The document discusses analytical customer relationship management (aCRM) and how it can be integrated with knowledge management (KM) using data mining techniques. It outlines that aCRM uses data analysis to generate customer profiles and identify behavior patterns to better understand customer needs. When combined with KM, aCRM systems can support customer knowledge acquisition by collecting customer data from various sources and using that to track customer behavior patterns and predict actions. This helps businesses retain profitable customers and address their needs more efficiently.

1. Knowledge Management Technology Discussion
Integration of Knowledge Management
and analytical CRM in business

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

3. 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.
aCRM
• 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.

4. 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.

5. 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

6. Brief introduction to aCRM
Analysis is done in every aspect of business
Customer
Analytics
Channel Marketing
Analytics Analytics
Service Sales
Analytics Analytics

7. How aCRM integrate with KM by using DM techniques
External Data
Operational
Internal Data
Customer
Data
Archive Data Warehouse
Production Data

8. How aCRM integrate with KM by using DM techniques
Customer
Knowledge
Warehouse
Operational
Customer Data mining
Data techniques & tools Customer Knowledge
Warehouse • Purchasing trends
• Clustering • Prediction for sales
• Classification • Prediction for
• Neural Network marketing
• Artificial
Intelligence

9. How aCRM integrate with KM by using DM techniques
External Data
Operational Customer
Internal Data Customer Data mining Knowledge
Archive Data
Data techniques & tools Warehouse
Warehouse
Production Data
Customer Knowledge
• Purchasing trends
Analytical CRM Process • 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

10. Application of Analytical CRM
3
1 Optimize marketing effectiveness
Customer acquisition, cross-selling, up-
2 selling, retention, etc.
Analysis of customer behavior to aid product and
3 service decision making
Management decisions, e.g. financial
4 forecasting and customer profitability analysis
5 Prediction of the probability of customer defection

11. Steps in analytical CRM process
Visualizing
Definitive analysis
Preparation
Problem formulation

12. Problem formulation
Segmentation of customers
Acquisition analysis
Relation analysis
Channel or approach analysis

13. Preparation
random sample survey
relevant variables
cases
spread in scores
definitive dataset

14. Definitive analysis
Statistical techniques
Data mining
Machine leaning techniques

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

16. The essential of acquiring customer knowledge
A
Who they are?
B
How they behave?
C
What pattern they follow?

17. Collect information from
Existing Defecting New
customers customers customers

18. 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

19. 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

20. Analytical CRM system model

21. Identifying strategically significant customers
1• The first group is the high lifetime value customers.
2• The second group of strategically significant
customers are “benchmarks”
3• The third group are customers who inspire changes
in the supplying company.
4• The final group are customers who absorb a
disproportionately high volume of fixed costs.

22. Tracking and modeling customer behavior patterns
Type of Behaviour
Tracking
behaviour pattern
Target
Predictive
Customer
analysis
groups
Behaviour Monitoring Behaviour
measures Changing
pattern

23. Tracking and modeling customer behavior patterns
• Select target customer groups.
• Developing measures to monitor customer
behavior
• Tracking and generating emerging patterns
• Predicting possible actions

24. Tracking and modeling customer behavior patterns
1 2
Developing measures
Select target
to monitor
customer groups customer behaviour
3 4
Tracking and generating
Predicting
emerging patterns possible actions

25. 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.

26. Thank You!