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Data analysis and data mining

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Title: Data analysis and data mining


1
Data analysis and data mining
2
DATA ANALYSIS
  • Successful data analysis requires progressing
    through the different stages in the analysis
    process.
  • Problem formulation identify it!
  • Preparations
  • Final analysis using statistical techniques or
    data mining techniques.
  • Visualisation or reporting

3
THE PROCESS FOR DATA MINING
4
consumer insight lies at the heart of all
marketing and communication strategy,and
that consumers are multi-faceted and complex
creatures,and that true consumer insight comes
only with a 360 view.
  • the 360 degree view

5
  • Data
  • Data are any facts, numbers, or text that can be
    processed by a computer. Today, organizations are
    accumulating vast and growing amounts of data in
    different formats and different databases. This
    includes
  • operational or transactional data such as, sales,
    cost, inventory, payroll, and accounting
  • nonoperational data, such as industry sales,
    forecast data, and macro economic data
  • meta data - data about the data itself, such as
    logical database design or data dictionary
    definitions

6
  • Information
  • The patterns, associations, or relationships
    among all this data can provide information. For
    example, analysis of retail point of sale
    transaction data can yield information on which
    products are selling and when.
  • Knowledge
  • Information can be converted into knowledge about
    historical patterns and future trends. For
    example, summary information on retail
    supermarket sales can be analyzed in light of
    promotional efforts to provide knowledge of
    consumer buying behavior. Thus, a manufacturer or
    retailer could determine which items are most
    susceptible to promotional efforts.

7
  • Data Warehouses
  • Dramatic advances in data capture, processing
    power, data transmission, and storage
    capabilities are enabling organizations to
    integrate their various databases into data
    warehouses.
  • Data warehousing is defined as a process of
    centralized data management and retrieval.
  • Data warehousing represents an ideal vision of
    maintaining a central repository-STORAGE -of all
    organizational data. Centralization of data is
    needed to maximize user access and analysis.
  • Dramatic technological advances are making this
    vision a reality for many companies. And, equally
    dramatic advances in data analysis software are
    allowing users to access this data freely.
  • The data analysis software is what supports data
    mining.

8
Data Mining
  • (sometimes called data or knowledge discovery) is
    the process of analyzing data from different
    perspectives and summarizing it into useful
    information - information that can be used to
    increase revenue, cuts costs, or both.
  • Data mining software is one of a number of
    analytical tools for analyzing data. It allows
    users to analyze data from many different
    dimensions or angles, categorize it, and
    summarize the relationships identified.
  • Technically, data mining is the process of
    finding correlations or patterns among dozens of
    fields in large relational databases.
  • Extremely large datasets
  • Discovery of the non-obvious
  • Useful knowledge that can improve processes
  • Can not be done manually.

9
Data Mining (cont.)
10
Data Mining (cont.)
  • Data Mining is a step of Knowledge Discovery in
    Databases (KDD) Process
  • Data Warehousing
  • Data Selection
  • Data Preprocessing
  • Data Transformation
  • Data Mining
  • Interpretation/Evaluation
  • Data Mining is sometimes referred to as KDD and
    DM and KDD tend to be used as synonyms

11
Data Mining Evaluation
12
Data Mining is Not
  • Data warehousing
  • SQL / Ad Hoc Queries / Reporting
  • Software Agents
  • Online Analytical Processing (OLAP)
  • Data Visualization

13
  • What can data mining do?
  • Data mining is primarily used today by companies
    with a strong consumer focus - retail, financial,
    communication, and marketing organizations.
  • It enables these companies to determine
    relationships among "internal" factors such as
    price, product positioning, or staff skills, and
    "external" factors such as economic indicators,
    competition, and customer demographics.
  • And, it enables them to determine the impact on
    sales, customer satisfaction, and corporate
    profits.
  • Finally, it enables them to "drill down" into
    summary information to view detail transactional
    data.
  • With data mining, a retailer could use
    point-of-sale records of customer purchases to
    send targeted promotions based on an individual's
    purchase history.
  • By mining demographic data from comment or
    warranty cards, the retailer could develop
    products and promotions to appeal to specific
    customer segments.

14
  • For example, Blockbuster Entertainment mines its
    video rental history database to recommend
    rentals to individual customers. American Express
    can suggest products to its cardholders based on
    analysis of their monthly expenditures.
  • WalMart is pioneering massive data mining to
    transform its supplier relationships. WalMart
    captures point-of-sale transactions from over
    2,900 stores in 6 countries and continuously
    transmits this data to its massive 7.5
    terabyte Teradata data warehouse. WalMart allows
    more than 3,500 suppliers, to access data on
    their products and perform data analyses.
  • These suppliers use this data to identify
    customer buying patterns at the store display
    level. They use this information to manage local
    store inventory and identify new merchandising
    opportunities. In 1995, WalMart computers
    processed over 1 million complex data queries.

15
  • Data mining consists of five major elements
  • 1. Extract, transform, and load transaction data
    onto the data warehouse system.
  • 2. Store and manage the data in a
    multidimensional database system.
  • 3. Provide data access to business analysts and
    information technology professionals.
  • 4. Analyze the data by application software.
  • 5. Present the data in a useful format, such as a
    graph or table.

16
Terms
  • Web mining searching and processing data on the
    internet is referred to this.
  • three types of webmining are listed as
  • Web structure mining
  • Web usage mining
  • Web content mining

17
Types
  • web structure mining places websites and the
    pages or items that contain in a network of
    connected websites.
  • Web usage mining focuses on browsing behavior
  • Web-content mining is all about discovering
    useful content on the worldwide web.

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Data Mining Motivation
  • Changes in the Business Environment
  • Customers becoming more demanding
  • Markets are saturated
  • Databases today are huge
  • More than 1,000,000 entities/records/rows
  • From 10 to 10,000 fields/attributes/variables
  • Gigabytes and terabytes
  • Databases a growing at an unprecedented rate
  • Decisions must be made rapidly
  • Decisions must be made with maximum knowledge

20
Data Mining Motivation
  • The key in business is to know something that
    nobody else knows.
  • Aristotle Onassis
  • To understand is to perceive patterns.
  • Sir Isaiah Berlin

21
Data Mining Applications
22
Data Mining ApplicationsRetail
  • Performing basket analysis
  • Which items customers tend to purchase together.
    This knowledge can improve stocking, store layout
    strategies, and promotions.
  • Sales forecasting
  • Examining time-based patterns helps retailers
    make stocking decisions. If a customer purchases
    an item today, when are they likely to purchase a
    complementary item?
  • Database marketing
  • Retailers can develop profiles of customers with
    certain behaviors, for example, those who
    purchase designer labels clothing or those who
    attend sales. This information can be used to
    focus costeffective promotions.
  • Merchandise planning and allocation
  • When retailers add new stores, they can improve
    merchandise planning and allocation by examining
    patterns in stores with similar demographic
    characteristics. Retailers can also use data
    mining to determine the ideal layout for a
    specific store.

23
SALES FORECASTING
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Data Mining ApplicationsBanking
  • Card marketing
  • By identifying customer segments, card issuers
    and acquirers can improve profitability with more
    effective acquisition and retention programs,
    targeted product development, and customized
    pricing.
  • Cardholder pricing and profitability
  • Card issuers can take advantage of data mining
    technology to price their products so as to
    maximize profit and minimize loss of customers.
    Includes risk-based pricing.
  • Fraud detection
  • Fraud is enormously costly. By analyzing past
    transactions that were later determined to be
    fraudulent, banks can identify patterns.
  • Predictive life-cycle management
  • DM helps banks predict each customers lifetime
    value and to service each segment appropriately
    (for example, offering special deals and
    discounts).

26
Data Mining ApplicationsTelecommunication
  • Call detail record analysis
  • Telecommunication companies accumulate detailed
    call records. By identifying customer segments
    with similar use patterns, the companies can
    develop attractive pricing and feature
    promotions.
  • Customer loyalty
  • Some customers repeatedly switch providers, or
    churn, to take advantage of attractive
    incentives by competing companies. The companies
    can use DM to identify the characteristics of
    customers who are likely to remain loyal once
    they switch, thus enabling the companies to
    target their spending on customers who will
    produce the most profit.

27
Data Mining ApplicationsOther Applications
  • Customer segmentation
  • All industries can take advantage of DM to
    discover discrete segments in their customer
    bases by considering additional variables beyond
    traditional analysis.
  • Manufacturing
  • Through choice boards, manufacturers are
    beginning to customize products for customers
    therefore they must be able to predict which
    features should be bundled to meet customer
    demand.
  • Warranties
  • Manufacturers need to predict the number of
    customers who will submit warranty claims and the
    average cost of those claims.
  • Frequent flier incentives
  • Airlines can identify groups of customers that
    can be given incentives to fly more.

28
Data Mining in CRMCustomer Life Cycle
  • Customer Life Cycle
  • The stages in the relationship between a customer
    and a business
  • Key stages in the customer lifecycle
  • Prospects people who are not yet customers but
    are in the target market
  • Responders prospects who show an interest in a
    product or service
  • Active Customers people who are currently using
    the product or service
  • Former Customers may be bad customers who did
    not pay their bills or who incurred high costs
  • Its important to know life cycle events (e.g.
    retirement)

29
Data Mining in CRMCustomer Life Cycle
  • What marketers want Increasing customer revenue
    and customer profitability
  • Up-sell
  • Cross-sell
  • Keeping the customers for a longer period of time
  • Solution Applying data mining

30
THE DIFFERENCE
  • upsell is to get the customer to spend more money
    buy a more expensive model of the same type of
    product, or add features / warranties that relate
    to the product in question.
  • A cross-sell is to get the customer to spend more
    money buy adding more products from other
    categories than the product being viewed or
    purchased.

L
31
  • heres no stock way to present product
    recommendations. Common labels for
    recommendations are
  • Recommended productsYou may also
    likeCustomers who bought X also
    boughtCustomers who viewed X also
    viewedFrequently bought togetherStuff you
    need (Radio Shack, for accessories)Stuff you
    may want (Radio Shack, for items in other
    categories)More from this (category, brand,
    author, artist)Looks hot withComplete the
    look

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Data Mining in CRM
  • DM helps to
  • Determine the behavior surrounding a particular
    lifecycle event
  • Find other people in similar life stages and
    determine which customers are following similar
    behavior patterns

34
Data Mining in CRM (cont.)
Data Warehouse
Data Mining
Customer Profile
Customer Life Cycle Info.
Campaign Management
35
Data Mining Techniques
36
  • Predictive modelling leverages statistics to
    predict outcomes
  •  Most often the event one wants to predict is in
    the future, but predictive modelling can be
    applied to any type of unknown event, regardless
    of when it occurred. For example, predictive
    models are often used to detect crimes and
    identify suspects, after the crime has taken
    place.
  • In many cases the model is chosen on the basis
    of detection theory to try to guess the
    probability of an outcome given a set amount of
    input data, for example given an email
    determining how likely that it is spam.

37
  • A decision tree is a decision support tool that
    uses a tree-like graph or model of decisions and
    their possible consequences, includingchance event
    outcomes, resource costs, and utility. It is one
    way to display an algorithm.
  • Decision trees are commonly used in operations
    research, specifically in decision analysis, to
    help identify a strategy most likely to reach
    agoal.

38
Predictive Data Mining
39
Prediction
Honest has round eyes and a smile
40
Decision Trees
  • Data

height hair eyes class short blond blue A tall blo
nd brown B tall red blue A short dark blue B tall
dark blue B tall blond blue A tall dark brown B sh
ort blond brown B
41
Decision Trees (cont.)
hair
dark
blond
red
Does not completely classify blonde-haired
people. More work is required
Completely classifies dark-haired and red-haired
people
42
Decision Trees (cont.)
hair
dark
blond
red
Decision tree is complete because 1. All 8 cases
appear at nodes 2. At each node, all cases are
in the same class (A or B)
eye
blue
brown
tall B short B
43
Decision TreesLearned Predictive Rules
44
Decision TreesAnother Example
45
Rule Induction
  • Try to find rules of the form
  • IF ltleft-hand-sidegt THEN ltright-hand-sidegt
  • This is the reverse of a rule-based agent, where
    the rules are given and the agent must act. Here
    the actions are given and we have to discover the
    rules!
  • Prevalence probability that LHS and RHS occur
    together (sometimes called support factor,
    leverage or lift)
  • Predictability probability of RHS given LHS
    (sometimes called confidence or strength)

46
  • In data mining, association rules are useful for
    analyzing and predicting customer behavior. They
    play an important part in shopping basket data
    analysis, product clustering, catalog design and
    store layout.
  • Association rules are if/then statements that
    help uncover relationships between seemingly
    unrelated data in a relational database or other
    information repository. An example of an
    association rule would be
  • "If a customer buys a dozen eggs, he is 80
    likely to also purchase milk.

47
Use of Rule Associations
  • Coupons, discounts
  • Dont give discounts on 2 items that are
    frequently bought together. Use the discount on
    1 to pull the other
  • Product placement
  • Offer correlated products to the customer at the
    same time. Increases sales
  • Timing of cross-marketing
  • Send camcorder offer to VCR purchasers 2-3 months
    after VCR purchase
  • Discovery of patterns
  • People who bought X, Y and Z (but not any pair)
    bought W over half the time

48
Product placement
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GOADANA
51
Clustering
  • The art of finding groups in data
  • Objective gather items from a database into sets
    according to (unknown) common characteristics
  • Much more difficult than classification since the
    classes are not known in advance (no training)
  • Technique unsupervised learning

52
The K-Means Clustering Method
10
9
8
7
6
5
Update the cluster means
Assign each of the objects to most similar center
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10
reassign
reassign
K2 Arbitrarily choose K objects as initial
cluster center
Update the cluster means
53
Chapter 8 customer segmentation
  • Segmentation is a research process in which the
    market is divided up into homogeneous customer
    groups that respond in the same way to marketing
    stimuli from the supplier.

54
CUSTOMER SEGMENTATION
55
Bonomo and Shapiro (1983) B2B
  • 5 criteria
  • Demographic factors industrial classification
    company size and location.
  • Operating variables technology, user status,
    customer capabilities,
  • Purchasing approaches how purchasing is
    organised, ..
  • Situational factors involves the urgency, the
    specific application and the order size.
  • Personal characteristics the values and norms of
    the employees working for the prospect or
    customer, their general loyalty and attitude to
    risk.

56
Segmentation technique
  • Markets can be segmented in a large number of
    ways.
  • the guideliness of the segmentation solution
    process
  • Measurable the size, purchasing power and
    characteristics of the segment can be measured,
  • Substantial the segments are large and
    profitable enough to serve.
  • Accessible the segments can be reached and
    served effectivelly.
  • Differentiable the segments are conceptually
    distinguishable and respond differently to
    different marketing stimuli.
  • Actionable effective programs can be formulated
    for attracting and serving the segments.

57
Segmentation research used in compiling the list
  • RFM- recency frequency monetary value
  • CHAID- chi squared automated interaction
    detection
  • CART- classification and regression trees

58
RFM
  • It was developed first.
  • Developed to identify the most attractive
    prospects.
  • Focusing on the frequency and the most recent
    transaction date in addition to the annual amount
    spent, produces better selections and higher
    response percentages.

59
CHAID and CART
  • A Chaid analysis produces a tree diagram.
  • At the top of the diagram, the response to the
    marketing campaigns are shown for the entire
    customer database. (8.2)
  • The organisation has 240.000 customers of which
    an average of 4.36 responds to a marketing
    activity. On the level below these customers are
    split according to the most discriminating
    significant segmentation criterion.

60
CART
  • it is often compared to CHAID.
  • Cart is not limited to numbers of variables and
    classes that can be included.

61
  • Customer
  • Organizational market

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Not all customers are the same
Highly profitable customer Mixed-profitability customer Losing customer
Highly profitable product
Profitable product
Mixed-profitability product _
Losing product _ _
65
Chapter 9
  • Retention and cross sell analyses

66
Retention
  • Holding on the customers.
  • Companies must arrive at definitions of former
    and current customers.
  • Does someone become a departing customer at the
    moment they no longer buy a certain product.
  • a consumer for example stop buying fresh meat at
    a particular market but continues to shop for a
    variety of packaged goods.

67
Customer Retention Strategies
  • Welcome
  • Reliability
  • Responsiveness
  • Recognition
  • Personalization
  • Reward Strategies

68
A welcome strategy
  • The organizations appreciation for the
    initiation of a relationship.
  • Creating a delightful surprise, making a good
    first impression
  • First touch additional customer information
  • Reassure the buyers that they have made the
    correct choices.
  • Treat like a first date. Dont overdo it!

69
Reliability
  • The organization can repeat the exchange time
    and time again with the same satisfying results.
  • Keep promise
  • Ensure consistent quality
  • Continuous promotion is still the key.

70
Responsiveness
  • The organization shows customers it really cares
    about their needs and feelings.
  • Loyal employees create loyal customers. Internal
    marketing.
  • Customer-contacted employees should have the
    authority as well as the responsibility for date
    to date operational activities and CRM decision.

71
Recognition
  • Special attention or appreciation that identifies
    someone as having been known before.
  • People respond to recognition.
  • Recognition and appreciation help maintain and
    reinforce relationships.

72
Personalization
  • Use CRM system to tailor promotions and products
    to the specific customers.
  • Offer engine take customer data after it is
    analyzed and applies it to create the offer or
    message that is appropriate to the individual
    customer. Ex., My site, Click stream analysis,
    free ride, etc.

73
Access strategy
  • Identify how customers will be able to interact
    with the organization.
  • General contact, product return, technical
    report, service representative, change a mailing
    address
  • Is the access quick and easy?

74
A Communication process
75
Cross-sell 
  • This is all about offering your customer items
    that can complement their purchase.  A retailer
    could offer software such as Microsoft Office, or
    perhaps a keyboard.
  • Think about when you are on Amazon.com and you
    see Best Value with the book you selected (in
    the below example the book, The Time Travelers
    Wife) and get another book (A Long, Long Time)
    at a bundled price a great cross-sell. Amazon
    also uses, Customers Who Bought This Item Also
    Bought which is another cross-selling
    opportunity.

76
Upsell vs cross sell
  • An upsell occurs during a purchase, where the
    customer is made aware of the ability to get even
    more of what he or she was looking for. For
    example, you can book an economy class trip to
    NEW YORK for 750, but for an additional 200,
    you can upgrade to business class and get more
    comfort.
  • A cross sell occurs either during or immediately
    after a purchase, where the customer is made
    aware of ways to accessorize the deal. For
    example, now that youve booked your trip to NEW
    YORK, you can, for an additional 350, get four
    nights at an upscale hotel on the beach along
    with a rental car.

77
UPSELL
  • Suggesting your customer buys the more expensive
    model of the same product or service or that
    they add a feature that would make it more
    expensive. With upsell youre suggesting they pay
    more in exchange for a better product or service.
  • For example
  • Buying a 42 TV instead of a 40
  • Upgrading from economy to business class for a
    flight
  • Adding an extended warranty

78
examples of Common Upselling Techniques
  • Jewelry Recommending a higher-quality and more
    expensive brand of the same product
  • Fast food Asking a customer if they would like
    to super size their meal
  • Fine dining Asking a customer if they would like
    a higher quality alcohol instead
  • Computers Asking a customer if they would like
    the same laptop with more hard drive space or
    more RAM
  • Electronics Asking customers if they would like
    an extended warranty plan to go along with their
    purchase
  • Electronics Asking a customer if they would like
    to upgrade from a 40 television to a 42
    television
  • SaaS Providing website customers a checkout
    option whereby they can pay for an entire years
    worth of service upfront at a lower per-month
    cost instead of signing up for the typical
    month-to-month service
  • Travel Asking a customer if they would like to
    upgrade from coach to first-class
  • Night clubs Asking a customer if they would like
    to upgrade their cover charge to VIP level.

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THE ONLINE ENVIRONMENT
  • CHAPTER 15

83
WWW-WORLD WIDE WEB
  • Web 1.0
  • Very first it was read only medium.
  • Webpages
  • Web 2.0
  • Web platforms, geocities, wordpress, facebook,
  • People can share their ideas, photos, videos,
    ideas, status,

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Google adwords
  • Fikrimuhim.

86
  • Lego factory story. Page 304305

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Search engines
  • Organic
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