Churn prediction based on text mining and CRM data analysis

Within quantitative marketing, churn prediction on a single customer level has become a major issue. An extensive body of literature shows that, today, churn prediction is mainly based on structured CRM data. However, in the past years, more and more digitized customer text data has become available, originating from emails, surveys or scripts of phone calls. To date, this data source remains vastly untapped for churn prediction, and corresponding methods are rarely described in literature. Filling this gap, we present a method for estimating churn probabilities directly from text data, by adopting classical text mining methods and combining them with state-of-the-art statistical prediction modelling. We transform every customer text document into a vector in a high-dimensional word space, after applying text mining pre-processing steps such as removal of stop words, stemming and word selection. The churn probability is then estimated by statistical modelling, using random forest models. We applied these methods to customer text data of a major Swiss telecommunication provider, with data originating from transcripts of phone calls between customers and call-centre agents. In addition to the analysis of the text data, a similar churn prediction was performed for the same customers, based on structured CRM data. This second approach serves as a benchmark for the text data churn prediction, and is performed by using random forest on the structured CRM data which contains more than 300 variables. Comparing the churn prediction based on text data to classical churn prediction based on structured CRM data, we found that the churn prediction based on text data performs as well as the prediction using structured CRM data. Furthermore we found that by combining both structured and text data, the prediction accuracy can be increased up to 10%. These results show clearly that text data contains valuable information and should be considered for churn estimation

Critical success factors for the successful customer relationship management: a conceptual case study

Customer Relationship Management (CRM) technology have integrated the latest information technology, including: internet and E-commerce, multi-media technology, data warehousing data mining and artificial intelligence. This is all about the value of customer relationship management. It congregate the scattering data through the process of analysis, it provide a comprehensive and holistic view of certain individual customers. Customer Relationship Management originated and prevailed among western companies, it has already spread in many East Asian countries, such as: Japan, Korean, India and China etc. In order to improve the existing CRM implementation process and enhance the success rate of the CRM implementation, we present the most important Critical Success Factors for the CRM implementation through literature reviews, the chosen CSFs were based on previous studies in the CRM implementation field, focus on the identification of CRM projects, whether they have achieved success or subject to obscure deficiency. Subsequently, the literature study will provide us a group of CSFs which considered to be a comprehensive summarization of those most important factors for CRM implementation projects. It is a challenging work, still some points are summarized

Management of Customer Relationship Management (CRM) Technological Attributes in Brazil: A B2B Relationship in the Software-Media Development Sector

Customer Relationship Management (CRM) represents a technological application based on the philosophy of Relationship Marketing and it recommends the interaction with high value consumers. Relating CRM to new social technologies, CRM 2.0 or social CRM deals with the relationship between companies and customers using online platforms such as blogs, RSS, forums and social network sites, among other facilities. Through a comparative study based on qualitative indicators, this article draws a relationship between CRM theory and practice. In two high technology organizations it was identified that, although the indicators are appropriate to the business practices, their usage and understanding are oriented by the nature of businesses and by the company characteristics. Empirical results show that technology structure, data collection and analysis tools and interactive sales tools favor CRM. In this paper, four variables are treated theoretically and empirically: Information Technology; Information tools, where the Database and the Storage of Debugged Data are included; Data Mining; and, the Sales Force Automation Facilities

A case study of predicting banking customers behaviour by using data mining

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model

Using a priori algorithm for supporting e-commerce system

The Internet technology has brought about a significant impact in doing business. It promotes the new way of doing business by enabling new system such as electronic commerce (e-commerce) to the worldwide users. Currently, the e-commerce system does not only provide electronic transactions like online payment, electronic cart shopping and ordering, and online tracking, but it must also be able to support a good relationship with their customers by providing a creative way in its business operations. It is because of many organizations having to maintain their customers by serving a good customer satisfaction. Lack understanding of the customers will cause an organization loss their customers and then would loss the company profit. This paper demonstrates the development of e-commerce system by focusing on the use of a Priori algorithm as supported feature in our e-commerce system. The feature is included to increase a good customer relationship management for the proposed system. It is hoped the proposed prototype would illustrate some practical ideas on how much advantages can be benefited from the e-commerce system and customer relationship management

Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data

Intelligent customer relationship management (ICRM) by EFLOW portal

Customer relationship management (CRM) has become a strategic initiative aimed at getting, growing, and retaining the right customers. A great amount of numeric data and even more soft information are available about customers. The strategy of building and maintaining customer relations can be described with 'if… then' rules acquired from experts. Doctus Knowledge-Based System provides a new and simplified approach in the field of knowledge management. It is able to cope with tacit and implicit rules at the same time, so decision makers can clearly see the satisfactory solution (then and there). It reasons both deductive and inductive, so it enables the user to check on the model graph why is the chosen solution in the given situation most appropriate. It is upgradeable with in telligent portal, which presents the personalized (body-tailored) information for decision makers. When we need some hard data from a database or a data warehouse, we have automatic connection between case input interface and the database. Doctus recognizes the relations between the data, it selects them and provides only the needed rules to the decision maker. Intelligent portal puts our experience on the web, so our knowledge base is constantly improving with new 'if… then' rules. We support decision mak ing with two interfaces. On the Developer Interface the attributes, the values and the 'if… then' rules can be modified. The intelligent portal is used as a managerial decision support tool. This interface can be used without seeing the knowledge base, we only see the personalized soft information. ICRM (intelligent Customer Relationship Management) helps customer to get the requested information quickly. It is also capable of customizing the questionnaires, so the customer doesn't have to answer irrelevant questions and the decision maker doesn't have to read endless reports