Discussion: Utilizing Market Basket Analysis in Diverse Scenarios

This discussion board post delves into the practical applications of market basket analysis, extending beyond typical retail scenarios to explore potential uses in areas like the military. It examines the nature of data required, the benefits derived from the analysis, and the key questions it aims to answer, including the element of time in predicting future choices. The post also differentiates market basket analysis from clustering, suggesting that the former is a specialized form of the latter, where customers select items based on their individual needs. It references algorithms that help companies crunch data and find interesting patterns, and it identifies the differences between online and offline purchasing patterns. The post concludes with references to support the discussion.

Running head: MARKET BASKET ANALYSIS
MARKET BASKET ANALYSIS
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1MARKET BASKET ANALYSIS
Answer to question 1.
The data of market basket analysis is quantitative data. It is quite common as well as
useful for both the customers and the business organizations (Olson, 2017). The chief aim of
market basket analysis actually is to understand what the products are which the customers
purposefully purchase together. For example, when a customer by is chicken then he also buy is
the spices vegetables and other materials for cooking that chicken or when a customer buys a
mobile phone then he or she also buy the accessories needed for operating the phone like ear
phones or phone cases.
Answer to question 2.
This helps the business persons to know the customers choices about one particular
object. Once it is known that the customers who will buy one product prefer or feeling necessity
to buy other related things, it is possible for different companies to attach themselves and market
those products together. This method will help them to make the customers of the product,
targeted prospects for another. This will help the shop managers to get the knowledge about the
products preferred by the customers so that they can stock huge amount of similar products along
with the main one (Kaur & Kang, 2016). They also can place those items optimally in their store
so that it becomes easy for the customers to pick similar things together (Shiokawa et al., 2017).
This will enhance the selling of different types of products together and bring profit for the stores
as well as those companies.
Answer to question 3.
The promise of market basket analysis is to understand the algorithms so that the
companies can crunch data and find the most interesting patterns and exploiting the business.

2MARKET BASKET ANALYSIS
Market basket analysis has closed connection with time for example the business organizations
can understand the demand of the market and what patterns are effective for reaching the
customers. In case of the online marketers it becomes easy for identifying the exact requirements
of the customers in no time. In online marketing the customers are provided with huge number of
choices from which they can choose one and get immediate choice for all the accessories needed
for using the first product. In that case the buying option becomes quite easy and less time
consuming. The marketers get immediate response to those products hence can stock them in no
time. In case of offline purchasing the markers do not get immediate reactions of the customers
about their purchasing or identify their purchasing patter that is why they wait for more time.
Answer to question 4.
Clustering is the method of accumulating a group of objects in a way that this group of
objects can be similar to the objects belonging in other different groups (Videla-Cavieres & Rios,
2014). On the other hand, market basket analyses the objects are not grouped like clustering
rather have individuality in their applications. Therefore, it can be said that the market basket
analysis is a special kind of clustering where the sellers offer different types of things differently
from which the customers choose one or similar objects according to their utility.

3MARKET BASKET ANALYSIS
References:
Kaur, M., & Kang, S. (2016). Market Basket Analysis: Identify the changing trends of market
data using association rule mining. Procedia computer science, 85, 78-85.
Olson, D. L. (2017). Market Basket Analysis. In Descriptive Data Mining (pp. 29-41). Springer,
Singapore.
Shiokawa, Y., Misawa, T., Date, Y., & Kikuchi, J. (2016). Application of market basket analysis
for the visualization of transaction data based on human lifestyle and spectroscopic
measurements. Analytical chemistry, 88(5), 2714-2719.
Videla-Cavieres, I. F., & Rios, S. A. (2014). Extending market basket analysis with graph mining
techniques: A real case. Expert Systems with Applications, 41(4), 1928-1936.