Bi crm presentation - Using Business Intelligence to Improve Customer Relations

1Proprietary and Confidential © ERP India – Vispi Munshi – erp-india.org

Using Business Intelligence to Improve Customer Relations

Vispi MunshiFounder - ERP [email protected]://www.erp-india.com



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.

Customer

DemandCreation

LeadGeneration

Lead Mgnt

OrderMgnt

Quoting

Contracts

ServiceMgmt

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

Leads

SALE

Referral

Repeat



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-ENDData Collection and

Integration Tools

ERP

Ecomm

CRM

Files

….

Data Mart

Forecasting, Mining

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.



- 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.

Data Analytics (New Age Decision Support Systems)



Multi-dimensional Data



High-end: Business Objects, OBIEE, Microstrategy, …

Open Source: http://en.wikipedia.org/wiki/Business_intelligence_tools

Data Analytics Tools in Market



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

Income

Job

Income Income

CarpenterEngineer Doctor

Bad Good Bad Good Bad Good

<30K

<40K

<50K

>50K

>90K

>100K

Predicting credit risk of a person with the jobs specified.



Case Study 2: Determining odd transactions using Clustering technique

Insurance frauds, manufacturing defects, Customer Extension ….

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.



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-ENDData Collection and

Integration Tools

ERP, CRM

Industry data

Sales

….

Customer Data Mart

Acquisition

Lead Closing

Retention

Relation Extension

Demand Creation

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



ConclusionQ&A

Thank You

[email protected]

98250 11489

Bi crm presentation - Using Business Intelligence to Improve Customer Relations

Data & Analytics

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