disscusion - a crm final

Integration of Knowledge Management and analytical CRM in business

Knowledge Management Technology Discussion

Outlines

• Background• Brief introduction to aCRM• How aCRM integrate with KM by using DM techniques• Future of KM enabled aCRM• Application of Analytical CRM

Background

• Nowadays, the Customer Relationship Management (CRM) has been widely used in business organizations, leading a success in developing and retaining customer to a great extent.

• However, in the initial stages sufficient attention was not paid to analysing customer data to target the CRM efforts.

• As aCRM is currently catching up and KM methodologies are progressing, the essence of aCRM and its value can be felt in an organization only with KM and data mining (DM) principles.

• This discussion report is to show the role of KM and analytical CRM in business based in data mining technologies.

aCRM

Brief introduction to aCRM

What is aCRM? •Data stored in the contact centric database is analysed through a range of analytical tools in order to generate customer profiles, identify behaviour patterns, determine satisfaction level, and support customer segmentation.


Advantages and benefits of implementing and using aCRM

Leads in making more profitable customer base by providing high value services

Helps in retaining profitable customers through sophisticated analysis and making new customers that are clones of best of the customers

Helps in addressing individual customer’s needs and efficiently improving the relationships with new and existing customers

Improves customer satisfaction and loyalty


Analysis is done in every aspect of business

Customer Analytics

Marketing Analytics

Sales Analytics

Service Analytics

Channel Analytics

How aCRM integrate with KM by using DM techniques

Operational Customer

Data Warehouse

External Data

Internal Data

Archive Data

Production Data


Data Warehouse

Data mining techniques & tools


• Clustering• Classification• Neural Network• Artificial

Intelligence

Customer Knowledge Warehouse

Customer Knowledge• Purchasing trends• Prediction for sales• Prediction for

marketing



Data Warehouse

External Data

Internal Data

Archive Data

Production Data

Data mining techniques & tools

Customer Knowledge Warehouse

Customer Knowledge• Purchasing trends• Prediction for sales• Prediction for

marketing• Better understand customer’s needs and purchasing trends.

• Supporting executives’ interaction with customers and • More efficiently and effectively decision making

Analytical CRM Process

Customer acquisition, cross-selling, up-selling, retention, etc.2

Management decisions, e.g. financial forecasting and customer profitability analysis4

Analysis of customer behavior to aid product and service decision making33

Optimize marketing effectiveness31

Prediction of the probability of customer defection5

Application of Analytical CRM

Steps in analytical CRM process

Visualizing

Definitive analysis

Preparation

Problem formulation

Segmentation of customers

Acquisition analysis

Relation analysis

Channel or approach analysis

Problem formulation

random sample survey

relevant variables

cases

spread in scores

Preparation

definitive dataset

Statistical techniques

Data mining

Machine leaning techniques

Definitive analysis

The results in such a way that it is understandable for the users

Visualizing

AWho they are?

BHow they behave?

CWhat pattern they follow?

The essential of acquiring customer knowledge

Collect information from

Existing customers

Defectingcustomers

New customers

Finding Suggestion

• aware of the power of analytical CRM systems and the strategic importance of gaining customer knowledge

• analytical CRM systems that can support customer knowledge acquisition need to be readily available and affordable

aware of the power of analytical CRM systems And the strategic importance

of gaining customer knowledge

analytical CRM systems that can support customer knowledge acquisition

need to be readily available and affordable

Finding Suggestion

Analytical CRM system model

Identifying strategically significant customers

• The first group is the high lifetime value customers.

• The second group of strategically significant customers are “benchmarks”

• The third group are customers who inspire changes in the supplying company.

• The final group are customers who absorb a disproportionately high volume of fixed costs.

11

22

33

44

TargetCustomergroups

Type ofbehaviour

Behaviourmeasures

Tracking

Monitoring

Behaviour pattern

BehaviourChangingpattern

Predictive analysis

Tracking and modeling customer behavior patterns


• Select target customer groups.

• Developing measures to monitor customer behavior

• Tracking and generating emerging patterns

• Predicting possible actions

Predicting possible actions

Tracking and generating emerging patterns

Select target customer groups

11

33

Developing measures to monitor

customer behaviour

22

44


Future of KM enabled aCRM

• Research scope will be further increased • CRM applications will continue to attempt to focus on the customer

first to build a long-lasting mutually beneficial relationship. – Getting to “know” more about each customer through data mining techniques and

build a customer-centric business strategy.

• E-relationship management or eRM that will synchronize cross-channel relationships. – Envisioned as an “e-partnering ecosystem” with a complex network of partners that

operate as an interconnected whole, spanning entire markets and industries.

Thank You!

disscusion - a crm final

Business