Practical Uses of Market Basket Analysis: Benefits and Examples

This discussion board post delves into the practical applications and benefits of market basket analysis, moving beyond typical retail examples to explore potential uses in fields like the military. It examines the nature of the data involved, highlighting how market basket analysis identifies products that customers frequently purchase together, such as chicken and spices or mobile phones and accessories. The post discusses the advantages of this analysis, including understanding customer purchasing patterns, optimizing product placement, and increasing sales. It also addresses the temporal aspect of market basket analysis and differentiates it from clustering, arguing that market basket analysis is a specialized form of clustering focused on individual item applications. Key metrics like support, confidence, and lift are mentioned, along with the Apriori algorithm. The discussion concludes by referencing set theory to refine association rules.

Running head: MARKET BASKET ANALYSIS
What the data is like.
The data of market basket analysis 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.
What type of benefit do you hope to get from market basket analysis? What major
questions are you attempting to answer?
Market Basket Analysis is the most common and essential types of the data analysis that
has been recently seen to widespread for the interpretation of the purchasing patterns of the
consumer. This analysis is commonly used for the retailing and marketing purpose. 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. One of the most common examples of the market basket analysis is that the

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MARKET BASKET ANALYSIS
supermarket store performing an analysis and study found out that the grocery items, together
with the baby products and beers sell well on Saturdays. The example illustrates the fact that the
couples tend to stock up their kitchen on supplies for themselves and their family before the
starting of the week (Gupta & Mamtora, 2014). The strength of this analysis lies on the fact that
by the effective usage of the data mining tools of the computer, it is not required for an
individual to assume the products the consumers will want to buy, but the sales data of the
consumer will speak for itself.
Is there anything being done in market basket analysis involving time?
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.
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.
Some of the commonly used metrics for the market basket analysis is support, confidence and
lift. Support means the percentage of the transactions that includes item set and are preferred for
being preferable in future transactions (Nengsih, 2015). Confidence illustrates the probability the
transactions contains items on the left hand side of the rule. Lift explains the probability of all the

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possible items of the rule divided by the probabilities of the items of the left and the right hand
side. The performance of the market based analysis and the identification of the potential rules
requires data mining algorithm commonly known as the Apriori algorithm
How does market basket analysis differ from clustering?
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. From the
explanation of the set theory, considering the sets (A, B) and (B,C) to be frequent whereas the
(A,C) is not, it is important to check the rule for (A,B,C) for narrowing down the possible
association of the rues of market basket analysis

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References:
Gupta, S., & Mamtora, R. (2014). A Survey on Association Rule Mining in Market Basket
Analysis. International Journal of Information and Computation Technology. ISSN,
0974-2239.
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.
Nengsih, W. (2015, May). A comparative study on market basket analysis and apriori association
technique. In Information and Communication Technology (ICoICT), 2015 3rd
International Conference on (pp. 461-464). IEEE.
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.