Download Data analysis and data mining

Slide 7.1
Data analysis and data mining
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Slide 7.2
DATA ANALYSIS
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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
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Slide 7.3
THE PROCESS FOR DATA MINING
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Slide 7.4
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the 360 degree view
consumer insight lies at
the heart of all marketing
and communication
strategy,and
that consumers are multifaceted and complex
creatures,and that true
consumer insight comes
only with a 360° view.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.5
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Data
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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
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Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.6
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.
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Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.7
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.
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.8
Data Mining
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(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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.9
Data Mining (cont.)
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Slide 7.10
Data Mining (cont.)
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Data Mining is a step of Knowledge Discovery in
Databases (KDD) Process
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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
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.11
Data Mining Evaluation
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Slide 7.12
Data Mining is Not …
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Data warehousing
SQL / Ad Hoc Queries / Reporting
Software Agents
Online Analytical Processing (OLAP)
Data Visualization
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Slide 7.13
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.14
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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.
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Slide 7.15
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.
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Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.16
Terms:
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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
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.17
Types:
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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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Slide 7.18
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.19
Data Mining Motivation
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Changes in the Business Environment
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Databases today are huge:
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Customers becoming more demanding
Markets are saturated
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
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.20
Data Mining Motivation
“The key in business is to know something that
nobody else knows.”
— Aristotle Onassis
PHOTO: LUCINDA DOUGLAS-MENZIES
PHOTO: HULTON-DEUTSCH COLL
“To understand is to perceive patterns.”
— Sir Isaiah Berlin
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.21
Data Mining Applications
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Slide 7.22
Data Mining Applications:
Retail
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.23
SALES FORECASTING
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Slide 7.24
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.25
Data Mining Applications:
Banking
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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).
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.26
Data Mining Applications:
Telecommunication
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Call detail record analysis
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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
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.27
Data Mining Applications:
Other Applications
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.28
Data Mining in CRM:
Customer Life Cycle
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Customer Life Cycle
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Key stages in the customer lifecycle
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The stages in the relationship between a customer and a
business
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)
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.29
Data Mining in CRM:
Customer Life Cycle
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What marketers want: Increasing customer
revenue and customer profitability
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Up-sell
Cross-sell
Keeping the customers for a longer period of time
Solution: Applying data mining
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.30
THE DIFFERENCE…
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.31
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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”
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.32
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.33
Data Mining in CRM
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DM helps to
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Determine the behavior surrounding a particular
lifecycle event
Find other people in similar life stages and
determine which customers are following similar
behavior patterns
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.34
Data Mining in CRM (cont.)
Data Warehouse
Customer Profile
Data Mining
Customer Life Cycle Info.
Campaign Management
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.35
Data Mining Techniques
Data Mining Techniques
Descriptive
Predictive
Clustering
Classification
Association
Decision Tree
Sequential Analysis
Rule Induction
Neural Networks
Nearest Neighbor Classification
Regression
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.36
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.37
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.38
Predictive Data Mining
Honest
Tridas
Vickie
Mike
Wally
Waldo
Barney
CrookedCRAZY
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Slide 7.39
Prediction
Tridas
Vickie
Mike
Honest = has round eyes and a smile
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Slide 7.40
Decision Trees
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Data
height
short
tall
tall
short
tall
tall
tall
short
hair
blond
blond
red
dark
dark
blond
dark
blond
eyes
blue
brown
blue
blue
blue
blue
brown
brown
class
A
B
A
B
B
A
B
B
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.41
Decision Trees (cont.)
hair
dark
blond
red
short, blue = B
tall, blue = B
tall, brown= B
{tall, blue = A}
Completely classifies dark-haired
and red-haired people
short, blue = A
tall, brown = B
tall, blue = A
short, brown = B
Does not completely classify
blonde-haired people.
More work is required
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.42
Decision Trees (cont.)
hair
dark
blond
red
short, blue = B
tall, blue = B
tall, brown= B
{tall, blue = A}
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)
short, blue = A
tall, brown = B
tall, blue = A
short, brown = B
eye
blue
short = A
tall = A
brown
tall = B
short = B
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.43
Decision Trees:
Learned Predictive Rules
hair
dark
blond
red
B
A
eyes
blue
A
brown
B
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.44
Decision Trees:
Another Example
Total list
50% member
0-1 child
$50-75k income
15% member
2-3 child
20% member
$75k+ income
70% member
4+ children
$50-75k income
Age: 20-40
45% member
$20-50k income
85% member
Age: 40-60
80% member
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.45
Rule Induction
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Try to find rules of the form
IF <left-hand-side> THEN <right-hand-side>
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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”)
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Predictability = probability of RHS given LHS
(sometimes called “confidence” or “strength”)
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.46
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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.
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.47
Use of Rule Associations
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Coupons, discounts
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Product placement
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Offer correlated products to the customer at the same
time. Increases sales
Timing of cross-marketing
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Don’t give discounts on 2 items that are frequently
bought together. Use the discount on 1 to “pull” the
other
Send camcorder offer to VCR purchasers 2-3 months
after VCR purchase
Discovery of patterns
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People who bought X, Y and Z (but not any pair)
bought W over half the time
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.48
Product placement
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Slide 7.49
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.50
GOADANA
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Slide 7.51
Clustering
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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
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Slide 7.52
The K-Means Clustering Method
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K=2
Arbitrarily choose K
objects as initial
cluster center
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Assign
each of
the
objects
to most
similar
center
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cluster
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Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
10
Slide 7.53
Chapter 8 customer segmentation
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.54
CUSTOMER SEGMENTATION
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Slide 7.55
Bonomo and Shapiro (1983) B2B
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.56
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.
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Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.57
Segmentation research used in compiling
the list
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RFM- recency frequency monetary value
CHAID- chi squared automated interaction
detection
CART- classification and regression trees
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.58
RFM
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.59
CHAID and CART
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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.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.60
CART
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it is often compared to CHAID.
Cart is not limited to numbers of variables
and classes that can be included.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.61
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Customer
Organizational market
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Slide 7.62
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.63
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.64
Not all customers are the same…
Highly profitable
customer
Highly profitable
product
++
Profitable product
+
Mixed-profitability
product
+
Losing product
Mixed-profitability
customer
Losing customer
+
+
_
_
_
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.65
Chapter 9
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Retention and cross sell analyses
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.66
Retention
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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….
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.67
Customer Retention Strategies
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Welcome
Reliability
Responsiveness
Recognition
Personalization
Reward Strategies
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
Slide 7.68
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!
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Reliability
The organization can repeat the exchange
time and time again with the same satisfying
results.
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Keep promise
Ensure consistent quality
Continuous promotion is still the key.
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Responsiveness
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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.
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Recognition
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Special attention or appreciation that
identifies someone as having been known
before.
People respond to recognition.
Recognition and appreciation help maintain
and reinforce relationships.
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014
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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.
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Access strategy
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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?
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A Communication process
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Cross-sell
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– 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.
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Upsell vs cross sell
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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.
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UPSELL
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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
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examples of Common Upselling
Techniques
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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.
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THE ONLİNE ENVİRONMENT

CHAPTER 15
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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,

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Google adwords

Fikrimuhim.
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
Lego factory story. Page 304*305
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Search engines

Organic
Peelen, Customer Relationship Management Powerpoints on the web, 2nd edition © Pearson Education Limited 2014