Machine learning is very advantageous for the retail industry. From client identification to predicting sales success, it benefits the retail sector in every way. Market basket analysis is one such common use of machine learning in the retail industry. Businesses use it as a cross-selling tool on their web platform.
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What is Market Basket Analysis?
Market basket analysis is a kind of data analytics that pinpoints commodities that people usually buy together. This study is typically carried out in the retail sector to comprehend consumer behavior and preferences. The outcomes of a market basket study may be utilized for a variety of tasks, including developing specialized marketing campaigns, enhancing product placement in stores, and controlling inventory.
Need for Market Basket Analysis
Market basket analysis is used to analyze items or products that buyers want to buy together. In the coming era, data will play an important role in making crucial business decisions. Market basket analysis is one such analysis that has many use cases:
- It helps a retail shop owner determine which products are purchased together.
- If used correctly, market basket analysis can significantly increase sales and customer satisfaction.
- It helps a shop owner make important decisions like product placements, offering special deals, and creating new product groupings.
Working of Market Basket Analysis
Market basket analysis is an association rule mining-based technique that is used in retail data analytics. Retailers apply algorithms such as the Apriori algorithm to large transaction datasets to uncover groupings or associations between products. Some common inputs include point-of-sale (POS) datasets and e-commerce order data.
By setting metrics like support, confidence, lift ratios, and conviction, a retailer can filter the output association rules to identify the most relevant relationships between items or products; these groupings are called itemsets.
Association rules use an IF -> THEN structure. For example, IF bread is purchased, THEN milk is purchased. Here, bread is called an Antecedent, and milk is called a Consequent.
Types of Market Basket Analysis
Market basket analysis is classified into two types:
Descriptive Market Basket Analysis
Descriptive market basket analysis searches the existing data for relationships and patterns that exist between the elements that form a market basket. The primary purpose of this type of research is to comprehend customer behavior, including the most common item combinations and the combinations of products that are bought together. With the use of descriptive market basket analysis, retailers can more profitably arrange products in their stores by determining which products are frequently purchased together.
Predictive Market Basket Analysis
Predictive market basket analysis is a type of market basket analysis that forecasts future purchases based on historical purchasing trends. In this kind of analysis, large amounts of data are evaluated using machine learning algorithms to make predictions about which products are most likely to be purchased together in the future. Using predictive market basket research, retailers can utilize data to make data-driven decisions about what products to carry, how much to charge for them, and how best to arrange their stores.
Differential Market Basket Analysis
Differential market basket analysis looks for differences between two sets of market basket data. This type of study is frequently used to compare the behavior of different client segments or the behavior of customers over time. With the aid of differential market basket analysis, retailers may adapt their marketing and sales strategies in response to changing consumer behavior.
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Algorithms Used in Market Basket Analysis
Multiple algorithms can be used in Market Basket Analysis. A few of the most well-established industry-level algorithms are:
Apriori Algorithm
It is a well-known algorithm for Association Rule mining. It finds association rules between these items and assists in locating frequently occurring item groupings in transactions. One of the limitations of the Apriori Algorithm is the frequent generation of item sets. Due to the enormous dataset, it must repeatedly search the database, which increases processing time and decreases performance. It makes use of the ideas of support and confidence.
AIS
AIS stands for Apriori Itemset Selection. It is one of the most common algorithms used in market basket analysis. The key idea behind AIS is to identify items that are frequently purchased together in transactions. It does this in multiple passes over the transaction data.
First, it counts the number of distinct objects that are supported, and then it determines which of those items are frequently found in the database. The technique generates candidate itemsets by enlarging enormous itemsets from each pass after each transaction scan. It determines the common itemsets between the itemsets of the previous pass and the items of the current transaction.
SETM Algorithm
SETM stands for Sequential Extended Top Down Mining. It is quite similar to the AIS algorithm. After counting the support for each item in the first pass, it ascertains which of them are frequently found in the database. Next, it enlarges the big itemsets from the previous phase to create the candidate itemsets. Furthermore, the SETM algorithm retrieves the transaction IDs (TIDs) of the transactions that were generated using the candidate itemsets. One disadvantage of the SETM algorithm is that it takes up a huge amount of storage space.
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Applications of Market Basket Analysis
Market basket analysis has several applications in various sectors; the most popular ones are mentioned below:
| Industry | Application |
| Retail | Retailers can use market basket research to identify products that customers frequently buy together, which can help them with price, marketing, and product placement decisions. Better client satisfaction and increased income may arise from this. |
| E-Commerce | E-commerce platforms can utilize market basket analysis to assess customer purchase data and identify which products are frequently bought in tandem. This information can be used to create customized product bundles and opportunities for upselling. |
| Healthcare | Healthcare firms can use market basket analysis to assess patient data and identify medicines or conditions that co-occur. This information could be used to reduce healthcare costs and improve patient outcomes. |
| Financial Services and Banking | Market basket analysis can be used by banks and financial organizations to evaluate client data and uncover trends in their purchasing habits. This data may be utilized to create customized marketing initiatives and boost consumer loyalty. |
| Telecommunications | Telecommunications firms can use market basket analysis to study consumer data and detect trends in their service consumption. This data may be utilized to enhance the customer experience and boost revenue. |
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Benefits of Market Basket Analysis
There are various advantages to conducting market basket analysis, including:
- Better Customer Understanding
Market basket research reveals customer behavior and purchase patterns, allowing firms to better understand their customers and their requirements.
- Increased Sales
Businesses can enhance sales and improve customer happiness by learning which goods are often purchased together.
- Improved Inventory Management
Market basket research can assist organizations in enhancing their inventory management by revealing which goods are commonly purchased together and which products are not selling well.
- Targeted Marketing
Market basket analysis can be used to create targeted marketing campaigns, as it provides information on which products are frequently purchased together and which products may not be selling well.
- Improved Customer Experience
By understanding customer behavior and purchasing patterns, businesses can make changes to improve the customer experience, such as making products more easily accessible or offering special promotions.
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Drawbacks of Market Basket Analysis
A few drawbacks of Market Basket Analysis are:
- Complexity
It can be complex and require specialized knowledge and expertise to implement and interpret.
- Data Quality
The accuracy of market basket analysis results depends on the quality of the data being analyzed. If the data is incomplete, outdated, or inaccurate, the results of the analysis will also be flawed.
- Limited Context
It provides information on which products are frequently purchased together, but it does not provide information on why these products are purchased together. This can limit the usefulness of the analysis for certain applications.
- Privacy Concerns
It relies on customer data, which can raise privacy concerns. Businesses must ensure that they comply with data privacy regulations and that they obtain the necessary consent from customers before using their data for analysis.
- Computational Costs
Market basket analysis can be computationally intensive, especially for large datasets. This can be a barrier for businesses with limited computational resources.
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Conclusion
Market basket analysis is an important method of data analysis that can reveal essential details about the behavior and buying habits of your customers. Businesses can make well-informed decisions that enhance sales, customer pleasure, and overall business performance by examining data on which goods are frequently purchased together.
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FAQs
Why do we use market basket analysis?
There are various benefits to using market basket analysis. It helps a retail shop owner make important decisions like product placements, offering special deals, and creating new product groupings.
Is market basket analysis a part of machine learning?
Yes, market basket analysis is a part of machine learning. It uses the mathematical concept of association rule mining. This concept can be applied through various machine learning algorithms like the Apriori algorithm, AIS, SETM, etc.
What is an Apriori Algorithm?
An Apriori Algorithm is a machine learning algorithm that implements Association rule mining on retail datasets or e-commerce datasets.