1. DATA MINING

2. •
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

3. •
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.

4. • 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

5. • 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.

6. • 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 preprocessing, 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.

7. DATA WAREHOUSING

8. • 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.

9. •
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

10. • Why Build a Marketing Database
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•
•
•
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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.

11. REASONS FOR FALIURE
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•
The Major Reasons For Failiure
􀂃 Underestimating the time and resource commitment to build or
maintain the database
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􀂃 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.
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􀂃 Inappropriate in scope -- too broad or too narrow
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􀂃 Not properly prioritizing deliverables – phased in approach
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Failure to shift the paradigm at the organization to a informationbased decision approach
􀂃

12. •
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. 􀂃
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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.

13. 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.

14. 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