disscusion - a crm final
DESCRIPTION
Intergration of Analitical CRM and KMTRANSCRIPT
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.
Brief introduction to aCRM
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
Brief introduction to aCRM
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
Operational Customer
Data Warehouse
Data mining techniques & tools
How aCRM integrate with KM by using DM techniques
• Clustering• Classification• Neural Network• Artificial
Intelligence
Customer Knowledge Warehouse
Customer Knowledge• Purchasing trends• Prediction for sales• Prediction for
marketing
How aCRM integrate with KM by using DM techniques
Operational Customer
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
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
Tracking and modeling customer behavior patterns
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!