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CUSTOMER RELATIONSHIP MANAGEMENT (CRM)docshare01.docshare.tips/files/15240/152402211.pdfSession...
Transcript of CUSTOMER RELATIONSHIP MANAGEMENT (CRM)docshare01.docshare.tips/files/15240/152402211.pdfSession...
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 integratingCRM 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
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
Session 3 8. CRM Software - Demo
9. Data mining
10. CRM people
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.
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
CRM implementationSteps 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 OMGdocument circulated to you
Example:PFD
It’s like your home – A team work
Team AERP
Team BSCMEnterprise Architect team
Team CCRM
Enterprise systems
Blue print reduces complexity
ERP SCM
CRMBI
(DW/DM)
Sup
plie
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Cu
sto
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Cost ResponseCostResponse Product / Service / Cash / Information flows
The technology factor-Web enabled
-Workflow, integrated process management, role based views, dash boards and reports
-Centralized database
-Secured transactions
-High speed processing
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
Integrated enterprise systems
(Open source Vs Proprietary)
CRM software modules overview
• Manage existing customer dataCustomer management
• Manage prospective customer dataProspect Management
• Manage rewards and cardsLoyalty management
• Manage inbound / outbound voice and non-voice requestsCall center management
• Manage customer service requests Service management
• Plan and execute targeted promotions via SMS/Email/Phone/PostPromotions management
• Customer data mining and reporting Marketing analytics and reports
Data mining
Determine purpose of data analysis
Decide orientation -Predictive or descriptive
Use appropriate
algorithm
Classification Regression
Link analysis Segmentation
Deviation detection
Five types of customer data analyses
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)
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 Q3 – 2009 Q3
Data mining models
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…
The CRM people characteristics
Field team
Discipline
Focus only on “In store customer experience”
Collect complete data
Effective execution of offers
Conflict resolution
Coordination with support team
Support team
Proactive in understanding customer needs
Focus only on “Presales and Post sales Customer experience”
Validate, enter and process data
Plan and execute targeted promotions
Preventive approach to conflict resolution
Coordination with field team
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