This research paper is focused on managing customer profitability in a competitive market by adapting dynamic and rapid competitive marketing strategies such as continuous data mining. The ever-changing customer behavior accounts for the unpredictable customer profitability, which in turn, causes inefficient and ineffective marketing planning. The use of data mining techniques develops a Customer Profitability Management (CPM) system that can be used to achieve marketing goals by allowing customers to migrate along pre-determined and desirable tracks. Hence, the CPM system is set up to emphasize continuous interplay between the active and reactive monitoring procedures to identify customer shifts in behavior.
Corporations continue to see customers as important assets and are increasingly devising ways and methods for estimating Customer Lifetime Value, which have been developed as a very important strategic marketing tool. The CLV Model has also been described in other management literature as ‘customer equity’ and ‘customer profitability’ which helps firms and corporations quantify customer relationships. Essentially, “customer profitability provides a metric for the allocation of marketing resources to customers and market segments.”
The complex interaction of the level of individual needs, marketing activity, brand perception, the competitive environment, and the influence of new technologies are the results of customer behavior. These have changed customer behavior drastically and have rendered inadequate the current CLV model, which predicts customers purchase behaviors that are based on past spending patterns or demographic characteristics. Therefore, in order to apply the CLV model effectively to a complicated open mar...
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...Customer Profitability Model: A Segment-Based Approach.” Journal of Service Research 5 (2002): 69-76.
Mulhern, Francis J. “Customer Profitability Analysis: Measurement, Concentration, and Research Directions.” Journal of Interactive Marketing 13 (1999): 25-40.
Wang, Hsiao-Fan and Wei-Kuo Hong. “Managing Customer Profitability in a Competitive Market by Continuous Data Mining” Industrial Marketing Management 35 (2006): 715-723. http://homepage.univie.ac.at/marcus.hudec/Lehre/WS%202006/Methoden%20DA/Profitabe%20CRM%20with%20Data%20Mining.pdf http://maxwellsci.com/print/rjaset/v4-5010-5015.pdf http://www.imanet.org/PDFs/Public/Research/SMA/Customer%20Profitability%20Management%20%281%29.pdf “Data Mining.” Accessed May 19, 2014. http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/
http://www.zentut.com/data-mining/advantages-and-disadvantages-of-data-mining/
RBC Financial Group uses a customer relationship management (CRM) strategy that provides a variety of services for a variety of clients. The strategy allows for individual customers to trust RBC and develop a personal relationship with each and every client. One major factor that allows CRM to operate effectively is the use of technologies and analytics to help classify each client’s financial situation. These customer profitability-based techniques allowed RBC to categorize their clients into A, B, and C groups so that the sales teams could optimize their efforts in catering to these different clients. This strategy holds the following strengths: optimizing sales efforts to different customers, easily accessible electronic sales leads, centralized and standardized financial decisions, and building personalized and sustainable customer relationships. There are a few weaknesses to the system though including the complexity in predicting future positions of companies despite the use of analytics as well as the complexity in creating consistency when using these
Soman,D & Marand, S (2009). Managing Customer Value: One Stage at a Time.: World Scientific Publishing. p9-14.
They brought different kinds of customer data, financial data, policy related data, purchase history to behavioral trending. They made the data available for reporting for trending, dashboards and Descriptive analysis. By coupling the customer data with Teradata CRM data, they were able to create segment of customer for special care and launch more targeted campaigns. By using the payment, purchase history they created customer behavioral prediction model to help the company to take personalized care of the customer which lead to increase in customer retention. By analyzing the weather patterns and inform the customer with claim process.
Data mining technology helps the companies to understand the needs of customer’s or other different segmentations through a huge modeling data and mathematical model of companies mass data stream and analysis from the massive data.[1]
In the competitive market, it’s a very big challenge for any organization to retain their customers and build a valuable place in the market to gain more customers.... ... middle of paper ... ... International Journal of Intelligent Technology, 1(1):104110, 2006. 13.
analytical aspirations become a reality. As CRM gains more knowledge about the lifecycle of the customer and their worth to the company they begin to seek ways to put this information in the hands of customer service representatives on the front lines through tools that use to data to create offers targeted to specific customers and accounts. By integrating analytics and using them to improve CRM as a distinctive capability Verizon moves through the third stage of competing on analytics Franklin, 2016).
1.1 Explain the value of customer service as a competitive tool Customer service is valued as a competitive tool by many organisations. It gives you the ability to gain customer loyalty while meeting the customer’s expectations. Staff will have the skills and knowledge that will provide a competitive edge. Most organisations are known for the quality of their customer service. This means that they are known for good customer service or poor customer service.
In general, nearly every time you surf or make a purchase online, information is collected on your actions. Then targeted advertising can be presented online, emailed, snail-mailed or even phoned to you. The business concept behind this is “best predictor of future behavior is relevant past behavior” (ala Dr. Phil). One company claims that retailers can increase their return on data mining investment by 1,000 percent.[i] The first step in understanding data mining is to look at the various ways t...
Data mining is continuingly growing in use throughout many different industries for a variety of business purposes. Some of these industries are financial and banking, healthcare, retail, etc. (Groth, 2000). One of the main uses of data mining is marketing a company’s products and or services to customers (Groth, 2000).
Many scholars believe that customer satisfaction has a crucial role in the success of a business, and is pivotal in increasing the overall profitability of the business (Kotier, 1991). Customer value is gained through the experience they receive from the goods or purchases they have obtained from a certain business. Customer value has various definitions and concepts, Holbrook (1999) stated that it is a kind of “interactive, mutual, and preferred experience”; but simply said, “the term customer value has many meanings.”
Hormazi, A. M., & Giles, S. (2004). DATA MINING: A COMPETITIVE WEAPON FOR BANKING AND RETAIL INDUSTRIES. Information Systems Management, 21(2), 62-71. Retrieved from https://tamiu.idm.oclc.org/login?url=http://search.proquest.com/docview/214122842?accountid=7081
The more profitable firms are those that are able to maintain their most valued customers throughout time. To satisfy a customer means to make him faithful and customer satisfaction becomes the index that measures the ability of the firm to produce income for the future.
Data mining as critical part of Knowledge Management process was presented by O. Folorunso and A.O. Ogunde in their article, “Data mining as a technique for knowledge management in business process redesign”. According to Mena (1999), Data Mining (DM) is the process of discovering actionable information, meaningful patterns, profiles and trends by sniffing through your data using pattern recognition technologies such as neural networks, machine learning and genetic algorithms.
100% of the respondents understand the value of good customer service and relationship as the way to retain their customers. The finding in the study was that, SMME owners and managers have a drive and understanding on meeting customer needs. Their approaches to customer retention are different but have the same goal that is to meet customer needs. These approaches ranges from, keeping customers happy at all times, providing variety of products or services, reliability, treating them as friends than customers, and partnering with the customers. In contrast to this, SMME stakeholders had a different view when it comes to SMMEs’ customer service and relationship. Both SMME stakeholders agreed that, SMMEs do not keep promises and that impact their relationship with their customers. The problem of failing to keep promises has resulted to customers leaving and finding other service providers. Based on the above argument, SMMEs owners and managers seem to have ways to keep their customers but they might not be working to their advantage. SMMEs stakeholders have confirmed that, SMMEs they lose their customers due to failure in keeping promises. This explains the low demand concern raised by respondent five.
One of the first steps to becoming a competitor is the widespread use of modeling and optimization. Instead of following basic statistical information, it is wise to look for ways to enhance profitability. To become successful at this, organizations use both internal and external information retrieved from outside sources for a vivid understanding of their consumers. Secondly, an enterprise approach is necessary. Through this approach, employees become proactive at finding out what items or processes are effective.