CRM

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

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Necessities for an effective Process Planning

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 ChangesDoes 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

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.• 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

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 CRM1. Conceptual Framework of Customer Relationship Management2. 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 Literature6. 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 APPLICATIONSThe CRM applications are a convergence of

1. Functional Components2. 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 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• 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





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 PreparationFor 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





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

• 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 VariablesStep 1: Create Analytical Customer View – “Flattening” the DataStep 2: Create Analytical VariablesStep 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 tablesStep 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 InsightStep 1: Preparing data samplesStep 2: Predictive ModelingStep 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 ModelingTwo 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

campaignGuidelines:

• 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 ModelCompare relative quality of prediction by comparing respective misclassification rates obtained on the test setExample of misclassification error rate or confusion matrix:





ActStep 1: Deliver Results to Operational SystemsStep 2: Archive ResultsStep 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 customerStep 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





CRM

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Transcript of CRM