Customer Relationship Management and Data Mining - Term Paper Example

Customer Relationship Management and Data Mining Student’s Name Course Professor’s Name University City (State) Date Customer Relationship Management and Data Mining Introduction OneStop Clothing Enterprises would benefit from hiring our company to analyze and provide a data analytics model for you. The data analytics model would implement CRM (customer relationship management) and data mining. CRM entails a set of enabling systems and processes, which provide support to a company’s strategy in creating long-term beneficial relationships with the specific consumers (Jenabi & Mirroshandel 2013). The clothes retail industry is highly competitive and saturated and OneStop Clothing Enterprises has to be selective in their strategies. Various companies are expanding their networks into other countries and cities, which provide varying services, prices, and items. The outcome is an increasingly competitive environment, which ensures the companies provide their services regarding the consumers’ personal preferences. Aims and Objectives The revolution of information and communication technology provides an opportunity for clothes retail companies to create better consumer relationships. In the past, CRM software was simplified. Currently, ICT has led to an increase of consumer data making CRM more complex. The complexity has made data mining become the forefront strategy in ensuring consumer relationships are beneficial. The aim is to use CRM and data mining to increase the company’s consumer attraction and retention. Data mining entails a process, which implements various data modeling and analysis techniques that discover the relationships and patterns in a relationship. The data is used in creating accurate predictions. The company would have the ability to choose the correct prospects where they can focus on, identify the correct consumers, and provide the correct additional products (Jenabi & Mirroshandel 2013). The CRM applications, which implement data mining, are known as analytic CRM. Through a combination of the capabilities to react directly to the consumers’ needs and offering the consumer with highly customized and interactive experiences, OneStop Clothing Enterprises would have the better ability for creating, nurturing, and sustaining a long-term relationship with their consumers. The strategic goal is changing the relationships into a profitable relationship through enhancing repeat purchases while reducing the consumer acquisition expenses. The marketers’ view of the world has changed since they need to have a better understanding of the consumers’ focus and behavior. Problem Background OneStop Clothing Enterprises needs to gather information on their current and potential consumers. The information acquired would be significant for the company. It would assist the company in knowing their consumers’ behavior and preferences. This would ensure they would provide products and services that are tailored towards specific groups of individuals. One way of acquiring the consumer information is using a consumer loyalty program. Data Analytics Scenario and Methodology OneStop Clothing Enterprises would have to follow seven basic components when collecting and organizing the consumer data. The first component is the creation of a database. This would serve as the basis for all CRM activities. Since the company has already implemented a web-based application, this task would be easy. The database should include information regarding consumer transactions, contacts, reaction to the marketing stimuli, and the descriptive information. The second component is data analysis. In the past, consumer databases were analyzed to identify the consumer segments. Discriminative and cluster analysis were implemented to group together consumers who had similar descriptive data and behavioral patterns (Ngai, Xiu, & Chau 2009). The data was used to create the direct marketing campaigns and different product offerings. The direct marketers used this strategy to focus on the most beneficial prospects for their catalogue mailings. The third component is consumer selection. After creation and analysis of the database, a clothes retail company considers the consumers who should be targeted. The first consumers to be selected are often the most required segments (greatest brand loyalty or highest purchasing rates). The other segments may also be chosen depending on specific additional factors. The goal of consumer selection is implementing consumer profitability analysis when separating consumers who offer long-term benefits from those who are hurting the profits (Jenabi & Mirroshandel 2013). The fourth component is consumer targeting. Consumer targeting is often done using mass marketing approaches. The approaches include print, radio, television, and social media advertising. They generate awareness while achieving other communication goals. Other methods include direct marketing approaches. They entail direct mail, telemarketing and direct sales for specific products and services. The fifth component is relationship programs. The goal of a relationship program is the delivery of better consumer satisfaction. Managers of clothes retail companies feel that consumers match the expectations and realizations of a product’s performance. It would be critical for the company to provide the performance at higher levels since the expectations rise based on the changing consumer needs and competition (Jenabi & Mirroshandel 2013). The management has also experienced the strong relationship between profitability and consumer satisfaction. Relationship programs include consumer services, loyalty programs, personalization, community building, and reward programs. The sixth component is privacy concerns. CRM and data mining is dependent on the databases holding the consumers’ information. Companies would need a vast amount of information on the potential consumers to provide better and personalized products. With the increased implementation of the internet, advocacy groups and consumers are concerned with the information collected, stored, and how the companies use the data. The last component is metrics. Due to the increased dependency on CRM, the management of OneStop Clothing Enterprises would have to analyze the products’ success in the selected market. Market and financial indicators like profit margins, market share, and profitability would always be significant (Ngai, Xiu, & Chau 2009). However, CRM has enhanced the significance of creating consumer-centric measures, which provide a better vision on the performance of the CRM programs and policies. According to Tama (2015), marketing strategies have changed to consumer-focused strategies from brand-focused strategies. Many companies are attempting to find the factors, which support consumer satisfaction. Consumer satisfaction is considered as part of the crucial business decisions. Satisfying consumers is the heart of clothes retail companies making it a significant factor due to many reasons. Satisfying consumers is a proportion between their expectations and their perceptions (Tama 2015). The services and products offered by OneStop Clothing Enterprises should meet the consumers’ expectations. Tama (2015) states that a company should have the capability to understand the determinant factors, which drive consumer satisfaction. The determining factors provide guidelines on how the products and services offered should be designed. Attributes associated with consumer satisfaction should be analyzed. The attributes are tangible and intangible attributes. Tangible attributes include store presentation and clothe quality. The intangible attributes include the store atmosphere (cleanliness) and staff behavior. In all retail outlets, the item quality, pricing, and range create the most significant attributes. The consumer experience would be influenced by the staff friendliness and environment. Gupta and Aggarwal (2012) state that implementing data mining into CRM would entail various basic steps. The steps are defining a business problem, creating the marketing database, exploring the data, preparing the data for a modeling purpose, creating the model, evaluating the model, and deploying the model and outcomes. Pawar (2016) states that CRM is significant for all companies and provides answers regarding the relationship between the organization and their consumers. Good companies would identify the consumer problems and increase the cohesion between the company and its consumers. According to Pawar (2016), using CRM makes it easier for OneStop Clothing Enterprises to choose the right consumer from a big set of the potential consumers. Data mining would assist a company in providing the most appealing and correct product and service to a current consumer. Consumer queries can be analyzed and a company can undertake the appropriate measures. Jenabi and Mirroshandel (2013) state that through data mining, a company can interact with its consumers and understand them. This would enhance their competitive advantage by making the appropriate marketing strategies and enhance the consumers’ value. Data Mining and Extraction Techniques Tama (2015) states that the capability of acquiring and managing the consumers is an important factor in maintaining a competitive advantage. Management of consumer relationships is a hard task since individual consumers have different expectations and preferences. Overcoming the issue would require an information-based strategy that would offer responsive decision-making capabilities. Technological developments associated with information management like data mining and data warehousing allow clothing retail companies to acquire and analyze individual consumer information. Machine learning and data mining confluence within the CRM domain is widely implemented as tools for relationship discovery among the data attributes. The CRM dimensions implemented include consumer identification, consumer attraction, consumer development, and consumer retention. The techniques that OneStop Clothing Enterprises can implement in data mining for CRM are segmentation, association, response modeling, deviation detection, churn detection (Kadiyala & Srivastava 2014). Segmentation is a discovery process where a company can identify new consumers, discover new ways of interacting with the consumers, and differentiating the consumers. The clothing retail companies can gain insights into the potential consumer profiles and find better methods of providing their services and products. Segmentation ensures the consumers are grouped into various categories based on their preferences (Kadiyala & Srivastava 2014). The groupings would be used for the target marketing strategies. Through segmentation, target-marketing strategies are personalized, which enhances the probability of a consumer responding to a specific marketing strategy. An example of segmentation is clothing created for very cold climates. People experiencing winter would need specific clothing to keep them warm. Association is another technique used in data mining. Association entails gaining information regarding consumers’ related interests that would provide an opportunity for cross selling (Kadiyala & Srivastava 2014). The information regarding a consumer’s specific interest would offer insight into the associated products a consumer is likely to purchase or the marketing campaigns that would attract their attention. Association is often implemented in retail stores. Similar products are placed next to each other ensuring a consumer can view a different product while purchasing what they wanted. Predictive modeling is a data mining technique that assists in attracting and retaining the profitable consumers. The technique implements tools like response models where a consumer’s behavior is predicted with a great certainty degree (Kadiyala & Srivastava 2014). Response models are implemented when evaluating and predicting the responses of the current consumers and responses of the potential future consumers to a new product. The technique is often used in catalog and credit industries. Future potential consumers are evaluated through analysis of the current population trends. Through the evaluation, individuals with various characteristics would be classified as worthwhile consumers or not. Response modeling is also used as a more effective target marketing strategy, which would save the company’s finances. C&A, a European based fashion retailer implemented this technique, which led a six percent increase in the response rates (Kadiyala & Srivastava 2014). The company was able to target their highly responding consumers through making tailored marketing strategies. The next technique is deviation detection. This technique is considered as the most significant data mining technique. Through forensic analysis, an individual can identify any deviations from the standard. A forensic analysis would reveal any unusual pattern in a specific consumer. The company would undertake a profitability analysis and decide if the consumer is worthwhile. Another technique is churn detection (Kadiyala & Srivastava 2014). Churn is where a consumer would switch and start using or purchasing the products of a competitor. A company can analyze the profiles of consumers and find out who is likely to leave. The company would then undertake a consumer retention strategy and a loyalty program that would ensure retention of the profitable consumers. eCRM is another technique that may be implemented. As the power of internet increases, many companies are ensuring their virtual presence is available (Gupta & Aggarwal 2012). E-commerce solutions are being implemented at higher rates, which are increasing the data available regarding consumers. Web data mining has acquired popularity among many retail companies due to increased use of applications and website shopping sites. OneStop Clothing Enterprises can use eCRM to enhance their target marketing strategies, implement consumer loyalty programs, and create a loyal consumer base. Web data mining performs various functions including association, segmentation, and response prediction. The company would make use of the log files, forms, and cookies. Cookies and log files would collect consumer data without the consumer being aware. Forms would gather specific information using the consumers’ cooperation and knowledge. Analysis of a click stream would track the number of times a specific item is viewed and provide conclusions regarding the consumer’s preferences. eCRM would also enhance the consumer relationships using improved consumer feedback and support. eBay is one of the companies that has achieved a success story in implementing eCRM. Through the program, the company has experienced productivity improvement, decreased employee turnover, and enhanced decision-making. The company can respond to their consumers immediately due to the online queries platform. The last technique is pattern management. This method entails extracting all the consumers’ data and arranging them into patterns. The company can discard a large amount of historical information. New information is matched with the existing patterns. The company can find any consistency through the match. New patterns are extracted and used to assess the consumers’ behaviors. Data Mining Models Data mining is a business-driven process, which aims at a consistent implementation of profitable knowledge. As a result, it can be used for forecasting and decision-making. Through data mining, consumers may be segmented into groups based on their needs and characteristics (Gupta & Aggarwal 2012). The data mining models include classification. Classification is a common data mining learning model. The aim of classification is building a model that would predict the future consumer behaviors. It classifies a database into various predefined classes that are based on a specific criterion. The common tools used in classification are decision trees, if-then-else rules, and neural networks. Clustering is another model that OneStop Clothing Enterprises can implement. Clustering entails the segmentation of heterogeneous populations into various homogenous clusters. The clusters are not known when the algorithm begins. Discrimination analysis is used when performing clustering. Forecasting is another model. It entails estimation of the future values using the record’s patterns. Forecasting is connected to modeling and all the logical relationships associated with the model. Survival analysis is a common tool implemented during forecasting. Regression is a data-mining model, which entails statistical estimation. Statistical estimation techniques are implemented when mapping a data object to a value that would provide the prediction value. Regression may be used by the company in modeling the casual relationships, testing scientific hypotheses, prediction, and curve fitting. Logistic regression and linear regression are the common tools used during regression. Sequence discovery is a data-mining model, which entails identification of patterns or associations over a specific period (Gupta & Aggarwal 2012). The goal of sequence discovery is modeling the state of a process, which generates a sequence. It also extracts and reports any deviations and new trends over a certain period. Set theory and statistics are the most common tools used during sequence discovery. The last model is visualization. Visualization entails data presentation is a way that the users can understand the complex patterns. Visualization is often used with other models. This ensures the data mining models are easily understood and a company like OneStop Clothing Enterprises can discover relationships and patterns. The visualization models often used include 3D graphs, SeeNet, and Hygraphs. OneStop Clothing Enterprises should combine various data mining models since it would provide adequate support for the CRM strategy. When creating the consumer loyalty program for the company, the consumers would be divided into segments. The segments would be clustered and the company would apply an association model on each cluster. The association model would show the relationships between a product and the specific cluster. In target marketing, the clusters would create initial classes that would be used in a classification model. The target marketing strategy would be supported the classification model since the prediction of the consumers’ behaviors and preferences are the company’s major concern. Outcome Evaluation and Deployment The model implemented to OneStop Clothing Enterprises would be evaluated in terms of accuracy based on the company’s goals and objectives. The results would be tested to verify its accuracy regarding the consumers’ loyalty. Our company will deploy and monitor the model for a full year. OneStop Clothing Enterprises would receive regular reports that they may use to develop strategies. During that year, the company staff would be trained on how to implement the model in their daily activities. Management would be shown how to interpret data when making decisions regarding specific strategies. Project Plan Task Duration Cost ($) Defining the business problem 1 month 600 Creating the marketing database 1 week 1,600 Exploring the data 1 week - Preparing the data for a modeling purpose 1 week - creating the model 1 month 2,400 evaluating the model 1 week 1,200 deploying the model and checking outcomes 1 month 4,800 References Gupta, G, & Aggarwal, H. 2012. Improving customer relationship management using data mining. International journal of machine learning and computing, vol. 2, no. 6, pp 874-876. Jenabi, G, & Mirroshandel, S. 2013. Using data mining techniques for improving customer relationship management. European online journal of natural and social sciences, vol. 2 no. 3, pp 3143-3149. Kadiyala, S, & Srivastava, A. 2014. Data mining for customer relationship management. International business and economics research journal, vol. 6, no. 1, pp 61-70. Ngai, E, Xiu, L, & Chau, D. 2009. Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications. Pp. 2592-2602. Pawar, R. 2016. Data mining techniques: techniques for enhancing customer relationship management in fast moving consumer goods industries. International research journal of multidisciplinary studies, vol. 2, no. 2, pp 1-4 Rodpysh, K, Aghai, A, & Meysam, M. 2012. Applying data mining in customer relationship management. International journal of information technology, control and automation, vol. 2, no. 3, pp 15-24. Tama, B. 2015. Data mining for predicting customer satisfaction in fast-food restaurant. Journal of theoretical and applied information technology, vol. 75, no. 1, pp 18-23. Read More