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  1. 1. CRM Process CRM Process is defined as any group of action that is instrumental in the achievement of the output of an operation system in accordance with a specified measure of effectiveness. The final objective of the CRM Process is to originate a powerful new tool for customer retention The CRM implementation and success rate purely depend upon the process, which includes the future, revenue, customer value, customer retention, customer acquisition and profitability. Benefits of CRM Process Ability to retain loyal and profitable customers and channels for rapid growth of the business project. Acquiring the right customers based on known characteristics which drives growth and increased profit margins Increasing individual customer margins, while offering right products at the right time A Closed Loop CRM Process Gathering information - About customers and data comes from both external and internal sources Perform Data aggregation - Here the data is merged and compressed into a complete view of the customer. A large customer data depository is produced. Create exploration warehouses - These are the extracts of customer data needed to support specific analysis. Such as customer profitability and predictive modeling. Execution of strategies - Execute these strategies by developing and launching marketing campaigns across targeted segments of customers. Campaign execution inevitably results in an interaction with customers. Finally, once a customer interaction takes place and the customer responds, capture that response and “recycle” it to use in the on-going learning, analysis, and refinement process Necessities for an effective Process Planning Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  2. 2. CRM Process is the systematic determination of the methods by which a project is to be implemented, economically and competitively. Process planning is the intermediate stage between project kick off and implementation. Creatively translate business innovation, structural transformation and value measures into CRM investment priorities Continually refine iteration approach based on the following five major areas. • Customer • Competition • Market • Growth • Technology innovation Process Selection Procedure • Major technological changes • Minor technological changes • Specific component choice • Process flow choice Major Technological Changes Does technology exist or not ? Are there competing technologies between which we should choose? Should technology be developed in the country itself? Should innovations be licensed from foreign countries? Minor Technological Changes Once the major technological choice is made there may be a number of minor technological process available Additional Modules – Customization of Software Director should be involved in evaluating costs and for consistency of the desired product Deciding the best combination of process in terms of costs and the operation process. Specific Component Choice What type of hardware is to be used? How effectively the technology is to be used? Process flow choice - The final process selection step determines how the CRM product will move through the system An Insight into E-CRM Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  3. 3. E-CRM/On Line CRM • Online CRM implies an additional means of communication and level of interaction with the customer where there is a real difference in the technology and its architecture, which allows for ease and self service to customers Need to Adopt E-CRM • Optimize the value of inter-active relationships. • Enable business to extend its personalized messaging to the web and E-Mail. • Coordinate Marketing initiatives across all customer channels. • Leverage customer information for more effective e-marketing and e business • Focus Business on improving customer relationships and earning a greater share of each of customers business through consistent measurement, assessment and “actionable” customer contact strategies. Basic requirements of E-CRM • Electronic channels-Web Personalized E-Messaging medium for fast interaction • Enterprise-touch and shape a customers experience across the entire organization • Empowerment-Customer Empowerment • Economics-provides the greatest return customer communication initiatives. • Assessment-Evaluation results • Outside information-use of customer sanctioned external information. Steps in building an E-CRM Solution • Define business objectives • Assess its current sophistication along the E-CRM continuum in terms of process and technology. • Define Business processes and changes needed to support its goals. • Plan and timetable to implement processes. • Erect the necessary data warehouse to attain a consolidated view of the customers • Select implement and integrate the required E-CRM applications. Three Dimensions of E-CRM • Acquisition Getting • Expansion • Retention Key E-CRM Features • Focused on process-reduces the technology gap. Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  4. 4. • Driven by a Data warehouse • Focused on a multi-channel view of customer actions Across channels. • Based on consistent metrics to asses customer actions across channels. • Built to accommodate the new market dynamics that place the customer in control. • Structured to identify a customers profitability or profit potential and determine effective investment allocation decisions accordingly • Scalable to meet growth and performance needs Introduction to CRM Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  5. 5. The approaches in marketing functions are constantly changing in tune with the changing challenges that are faced at the market fronts. Today, marketers consider retaining of customers as a much more challenging job than acquiring customers in the context of growing competitive forces. Thus, the traditional transactional approach of marketing became insufficient to achieve the marketing goals and necessitated the emergence of a new approach namely Relationship Marketing. Traditional Transaction Approach: The focus on this type of approach is on individual transaction and does not concern continuous relationship with customers. It does not contain a strategic long term perspective. The customers are viewed as outsiders to the business. Very little attention is paid to the customer services and customer commitments. Relationship Marketing: This focuses on continuous multiple transactions rather than isolated individual transactions. The customers are viewed as insiders to the business and aims to build a long -term and never -ending relationship with them. It focuses on developing a hard core loyal customers with the idea of retaining them forever. The relationship Marketing approach has gradually taken the shape of Customer Relationship Management. Customer Relationship Management focuses more widely on customers and on the entire functions connected. Definition of Customer Relationship Management: “a management process of acquiring customers by understanding their requirements, retaining customers by fulfilling their requirements more than their expectations and attracting new customers through customer specific strategic marketing approaches. The process invites total commitment on the part of the entire org in evolving and implementing relationship strategies that would be rewarding to all concerned” Significance of CRM: The following are some of the points that gives an overview about the importance of Customer Relationship Management • Reduction in customer recruitment cost. • Generation of more and more loyal customers. • Expansion of customer base. • Reduction in advertisement and other sales promotion expenses. • Increase in the number of profitable customers. • Easy introduction of new products. • Easy business expansion possibilities. • Emerging Concepts of CRM 1. Conceptual Framework of Customer Relationship Management Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  6. 6. 2. CRM: A Research Agenda 3. Implications of Globalization on Customer Relationship Management 4. Effective Customer Relation Management Through Customer Knowledge Management 5. A Generalized Model for the Structure of Business Relationships: A Meta-analysis of Relationship Literature 6. Regain Management: Issues and Strategies 7. Winning Markets through Effective Customer Relationship Management CRM Portal NEED FOR CRM  Reduction in customer recruitment cost  Generation of more and more royal customer  Expansion of customer base  Reduction in advertisement & other sales promotion expenses  Benefiting customer selectivity approach  Increase in number of profitable customers  Easy introduction of new products  Easy business expansion possibilities  Increase in customer partnering CRM APPLICATIONS The CRM applications are a convergence of 1. Functional Components 2. Channels And the Functional components include:  Sales Applications  Marketing Applications  Customer Service and Support Application SALES APPLICATIONS  Sales Forecasting  Proposal Generation and Management  Compensation  Pricing  Territory Assignment and Management  Expense Reporting MARKETING APPLICATIONS  Marketing Campaign Planning, Execution and Analysis.  Budgeting and Forecasting  A Marketing Encyclopedia  List Generation and Management CUSTOMER SERVICE AND SUPPORT APPLICATIONS  Customer care Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  7. 7.  Field Service  Problem and Solution Database  Service agreements and Contracts Data Mining • Applications of Data Mining • Involvement of the three main groups participating in a data-mining project Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  8. 8. • Overview of the Data Mining Process • CRM at Work: Credite Est and Yapi Kredi Applications of Data Mining • Reducing churn with the help of predictive models, which enable early identification of those customers likely to stop doing business with the company • Increasing customer profitability by identifying customers with a high growth potential • Reducing marketing costs by more selective targeting Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  9. 9. Involvement of Business, Data Mining and IT Resources in a Typical Data Mining Project • Data mining group: – Understand the business objectives and support the business group to refine and sometimes correct the scope, and expectations – Most active during the variable selection and modeling phase – Share the obtained customer insights with the business group • IT resources: – Required for the sourcing and extraction of the required data used for modeling • Business group: – Involved in checking the plausibility and soundness of the solution in business terms – Takes the lead in deploying the new insights into corporate action such as a call center or direct mail campaign Manipulations to Data Set • Column manipulations: – Transformation – Derivation – Elimination Row manipulations – Aggregation – Change detection – Missing value detection – Outlier detection Data Preparation For modeling, incoming data is sampled and split into various streams as: • Train set: Used to build the models • Test set: Used for out-of-sample tests of the model quality and to select the final model candidate • Scoring data: Used for model-based prediction , ‘large’ as compared to other data sets Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  10. 10. Define Business Objective • Modeling of expected customer potential, in order to target acquisition of customers who will be profitable over the whole lifetime of the business relationship • Distinguish between customers with a target variable equal to zero and customers with a target variable equal to one • Establish likelihood threshold levels above which business group think a prospect should be included in the marketing campaign • Define the set of business or selection rules for the campaign (e.g.: , the customers that should be excluded from or included in the target groups) • Define the details of project execution specifying the start and delivery dates of the data mining process, and the responsible resources for each task • Define the chosen experimental setup for the campaign • Define a cost/revenue matrix describing how the business mechanics will work in the supported campaign and how it will impact the data mining process • Establish the criteria for evaluating the success of the campaign • Find a benchmark to compare against results obtained in the past for the same or similar campaign setups using traditional targeting methods, and not predictive models Cost/Revenue Matrix Get Raw Data Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  11. 11. • Identify, extract and consolidate raw data in a database (often called “Analytical Data Mart”) • Check the quality of the analytical raw data - technical checks as well as ensuring that the data makes sense in the given business context Get Raw Data (contd.) • Step 1: Looking for Data Sources – Mixed top-down and bottom-up process, driven by business requirements (top) and technical restrictions (bottom) • Step 2 : Loading the Data – Define how the data will be imported into the data mining environment • Checking Data Quality – Technical aspects of the data: primary keys, duplicate records, missing values – Business context: realistic data Step 1: Looking for Data Sources • Data warehouse infrastructures with advanced data cleansing processes can help ensure that you are working with high-quality data • Build a (simple) relational data model onto which the source data will be mapped Step 2: Loading the Data • Define further query restrictions , prepared by IT teams , for execution at pre-defined time windows in batch mode • Deliver extracted data to the data mining environment in a pre-defined format • Further processing and using data to fill previously defined data model in the data mining environment as part of the ETL process (Extract-Transform-Load) Step 3: Checking Data Quality • Assess and understand limitations of data resulting from its inherent quality (good or bad) aspects • Create an analytical database as the basis for subsequent analyses • Carry out preliminary data quality assessment – To assure an acceptable level of quality of the delivered data – To ensure that the data mining team has a clear understanding of how to interpret the data in business terms • Data miners have to carry out some basic data interpretation and aggregation exercises Identify Relevant Predictive Variables Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  12. 12. Step 1: Create Analytical Customer View – “Flattening” the Data Step 2: Create Analytical Variables Step 3: Select Predictive Variables Step 1: Create Analytical Customer View – “Flattening” the Data • Individual customer constitutes an observational unit for data analysis and predictive modeling • All data pertaining to an individual customer is contained in one observation (row, record) • Individual columns (variables, fields) represent the conditions at specific points in time or a summary over a whole period • Definition of the target or dependent variable- values should be generated for all customers and added to the existing data tables Step 2: Create Analytical Variables • Introduce additional variables derived from the original ones • When needed, transform variables to get new and more predictive variables • Increase normality of variable distributions to help the predictive model training process • Missing value management is key for enhancing the quality of the analytical data set Step 3: Select Predictive Variables • Inspect the descriptive statistics of all univariate distributions associated to all available variables • Exclude those variables: • which take on only one value (i.e. the variable is a constant) • with mostly missing values • directly or indirectly identifying an individual customer • showing collinearities • showing very little correlation with the target variable • Containing personal identifiers • Define a threshold missing value count level above which the field would be excluded from further analysis (e.g. more than 95% missing values) • Check if all variables have been mapped to the appropriate data types Gain Customer Insight Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  13. 13. Step 1: Preparing data samples Step 2: Predictive Modeling Step 3: Select Model Step 1: Preparing Data Samples • Analyze if sufficient data is available to obtain statistically significant results • If enough data is available, split the data into two samples: – the train set to fit the models – the test set to check the model’s performance on observations that have not been used to build it Step 2: Predictive Modeling Two steps: • The rules (or linear/non-linear analytical models) are built based on a training set • These rules are then applied to a new dataset for generating the answers needed for the campaign Guidelines: • Distinguish between different types of predictive models obtained through different modeling paradigms: supervised and un-supervised modeling • Find the right relationships between variables describing the customers to predict their respective group membership likelihood: purchaser or non-purchaser, referred to as scoring (e.g. between 0 and 1) • Apply unsupervised modeling where group membership is not known beforehand Step 3: Select Model Compare relative quality of prediction by comparing respective misclassification rates obtained on the test set Example of misclassification error rate or confusion matrix: Act Step 1: Deliver Results to Operational Systems Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates
  14. 14. Step 2: Archive Results Step 3 Learn Step 1: Deliver Results to Operational Systems • Apply the selected model to the entire customer base • Prepare score data set containing the most recent information for each customer with the variables required by the model • The obtained score value for each customer and the defined threshold value will determine whether the corresponding customer qualifies to participate in the campaign • When delivering results to the operational systems, provide necessary customer identifiers to unambiguously link the model’s score information to the correct customer Step 2: Archive Results • Each data mining project will produce a huge amount of information including: – raw data used – transformations for each variable – formulas for creating derived variables – train, test and score data sets – target variable calculation – models and their parameterizations – score threshold levels – final customer target selections • Useful to preserve especially if the same model is used to score different data sets obtained at different times Step 3: Learn • Referred to as “closing the loop” • Obtain the facts describing performance of data mining project and business impact • Obtained by monitoring campaign performance while it is running and from final campaign performance analysis after the campaign has ended • Detect when a model has to be re-trained Get MBA study materials, articles, order business templates and stock market updates from or or or Give your valuable feedback Join to get updates