No Plagiarism PleaseYou should WRITE on and complete the assignment on the SAME file that I uploaded it. Before you think about how you might use text mining, think about the presence your business will have on the Internet and in Social Media. How will you use the Web in your business? Will it be used to conduct business? Or just for information? Write a paragraph describing your presence on the World Wide Web. How will you use Social Media in your business? What specific accounts are you likely to maintain for your business? Write a paragraph describing your Social Media presence.Thinking about your use of the Web and Social Media as described previously, describe a few examples of how you could utilize text, web, or social media anaytics.
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Business Introduction
View Tours and Travels is a tour company that aims at providing the best services to its
clients all over the world. There has been a gap in the tours and travel industry especially during
the festive seasons where most people look for destinations to spend the holidays, hence the idea
for this company (Elegantjbi.com, 2019). The company makes it easier for people to travel to
various destinations in the world by booking flights, securing accommodation, food and recreation
activities.
The company is accessible through the official website as well as application software that
give various suggestions based on what the client’s needs may be. A database with details on
various destinations such as flight prices, types of food and recreational activities in a specific area
are provided making it easier for people to decide on their ultimate holiday destinations
(Elegantjbi.com, 2019). These tools improve operations, enhance decision making and ease access
of data needed by the clients.
View Tours and Travels has 15 branches all over the world employing 1200 people
collectively. The head office is located in New York. The company generates, collects and
analyzes a large volume of data and uses holiday travel forecast to find out the demand of the
available destinations. Prices vary with seasons since there is more demand during holidays.
However, discounts and offers are also awarded to clients once they meet a certain threshold.
The most important task in this business is an analysis which needs to be up to date in order
to monitor targets, communicate with potential clients, keep track of performance and forecast
trends for new interesting destinations (Elegantjbi.com, 2019). The company, therefore,
understands the customer demographic making it easy to reach out in case of marketing.
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Business Intelligence PROJECT CH 2
View Tours and Travels business deals with several activities like flight ticketing, hotel
booking, and paying for bills and utilities. In this industry, there are various types of data used to
capture all the data in this field at once. These examples include; conversation optimization in
real-time, Niche targeting and dynamic pricing strategy. These data types are obtained from
various agencies including the airport, ticketing agencies, and the hospitality businesses. By
using these methods of obtaining data, it is possible to predict the traffic of people leaving for
vacations to various places; it will also help the ticketing companies when it comes to booking
for their fights properly to avoid cancellation due to unavoidable circumstances.
Since the type of data obtains from the company are dealing with various activities, most
of it is quantitative a discrete as it involves people and currencies. Since the business get these
two sets of data, should be tabulated and plotted on a bar or line graph. Also, a pie chart could be
possible to implement for those people looking for an easier way of representing this data. For an
in-depth analysis that is used by the Niche targeting data type, representation of these data on
histogram would be helpful, especially when determining the frequency and the intensity of these
activities in a certain time.
•
Traveling Agents
•
Revenue growth
•
Customer growth
These three Items presented above are services presented by View Tours and Travels to its
clients. Monitoring this item would enable the company to create a way of handling the intensity
at which the people use each of these items at a time.
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View tours and travel Dashboard
Data Cycle By Month
20
10
0
Category 1 Category 2 Category 3 Category 4 Category 5 Category 6 Category 7 Category 8 Category 9 Category 10
Series 1
Traveling Agents
Series 2
Revenue Growth
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Series 3
Customer growth
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Chapter 3
Views and tours business collect copious amounts of data and information about clients,
tourist destinations and pricing across the various currencies used by customers. Thus a data
warehouse is an imperative requirement for the business to store, consolidate and analyze the
data it collects. The business has a chain of fifteen branches spread across the world which
warrants a robust three-tier architecture for the data warehouse. The segregation of the model
will provide reliability and speed that result in customer satisfaction and streamlined business
processes across all of the branches.
Additionally, the growth and current size of the business will make use of the enterprisewide data warehouse design which will give the business better reporting and data consolidation
mechanisms for all of the departments in the industry. Data can be collected and summarized
effectively and efficiently at one data point that will ensure the cohesiveness of each department
in the company. Moreover, the large amount of data collected by the company will need a stable
and scalable system that will be able to grow with the business.
Establishing a data warehouse for an exponentially growing company can be a daunting
task in terms of the technical requirements and implementation (Payton and Handfield, 2003).
However, the best implementation for views and tours will be to maintain their own data
warehouse. This is because the data collected by the business needs to be as secure as possible in
lieu of privacy terms and the sensitive nature of the data. An in-house solution for data
warehousing will provide the most secure mode of data storage and management. Although this
comes with numerous challenges including, the expensive nature of proper equipment and expert
personnel required to operate them.
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Chapter 4
Data Mining Applications in Business and in Views Tours and Travels.
Data mining applications can be used by businesses to analyze the consumers’ buying
patterns through basket market analysis. The company will be able to predict what the consumer
will most likely want to buy based on previously purchased items and thus giving the business a
competitive edge as the will be able to accurately anticipate and meet consumer needs. View
Tours and Travels will be able to analyze data on customer travels, booking and flight
information so as to be able to best predict and present relevant tour suggestions to the customer.
Additionally, Data Mining applications can be used to help implement good customer
relationship management. Where the data mined will be analyzed to come up with strategies to
get, retain and maintain loyal customers to the business. One gets specific data on what to do to
retain specific customers and thus they are able to build the consumer-business relationship.
When Views Tours and Travels knows and anticipates the customer needs, they are able to better
create and maintain a good relationship based on understanding and good communication.
Data Mining Applications are used to easily detect fraud through patterns of nonfraudulent and fraudulent activity records that are collected. These data are then used to build an
algorithm that can easily identify whether an activity is fraudulent or not and thus preventing the
business from incurring unnecessary loses.
Another use of Data Mining in business is that it is used to better classify customers
through the information collected into the specific customer tiers that can best cater to their
needs and thus improving market efficiency. Views Tours and Travels will be able to know the
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kind of specialized offers to present to a specific customer group so as to enhance their
experience and satisfaction.
In Data Mining Applications, there is a need for privacy as the data being collected is
sensitive. So the application is heavily encrypted with defensive measures like user
authentication that prevents intrusions. When an unauthorized person tries to access the data in
View Tours and Travels they are immediately detected, blocked out and reported to the relevant
persons.
Before we had data mining in our company, it was really hard, but not impossible to keep
up with customer needs. We had to constantly go over customers data or just make general
suggestions of places to visit and stay to our customers and so we were not being very market
effective but since we started applying data mining methods it has been like a whole new market
opened up for us as we were able to better relate to our customers and deliver experiences that
exceeded their expectations.
The CRISP-DM Data Mining Process
The Cross Industry Standard Process for Data Mining is an analytic model that is used
most commonly to define common approaches used in data mining. It is made up of six main
steps which are;
I.
Understanding the business
One needs to first conduct a SWOT analysis on the business, determine the
business objectives and goals of the business. With the information you get from
the business analysis you need to then set up a data mining plan and the goals you
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hope to achieve through the data mining process before you can move to the next
step of the cycle.
II.
Understanding the data
At this stage, you collect all the raw data, describe it, explore it and then you
verify the quality of the data. This will help give you an understanding of the
business and to which information is relevant to your business goals and which is
not.
III.
Preparation of the data
Data preparation involves selection of data, description of the data, data
exploration and verification of the quality of the data. This allows the business to
determine the relevant data that can be used to determine any patterns that may be
used to select the best modeling technique.
IV.
Modeling
The business then uses the already prepared data to select a modeling technique,
generate test design (prototype), and build a workable model and then asses the
models’ performance.
V.
Evaluation
Here the business evaluates the results i.e.; the approved model, business success
and mining results. After evaluation, a review of the whole process is done so that
the business can be able to determine the next steps of the cycle which can either
be a deployment or throwing out the whole model and starting again.
VI.
Presentation of the information gathered to the stakeholders.
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Before the information can be presented to any stakeholders, first the business
needs to come up with a deployment plan, monitor and maintain the plan, produce
a final report and then review it to eliminate any errors. After all that is done if the
information is deemed usable and meets all the requirements, it can then be
presented to the stakeholders and be used to determine new business objectives.
These steps are a cycle, a continuous never-ending process because markets change and
business need to evolve because a stagnant business is a dying business.
Data Mining Methods
1. Anomaly detection
This data mining method is used to detect when something is odd. When does not fit
into the regular pattern established from all the data you have.
2. Associate learning/ market-basket analysis
This method is used to detect when certain pairs correlate to each other, i.e., the
purchase of item Y is usually associated with the purchase of item X. although
according to Giraud-Carrier, “Association learning goes beyond simple correlation
because it extends beyond pairs and can account for larger groupings of items.”
3. Cluster detection
This method is used to determine different subgroups within a dataset. This is kind of
like ‘labeling’ humans and putting them into different categories that have more
similar characteristics.
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4. Regression
A measure based on the relationship of various variables in the data set in order to
make future predictions on consumer behavior.
Views Tour and Travels could use the association learning method of data mining to determine
which customers to target for certain deals, coupons or advertising. For example, a client who
booked a Disney cruise line for Christmas and booked a carnival cruise line for new-years often
likes traveling on cruises and thus will receive more adverts on cruises.
Another example using anomaly detection method: if a customer usually books adventure tours
and then suddenly they book a fully escorted tour which is not normal for them, this will register
as an anomaly in the system because it does not fit the already established pattern.
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References
Caro, L. M., & García, J. A. M. (2008). Developing a multidimensional and hierarchical service
quality model for the travel agency industry. Tourism Management, 29(4), 706-720.
Elegantjbi.com. (2019). Business Intelligence Tool | BI Tool | BI Software | ElegantJ BI.
[Online] Available at: https://www.elegantjbi.com/ [Accessed 17 Jan. 2019].
Payton, F.C., & Handfield, R. (2003). Data Warehousing Implementation and Outsourcing
Challenges: An Action Research Project with Solectron. CAIS, 12, 43.
Wainstein, l. (2018). Data Warehouse Architecture — An Overview. [Online] Techburst.io.
Available at: https://techburst.io/data-warehouse-architecture-an-overview-2b89287b6071
[Accessed 9 Feb. 2019].
Lukasz Kurgan and Petr Musilek (2006); A survey of Knowledge Discovery and Data
Mining process models. The Knowledge Engineering Review. Volume 21 Issue 1, March 2006,
pp 1–24, Cambridge University Press, New York, NY, USA doi: 10.1017/S0269888906000737
[Accessed 3 March 2019].
Pete Chapman, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin
Shearer, and Rüdiger Wirth (2000); CRISP-DM 1.0 Step-by-step data mining guides. [Accessed
3 March 2019].
What IT Needs To Know About The Data Mining Process Published by Forbes, 29 July
2015, retrieved March, 2019, 3
Shearer C., The CRISP-DM model: the new blueprint for data mining, J Data
Warehousing (2000); 5:13—22. Retrieved 3 March 2019
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