Download CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics

CUSTOMER RELATIONSHIP
MANAGEMENT (CRM)
CII Institute of Logistics
Session map
Session1
Session 2
• Introduction
• The new focus on customer loyalty
• CRM and Business Intelligence
• CRM Marketing initiatives
• CRM in e-business
• Partner relationship management
• Planning CRM programme
• Preparing CRM business plan
Session 3
Session 4
• Understanding and integrating CRM
with the business process
• Tools for CRM
• Choosing the CRM tool
• Putting the CRM to work
• CRM through new product development
• Channel management and CRM
• Catalytic measures to improve CRM
• Best practices in outsourcing CRM
Session 3
1.
Recap sessions1and 2
8.
CRM Software - Demo
2.
CRM implementation
9.
Data mining
3.
PFD overview (OMG – BPMN)
10.
CRM people
4.
Blue print
5.
Case study – CRM
implementation
6.
Technology
7.
CRM S/W modules
Session Summary
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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.
Session summary
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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
CRM implementation
Steps
Areas to focus
Define purpose
Customer acquisition, retention, enhancement
Define processes
Use process mapping tools (Ex. BPMN)
Create blue print
Blue print provides simple view of integrated process and data
flow across the enterprise
Use technology
Evaluate based on current industry standards (Process management,
workflow management, data warehousing and data mining)
Identify and train people
Attitude towards customers and process orientation
Execute customer centric programmes
Design and redesign marketing programmes based on insights
gained through customer data mining
Process flow diagram notations
For details refer OMG
document circulated to you
Example:
PFD
It’s like your home – A team work
Team A
ERP
Enterprise systems
Team C
CRM
Enterprise Architect team
Team B
SCM
Cost
Response
ERP
SCM
CRM
BI
(DW/DM)
Product / Service / Cash / Information flows
Customers
Suppliers
Blue print reduces complexity
Response
Cost
The technology factor
-Web enabled
-Workflow, integrated process management, role
based views, dash boards and reports
-Centralized database
Enterprise resource planning for intra business
efficiency
-Secured transactions
-High speed processing
Integrated enterprise systems
(Open source Vs Proprietary)
Supply chain management and Customer relationship
management for inter business efficiency
Data warehousing and data mining for business
intelligence, supplier intelligence and customer
intelligence
CRM software modules overview
Customer management
• Manage existing customer data
Prospect Management
• Manage prospective customer data
Loyalty management
Call center management
Service management
Promotions management
Marketing analytics and reports
• Manage rewards and cards
• Manage inbound / outbound voice and non-voice requests
• Manage customer service requests
• Plan and execute targeted promotions via SMS/Email/Phone/Post
• Customer data mining and reporting
Data mining
Five types of customer data analyses
Determine purpose of data
analysis
Classification
Regression
Decide orientation Predictive or
descriptive
Link analysis
Segmentation
Use
appropriate
algorithm
Deviation
detection
Query examples
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Database

Find all credit applicants with last name of Smith
Identify customers who have purchased more than $10,000 in the last
month.
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Find all customers who have purchased milk
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Data Mining
 Find all credit applicants who are poor credit risks. (classification)
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Identify customers with similar buying habits. (Clustering)
Find all items which are frequently purchased with milk. (association
rules)
Types of data analyses
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Classification
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Regression analysis (Predict using dependent and independent variables – Bi-variate / multivariate)
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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
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Directly related or inversely related, strong connection or weak connection between variables to understand trends
and patterns
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Market basket analysis – customer buys product A, B, C may also buy D
Segmentation or Cluster analysis
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Class A / B / C products, Low / Med / High spend customers
Identify customers with similar buying habits (Monthly provisions and personal care items together)
Deviation detection
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Sales volume Vs Stock outs 2008 Q3 – 2009 Q3
Data mining models
In simple terms
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Data mining tools
Summarization (Tables and measures of dispersion)
 Visualization (Graphs)
 Modeling (Predictive and descriptive algorithms)
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 RFM, Association rules, Time series,
Regression, Decision
trees, Case based reasoning, clustering…
The CRM people characteristics
Corporate team
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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
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Field team
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Support team
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Discipline
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Proactive in understanding customer needs
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Focus only on “In store customer experience”
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Focus only on “Presales and Post sales Customer experience”
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Collect complete data
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Validate, enter and process data
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Effective execution of offers
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Plan and execute targeted promotions
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Conflict resolution
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Preventive approach to conflict resolution
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Coordination with support team
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Coordination with field team