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Unit 5: Closed Loop Marketing

reporting system. Knowing the future derives enlightened people to seize the opportunity. The Notes
knowledge can be applied again and again in a changing business environment this represents
a major step forward in information technology, towards the ultimate goal of “continuous
insight,” where the system will one day constantly monitor events and automatically adapt to
a new environment.
As the next step towards this goal, companies should make data mining an integral and continuous
part of their business processes. Having built a model, they can regularly calibrate its accuracy
and revise it when necessary or on a scheduled basis. They can continue to build more
sophisticated and more pin-pointed models, such as around customer segments that they identify.
They can map customers in to segments and follow and predict their progress from one segment
to another. They can develop “customer lifetime value” models to guide their marketing and
product development efforts during the next campaign. Data mining then becomes a way of life
and a means of staying ahead of the competition.

Did u know? Data mining algorithms that address tasks have four basic components:
1. Model or Pattern Structure: determining the underlying structure or functional
forms that we seek from the data
2. Score Function: judging the quality of a fitted model

3. Optimization and Search Method: optimizing the score function and searching
over different model and pattern structures
4. Data Management Strategy: handling data access efficiently during the search/
optimization

5.2.2 Statistical Perspective on Data Mining

The information age has been matched by an explosion of data. This surfeit has been a result of
modern, improved and, in many cases, automated methods for both data collection and storage.
For instance, many stores tag their items with a product-specific bar code, which is scanned in
when the corresponding item is bought. This automatically creates a gigantic repository of
information on products and product combinations sold. Similar databases are also created by
automated book-keeping, digital communication tools or by remote sensing satellites, and
aided by the availability of affordable and effective storage mechanisms – magnetic tapes, data
warehouses and so on. This has created a situation of plentiful data and the potential for new and
deeper understanding of complex phenomena. The very size of these databases however means
that any signal or pattern may be overshadowed by “noise”.

Consider for instance the database created by the scanning of product bar codes at sales checkouts.
Originally adopted for reasons of convenience, this now forms the basis for gigantic databases
as large stores maintain records of products bought by customers in any transaction. Some
businesses have gone further: by providing customers with an incentive to use a magnetic-
striped frequent shopper card, they have created a database not just of product combinations but
also time-sequenced information on such transactions. The goal behind collecting such data is
the ability to answer questions such as “If potato chips and ketchup are purchased together, what
is the item that is most likely to be also bought?”, or “If shampoo is purchased, what is the most
common item also bought in that same transaction?”. Answers to such questions result in what
are called association rules. Such rules can be used, for instance, in deciding on store layout or on
promotions of certain brands of products by offering discounts on select combinations.
Applications of association rules transcend sales transactions data — indeed.

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