Data Mining Applications

1
Data Mining Applications

• Fortune (Financial Magazine) in its annual report
  of the best 500 companies. 80 of them are using
  data mining for decision support.
• Example Detecting unusual calling patterns.

2
Data Mining - Application

• The main three business areas where data mining
  is applied are
• (1) Market Management
• Target Marketing
• Customer relationship management
• Market basket analysis
• Cross Selling
• (2) Risk Management
• Forecasting
• Customer retention
• Improved underwriting
• Quality control
• Competitive Analysis
• (3) Fraud Management
• Fraud detection

3
Market Management Applications

• Market management is one of the most
  well-established application areas for data
  mining.
• The organization builds the database of customer
  product preferences and lifestyles from such
  sources as credit card transactions, loyalty
  cards, warranty cards, discount coupons, entries
  to free prizes drawings and customer complaint
  calls.
• Data mining algorithms then surf through the data
  looking for clusters of model consumers who all
  share the same characteristics (examples income,
  interests and spending habits).

4
Determining customer purchasing patterns over time

• Examples
• The sequence in which they take up financial
  services as their family grows
• How they change their cars.
• Converting a single bank account to a joint
  account indicates marriage which could lead to
  future opportunities to get loans, insurance,
  study fees....
• By understanding these patterns the organization
  can advertise just-in-time.

5
Improving Catalog Telesales

• The goal is track the products its customers
  order most frequently as well as to suggest the
  purchase of those products in future order.
• Some products associations are obvious Camera
  Films, Radio Batteries...

6
Loyalty Cards

• To reward your frequently buyers.
• Cardholders get special treatment such as
  exclusive discounts on selected items, to
  encourage them to do more shopping at the shop
  and less likely to visit the competition.

7
Turning external influences to advantages

• The mining discovered -in an insurance company-
  two groups of customers who appeared at first
  sight to be similar because they had similar
  levels of income and savings. Even the company
  tried to merge them but after the analysis they
  found that when one group decided to invest the
  other group decided to cash in its policies.

8
They found later that the reason was because of
the different fiscal treatment each group
received from the government
Payments as of Total
30 25 20 15 10 5 0
1988 1989 1990
1991 1992
9
Getting more out of store promotions

• A data mining system found that shoppers who were
  coming into a store were gravitating to the left
  side of the store for the promotional items and
  were not necessarily shopping the whole store. As
  a result the manager added apparel, which was
  stocked on the right side of the store, to the
  promotion and launched a similar mid week
  promotion. As a result, sales of all products
  including apparel increased.

10
Risk Management Applications

• Risk associated with insurance or investments.
• Risk associated to business risks arising from
  competitive threat.
• Risk associated to poor product quality.
• Risk associated to customer attrition (i.e. The
  loss of customers, especially to competitors.
  Examples in the retails, finance and
  telecommunications fields).
• The idea here is to build a model of a vulnerable
  customer who shows characteristics typical of one
  who is likely to leave for a competitive company.

11

• For example customer losses may frequently
  follow a change of address or a recent protracted
  exchange with an agent of the company.
• One US bank uses such models to predict the loss
  of customers up to one year in advance.
• Another bank analyzes more than one million
  credit card account histories to ensure that it
  is not over expected to high rates of attraction.

12

• Retail organizations uses data mining to better
  understanding the vulnerability of certain
  products to competitive offerings or changing
  consumer purchasing patterns. Historical
  purchasing patterns of customers are analyzed to
  identify groups of customers with low product or
  brand loyalty.
• A historical of bad and good loan histories is
  used to develop a profile of a bad and good loan
  applicant.

13

• Telecommunication companies have several billion
  dollars in uncollectible debts every year. Data
  mining can build models that help predict whether
  a particular account is likely to be collectible
  and is therefore worth going after.
• Competitive Intelligence (CI) is the process of
  collecting, analyzing and disseminating
  information about industry developments or market
  trends to enhance a companys competitiveness.

14
Forecasting Financial Future

• If changes in financial behavior can be
  predicted, the organization can adjust its
  investment strategy and capitalize on the
  predicted changes.
• Financial Engineering The application of
  advanced quantitative techniques such as
  statistics and data mining in the area of risk
  management.
• Example The ability to forecast the right price
  of a future which is a contract that allows
  someone to buy something at a certain price on a
  certain date in the future.
• A model is used to predict the future price
  changes.

15
Pricing Strategy in a Highly Competitive Market

• A chain of gasoline stations used data mining to
  develop profitable pricing strategies in a very
  competitive marketplace, by developing a model
  that helps to determine
• Appropriate pricing for its products on a day-to
  day basis, with a view to maximizing sales and
  profits.
• Sales volumes and profitability.
• The likely competitive reaction to their price
  changes.
• The likely profitability of a new station.

16
Fraud Management Applications

• Those sectors suffer more than most - especially
  those where there are many transactions such as
  health care, retail, credit card service and
  telecommunication.
• The goal is to use historical data to build a
  model of fraudulent behavior and then use data
  mining to help identify similar instances of this
  behavior.
• Detecting Telephone Fraud Example some of the
  more important elements (patterns) in building
  the model are the destination of the call,
  duration, time of day and week.

17
Detecting inappropriate medical treatments

• An insurance company maintains computerized
  records of every doctors consultation in
  Australia, including details on the diagnosis,
  prescribed drugs and recommended treatment.
• Using traditional data analysis techniques they
  noticed a rapid increase in the number of
  prescribed pathology tests.
• Using data mining they were able to identify
  which combinations of tests were commonly used,
  they were able to detect these invalid
  combinations and to no longer accept them for
  benefit payment, and they were able to identify
  that in many cases that a certain test has been
  used at given symptoms.

18
Future Application Areas

• Text Mining Words are analyzed in context, for
  example the word memory used in a medical
  article or a computer article.
• Web Analytics to develop insights into users
  behavior on the internet. For example today
  hypertext are typically fixed, the site
  developers have provided the most likely links by
  trying to second guess what the user wants to do
  next. With data mining, historical user browsing
  patterns can be analyzed to dynamically suggest
  related sites for users to visit.

19
When Things Go Wrong?!

• A class of divorced women
• A data mining system discovered that divorced
  women have distinctly different shopping pattern
  from those of either single or married women.
• After analyzing the data they found that the data
  on martial status was much less accurate than the
  other data because of cultural norms.

20

• Missing the Point
• While preparing to mine a database of hospital
  patient admission records. They found this
  strange graph about the temperature. Then they
  discovered that the nurse was likely to have the
  temperature 37oC recorded as either 36.9oC or
  37.1oC.

Population
35o 36o
37o 38o Temperature