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E-CRM E CRM is the process of maximising sales to the existing customer, encouraging continuous relationships through the use of digital communications technologies such as operational databases, personalized web messages, Customer Service, Email and Social Media Marketing. E –CRM : A changing perspective The business goals of CRM have changed little over the past 10 years. While many of the business goals of CRM have seen little change over the last 10 years, today most CRM programs, applications, and services depend more heavily on IT than in the past. These programs, software applications, and services constitute part of what is known as electronic CRM (e-CRM). Electronic CRM (e-CRM) is the electronically delivered or managed subset of CRM. It arises from the consolidation of traditional CRM with the e-business applications marketplace and covers the broad range of information technologies used to support a company’s CRM strategy. Features of E –CRM eCRM implies capabilities like self service knowledge bases, automated email response, personalization of web content, online product bundling and pricing. eCRM gives Internet users the ability to interact with the business through their preferred communication channel. It also allows business to offset expensive customer service agents with technology. eCRM puts much emphasis on the customer satisfaction and reduced cost through improved efficiency. eCRM use customer data for personalization, cross-selling and upselling. Sales Force Automation(SFA )and Enterprise Marketing Automation(EMA) is integrated in the eCRM. Advantages of e-CRM Monitoring, analysing and responding to conversations through social listening tools. Find the best ways to get involved, influence sales and generate leads by understanding social platforms. Develop customer relationship tools/self-help/service and support. Using CRM based conversations to enhance online offers. Supporting collaboration within the organisation through e-Business processes. Enhance customer experience and add value to the business. Reducing costs in customer targeting (customising emails on large scale therefore reducing costs for direct mail). Increasing the meaning of information. Better and relevant understanding of customers and relationship dynamics. Encouraging customer relationship/customer development and retention through loyalty programmes. Customer life-cycle management (CLM) encompasses a number of major management activities. Different authorities have different views of the specific activities falling under the CLM rubric. For many the cycle includes: ◗ Acquire customers. Involves the processes of identifying and obtaining new customers. Lead generation, sales promotions, marketing campaigns, and customer registration are some of the services associated with customer acquisition. ◗ Retain customers. Involves the processes of caring for customers and maintaining long-term relationships. Technical support, loyalty marketing, customer satisfaction assurance, and collections and billing are some of the services associated with customer retention. ◗ Growth. Involves the processes of enhancing the value of customers to the business. New product launches, account management, loyalty marketing, affinity programs, and up-sells and cross-sells are services associated with customer growth and development. An e-CRM system is to be accessible to customers including company users and administrators from all access support points, round-the-clock on a fundamental to e-CRM, the system must also have 24/7 basis. E-CRM in Business In business, e-CRM is used in – Customer-facing applications. These include all the areas where customers interact with the company: call centres, including help desks; sales force automation; and field service automation. Such CRM applications basically automate information flow or support employees in sales or service. It allows customers to communicate and interact with a company in whatever way they choose—voice, fax, e-mail, and Web interactivity. Autoresponders, also called infobots are e-mail on demand. Sales force automation (SFA) applications support the selling efforts of a company’s sales force, helping salespeople manage leads, prospects, and customers through the sales pipeline. Field service automation applications support the customer service efforts of field service reps and service managers. These applications manage customer service requests, service orders, service contracts, service schedules, and service calls. Customer-touching applications. In this category, customers interact directly with the applications. Notable are self-service activities, such as FAQs; campaign management; and general-purpose EC applications. Many companies provide customers with tools to create their own individual Web pages (e.g., MyYahoo!). Not only can a customer pull information from the vendor’s site, but the vendor can also push information to the consumer. In addition, these Web pages can record customer purchases and preferences. Typical personalized Web pages include those for bank accounts, stock portfolio accounts, credit card accounts, and so on. On such sites, users can see their balances, records of all current and historical transactions, and more. American Airlines is an example of one company that uses personalized Web sites to help increase the bottom line. The Web environment provides an opportunity for customers to serve themselves known as Web self-service. Probably the best known and most frequently used Web self-service systems are the package tracking systems provided by FedEx and UPS. Self-tracking refers to systems, like that of FedEx, where customers can find the status of an order or service in real. Most Web sites provide a “frequently asked questions” (FAQ) page. An FAQ page lists questions that are frequently asked by customers along with the answers to those questions. Customer-centric intelligence applications. These are applications that analyse the results of operational processing and use the results of the analysis to improve CRM applications. Reporting, data warehousing, and data mining are the prime topics here. Reporting - Reports can range from simple lists or charts of data and information to more complex analyses of CRM performance metrics. Usually, reports come in one of two forms—standardized (predefined) or query-based (ad hoc). Online analytical processing (OLAP) - Medium and large corporations often organize and store data in a central repository called a data warehouse. A data warehouse has a specialized (star schema or hierarchical) structure that makes it easy to view and analyze measures from a variety of dimensional perspectives (e.g., comparing sales data for different products sold at different stores at different times). Data mining - Data mining is another analytic activity that involves sifting through an immense amount of data to discover hidden patterns. Online networking applications. Online networking refers to methods that provide the opportunity to build personal relationships with a wide range of people. These include chat rooms, blogs, wikis, and discussion lists. Representative online networking tools and methods include the following: Forums. Available from Internet portals, forums offer users the opportunity to participate in discussions as well as to lead forums on “niche” topics (see Badjatia 2009 for discussion of the role of forums in online CRMs). Chat rooms. Found on a variety of Web sites, they offer one-to-one or many-to many real-time conversations. Usenet groups. These are collections of online discussions grouped into communities. Usenet groups existed well before the advent of the Web. Blogs and wikis. Blogs and wikis are becoming a major online networking tool. Blogs enable companies to approach focused segments of customers. Technologies of e-CRM Voice Portal - A voice portal (sometimes called a vortal) is a Web portal that can be accessed entirely by voice. Ideally, any type of information, service, or transaction found on the Internet could be accessed through a voice portal. mobile user with a cellular telephone might dial in to a voice portal Web site and request information using voice or Touchtone keys and receive the requested information from a special voice-producing program at the Web site. Voice portal interaction may involve audible speech, speech recognition or a telephone keypad interface. A consumer voice portal provides general access to information; an enterprise voice portal provides customized access to customer support. Consumer voice portals appeared in the late 1990s. Tellme Networks and Quack.com were among the first providers. Live-agent portals are introducing greater automation through speech recognition and text-to-speech technology. Enterprise voice portals manage inbound and outbound voice traffic and agent controls to manage calls within the enterprise. Web phones - WebPhone is a Voice over Internet Protocol (VoIP) service. WEB PHONE: A cellphone with Web access. Most cellphones are Internet capable and have access to the Web, e-mail and other Internet facilities. It is a technology that allows you to make voice calls using a broadband Internet connection instead of a regular (or analog) phone line. Some VoIP services may only allow you to call other people using the same service, but others may allow you to call anyone who has a telephone number including local, long distance, mobile, and international numbers. BoTs - A bot (short for "robot") is a program that operates as an agent for a user or another program or simulates a human activity. On the Internet, the most ubiquitous bots are the programs, also called spiders or crawlers, that access Web sites and gather their content for search engine indexes. A shopbot is a program that shops around the Web on your behalf. A knowbot is a program that collects knowledge for a user by automatically visiting Internet sites and gathering information that meets certain specified criteria. Virtual Customer Representative - In customer relationship management (CRM), a virtual agent (sometimes called an intelligent virtual agent, virtual rep or v-rep) is a chatterbot program that serves as an online customer service representative for an organization. Because virtual agents have a human appearance and respond appropriately to customer questions, they lend automated interactions a semblance of personal service. Combining artificial intelligence (AI) with a graphical representation, virtual agents are increasingly used in CRM to help people perform tasks such as locating information or placing orders and making reservations. Virtual agents are usually scripted to respond to a wide variety of questions and remarks. Virtual agent is also used to refer to a human agent who works over the Internet at some distance from the employer's organization. Customer Relationship Portals - A CRM portal is a way to open up the CRM system to people who are not CRM system users. This allows them to work with and get information from the company in an efficient way, regardless of the time of the day. CRM portals usually ask the user to log in. When this is done, they are connected to the CRM system, and have access to those items that the portal is set to allow. A Customer Portal may allow “SelfSupport.” A customer with a problem can search the company’s knowledge base to see if they can find a solution. In CRM, it is important to record information about each ‘touch’ that your company has with its prospects and customers. The CRM portal makes this possible for each portal interaction. You are able to track who enters the portal, as well as the information and actions they initiate. This helps enrich the data collected about each person. The idea is to provide a means for those constituencies outside the CRM system to interact with your company and to get the materials that they need, without involving a customer support agent or a sales person. The company’s customers can log into the portal and access such things as: • Enter Support Tickets and check status. • Enter Returned Material Authorizations (RMA) and check status. • See order status from the Order system. • See invoice status from the Accounting system. • Read or download Product literature and company news. • Download software updates. • Read and Print CRM reports, such as Service history report. Functional Components of CRM (Stated in Unit -1 ) 1. Operational: Because of sharing information, the processes in business should make customer’s need as first and seamlessly implement. This avoids multiple times to bother customers and redundant process. 2. Analytical: Analysis helps company maintain a long-term relationship with customers. 3. Collaborative: Due to improved communication technology, different departments in company implement (intraorganizational) or work with business partners (interorganizational) more efficiently by sharing information. Database Management A Database is a computer based software application (program) that allows input of information in a related and structured way for processing and reporting. A CRM is a database that has been designed to track interactions / interventions between an organisation and its ‘customers’. A CRM will store details on organisations / projects we work with and support, it will provide an effective way to communicate electronically and allow us to record activities that people / organisations have participated in and most importantly allow us to create reports quickly and dynamically to evidence our impact. A database management system (DBMS) is system software for creating and managing databases. The DBMS provides users and programmers with a systematic way to create, retrieve, update and manage data. Database Management consist of: 1. Database Construction: To have a 360 degree view of the customer, the CSR would require the assistance of software tools such as next generation integrations and transformation platforms that are capable of handling the complexities of transforming bare facts into useful data for efficient customer service. A unified view of customer would further mean maintaining hierarchal views of customers, linked to their transactional histories and enhanced with external demographics, dates & behavioural patterns obtained through various interactions and transactions that the customer previously had with the organisation. 2. Database Warehousing – A Data Warehouse is the main repository of the organisation’s historical data. It contains the raw material for the management decision support system. This technique is used to develop and use customer data to check profile, retention & loyalty patterns. It provides valuable input for retaining customers and developing products & service for future. A typical CRM cycle consists of front end operations that interact with the customers (call centres, target market initiatives etc.) and obtain data about him/her. This is typically consolidated from various contact points and fed into a data warehouse. The data warehouse consolidates not only the transaction data but also the data obtained from outside sources such as census and provides a fertile ground for analysis. By using data warehouse, an analyst can perform complex queries & analysis such as data mining without slowing down the operational system. 3. Data Warehouse Architecture – To design an effective and efficient data warehouse, we need to understand and analyze the business needs and construct a business analysis framework. Each person has different views regarding the design of a data warehouse. These views are as follows: • The top-down view - This view allows the selection of relevant information needed for a data warehouse. • The data source view - This view presents the information being captured, stored, and managed by the operational system. • The data warehouse view - This view includes the fact tables and dimension tables. It represents the information stored inside the data warehouse. • The business query view - It is the view of the data from the viewpoint of the end-user. Three-Tier Data Warehouse Architecture Generally a data warehouses adopts a three-tier architecture. Following are the three tiers of the data warehouse architecture. • Bottom Tier - The bottom tier of the architecture is the data warehouse database server. It is the relational database system. We use the back end tools and utilities to feed data into the bottom tier. These back end tools and utilities perform the Extract, Clean, Load, and refresh functions. • Middle Tier - In the middle tier, we have the OLAP Server that can be implemented in either of the following ways. By Relational OLAP (ROLAP), which is an extended relational database management system. The ROLAP maps the operations on multidimensional data to standard relational operations. By Multidimensional OLAP (MOLAP) model, which directly implements the multidimensional data and operations. • Top-Tier - This tier is the front-end client layer. This layer holds the query tools and reporting tools, analysis tools and data mining tools. The following diagram depicts the three-tier architecture of data warehouse: Data Mining Data mining, by its simplest definition, automates the detection of relevant patterns in a database. It is the non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Data mining uses well-established statistical and machine learning techniques to build models that predict customer behavior. Today, technology automates the mining process, integrates it with commercial data warehouses, and presents it in a relevant way for business users. Data mining extracts information from a database that the user did not know existed. Relationships between variables and customer behaviors that are non-intuitive are the jewels that data mining hopes to find. And because the user does not know beforehand what the data mining process has discovered, it is a much bigger leap to take the output of the system and translate it into a solution to a business problem. Advantage of Data Mining 1. Data mining helps marketing users to target marketing campaigns more accurately; and also to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. 2. If the necessary information exists in a database, the data mining process can model virtually any customer activity. The key is to find patterns relevant to current business problems. 3. Typical questions that data mining addresses include the following: Which customers are most likely to drop their cell phone service? • What is the probability that a customer will purchase at least $100 worth of merchandise from a particular mail-order catalog? • Which prospects are most likely to respond to a particular offer? Answers to these questions can help retain customers and increase campaign response rates, which, in turn, increase buying, cross-selling, and return on investment (ROI). 4. Data mining builds models by using inputs from a database to predict customer behavior. This behavior might be attrition at the end of a magazine subscription, cross-product purchasing, willingness to use an ATM card in place of a more expensive teller transaction, and so on. The prediction provided by a model is usually called a score. A score (typically a numerical value) is assigned to each record in the database and indicates the likelihood that the customer whose record has been scored will exhibit a particular behavior. For example, if a model predicts customer attrition, a high score indicates that a customer is likely to leave, whereas a low score indicates the opposite. After scoring a set of customers, these numerical values are used to select the most appropriate prospects for a targeted marketing campaign. 5. Database marketing software enables companies to deliver timely, pertinent, and coordinated messages and value propositions (offers or gifts perceived as valuable) to customers and prospects. Today's campaign management software goes considerably further. It manages and monitors customer communications across multiple touch-points, such as direct mail, telemarketing, customer service, point of sale, interactive web, branch office, and so on. 6. Increasing Customer Lifetime Value - Consider, for example, customers of a bank who use the institution only for a checking account. To persuade these customers to keep their money in the bank, marketing managers can use campaign management software to immediately identify large deposits and trigger a response. The system might automatically schedule a direct mail or telemarketing promotion as soon as a customer's balance exceeds a predetermined amount. Based on the size of the deposit, the triggered promotion can then provide an appropriate incentive that encourages customers to invest their money in the bank's other products. Data Mining Tools & Techniques Association Association is one of the best-known data mining technique. In association, a pattern is discovered based on a relationship between items in the same transaction. That’s is the reason why association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently purchase together. Retailers are using association technique to research customer’s buying habits. Based on historical sale data, retailers might find out that customers always buy crisps when they buy beers, and, therefore, they can put beers and crisps next to each other to save time for customer and increase sales. Classification Classification is a classic data mining technique based on machine learning. Basically, classification is used to classify each item in a set of data into one of a predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. In classification, we develop the software that can learn how to classify the data items into groups. For example, we can apply classification in the application that “given all records of employees who left the company, predict who will probably leave the company in a future period.” In this case, we divide the records of employees into two groups that named “leave” and “stay”. And then we can ask our data mining software to classify the employees into separate groups. Clustering Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique. The clustering technique defines the classes and puts objects in each class, while in the classification techniques, objects are assigned into predefined classes. To make the concept clearer, we can take book management in the library as an example. In a library, there is a wide range of books on various topics available. The challenge is how to keep those books in a way that readers can take several books on a particular topic without hassle. By using the clustering technique, we can keep books that have some kinds of similarities in one cluster or one shelf and label it with a meaningful name. If readers want to grab books in that topic, they would only have to go to that shelf instead of looking for the entire library. Prediction The prediction, as its name implied, is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables. For instance, the prediction analysis technique can be used in the sale to predict profit for the future if we consider the sale is an independent variable, profit could be a dependent variable. Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction. Sequential Patterns Sequential patterns analysis is one of data mining technique that seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period. In sales, with historical transaction data, businesses can identify a set of items that customers buy together different times in a year. Then businesses can use this information to recommend customers buy it with better deals based on their purchasing frequency in the past. Decision trees The A decision tree is one of the most common used data mining techniques because its model is easy to understand for users. In decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers. Each answer then leads to a set of questions or conditions that help us determine the data so that we can make the final decision based on it. For example, We use the following decision tree to determine whether or not to play tennis: Starting at the root node, if the outlook is overcast then we should definitely play tennis. If it is rainy, we should only play tennis if the wind is the week. And if it is sunny then we should play tennis in case the humidity is normal. We often combine two or more of those data mining techniques together to form an appropriate process that meets the business needs.