CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics

Transcription

1 CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics

2 Session map Session1 Session 2 Introduction The new focus on customer loyalty CRM and Business Intelligence CRM Marketing initiatives Session 3 Session 4 Understanding and integratingcrm with the business process Tools for CRM Choosing the CRM tool Putting the CRM to work CRM in e-business Partner relationship management Planning CRM programme Preparing CRM business plan CRM through new product development Channel management and CRM Catalytic measures to improve CRM Best practices in outsourcing CRM

3 Session 3 1. Recap sessions1and 2 2. CRM implementation 3. PFD overview (OMG BPMN) 4. Blue print 5. Case study CRM implementation 6. Technology 7. CRM S/W modules 8. CRM Software - Demo 9. Data mining 10. CRM people

4 Session Summary Customer relationship programmes should result in customer acquisition, retention and enhancement to retailers. Programme design, people, processes and automation are key components for successful customer outcomes. Multi-dimensional views and deeper insights into consumer data are critical for good programme design.

5 Session summary To become customer centric, firms should shift focus from product to customer Customer segmentation helps in identifying profitable segments and deliver high value Enterprises can gradually move up in CRM maturity levels Customer satisfaction does not guarantee loyalty Continuous efforts a necessary to refocus on customer needs to be successful and profitable in competitive market

6 CRM implementation Steps Define purpose Define processes Create blue print Use technology Identify and train people Execute customer centric programmes Areas to focus Customer acquisition, retention, enhancement Use process mapping tools (Ex. BPMN) Blue print provides simple view of integrated process and data flow across the enterprise Evaluate based on current industry standards (Process management, workflow management, data warehousing and data mining) Attitude towards customers and process orientation Design and redesign marketing programmes based on insights gained through customer data mining

7 Process flow diagram notations For details refer OMG document circulated to you

8 Example: PFD

9 It s like your home A team work Team A ERP Enterprise systems Team C CRM Enterprise Architect team Team B SCM

10 Suppliers Customers Blue print reduces complexity ERP SCM CRM BI (DW/DM) Cost Response Product / Service / Cash / Information flows Response Cost

11 The technology factor -Web enabled -Workflow, integrated process management, role based views, dash boards and reports -Centralized database -Secured transactions -High speed processing Integrated enterprise systems (Open source Vs Proprietary) Enterprise resource planning for intra business efficiency Supply chain management and Customer relationship management for inter business efficiency Data warehousing and data mining for business intelligence, supplier intelligence and customer intelligence

12 CRM software modules overview Customer management Prospect Management Loyalty management Call center management Service management Promotions management Marketing analytics and reports Manage existing customer data Manage prospective customer data Manage rewards and cards Manage inbound / outbound voice and non-voice requests Manage customer service requests Plan and execute targeted promotions via SMS/ /Phone/Post Customer data mining and reporting

13 Data mining Five types of customer data analyses Determine purpose of data analysis Classification Regression Decide orientation - Predictive or descriptive Use appropriate algorithm Link analysis Deviation detection Segmentation

14 Query examples Database Find all credit applicants with last name of Smith Identify customers who have purchased more than $10,000 in the last month. Find all customers who have purchased milk Data Mining Find all credit applicants who are poor credit risks. (classification) Identify customers with similar buying habits. (Clustering) Find all items which are frequently purchased with milk. (association rules)

15 Types of data analyses Classification Class A / B / C products, Low / Med / High spend customers Regression analysis (Predict using dependent and independent variables Bi-variate / multivariate) 2009 Diwali sales INR 30Mn in Delhi because of TV promotions costing INR 3Mn, What would be 2010 Diwali sales? Link analysis or Correlation analysis Directly related or inversely related, strong connection or weak connection between variables to understand trends and patterns Market basket analysis customer buys product A, B, C may also buy D Segmentation or Cluster analysis Identify customers with similar buying habits (Monthly provisions and personal care items together) Deviation detection Sales volume Vs Stock outs 2008 Q Q3

16 Data mining models

17 In simple terms Data mining tools Summarization (Tables and measures of dispersion) Visualization (Graphs) Modeling (Predictive and descriptive algorithms) RFM, Association rules, Time series, Regression, Decision trees, Case based reasoning, clustering

18 The CRM people characteristics Corporate team Focus on business objectives Focus on full customer experience Analyze data Plan marketing strategy Resolve escalated conflicts Evaluate performance of Field and Support teams Adapt to changing demand Field team Support team Discipline Proactive in understanding customer needs Focus only on In store customer experience Focus only on Presales and Post sales Customer experience Collect complete data Validate, enter and process data Effective execution of offers Plan and execute targeted promotions Conflict resolution Preventive approach to conflict resolution Coordination with support team Coordination with field team