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. the 360 degree view
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.

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.

19. 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
PHOTO: HULTON-DEUTSCH COLL
PHOTO: LUCINDA DOUGLAS-MENZIES

21. Data Mining Applications

22. Data Mining Applications: Retail
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 cost–effective 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

25. Data Mining Applications: Banking
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 customer’s lifetime value and to service each segment appropriately (for example, offering special deals and discounts).

26. Data Mining Applications: Telecommunication
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 Applications: Other 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 CRM: 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
It’s important to know life cycle events (e.g. retirement)

29. Data Mining in CRM: Customer 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. here’s no stock way to present product recommendations
here’s no stock way to present product recommendations. Common labels for recommendations are:
“Recommended products” “You may also like” “Customers who bought X also bought” “Customers who viewed X also viewed” “Frequently bought together” “Stuff 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 with” “Complete the look”

33. 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
Customer Profile
Data Mining
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  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
Tridas
Vickie
Mike
Honest
Barney
Waldo
Wally
Crooked-
CRAZY

39. Prediction
Tridas
Vickie
Mike
Honest = has round eyes and a smile

40. Decision Trees Data height hair eyes class short blond blue A
tall blond brown B
tall red blue A
short dark blue B
tall dark blue B
tall blond blue A
tall dark brown B
short blond brown B

41. Decision Trees (cont.) hair dark blond red short, blue = B
tall, blue = B
tall, brown= B
{tall, blue = A}
short, blue = A
tall, brown = B
tall, blue = A
short, brown = B
Does not completely classify
blonde-haired people.
More work is required
Completely classifies dark-haired
and red-haired people

42. Decision Trees (cont.) hair eye dark blond red short, blue = B
tall, blue = B
tall, brown= B
{tall, blue = A}
short, blue = A
tall, brown = B
tall, blue = A
short, brown = B
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
short = A
tall = A
tall = B
short = B

43. Decision Trees: Learned Predictive Rules
hair
eyes B A
dark
red
blond
blue
brown

44. Decision Trees: Another Example

45. Rule Induction Try to find rules of the form
IF <left-hand-side> THEN <right-hand-side>
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
Don’t 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

50. 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 Method1 2 3 4 5 6 7 8 9 10 10 9 8 7 6 5
Update the cluster means 4
Assign each of the objects to most similar center 3 2 1 1 2 3 4 5 6 7 8 9 10
reassign
reassign
K=2
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 İt 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 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

64. 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 organization’s 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. Don’t 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 Traveler’s 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 you’ve 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 you’re 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 year’s 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.

82. THE ONLİNE ENVİRONMENT
CHAPTER 15

83. WWW-WORLD WİDE 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,

85. Google adwords
Fikrimuhim.

86. Lego factory story. Page 304*305

87. Search engines
Organic