1. DATABASE MARKETING

2. What is data?
It is not information
It is not knowledge
Data, by itself, is a fact, or multiple
facts, or a set of values that is
available in a structured manner.
It is a kind of raw material
WHAT DOES SUCH DATA MEAN?

3. Seen out of context, without
interpretation and application of
human mind it does not mean
anything.
It is just like a nugget, an object
IT DOES NOT HAVE VALUE RELEVANCE,
OR SIGNIFICANCE

4. With interpretation, or application of
human intelligence it becomes—possibly– a
source of useful information and therefore
valuable to your business.
WHEN HUMAN INTELLIGENCE IS APPLIED
TO STUDYING DETAILS, INTERPRETING
THEM AND IDENTIFYING PATTERNS THAT
MAY BE USEFUL DATA BECOMES VALUABLE
INFORMATION
Therefore, collecting and storing data is
pertinent only if is a part of a strategic
plan.

5. DATABASES
WHAT IS A DATABASE
Database is a set of collection of such
structured facts stored in physical files and
managed by database management
systems. (DBMS)
It is collection of facts which is potentially
useful to the organisation when studied in
detail and interpreted for a purpose.

6. DATABASE contd/Organised collection of comprehensive data about individual
customers or prospects including geographic,
demographic,psychographic and behavioral data.
It gives a 360-degree view of customers and how they behave
A company is as good as its customers data base.
In consumer marketing the customer database might contain
customers’
a)demographics— age, income, family members, birthdays
b)Psychographics– Activities, interests, opinion
c) RFM of past purchases—recency , frequency and monetary value of
past purchases

7. The DBMS is a system that handles
the data so that you can access it in
a variety of ways.
DATABASE MARKETING
IT IS A TYPE OF BUSINESS METHOD.
A PROCESS WHEREBY DATABASE IS
TURNED INTO A BUSINESS
DECISION

8. WHY DATABASE MARKETING
Why Database Marketing?
Informational Reasons
Availability of abundance data
Permission to collect them
Technological Reasons
Technology to collect and warehouse data
Technology and computing power available to
analyze them

9. Availability of commercial software, some of
which are even “user friendly” (e.g. XL miner,
SAS enterprise miner)
Commercial Reason
Interest in generating intelligence to remain
competitive main market share
Drive to be efficient

10. DATABASE MARKETING-DEFINITION
A More Inclusive Definition of Database
Marketing
Database marketing is a systematic
approach to the gathering, consolidation,
and processing of marketing databases to
learn more about customers and
competitors, select target markets,
compare customers' value to the
company, provide more specialized
offerings, as well as make other marketing
and strategic decisions.

11. Data Base
MEMORY
Other
Data
Customer
Data
Transactional
Data
Data Warehousing
Data Base
Data Analytic
Tools/Software
M
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12. DATA QUALITY
VALUE OF DATA.
For data to be useful it must have certain characteristics to
make it “good data”
1 ACCURACY
A simple incorrect data entry is the most common form of
data error
2 INTEGRITY
Is the structure of data and relationship among entities and
attributes maintained consistently?
Eg “Qualifications: Graduate” used across the system or
“Qualifications=Graduate” “qualifications graduate’ also
used?

13. COMPLETENESS
Is all the data complete?
VALIDITY
Does the data value within acceptable range of business?
TIMELINESS
Is the data available when required?
ACCESSBILITY
Is the data easily accessible, understandable and usable?
It is very essential for the team in charge of data to
understand the user of dat.

14. DATA MINING

15. In the past, there was never a high level of precision when
it came to marketing your products
Trying to compare traditional marketing strategies with
data mining is like trying to compare a precision guided
missile to blindly throwing darts at a board. There is no
comparison.
The advancement of artificial intelligence, neural networks,
and computer algorithms has led to a world in which the
technology is capable of learning. Because of this, it can
look for relationships that my not be obvious to the user

16. Data mining is a technique that enables the company to
discover connections, patterns, and relationships in a set of
data with a high degree of accuracy and precision.
It is not a business solution
However, the patterns that are discovered by data mining
tools are only relevant if they are directly related to solving
a specific problem. Otherwise, any patterns that are
discovered is useless. Also, you have to look at the
coverage of your data mining tool.

17. Typical tasks addressed by data mining
include:
Rate customers by their propensity to
respond to an offer
Identify cross-sell opportunities
Detect fraud and abuse in insurance and
finance
Estimate probability of an illness reoccurrence or hospital re-admission
Isolate root causes of an outcome in
clinical studies

18. Determine optimal sets of parameters for a
production line operation
Predict peak load of a network
Without proper analytical tools, discovering useful
knowledge hidden in huge volumes of raw data
represents a formidable task.
The exponential growth in data, diverse nature of
data and analysis objectives, the complexity of
analyzing mixed structured data and text are
among the factors that turn knowledge discovery
into a real challenge.

19. Data Mining provides tools for automated learning
from historical data and developing models to
predict outcomes of future situations. The best
data mining software tools provide a variety of
machine learning algorithms for modeling, such
as Regression, Neural Network, Decision Tree,
Bayesian Network, CHAID, Support Vector
Machine, and Random Forest, to name a few.
Yet, data mining requires far more than just
machine learning. Data mining additionally
involves data pre-processing, and results
delivery.
Data pre-processing includes loading and
integrating data from various data sources,
normalizing and cleansing data, and carrying out
exploratory data analysis.

20. DATA WAREHOUSING

21. 
We build a marketing data warehouse for the main
purpose of more efficiently and profitably servicing
our customers and prospects today and in the future

Example-- a publisher of several titles that also
offers special online content such as webcasts or
paid reports..

Without such a database a publisher would not know
how to cross sell the various titles or online products
or services most effectively. The fulfillment files for
each would most likely be separate and distinct and
not allow for an efficient usage of information for
promotional decision purposes. And, even if all were
fulfilled from the same source, the data would likely
not be integrated at a customer level nor easily
accessible for marketing.

22. Although a fulfillment file contains a wealth of information, its
structure is rigid (built for fulfillment rather than marketing) and it
lacks complete information about the customer and typically deals
with only one product. Thus, alone it will not meet all of the needs
to efficiently target communications to your customers or
prospects across the company.
What are the key points that must be taken into consideration to
ensure success in the build of a marketing data warehouse.
Why Build a Marketing Data Warehouse
Top Reasons Why We Fail
What Functional Areas it Must Support
Key Profit Drivers
Who Will be Using the Data Warehouse and what it must
deliver
What should be stored on the Data Warehouse
How often should the Data Warehouse be updated
Should the Database be Built In-house or Outsourced
Quantifying Profit

23. Why Build a Marketing Database
There are three key functions of a marketing database:
􀂃 To most efficiently maintain your data in an organized
fashion
􀂃 To better support corporate functions
􀂃 To better support marketing functions
All of which will allow you to better service your customer
and hence maximize revenue
The pinnacle is of course to be able to calculate and
forecast customer lifetime value
The foundation of the database build is the customer
contact information. From this we accumulate historical
purchase and promotional data called our marketing data.

24. REASONS FOR FALIURE
The Major Reasons For Failiure
􀂃 Underestimating the time and resource commitment to
build or maintain the database
􀂃 Not having the right support team in place once the
database is delivered even if outsourced
􀂃 Not having a plan in place regarding how you will use
the database once delivered and how you will quantify the
benefits.
􀂃 Inappropriate in scope -- too broad or too narrow
􀂃 Not properly prioritizing deliverables – phased in
approach
Failure to shift the paradigm at the organization to a
information-based decision approach
􀂃

25. Thinking that if you build the database profits will come
Failure to realize that your number one priority in the build
is getting the data right. 􀂃
Failure to fully assess costs of “add ons” relative to total
database costs versus their benefits.
􀂃
It is not any one reason that causes
failure but a combination of the
above.

26. KEY PROFIT DRIVERS
--The major profit drivers can be
--Demographic Profiling,
--Mkt Research support
--CRM Strategies
--Lifetime Value analysis
--Customer Acquisition Models
--Retention Models
--Strategic Corporate Reports
Some functions can be performed just by virtue of having all of
the data readily available and in one place, others require the
application of quantitative sophistication to complete.

27. WHO WILL BE USING DATABASE
Keep in mind that there are many individuals throughout the
organization who will utilize a marketing database. However,
typically many of these needs are related in one form or
another. As such, you will want to involve all in the decision
process of what the database should ultimately accomplish.
Many individuals will need similar access to the database and
what is has to offer. For example, all functional
areas/individuals will want some form of “dashboards” or other
reports.
To ensure success and keep the cost of the database in check,
you must understand the relationships between the individuals
so that priorities can be properly set regarding deliverables and
features. It is best to build in phases ---start out small and
build on each success. Remember this is a marketing
database.
The primary focus must first be to meet marketing’s needs.
Other divisions can then follow