Critical Review and Assessment of a Data Mining Journal Article

This report provides a critical review of a data mining journal article focusing on the application of data mining techniques in the online retail industry, specifically emphasizing customer-centric intelligence. The review explores how businesses leverage data mining to understand customer behavior, segment customers using techniques such as the RFM model (Recency, Frequency, Monetary), and apply clustering algorithms for targeted marketing strategies. The journal article highlights a case study where a small online retail company utilized data mining, employing the SAS technique to analyze customer data and identify distinct customer clusters based on purchasing patterns. The analysis reveals how these insights enable the development of more effective marketing strategies, leading to improved customer retention, increased sales, and enhanced competitive advantage. The report underscores the importance of data mining in modern business operations, emphasizing its role in extracting valuable insights from large datasets to inform strategic decision-making and drive business growth.

Find a journal regarding Data
Mining and review the information
in it and provide a critical
assessment of the ideas and
argument that are being prese
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Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
CONCLUSION ...............................................................................................................................3
REFERENCES................................................................................................................................4
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INTRODUCTION
In rapid change of technology it has become easy for business to grow and develop.
There are many new tool and application which are emerged and is used by business. They are
able to get crucial data and info. Data mining has become a modern and advance tool for
business to extract data and find out useful information. However, retail sector has benefited to
great extent by using data mining. In this there are various types of techniques which is applied.
The article focus on use of customer centric intelligence for online retail business. Through this,
they will be able to understand their customers in effective way. Furthermore, they will identify
their needs and do marketing accordingly. In addition there are many other techniques as well
such as recency, frequency, monetary, etc. that is useful in segregating customers. Data mining is
based on algorithms which is having a specific set of standards. It has been identified that there
is high rise in use if data mining by online retailers in UK. However, online shoppers in the UK
spent an estimated £50 billion in year 2011, a more than 500% increase compared with year
2015. There are many purpose of using data mining for business. It depends on business needs
and their products or services. Usually, from large dataset it is difficult to analyse it and generate
new info so, data mining is process to examining pre data set by which it becomes easy to find
out new info this info is useful for business to gain competitive advantage or find out variations
in the market. Basically, there are 3 concept in it which are machine learning, statistics and AI.
Usually, statistics is more useful for business as it provides crucial and useful info which can be
applied. The journal focus on things statistics in data mining. In recent times, online shopping
has several new features such as each customer pattern of shopping, decision making, etc. Can be
identified and evaluated, each customer's order is usually associated with a delivery address and
each customer has an online store account with essential contact and payment information. These
desirable, special online shopping characteristics have enabled online retailers to treat each
customer as an individual with personalized understanding of each customer.
Here. Customer centric technique is used to identify individual customer needs. Also, it provide
data about what type of customers, change in sales pattern, etc. With help of it, business are also
market their products and services. Hence, data mining is been integrated within business
operations. Besides that, the recency frequency model in data mining is used to generate profit. It
provide data about various markets and sales of products.
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The use of customer centric has enabled business to understand customers in more depth. They
get info about change in buying behavior, what specific thing they focus on, Pricing strategy, etc.
There are large dataset available in retail set, so data mining extract data and produces new
information. This is analysed and used by business to find out how customers have reacted and
what can be done to retain them.
Moreover, the clustering algorithm is applied in data mining in which main purpose is to find out
variable within customer cluster. So, organization focus on those variables and try to remove
them. It also gives insight on specific customer cluster and their variables. Therefore, now on
basis of cluster marketing is done. It makes things easy to relate marketing with customer
behavior. This allows in specifying things in clear way. Further, cluster analysis is evaluated via
decision tree. It provide a path about how customer has interacted with company product.
However, in the journal it has been described about how data mining is used by small online
retail company to understand their customers. In that cluster technique is applied on basis of
which customer are segregated. The SAS technique is used to analyse data. It us because SAS
provide statistical data which is interpreted to obtain outcomes.
By analysis journal it was identified that data mining enabled them to segregate customers on
basis of clusters. They got to know that many things about it. Here, in first cluster the profit
generated was least. The customers did not buy products frequently. Basically, purchasing was
done after July month. But on contrary, cluster 5 customer frequently purchased it. They were
highly profitable cluster. Along with it, in cluster customer purchased but less than cluster 5 one.
Thus, they are second profitable group. So, it clearly states that how an online retailer was able to
segregate customers on basis of purchasing with help of data mining technique of customer
centric. By this it will be easy for them to develop marketing strategy and target them on basis
of purchasing pattern. Besides that, it has to be change in things related to how marketing is
done. This makes it easy to retain customer and apply a new way of approach in order to do
marketing. The knowledge gained by business is applied in their operations. It helps in bringing
out some new changes and outcomes. Moreover, they are able to compete with rivals in the
market. There is also rise in sales and profits.
Henceforth, there are many other applications and techniques of data mining which help in
providing new info the statistics is mostly used as it gives data that is accurate and reliable. It is
used to a great extent and support in increasing sales and profits which is main goal of business.
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Through it they are able to gain competitive advantage. When business is able to understand their
customers they easily retail them for long term. However, as customer is king of marketplace
thus it is necessary to focus on them rather than other aspects. Therefore, data mining has amde it
easy for organization to understand customer in better way. They get info about change in buying
behavior, what specific thing they focus on, Pricing strategy, etc. There are large dataset
available in retail set, so data mining extract data and produces new information. This is analysed
and used by business to find out how customers have reacted and what can be done to retain
them.
CONCLUSION
So, data mining is an emerging technology which is used by business. It has resulted in providing
data about many things and analysis it. In this there are various types of technique which is
applied such as customer centric, recency frequency model, etc. However, customer centric is
applied to understand customer in better way. In that, many things are identified regarding
customer behavior, pattern, decision making, etc. The recency frequency model is used to
increase sales and profits in the market. This is done by changing way of marketing and its
pattern.
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REFERENCES
Books and journals
Chen, D., Sain, S.L. and Guo, K., 2012. Data mining for the online retail industry: A case study
of RFM model-based customer segmentation using data mining. Journal of Database
Marketing & Customer Strategy Management, 19(3), pp.197-208.
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