Bi crm presentation - Using Business Intelligence to Improve Customer Relations

Using Business Intelligence to Improve Customer Relations
Vispi Munshi
Founder - ERP India
vmunshi@erp-india.com
http://www.erp-india.com
Proprietary and Confidential © ERP India – Vispi Munshi – erp-india.org 1

Today’s Agenda
- Introduction
- What is Customer Relationship Management (CRM)
- Key CRM Functions
- What is Business Intelligence
- Data Analytics, Dashboards
- Data Mining, Predictive Analytics
- Examples of using BI in CRM
- Blueprint of a Customer Intelligence System
- Measuring Customer Relations performance, Scorecard
- Conclusion, Q&A

Ground Rules
- Please turn mobile ringer to SILENT MODE
- If you have to take a call, please leave the room and then start talking
- Questions can be asked any time
- Before asking question, raise your hand
- No talking with each other
- Disagreements allowed, but no disrespect
- Timeframe awareness
- Lets learn from each other and increase our knowledge

Introductions
- Full Name
- Where do you work (Company/Consultant/Student etc)
- Your job designation and responsibilities
- Any BI tool you have already used?
- Any CRM experience?
- Expectation from this workshop

What is Customer Relationship Management
CRM is about identifying and acquiring new customers, and
maintaining and enhancing relations with valued customers.
Demand
Creation
Service
Mgmt
Customer
Lead
Generation
Lead
Mgnt
Contracts
Order
Mgnt
Quoting
Opportunity
Mgnt

Key CRM functions
- Demand Creation (Marketing Campaigns, Promotions)
- Identify and Acquire customers (Sales Lead management)
- Proposal/Quotation and Order processing
- Order Management
- Contract Management
- Service Management
- Sales Channel Management
- Loyalty Programs
- Surveys (for feedback)

Identifying and Acquiring Customers
Call Center
Field Staff
Your Web Site
Partners
External
Sources
Suspects
Prospects
Referral
Leads
Repeat
SALE

What is business intelligence?
Business Intelligence is a term generally
used to identify a class of Information
System applications useful for supporting
operational, tactical and strategic decision
making of a organization.
BI deals with producing (and presenting)
Accurate, Relevant and Timely (ART)
INFORMATION from integrated data.
Not Accurate: Users loose faith in system
Not Relevant: Users ignore the system
Not Timely: Users find alternatives to the
system

Benefits
- Sales Optimization
- Cost Reduction, Planning and Budgeting
- Inventory Optimization
- Purchase Optimization
- HR Optimization
- Production / Manufacturing Optimization
- Demand Forecasting
- Market Competitive Analysis and Customer Relationship Optimization
- Supply Chain (Sales and Distribution) Optimization
- Most Important is ART Information to support all levels of Decision Making
- Aim is to replace/reduce Excel based Analysis

BI System Anatomy
BACK-END
Data Collection and
Integration Tools
Forecasting,
Mining
ERP
Ecomm
CRM
Files
….
Data
Mart
PRESENTATION
FRONT-END
Analytics, Visualization,
Dashboards, …
DISTRIBUTION
Alerts, Email, Mobile,
Intranet ….

Backend BI Tools: Used for collection, integration and consolidation
of data from various sources.
Intermediate BI Tools: Allows users to create forecasts and identify
trends (data mining) from the data.
Frontend BI Tools: Provides users with a interactive and intuitive
interface to view the information generated.

Data Analytics (New Age Decision Support Systems)
- Based on multi-dimensional view of data (cubes)
- Pivot Table demo
- Features for drilling in and across data
- Slicing and dicing of data
- Graphics capabilities
- Similar to Excel
- Exception reporting
- Data Analyst would use such tools and create Reports/Analysis/Graphs (objects)
- These objects would be supplied to Executives using Dashboards, Alerts etc.

Multi-dimensional Data

Data Analytics Tools in Market
High-end: Business Objects, OBIEE, Microstrategy, …
Open Source: http://en.wikipedia.org/wiki/Business_intelligence_tools

Dashboards and Alerts
- Dashboards are collection of reports/analysis/graphs
- Alerts are specific events that the system finds and sends a email/sms etc to
the executive
- Key Performance Indicators (KPI’s) can be set and linked to alerts
- Linking across objects provides executive with ability to go through the
generated information in a very intuitive manner

Data Mining
- Also known as KDD (Knowledge Discovery in Databases)
- Involves use of large data sets
- Involves uses of Statistical Methods, Database Systems and Artificial Intelligence
- The objective is to discover patterns or knowledge from existing data
- Involves four step process: Data Preparation (classification), Hypothesis (user provided
guidance), Discovery (of knowledge) and Validation (of discovered knowledge against
hypothesis)
- Techniques: Classification, Clustering, Association Rules, Regression
- High end tools: SPSS, SAS, …
- Open Source: http://en.wikipedia.org/wiki/Data_mining

Big Data
- Very large datasets (peta bytes), which cannot be handled by regular
database systems
- Applications such as bio-informatics, space research, large banks, large
ecommerce portals etc. require big data systems
- Data mining requires big data systems to generate meaningful knowledge
- Hadoop is a leader in this area

Data Visualization
- Presents a intuitive graphic representation of the data and generated
knowledge
- The visualization is a embedded feature in most Analytics and Mining tools

Predictive Analytics
- Combines Data Analytics and Statistical techniques to provide a interactive
tool
- encompasses a variety of statistical techniques from modeling, machine
learning, and data mining that analyze current and historical facts to
make predictions about future, or otherwise unknown, events
- Recency, Frequency, Transaction Value, Demographics
- Classification, Clustering, Association Rules, Regression
- http://en.wikipedia.org/wiki/Predictive_analytics

Case Study 1: Bank using Data Mining (decision tree classification
technique) to predict credit risk
Job
Carpenter
Engineer Doctor
Income
Income Income
<30K >50K <40K >90K <50K
>100K
Bad Good Bad Good Bad Good
Predicting credit risk of a person with the jobs specified.

Case Study 2: Determining odd transactions using Clustering technique
Clustering algorithms find
groups of items that are
similar. … It divides a data
set so that records with
similar content are in the
same group, and groups are
as different as possible from
each other.
Insurance frauds, manufacturing defects, Customer Extension ….

Case Study 3: Market Basket Analysis using Association Rules
• “An association algorithm creates rules that describe how often
events have occurred together.”
Example: When a customer buys a hammer, then 90% of the time they
will buy nails. HIGH SUPPORT
Example: When a customer buys a hanger set, then 3% of the time they
will buy nails. LOW SUPPORT
If SUPPORT between item pair is more than say 30% then the item pairs
can be placed on same shelf.
If SUPPORT between item pair is more than say 50% then they can be
packaged together.

More Ways to Improve Customer Relations
- Customer Profitability Analysis: which customers to value?
- Targeting Marketing Promotions: identifying suspects using data mining (clustering)
- Ecommerce Site User behavior analysis: Make special offers to convert suspects to
prospects and leads, depending on which pages of the site the suspect has visited
- Call Center Log Analysis: identifying leads using data mining or predictive analysis
- Customer Retention: identify potential leads, renewal offers
- Customer Relation Extension: identify potential leads based on service records

Customer Intelligence System
BACK-END
Data Collection and
Integration Tools
ERP,
CRM
Industry
data
Sales
….
Customer
Data
Mart
Demand Creation
Acquisition
Lead Closing
Retention
Relation Extension
Relation Recovery

Measuring CRM Performance: Customer Relation Scorecard
New Relations: 5678, 7%
Acquisition Cost: 2500/-
Lead Closing %: 34%
Renewal %: 77%
Extended Relations: 345, 1.3M
Recovered Relations: 45

Conclusion
Q&A
Thank You
vmunshi@erp-india.com
98250 11489

Bi crm presentation - Using Business Intelligence to Improve Customer Relations

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