DATA MINING FOR BUSINESS INTELLIGENCE 1 and 2

1
DATA MINING FORBUSINESS INTELLIGENCE(1 and
2)
2

• Data Mining A process for extracting information
  from large data sets to solve business problems.
• Data Warehouse A large database created
  specifically for decision support throughout the
  enterprise. It usually consists of data extracted
  from other company databases. This data has been
  cleaned and organized for easy access. Often
  includes a metadata store as well.

3

• Data Mining
• Data mining is defined as the process of
  extracting significant and potentially useful
  patterns in large volume of data. In other words,
  data mining is the search for the relationships
  and global patterns that exist in large databases
  but are hidden among vast amount of data. This
  relationship represents valuable knowledge about
  the database, if the database is a faithful
  mirror of the real world registered by database.

4

• Some application areas
• Sales and Customer Service
• Market Basket Analysis (Analysis of
  transactional databases to find sets of items
  that appear frequently together in a single
  purchase) have already shown phenomenal gains in
  cross-selling, floor and shelf layout, better
  layout of catalog and web pages, effective
  promotion schemes)
• (ii) Customer Retention
• -Identifying patterns that leads to
  defection of customers and suggesting preventive
  measures for the current customers
• (iii) Risk Assessment and Fraud Detection
• -a mail order retailer can identify payment
  patterns from different customers at the same
  address, identifying potentially fraudulent
  practices by an individual using different names
• - An insurance company can identify
  client who may have different kinds of policies
  totaling more than an acceptable level
• - A bank can identify companies that may
  be in financial jeopardy before extending a loan
  to them
• (iv) Customer Segmentation
• (v) Product Grouping

5

• TYPES OF KNOWLEDGE EXTRACTED USING DATA MINING
• Association Rule
• Classification
• Clustering
• Feature Selection
• Factor Analysis
• Sequence Mining
• Regression

6

• ASSOCIATION RULE
• Association rule is a type of data mining that
  correlates one set of items or events with
  another set of items or events. It employs
  association or linkage analysis, searching
  transactions from operational systems for
  interesting patterns with a high probability of
  repetition.
• Algorithms
• A priori
• Partitioning
• Dynamic Itemset Counting
• Frequent Pattern Tree Algorithm

7
Examples of Association Rules

• When people buy butter, they also buy bread 70
  of the time (Association)
• When people buy Pepsi, they also buy Lays chips
  70 of the time, on Sunday evenings (Temporal
  Association)
• (iii) 70 of the readers who buy a DBMS book also
  buy a Data Mining book after a semester
  (Sequence Rule, a type of Temporal Association)
• (iv) When people buy coke, they do not buy coffee
  95 of the time (Negative Association)

8
Strength of an association rule defined under
the framework of support, confidence and
lift

• Support of an itemset in a transaction database
  is defined as the percetntage of occurrence of
  the itemset, out of all the transactions.
• XgtY holds with support s , if s of the
  transactions in the database contain X and Y
  both,

9

• For a given transaction database, if X and Y
  represent two items/itemsets such that
• XnY?, i.e., there is no common item
  in them, we say X associates Y and
  represented as XgtY

10

• Confidence of an association rule XgtY is
  defined as the percentage of transactions
  containing X and Y both, out of all the
  transactions containing X.
• XgtY holds with confidence c, if c of the
  transaction in the database that supportsX also
  supportsY

11

• Lift of an association rule XgtY
• The lift ratio is the confidence of the rule
  divided by the confidence assuming independence
  of consequent from antecedent. A lift ratio
  greater than 1.0 suggests that there is some
  usefulness to the rule. The larger the lift
  ratio, the greater is the strength of the
  association.
• Lift Confidence/Percentage Support of Y

12

• Various types of association rules
• Ordinary Association Rule
• -Boolean Type
• - Quantitative Type
• - Categorical Type
• Temporal Association Rule
• -Boolean Type
• -Quantitative Type
• -Categorical Type
  
• Spatial Association Rule
• -Boolean Type
• -Quantitative Type
• -Categorical Type

13

• Transaction of items

14

• Total no. of transactions 10
• n(I1) 7, n(I2) 2, n(I3) 5
• n(I1and I2) 2,
• n(I1 and I3) 4,
• n(I2 and I3) 1,
• n (I1, I2 and I3) 1
• Find all the association rules for
• min_support 30 and min_confidence 60 and
  min_lift 1.

15

16

• .

17
(No Transcript)
18
(No Transcript)
19

• Case-I
• Maximize return on investment in retail
  industry Customer rewards programs
• (PublicationsArticles Whitepapers, Inductis
  Retail Rewards Solutions, by Dr. Matt Hasan, May
  2007)  
• Optimally Manage Churn by Leveraging Each
  Shoppers Inherent Loyalty Intensity.

20

• Recent research of retail customer rewards
  programs industry sources reveal the following
  interesting facts
• Reward programs, especially card based ones, are
  widespread in the retail industry
• Programs have very high adoption rates due to no
  joining fee and low barriers to entry 80 of the
  customers have a loyalty card
• 54 of shoppers have multiple loyalty cards from
  competing retailers
• 80-90 of grocery purchases are made with a
  loyalty card
• Less than 30 of shoppers say reward programs
  have a major impact on their shopping decisions
• 40 of long term reward program members never
  redeem their rewards
• Shoppers with emotional ties to a store (or
  chain) tend to shop there even if they have to go
  further or pay more
• Majority of retail rewards programs use just
  cumulative dollar value of purchases to allocate
  rewards some use transaction data to match
  coupons and discounts to customer buying
  patterns

21

• Success Story of a Leading Retail Chain in USA
  Background Store sales were declining with
• The average shelf life of store merchandise was
  higher than industry norms
• Rewards/Loyalty program, including discounts and
  coupons were attracting less-profitable
  customers, without significantly impacting the
  top line.
• Using the SCWM (Strategic Customer Worth
  Management - Developed by Inductis)
• They were able to reverse a decline in sales
  while decreasing expenditures on rewards program,
  resulting in a gross margin improvement of 17.
• Strategic Customer Worth Management (SCWM) SCWM
  is a proven enterprise solution based on unique
  true customer worth evaluating framework that
  determines the optimal treatment and rewards to
  offer to acquire, retain, or expand business with
  each customer.

22

• Current Rewards/Loyalty Programs
• Point of Sale Data
• Customer Database
• Product/ Discount Database
• - Rewards frequent and high spending shoppers
• - Provides broad demographic and transaction data

23

• Enhancements/Additions Based on SCWM
• - Personalized Customer Interaction
• Predictive Modeling
• Matches incentives to customer profile
• Scores customers for true economic worth
• Provides details on inherent loyalty (emotional)
  profile of customers in addition to demographic
  and transaction data

24

• Approach
• They applied unique SCWM framework to perform
  in-depth customer segmentation of intrinsic
  loyalty, as well as preferences for merchandise
  and communications channel. They used in-store
  survey data as well as geodemographic data from
  consumer research services
• Aligned the loyalty programs, merchandizing, and
  communications channels of each store type
  according to the profile of the profitable
  customers of that store type
• Implemented a targeted direct mail program
  designed to resonate with shoppers matching the
  profitable customer profile of each store type
• Designed and executed a rewards program based on
  true customer worth and inherent loyalty
  coefficient of each customer segment
• Instituted an ongoing survey and tracking system
  to collect data on customer demographics and
  browsing, purchasing, and loyalty patterns, to
  create a virtuous circle of understanding
  customers and creating programs that retaintheir
  loyalty

25

• Results
• Average revenue per store increased by 5
• Among those stores which were previously the
  worst performers, average revenue per customer
  increased by 14
• Traffic increased by an average of 11, with some
  stores seeing an increase of as much as 20
• Expenditures on rewards programs decreased by 8,
  as targeted rewards programs replaced some
  sales-coupons, and discounts
• Gross margin increased by 17
• They applied unique SCWM framework to perform
  in-depth customer segmentation of intrinsic
  loyalty, as well as preferences for merchandise
  and communications channel. They used in-store
  survey data as well as geodemographic data from
  consumer research services
• Aligned the loyalty programs, merchandizing, and
  communications channels of each store type
  according to the profile of the profitable
  customers of that store type
• Implemented a targeted direct mail program
  designed to resonate with shoppers matching the
  profitable customer profile of each store type
• Designed and executed a rewards program based on
  true customer worth and inherent loyalty
  coefficient of each customer segment
• Instituted an ongoing survey and tracking system
  to collect data on customer demographics and
  browsing, purchasing, and loyalty patterns, to
  create a virtuous circle of understanding
  customers and creating programs that retaintheir
  loyalty

26
(No Transcript)
27
.
28