Download Customer Relationship Management

This is Week 12
Customer Relationships
Some Aspects of Security
CSE3180 Semester 1 2005 Week 12 CRM / 1
Customer 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.
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Customer Relationship Management
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
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Customer Relationship Management
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
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Customer Relationship Management
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’)
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Customer Relationship Management
This could be set up as in the diagram
Top
Big
1% of customers
4% of customers
Medium
15% of customers
Small
Inactive
80%
Prospects
Suspects
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Customer Relationship Management
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%
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Customer Relationship Management
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% by 6 customers, the next 4% (big)
by 12 customers …. and so on.
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Customer Relationship Management
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.
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Customer Relationship Management
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)
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Customer Relationship Management
Customer Marketing: Basic Data
Name
Address
Purchasing dates
Purchasing Amounts
Purchasing values
Patterns of Purchasing
Subsidiary organisations
Methods of selling
Names of sales representatives ……………..
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Customer Relationship Management
Customer marketing : Diagnostic data
Interviews with customers and prospective customers
Value of customers
Behaviour of customers
Satisfaction of customers
Customer focus
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Customer Relationship Management
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
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Customer Relationship Management
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
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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
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Data 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.
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Data Mining
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 ?
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Data Mining
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’
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
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Data Mining
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
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Data Mining
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
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Data Mining
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
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Data Mining
What are Association Rules ?
There are 3 factors explored
Confidence
Support
Leverage (sometime called lift)
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Data Mining
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
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Data Mining
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
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Data Mining
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
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Data Mining
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.
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Data Mining
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’.
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Data Mining
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
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A 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
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Data Mining
These are some of the Data Mining functions, techniques and
applications
Category
Function
Algorithm
Application
Predictive 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
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Data Mining
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 ?’
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Data Mining
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
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Data Mining
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
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Customer Relationship Management
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’ ?
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Customer Relationship Management
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.
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Data Mining
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.
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Customer Relationship Management
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 ?
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Customer Relationship Management
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
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Customer Relationship Management
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.
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Customer Relationship Management
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.
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Customer Relationship Management
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
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Data 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
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Data Quality
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 ?
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Data Quality
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 …..
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Data Quality
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 CSE3180 Semester 1 2005 Week 12 CRM / 45
Customer Relationship Management
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).
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Customer Relationship Management
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
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Acknowledgements
•
•
•
•
•
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
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Database Security
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Database 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
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Database Security
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
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Database Security
Other Tools:
Security Logs, Audit Trails, Encryption
• Data Encryption Standard
• Public Key Encryption
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Security
user name
User
Application
Security Table
Authority Checks
(grants)
Database
Access authority
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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
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Server 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
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Server Security
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 ..)
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Server Security
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
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Server Security
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
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Server Security
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
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Server Security
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
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Server Security
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
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Server Security
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.
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Server Security
RESOURCE MANAGEMENT
Generally associated with CPU processing time
per statement (transaction), disk I/O’s per statement
(transaction), and disk space per user.
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Server Security
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 .....
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Oracle 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)
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Oracle Security
• Role
- Create (create a role)
- Create Like (an existing role)
- Remove (delete nominated role)
- Revoke Privilege
- Add Privilege
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And Microsoft Access ?
There are a number of privileges available to
the System Administrator.
They are similar in application to the Security features
of 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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