Knowledge Management Technology DiscussionIntegration of Knowledge Management and analytical CRM in business
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. 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.
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 Channel Marketing Analytics Analytics Service Sales Analytics Analytics
How aCRM integrate with KM by using DM techniques External Data Operational Internal Data Customer Data Archive Data Warehouse Production Data
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
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
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
Steps in analytical CRM process Visualizing Definitive analysis Preparation Problem formulation
Problem formulation Segmentation of customers Acquisition analysis Relation analysis Channel or approach analysis
Preparation random sample survey relevant variables cases spread in scores definitive dataset
Definitive analysis Statistical techniques Data mining Machine leaning techniques
Visualizing The results in such a way that it is understandable for the users
The essential of acquiring customer knowledge A Who they are? B How they behave? C What pattern they follow?
Collect information from Existing Defecting Newcustomers customers 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
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
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.
Tracking and modeling customer behavior patterns Type of Behaviour Tracking behaviour pattern Target Predictive Customer analysis groups Behaviour Monitoring Behaviour measures Changing pattern
Tracking and modeling customer behavior patterns • Select target customer groups. • Developing measures to monitor customer behavior • Tracking and generating emerging patterns • Predicting possible actions
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
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.