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How to Prepare your Mind/Body to Give Great Speeches
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Speed as a Trend
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Top 5 Hottest Homebased Businesses to Start In 2006
2006 is right around the corner and we want to help you get a jump start on the hottest businesses you can start from the comfort of your own home. As times change, people have different needs. I have done a great deal of research based on what the...
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Data Mining And Modelling
The important processes that have to be clearly delineated for
Data Mining, Analysis and Modelling are:
Data model: what data will be available and how will it flow?
Data gathering: how will data be gathered both in physical and
technological terms? Data gathered: what data will be gathered?
Data types: what types of data will be gathered? Data
formatting: how will data be held? Data warehousing: where will
data be held? Data mining: how will we retrieve data from the
warehouse? Information modelling: how will we create models and
what of? Information access: how will we access the data models
and reports? Presentation & reporting: on what will we report?
Most companies want to know essential information about
customers at every point of contact, for example:
Lifetime value X sell and upgrade potential Acquisition cost
Channel preferences Loyalty/retention Purchase behaviour patterns
Much of the data that they have will have different frequencies
of change, refreshment or occurrence. It will be kept for
different periods. In some cases, aggregated data may be kept
rather than source data. All of these factors effect the data
modelling exercise and the eventual modelling software
requirements.
Turning the data into useful information requires:
Identifying the issue(s) Assembling the data set(s) Building
models Verify models Interpretation of the results
Automation of
the delivery
Thereafter, modelling tools and techniques have to be used.
These can be divided into two groups: theory driven and data
driven.
Theory driven modelling (hypothesis testing) attempts to
substantiate or disprove preconceived ideas. Theory driven
modelling tools require the user to specify most of the model
based on prior knowledge and then tests to see if the model is
valid.
Data driven modelling tools automatically create the model based
on patterns they find in the data. This also needs to be tested
before it can be accepted as valid.
Modelling is an iterative process with the final model usually
being a combination of prior knowledge and newly discovered
information. The engine(s) tools and techniques include:
Statistical techniques Data driven tools Correlation Cluster
analysis t-tests Factor analysis Analysis of Variance CHAID
(Chi-square Automatic Interaction Detector) decision trees
Linear regression Visualisation tools Logistic regression Neural
networks Discriminant analysis
About the author:
Richard Hill is a director of E-CRM Solutions and has spent many
years in senior direct and interactive marketing roles. E-CRM
http://www.e-crm.co.uk helps you to grow by getting you more
customers that stay with you longer. We provide practical
solutions that pay for themselves. We help you to make sure that
your marketing works.
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