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)

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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