis in the core strategies formulated by an organisation. Secondly, senior management commitment to CRM ensures a well balanced implementation procedure, thirdly the training and recruitment of software personnel also determines the success of CRM implementation and effective of customer relations. Informed project planning and scheduling; Timely feedback and monitoring of CRM strategies; Application of flawless and secure communication channels. Ensuring fluid transition from one CRM strategy to another. Lastly, the cost effectiveness of an organisation’s CRM policy.
This research proposal has tried to critically address all factors that an organisation should seek to consider prior to investing in a CRM strategy based on data mining. Through proper consideration of all possible critical success factors, an organisation can be able to successfully implement CRM strategies. This leads to better insights as to the appropriate relations with customers with the sole aim of improving on the targeted customer’s satisfaction, better profitability through gaining a competitive edge through the acknowledgement of critical success factors.