Week12.ppt

CSE3180 Semester 1 2003 Week 12 CRM / 1

Customer Relationship ManagementCustomer Relationship Management

Customer Relationship Management is ‘an organisational discipline which includes the identification, attraction and retention of the most valuable customers in order to sustain profitable growth’. (the Economist)

It could also be the process of making and keeping customers and maximising their profitability, behaviour and satisfaction.



There are some other ‘givens’:

1. As a general rule (which seems to be accurate in many instances), 80% of revenue (or profit) is derived from 20% of a Company’s customers.

2. A Company needs customers

3. A Company needs to make profits from those customers

4. Customers should have high levels of satisfaction in transacting their business



5. The return on investment requirement leads to the selection and recognition of ‘the most valuable’ customers.

A general analysis of customers will probably show that

80% of customers come from these groups

small (purchases)

inactive (on mailing lists, or Direct Buy catalogues)

prospective - identified clients which could lead to a sale

Inactive - have been customers, but have not bought anything for a period



In the ‘top 20%’ are these customers

the best customers make up about 1%

the big customers make up about another 4%

medium customers make up about 15%

(notice the unquantified ‘best’, big’, medium’)



This could be set up as in the diagram

1% of customers

4% of customers

15% of customers

80%

Top

Big

Medium

Small

Inactive

Prospects

Suspects



A survey of a European company showed:

2150 customers Revenue $M10

Profit $900,000 (approx)

80% of its customers provided 20% of revenue

Another 5% of its customers provided 29%

Another 4% of its customers provided 27%

and 1% of its customers provided 24%

Some rough calculations showed that the average revenue per customer was $4650, profit per customer was $418, and Return on Investment (ROI) was about 9%



One of the aims of CRM analyses is to ‘accurately’ assess which grouping of customers would provide the optimum result

or, what percentage of each group could be targetted for improvement

This utilised ‘what if ’ modelling - what would be the result of say increasing the top 1% from by 6 customers, the next 4% (big) by 12 customers …. and so on.



The main problem is ‘knowing’ which groups and the relative numbers in each group to concentrate on

This is where well planned and constructed CRM databases are are essential

They should contain the required information

Information is one aspect BUT the major effort is involved in extracting ‘intelligence’ from the information stored.



Marketing and Sales statistical process control techniques are needed.

What ‘data’ is required ?

Try this :-– customer value (profit per customer, lifetime value,

NPV)– customer behaviour (revenue/customer, lifetime,

share of customer)– customer satisfaction (satisfaction scores,

defection risk, cross selling potential)



Customer Marketing: Basic Data

Name

Address

Purchasing dates

Purchasing Amounts

Purchasing values

Patterns of Purchasing

Subsidiary organisations

Methods of selling

Names of sales representatives ……………..



Customer marketing : Diagnostic data

Interviews with customers and prospective customers

Value of customers

Behaviour of customers

Satisfaction of customers

Customer focus



Customer Marketing : Decisions

Proceed or Not to Proceed on results

Which are the target improvement groups

Plan the processes for data collection and aalyses

Customer Marketing : Audit

Measure, remeasure and confirm results

Analyse the results



A working profile:

Many organisations collect and generate large volumes of data to assist them in their day to day operations.

Many organisations have ‘data warehouses’ to access this collected data

However, the difficult part is the detection of ‘important’ content of the stored data.

And this is where ‘data mining’ techniques are useful


Data Mining ?Data Mining ?

Data mining is being increasingly used to to assist in Management making better decisions in daily operations

One example is that of identifying ‘suitable candidates’ and products for cross selling

Association analysis (or market basket analysis) identifies relationships and associations among the items (or services) which customers purchase.

There is now awareness that the combination of profitability analysis and basic associations analysis can be very effective


Data MiningData Mining

Cross selling can be a major strategy for some organisations (- is it applicable to Monash University ?)

It is know that when customers have multiple association with a business, such as a bank, they are less likely to move their business to a competitor.

The loss rate for customers who have 2 accounts with a bank is estimated to be about 55%

For Customers who have 4 or more products and services with a bank, the loss rate is close to zero.



There are 2 other aspects :

1. Cross selling improves customer retention

2. It is more profitable to sell more products or services to an existing customer than to obtain a new customer

Did you know that it is generally accepted that credit card companies only start to make money in the 3rd year of doing business with a customer ?



Two of the BIG questions are :

What is the product to sell ?

To whom is the product sale directed ?

They can be part answered by a combination of ‘intuition’ (which you saw way back in Lecture 1), and by the use of data mining analysis or analyses.

In the banking industry, mortgage owners are encouraged to think about home equity loans - this is ‘intuition’ but the bank (or company) may be unaware of other opportunities with the customer



Data mining can be seen as a technique of deriving information from data

One of the techniques is called ‘association analysis’

This can identify products (or services) which can be highlighted and cross-sold to to customers

A company’s business strategy will lead to some selected products being promoted for marketing



The combination of ‘intuition’ and data mining is a sound decision

Let’s assume that the ‘cross product’ has been decided

The next step is to decide who is the ‘prospective customer’

This requires more research and analysis



There are several approaches :

1. To use association analysis to rework those customers who have been previously targeted but have not taken up the cross-sell offer

2. Another approach is to build a predictive model to show who is likely to buy specified products or services

3. Another approach is to build a model to predict the likelihood of customers, identified by association rules only, buying a product



What are Association Rules ?

There are 3 factors explored

Confidence

Support

Leverage (sometime called lift)



Confidence :

Is based on the probability that if customers buy Product a, they will also buy Product B (or in SQL like terms A determines B).

Support:

This is the frequency of occurrence of the rule in the set of records available

Leverage:

This is a complex item, but it can be stated as it being a multiplier of the probability of B in the presence of A, as opposed to the probability of B with no influence of A



You are aware that most organisations are interested in ‘profitability’ which is linked to ‘return on investment’.

2 of the ‘danger’ indicators are low or negative profitability

It’s a good move to include some form of profitability analyses with association analyses



Let’s go back to our starting example of the ‘company’ which had analysed its consumer base.

The top 1% of its customers resulted in an average of $114,000 revenue, $45,600 profit and 114% Return on Investment



The ROI reduces as the analyses approach the 80% of customers who create to 20% of revenue

At this level the revenue from each customer (average) is $1160,the profit drops to $500 and the ROI drops to -53% or (53%) - not a good number as a previous Australian Treasurer was heard to say.



An interesting aspect is that in the 80% customer bracket, experience shows that about 5 to 10% of the inhabitants have a high potential to move ‘up the ladder’ and become high-revenue, high-profit and high ROI customers

Conversely, customer identified as having ‘downwards’ profiles are normally discarded or dropped from the next marketing campaign.They may be encouraged by email or by a special promotion to ‘do better’.



As a typical profit embedded association rule :-

Visa Gold with high profitability house loan with high profitability with

support of 0.22

confidence 10.7

leverage 13.3

This is interpreted as :

when a customer has a Visa Gold card (a high profitability item), the customer is also likely to have a housing loan (high profitability) in 10.7% of cases, and this is 13.3% more likely in the overall record population of the data warehouse


A Data Mining ProcessA Data Mining Process

Extract product holding and service information for each customer

Calculate profit for each product or service

Categorise profit level for each product or service

Prepare data in a format for data mining tool use

Run association analysis with product/service embedded with profitability

Profile customer characteristics based on identified rules

Calculate the number of customers who can be cross-sold

Set up Communication channels and messages



These are some of the Data Mining functions, techniques and applications

Category Function Algorithm ApplicationPredictive Model Classification Decision Tree Targetting Marketing

Neural Networks Risk Analyses

Classification Customer Retention

Discrimination Fraud detection

Logistic Regression Bankruptcy Prediction

Forecasting Time series Statistical Time Sales Forecasting,

Forecasting series

Box-Jenkins model Interest Rate predictions

Company Loss Forecasts



The previous overheads showed you the ‘highs’ of the application of computer bases models linked with Customer Relationship Management applications

Information Technology is an incurably ‘super-optimistic’ environment

On the next overheads there are some items which may cause you to wonder ‘are the new IT techniques are successful as they seem to be ?’



The message to Management seems to be

‘learn everything about your customers’ and somehow you will be guided by all that information to deliver the goods and services which will make them happy and loyal to your company’.

Loyalty can produce profits, reduction of costs, growth and other benefits including a good return on Investment



However there is a risk involved.

There may be a wide gap between the gathering of all of the customer information and insight which may be revealed of the customers’ preferences

There is the possibility of alienating, or turning off, more customers than are being satisfied

There is a possibility that energy is being spent of what may be counter-productive results



The type of relationship between a business and its customers will vary from by the type of business and of course by individual customers

Is interaction necessary ?

Why should the customers feel that it is important to them that efforts are being made to discover more about their buying habits - and perhaps their lifestyles ?

Could customers feel that this is ‘intrusive’ and ‘not necessary’ ?



Would the customers like to be not part of the data gathering industry - and the resulting analyses ?

A conundrum: if a business fails to build a relationship with customers who value relationships, and instead focuses on what are seen as cost cutting measures, they may go elsewhere

Alternatively, if attempts are made to build relationships with customers who are more focussed on products and services, they also may go elsewhere.



Information Technologists, because of their skills, tend respond to a problem with technology, and particularly in so in the current environment where there is a high level of interest in Customer Relationships and their Management.

But it may be that more technology, or expensive technology, may not the the solution - it may make the problem worse.



What if a relationship was defined as :

A vendor with every company which made or sold every product a customer used last month, or last quarter ?

Where would the ‘data gathering’ stop ?



Is the focus on customer relationship manipulation rather than customer relationship management ?

The largest scale data gathering system is not necessarily the best one on the grounds that it is available.

A smaller-scale model might produce better results



A few suggestions:

Profile the best customers.

Determine who they are ( ? criteria) and what they buy.

Use this as the starting point of mapping the full life cycle of the ‘valuable’ customers’ dealing with the company

Map onto a timeline what events happen, and when they happen - and note the time intervals for each ‘valued’ customer.



Loyalty is more than capturing an account code, an email address, a telephone number at each transaction

Good relationships and trust are a 2 way mechanism - which take time, flexibility and minimum pressure

Hopefully, you are not totally confused, but are garnering some ideas which indicate that much skill, as well as effort, is required in successful CRM applications.



Our attention will turn now to another aspect of Information and this is the need to ensure high levels of ‘data quality’ - or the quality of data must be very high

Data quality is essential if information about customers is to produce clear, accurate and consistent information


Data QualityData Quality

How could data not be accurate ?

(or, if you like, inaccurate ?)

Missing content of fields

‘Old’ or rarely used data

An incorrect, but logical numeric address e.g. 90 Dandeong Road, instead of 900 Dandenong Road

Reversal of number in a phone contact

An unnotified change of address

Changed item numbers

Illogical or non-active web addresses



What are some of the effects ?

Multiple mail outs to the same address

No mailouts to an important address

Which is likely to be the worse of these two alternatives ?



In the health system, it is common for multiple record systems to exist - and this can mean multiple records for the same person, BUT there may be no way of tying all of the records for the same patient together

CRM applications, by their nature, invariably ‘bring together’ many pieces of data about the same entity - a customer, a supplier, a product, a process …..



There are software based ‘data cleaning’ services

Madison Information technologies

Evoke Software

MetaRecon from Metagenix

Group 1 Software - Enterprise Data Quality and HotData

Their objective ? To redo or reconstitute data so that it becomes suitable to produce

clear

accurate

consistent information



Some Pointers for Success with CRM

1. Determine and Maintain the focus of the application

2. Design the CRM territory correctly

3. Balance Detail and Summary data sets

4. Use the correct data for the application

5. Stay in synchronisation - develop the whole CRM strategy before using technology (which rarely addresses everything).



6. Plan for Today - Anticipate benefits of emerging technology

7. Develop and Action Plan

8. Integrate and associate Customer data

9. Share, don’t put data in walled environments

10. Use all communication channels available


AcknowledgementsAcknowledgements

• J.L. Weldon - EDS CRM Services, New York• B.Grime - Customer Marketing Institute• F.Reichheld - Bain and Company• S.Liu - IBM Global Business Services• J.Yap - IBM International Global Services


Database SecurityDatabase Security



• DATABASE SECURITY is the protection of a database from

• unauthorised access• unauthorised modification• destruction

• Privacy is the right of individuals to have some control over information about themselves

• Integrity refers to the correctness, completeness and consistency of stored data



Some Random Ideas:

• Physical Access controls - badges, closed circuit TV, guards...

• Terminal Authentication User I/D’s, Passwords

(System Level and Database Level)• Authorisation - Authorisation Rules

(which users can access what information

What operation users can invoke

Read Only, Read/Write, Update, Delete• User Views - non updatable access, but access to latest

level of information


Database Security Database Security

Other Tools:

Security Logs, Audit Trails, Encryption

• Data Encryption Standard• Public Key Encryption


Security Security

User

Application

Database

Security Table

user name

Authority Checks(grants)

Access authority


Security Security

Some perceptions:

1. Security is often an afterthought

2.Organisations often have no upfront planning of system-wide security

3.When systems are distributed, security reaches beyond individual databases and into the operating systems

4.No tools specifically available for either client/server or distributed database

D.Burleson, DBMS. Author of Distributed Databases


Server SecurityServer Security

1. First layer - LAN or Host Computer Operating System

(1) Login / valid username / password

(2) Privileges / permissions on directories

and files (read/write/execute/delete)

Operating System controls



2. Second Layer - Database Server

(1) Valid user accounts / password

(some servers use operating system authentication

- eliminates a level of security checking)

(2) Privileges / permissions

Database Administrator - GRANT and REVOKE

commands

Examples: Create, Alter, Drop database objects .....

(Databases, Tables, Views, Procedures ..)



More examples: Create, Alter, Drop Database Users

Start Up and Shut Down the Database Server

Customise Specific Jobs or Locations Privileges

Different Administrators and Different Functions



OBJECT PRIVILEGES

All database servers control access to :

Tables, Views, Procedures with Object Privileges

Examples: Select, Insert, Update, Delete privileges on

tables and views

References privilege (associated with referential

integrity constraints and Rules/Procedures

Execute - controls the ability to execute a Procedure



Some syntax:

GRANT select, update on nameinfo

to user1, user2, user3

GRANT execute on deletenameinfo to user4

with GRANT OPTION

[2 items here - deletenameinfo is a Procedure

and the GRANT OPTION delegates the privilege to other users.(User4 can pass on the privilege)

GRANT select (userid, username) on business to

user1, user3, user4



A result of the application of attribute lists and object privileges.

IF a server cannot insert a value for a not-null attribute, AND the attribute does not have a default attribute value, all INSERT statements on the table will :

(a) be suspended Y/N

(b) override the not-null condition Y/N

(c) fail Y/N



PRIVILEGE MANAGEMENT• Difficult to manage large numbers of users with individual

privileges

• In real life many users have the same privileges

• Common privilege users are normally associated with GROUPS (as with Unix, VMS)

A Group Privilege change affects all members of the group



ROLE Privileges

Privileges dynamically available to users of a database system during the running of an application

When the system ends, or the user quits the application, the privileges assigned to the

user(s) are disabled.



RESOURCE MANAGEMENT

Generally associated with CPU processing time

per statement (transaction), disk I/O’s per statement

(transaction), and disk space per user.



AUDITING USERS

Some server software supports the audit and analysis of individual users (Student Network system at Monash)

This facility will ‘finger’ a user who:

is deleting (or attempting to) rows from a table

requesting delete table functions

altering table names .... etc .....


Oracle SecurityOracle Security

• Security Manager

Menu Options:

- Create (a new user)

- Create Like (an existing user)

- Remove

- Revoke Privilege (remove a selected privilege)

- Add Privilege to user

- Change Account Status (enable/disable access)


Oracle SecurityOracle Security

• Role

- Create (create a role)

- Create Like (an existing role)

- Remove (delete nominated role)

- Revoke Privilege

- Add Privilege


And Microsoft Access ?And Microsoft Access ?

There are a number of privileges available tothe System Administrator.

They are similar in application to the Security featuresof Oracle, but are more limited.

Access in Network mode offers more security features.

And if you have time you could research the Security aspects of SQLServer

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