MIB IIBM CASE STUDY ANSWER SHEETS - User-generated content is an indispensable part of today’s industry as every other company needs user data to sell and buy products and provide the best possible support
MIB IIBM CASE STUDY ANSWER SHEETS - User-generated content is an indispensable part of today’s industry as every other company needs user data to sell and buy products and provide the best possible support
MIB IIBM CASE STUDY ANSWER SHEETS - User-generated content is an indispensable part of today’s industry as every other company needs user data to sell and buy products and provide the best possible support
For answersheets contact
info.answersheets@gmail.com
+91 95030-94040
Data Mining and
Predictive Analytics
a)
Additional acquaintance used by a learning algorithm to facilitate the learning
process
b)
A neural network that makes use of a hidden layer
c)
It is a form of automatic learning.
d)
None of these
II.
Querying of unstructured textual data is referred to as
a)
Information access
b)
Information updation
c)
Information manipulation
d)
Information retrieval
III.
A manual component to data mining, consists of preprocessing data to a form
acceptable to
a)
Variables
b)
Algorithms
c)
Rules
d)
Processes
IV.
A manual component to data mining, consists t processing data in form of
a)
Discovered processes
b)
Discovered algorithms
c)
Discovered features
d)
Discovered patterns
V.
Patterns that can be discovered from a given database, can be of
a)
One type only
b)
No specific type
c)
More than one type
d)
Multiple type always
VI.
Analysis tools precompute summaries of very large amounts of data, in order to
give
a)
Queries response
b)
Data access
c)
Authorization
d)
Consistency
VII.
Data can be store , retrieve and updated in …
a)
SMTOP
b)
OLTP
c)
FTP
d)
OLAP
VIII.
Which of the following is a good alternative to the star schema?
a)
snow flake schema
b)
star schema
c)
star snow flake schema
d)
fact constellation
XI.
Background knowledge is…
a)
It is a form of automatic learning.
b)
A neural network that makes use of a hidden layer
c)
The additional acquaintance used by a learning algorithm to facilitate the
learning process
d)
None of these
X.
Which of the following is true for Classification?
a)
A subdivision of a set
b)
A measure of the accuracy
c)
The task of assigning a classification
d)
All of these
Part Two:
1.
What are data mining techniques? (5)
2.
What are the applications of data mining? (5)
3.
Why is data mining important? (5)
4.
Differentiate Between Data Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated
content is an indispensable part of today’s industry as every other company
needs user data to sell and buy products and provide the best possible support
to its users and clients. While user data is important, it needs to be
processed to make it relevant for the company. Data mining is the most
important tool to process such data and make it relevant and useful.
The
decision tree algorithm with the apriori algorithm can be used to support the
needs of the client.
To
explain this problem, we will turn to smart technology –something that makes
our lives easier. Whenever we install any application in our smartphone, we are
asked for permission for the installation, but we do not pay too much attention
to the information these application require to be installed. In the process,
we unknowingly disseminate varied information on maps,
massages, contacts, etc. With the help of this information the application,
besides collating customer data, also tries to support the users to make their
life easier and at the same time makes them dependent on the application in the
near future.
Once
the user information is gathered, the data is analysed to get the required
information so as to give the best information to the algorithm at different
times. This type of analysis starts from data pre-processing steps, steps that
have already been explained in Chapters 1 and 2. However, for this type of data
pre-processing the information gain happens by designing the decision tree at
different levels-the depth decision tree or 2-10 level decision tree as well.
Each
data gives a valid point of information and these points are used in designing
the clusters among different types of data but they are very centric in
information as they provide the information of different users according to
same contents. The frequency of the matching data is processed by means of
decision tree under info gain and Apriori.
It
is a common experience nowadays for different applications to recommend the
same item for buying from different applications or portals, Users are also
able to exercise their choices when it comes to reading the news by selecting
the content that is more liked. Through their preferences, they provide the
application information about the cognitive behavior of users. This allows prediction
of the way a particular4 consumer behaves and recommendations are accordingly
tweaked. Most studies of systems or online reviews so far have used only
numeric information about sellers or products to examine their economic impact.
The understanding that text matters has not been fully realized in electronic
markets or in online communities. Insights derived from text mining of
user-generated feedback can thus provide substantial benefits to businesses
looking for competitive advantages.
Let
us summarise some of the chief benefits utiling user-centric data:
It saves money: Since the users themselves provide relevant
content for prediction and subsequent recommendations, users data need not be
bought and efficiency in terms of time and costs in increased.
It provides variety: By using the user data,
the customer can be
apprised of various new features or upgrades to the existing product. Further,
the user gets to know about the discounts being offered and can avail the
support extended to the end user.
It offers a voice to the user: The company
is in a position to offer
individual customers different products as per individual preferences and a
user can provide any specific information of the item he /she wants to use
These
benefits of user-centric data should be firmly kept in mind to make such data
more predictive and relevant in our fast-paced technological era.
 |
| MIB IIBM CASE STUDY ANSWER SHEETS - User-generated content is an indispensable part of today’s industry as every other company needs user data to sell and buy products and provide the best possible support |
Questions
1.
What do you understand by user generated content? (10)
2.
Do you really think user generated content is effective? (10)
Caselet 2
Big
data is the collection and cross-referencing of large numbers and varieties of
data sets that allows organizations to identify patterns and categories of
cardholders through a multitude of attributes and variables. Every time
customers use their cards, big data suggests the products that can be offered
to the customers. These days many credit card users receive calls from
different companies offering them new credit cards as per their needs and
expenses on the existing cards. This information is gathered on the basis of
available data provided by vendors.
There
are quite a few option available to customers to choose from. Sometimes
customers even switch their existing credit card companies. But competition may
not always work in the best interests of consumers. It also involves
bank’s profit. Competition may also be focused on particular features of credit
cards that may not represent long-term value or sustainability.
Those
paying interest on balances may be paying more than they realize or expect.
Some consumers use up their credit limits quickly or repeatedly make minimum
payments without considering how they will repay their credit card debt. A
proportion of consumers may also be over-borrowing and taking on too much debt,
and there are signs that some issuers may profit more from higher risk
borrowers (by which we mean customers at greater risk of credit default).
With
the launch of this credit card market study, we intend to build up a detailed
picture of the market and assess the potential identified issues. We plan to
focus on credit card services offered to retail consumers by credit card
providers, including banks, mono-line issuers and their affinity and co-brand
partners.
While
mass marketing continues to dominate most retailers’ advertising budgets,
one-to-one marketing is growing rapidly too. In this case study, you will learn
how to improve performance by communicating directly with customers and
delighting them with relevant offers. Personalised communication is becoming a
norm. Shoppers now expect retailers to provide them with product information
and promotional offers that match their needs and desires. They count on you to
know their likes, dislikes and preferred communication method-mobile device,
email or print media.
On
the surface, generating customer-specific offers and communications seems like
an unnerving task for many retailers, but like many business problems, when
broken into manageable pieces, each process step or analytical procedure is
attainable. First, let’s assume you have assembled promotions that you intend to
extend as a group of offers (commonly called “offer bank”) to individual
customers. Each offer should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group
Basket builder to increase the customer’s
basket size
Trip builder to create an additional trip or
visit to the store or an additional e-commerce
session
Reward to offer an incentive to loyal
customers
Questions
1.
How Big data used in this case study- Define? (20)
Section C: Applied Theory (30 marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of "data Mining"? (15)
For answersheets contact
info.answersheets@gmail.com
+91 95030-94040
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