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What Is Market Basket Analysis In Data Mining?

Author: Lovely Sharma
by Lovely Sharma
Posted: Jul 06, 2020

Market Basket Analysis

Market basket analysis is a data mining technique to promote sales. The retailers or merchants come across the purchasing patterns of customers. With the help of some expert analysts, they dig out what is not easily seen in the customers’ datasets, especially the purchase history. When they go deep with their analytics, it reveals a crystal clear picture of the products that a particular customer is interested in and also, what the other products could be that they would likely to purchase together.

Application of Market Basket Analysis

The market embraced this amazing sales growth technique with whole heart because of the advent of electronic POS or Point of Sale systems. These are some visible systems, such as a screen, a scanner and a CRM application, that you can see at the counter of the shopping malls. Certainly, it’s just because of the digital era or transformation that has blown up the use of electronic systems like POS for easy recording, processing and analysis of large volume of purchase data.

Besides, there are a few more areas where it is harnessed for analysis. These are:

  • Analysis of credit card purchases
  • Analysis of telephone calling patterns
  • Determining fraudulent medical insurance claims
  • Analysis of telecom service purchases

In the nutshell, this analysis can be executed where you need go deep with the purchase patterns of some products or services.

Requirements for This Basket Analysis

  • Database: To start with, the
data mining companies need databases. The implementation of this sale-triggering analysis is possible when you have statistics of all purchases.

  • Data Science: The expertise in data science assists you to research and pass through suitable methods like clustering, progression and many other mining techniques for figuring out some useful patterns.
  • Algorithmic computer programming skills: The programming algorithm is a computing procedure that tells computer precisely about what steps to follow up for solving a problem to achieve a goal. For sure, it requires some inputs and whatever result programmers get at the end is considered as an output.

If you extract a part of customers’ web journey on an eCommerce store, you can have an ideal database to execute this analysis. Now, what all you need to do is to create an Excel sheet for analyzing transactions. Just get deep with the shopping basket analysis, monitoring transactions in respect to shopping cart data. Then, create two sheets, A) the shopping basket item groups wherein you would place all items that are frequently purchased together. B) the shopping basket rules, which shows how those products are related to one another.

Types of Market Basket Analysis

  • Predictive Analysis

This analysis is aimed at foreseeing the items purchased in a sequence so that the merchant can presume what to throw as recommendation for cross-selling. It is generally carried out on the items purchased previously.

  • Differential Market Analysis

This considers purchase data across different stores and different customer groups in different times of the day, month or year. Therefore, a valuable insight can be drawn, which can lead to a new product to be added in the offerings for driving more and more sales.

Association Algorithm To Be Used For This Analysis

The association rules represent the best tweak for predicting the likelihood of products, which are being purchased together. It sticks to counting the frequency of items that happen together so that one can determine its association, which can be often or more frequent.

These association rules are carried out with AIS, SETM and Apriori. Out of these three, the Apriori algorithm is prominently used by data scientists for the market basket analysis, which enables identification of frequently bought items in the database. Upon noticing it, they measure up their frequency to analyse the opportunity for expanding it largely.

The arules package for R also smoothly goes with the mining when it is done using the R programming language. The best part is that it also supports the Apriori algorithm along with others, such a arulesNBMiner, opusminer, RKEEL and RSarules.

Its Benefits

As mentioned in the beginning, it is to trigger exponential sales. Besides, the merchants also find out ways to satisfy customers exploring the patterns that carry the intelligence for business advantages.

Once you have an idea about the products being purchased together, you can easily optimize the product placement. Even, you can come with special offers that have some new products for stimulating combined sales.

This is an effective technique to know about how to create leads of some additional products while making shopping experience the most productive and valuable for customers. Gradually, it can lead to brand loyalty towards the company.

About the Author

Being a digital business strategist, Lovely has gained grounds in the digital transformation. He lets the performance speak about the plus and minus of what is done, which helps him drive to winning strategies over the internet.

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Author: Lovely Sharma

Lovely Sharma

Member since: May 20, 2019
Published articles: 14

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