How-To: Market Basket Analysis

by mrspectrumdesigns | Jun 28, 2024 | All Articles, Tutorial Hub: Tech & Business How-To's

Market Basket Analysis (MBA) is a data mining technique primarily used in retail to understand the purchase patterns of customers by identifying associations between different items bought together. By scrutinising transactional data, MBA unveils patterns that inform strategic decisions, such as product placement, promotional strategies, and inventory management.

This article explores the core principles of Market Basket Analysis, highlighting its importance in today’s retail and e-commerce environments. It also offers a detailed, step-by-step guide for performing a successful Market Basket Analysis, equipping professionals with the knowledge to utilize this valuable tool to boost sales and improve customer satisfaction.

When to Use a Market Basket Analysis

Ideal Situations for Market Basket Analysis

Market Basket Analysis is best utilized when you want to gain insights into customer purchasing patterns. It’s particularly effective for identifying product pairings that are frequently bought together. This analysis can be valuable during seasonal sales, when launching new products, or when optimizing product placement in stores or on websites. Retailers often use it to create effective cross-selling strategies and to design promotions that target specific customer behaviors.

Who Should Be Using Market Basket Analysis

Market Basket Analysis is not just for large retailers. While big-box stores and supermarkets frequently employ this technique, it’s also beneficial for small and medium-sized businesses, e-commerce platforms, and even service providers. Any business that has access to transactional data and is looking to understand and predict customer behavior can benefit from this analysis. Marketing teams, data analysts, and business strategists are typically the professionals who should be skilled in conducting and interpreting Market Basket Analysis.

Third-Party Companies and Tools

There are numerous third-party companies and tools available to assist businesses in performing Market Basket Analysis. Companies like IBM, SAS, and Oracle offer comprehensive analytics solutions that include market basket analysis capabilities. For those looking for more specialized tools, software like RapidMiner, KNIME, and Tableau can be incredibly useful. These tools often provide user-friendly interfaces and robust data processing capabilities, making it easier for businesses to conduct detailed analyses and gain actionable insights. Additionally, many of these tools come with built-in algorithms for association rule mining, which is essential for performing Market Basket Analysis.

Step-by-Step Guide to Conduct Market Basket Analysis

Step 1: Data Collection

Data collection is the foundational step in conducting a Market Basket Analysis. It involves gathering all relevant transactional data, which serves as the raw input for subsequent analysis. Here’s a detailed breakdown of this crucial step:

Identifying Data Sources

Begin by identifying the sources from which you will collect your data. The most common sources include:

1. Point-of-Sale (POS) Systems: These systems capture each transaction made at the checkout counter, including details about the items purchased, quantities, and timestamps.
2. E-commerce Platforms: Online stores record transaction data that includes customer purchases, shopping cart contents, and browsing history.
3. Customer Loyalty Programs: These programs often track detailed purchase histories linked to individual customers, providing rich data for analysis.
4. Inventory Management Systems: These systems can offer insights into stock levels and turnover rates, complementing transaction data.

Data Requirements

To perform a Market Basket Analysis, your dataset should include the following key elements:

1. Transaction ID: A unique identifier for each transaction.
2. Item or Product ID: Identifiers for each product or item purchased.
3. Quantity: The number of units of each item purchased (optional but useful for detailed analysis).
4. Timestamp: The date and time of each transaction (optional but can be used for temporal analysis).

Data Extraction

Once you have identified the data sources, the next step is to extract the data. This can be done through:

1. Direct Database Queries: Writing SQL queries to pull data directly from relational databases.
2. API Access: Using APIs provided by POS systems or e-commerce platforms to retrieve data programmatically.
3. CSV or Excel Files: Exporting data from software tools into CSV or Excel files for easier handling and manipulation.

Data Cleaning

Raw data is often messy and may contain errors or inconsistencies. Data cleaning involves:

1. Removing Duplicates: Ensure each transaction is recorded only once.
2. Handling Missing Values: Decide how to treat missing data points, whether by removing incomplete records or imputing missing values.
3. Correcting Errors: Look for and correct any anomalies, such as incorrect product IDs or impossible timestamps.

Data Formatting

The cleaned data needs to be formatted properly to facilitate analysis. Typically, you will want to organize the data into a transactional format where each row represents a single transaction and lists all items purchased in that transaction. This can be achieved through:

1. Transaction-Item Matrix: Creating a matrix where rows represent transactions and columns represent items, with binary values indicating the presence or absence of an item in each transaction.
2. Transaction Lists: Compiling lists of items purchased per transaction, often used in association rule mining algorithms.

Data Storage

Finally, store the cleaned and formatted data in a secure, easily accessible location. This could be a database, a data warehouse, or a simple file system, depending on the size of your dataset and your analytical needs. Ensure that the storage solution allows for efficient retrieval and processing of the data.

Step 2: Data Preprocessing

Data preprocessing is a critical step in preparing your dataset for Market Basket Analysis. This step ensures that the data is in the right format and is clean, consistent, and ready for analysis. Here’s a detailed guide on how to preprocess your data:

Data Cleaning

1. Remove Duplicates: Ensure that each transaction is unique. Duplicates can skew the analysis and lead to incorrect conclusions. Use tools or scripts to identify and remove any duplicate records.
2. Handle Missing Values: Assess your dataset for any missing values. Depending on the extent and nature of the missing data, you can choose to:
   • Remove transactions with missing values if they are few.
   • Impute missing values if possible, using techniques such as mean imputation for numerical data or the most frequent value for categorical data.
3. Correct Errors: Look for and correct any obvious errors in the data. This includes fixing incorrect product IDs, ensuring dates and times are in the correct format, and verifying that quantities are realistic (e.g., no negative quantities).

Data Transformation

1. Standardize Formats: Ensure that all data fields are in a consistent format. For example, dates should be in a uniform format (e.g., YYYY-MM-DD), and product IDs should be standardized (e.g., same length and format).
2. Encode Categorical Variables: Market Basket Analysis typically requires that categorical data, such as product IDs, be converted into a format that can be processed by analysis algorithms. This often means converting product IDs into numerical codes if they are not already numeric.
3. Discretize Continuous Variables: If your dataset includes continuous variables that need to be considered categorically (e.g., transaction amounts, age of customers), discretize them into categories (e.g., transaction amount ranges, age groups).

Data Aggregation

1. Aggregate Transactions: Transform your data into a format suitable for Market Basket Analysis. This usually means converting a list of individual transactions into a transactional format where each row represents a single transaction and includes all items bought in that transaction.
2. Transaction-Item Matrix: Create a transaction-item matrix (also known as a binary matrix), where rows represent transactions and columns represent items. The values in the matrix indicate whether a specific item was purchased in a particular transaction (1 for purchased, 0 for not purchased).

Data Reduction

1. Filter Out Infrequent Items: To reduce noise and improve computational efficiency, filter out items that appear in very few transactions. Set a threshold (e.g., items appearing in less than 1% of transactions) and exclude these items from the analysis.
2. Sampling: If your dataset is exceptionally large, consider using sampling techniques to create a representative subset of the data for initial analysis. This can speed up the preprocessing and initial analysis stages.

Final Preparation

1. Normalize Data: Ensure that the data is normalized if required by the analysis algorithms. Normalization can help in dealing with items that are purchased in very large quantities, ensuring they don’t disproportionately influence the analysis.
2. Partition Data: If you plan to validate your results, partition your data into training and test sets. This allows you to perform analysis on one set of data and validate the results on another, improving the reliability of your findings.
3. Save Preprocessed Data: Store the preprocessed data in a format that can be easily accessed and used by your Market Basket Analysis tools. Common formats include CSV files, databases, or dataframes in programming environments like Python or R.

Step 3: Selecting the Algorithm

Selecting the right algorithm is crucial for effective Market Basket Analysis. The choice of algorithm depends on the size and nature of your dataset, as well as your specific analysis objectives. Here, we’ll explore some of the most commonly used algorithms: Apriori, FP-Growth, and Eclat.

Apriori Algorithm

Overview

The Apriori algorithm is one of the earliest and most popular algorithms used in Market Basket Analysis. It works by identifying frequent itemsets and then deriving association rules from these itemsets.

How It Works

1. Generate Candidate Itemsets: The algorithm starts by identifying individual items that meet a minimum support threshold. It then iteratively combines these items to generate larger itemsets.
2. Prune Infrequent Itemsets: In each iteration, itemsets that do not meet the minimum support threshold are pruned.
3. Generate Association Rules: Once frequent itemsets are identified, the algorithm generates association rules by calculating confidence for rule subsets.

When to Use

• Suitable for smaller to medium-sized datasets.
• Useful when interpretability is important, as the rules generated are easy to understand.

FP-Growth Algorithm

Overview

The FP-Growth (Frequent Pattern Growth) algorithm is an advanced version of the Apriori algorithm designed to address its inefficiency with large datasets. It uses a divide-and-conquer strategy to find frequent itemsets without candidate generation.

How It Works

1. Construct FP-Tree: The algorithm first scans the dataset to build a compact data structure called the FP-tree, which stores the dataset in a compressed form.
2. Mine the FP-Tree: The algorithm then recursively mines the FP-tree to extract frequent itemsets directly.

When to Use

• Ideal for large datasets where Apriori is computationally expensive.
• Suitable when the dataset has a high degree of redundancy, as FP-Growth efficiently compresses the data.

Eclat Algorithm

Overview

The Eclat (Equivalence Class Clustering and bottom-up Lattice Traversal) algorithm is another popular choice for Market Basket Analysis. It uses a depth-first search strategy to find frequent itemsets.

How It Works

1. Transform Data: The algorithm transforms the transaction dataset into a vertical data format, where each item is associated with a list of transaction IDs (TIDs) in which it appears.
2. Intersect TIDs: It then finds frequent itemsets by intersecting these TID lists to count the occurrences of itemsets.
3. Generate Frequent Itemsets: The algorithm recursively intersects TID lists to generate larger frequent itemsets.

When to Use

• Effective for both dense and sparse datasets.
• Suitable when memory usage is a concern, as Eclat can be more memory-efficient than Apriori.

Choosing the Right Algorithm

Considerations

1. Dataset Size:
   • For small to medium-sized datasets, Apriori is often sufficient.
   • For large datasets, FP-Growth is generally more efficient.
2. Data Sparsity:
   • For dense datasets with many items per transaction, FP-Growth and Eclat perform well.
   • For sparse datasets, Apriori and Eclat are good choices.
3. Computational Resources:
   • Apriori can be resource-intensive, especially for large datasets.
   • FP-Growth and Eclat are more efficient and scalable.
4. Ease of Interpretation:
   • Apriori’s results are easy to interpret, making it a good choice for those who need straightforward association rules.
   • FP-Growth and Eclat can handle more complex datasets but may require more effort to interpret the results.

Practical Tips

• Start with Apriori for its simplicity and ease of implementation.
• If performance issues arise, especially with larger datasets, switch to FP-Growth or Eclat.
• Experiment with different algorithms to see which one performs best with your specific data and analysis goals.

Step 4: Applying the Algorithm

Once you have selected the appropriate algorithm for your Market Basket Analysis, the next step is to apply it to your preprocessed data. This involves using specific libraries and tools, writing code, and interpreting the results. Below, we’ll outline the necessary libraries, steps to follow, and provide code snippets for applying the Apriori, FP-Growth, and Eclat algorithms using Python.

Necessary Libraries

To perform Market Basket Analysis in Python, you’ll need the following libraries:

• pandas for data manipulation
• mlxtend for implementation of the Apriori, FP-Growth, and Eclat algorithms
• numpy for numerical operations

You can install these libraries using pip:

bashCopy code
pip install pandas mlxtend numpy

Applying the Apriori Algorithm

Steps

1. Load and Prepare Data: Import the transaction data and preprocess it as needed.
2. Generate Frequent Itemsets: Use the Apriori algorithm to generate frequent itemsets.
3. Generate Association Rules: Derive association rules from the frequent itemsets.

Code Snippet

pythonCopy code
import pandas as pd
from mlxtend.frequent_patterns import apriori, association_rules

# Load data
data = pd.read_csv('transactions.csv')

# Convert data into a one-hot encoded dataframe
basket = data.groupby(['TransactionID', 'ItemID'])['ItemID'].count().unstack().reset_index().fillna(0).set_index('TransactionID')
basket = basket.applymap(lambda x: 1 if x > 0 else 0)

# Generate frequent itemsets
frequent_itemsets = apriori(basket, min_support=0.01, use_colnames=True)

# Generate association rules
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)

print(rules)

Applying the FP-Growth Algorithm

Steps

1. Load and Prepare Data: Similar to the Apriori algorithm, import and preprocess the data.
2. Generate Frequent Itemsets: Use the FP-Growth algorithm to generate frequent itemsets.
3. Generate Association Rules: Derive association rules from the frequent itemsets.

Code Snippet

pythonCopy code
import pandas as pd
from mlxtend.frequent_patterns import fpgrowth, association_rules

# Load data
data = pd.read_csv('transactions.csv')

# Convert data into a one-hot encoded dataframe
basket = data.groupby(['TransactionID', 'ItemID'])['ItemID'].count().unstack().reset_index().fillna(0).set_index('TransactionID')
basket = basket.applymap(lambda x: 1 if x > 0 else 0)

# Generate frequent itemsets using FP-Growth
frequent_itemsets = fpgrowth(basket, min_support=0.01, use_colnames=True)

# Generate association rules
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)

print(rules)

Applying the Eclat Algorithm

Steps

1. Load and Prepare Data: Import and preprocess the data similarly to the previous algorithms.
2. Generate Frequent Itemsets: Use the Eclat algorithm to generate frequent itemsets. Note that Eclat is not directly available in mlxtend, so we’ll need to implement a basic version of it.
3. Generate Association Rules: This step is similar to the other algorithms but may require custom implementation depending on your Eclat implementation.

Code Snippet

Since mlxtend does not provide a built-in Eclat implementation, here’s a basic example using a custom function:

pythonCopy code
import pandas as pd
from itertools import combinations

# Load data
data = pd.read_csv('transactions.csv')

# Convert data into a list of transactions
transactions = data.groupby('TransactionID')['ItemID'].apply(list).tolist()

# Custom Eclat function
def eclat(transactions, min_support=0.01):
    itemsets = {}
    for transaction in transactions:
        for item in transaction:
            if item in itemsets:
                itemsets[item] += 1
            else:
                itemsets[item] = 1

    n_transactions = len(transactions)
    frequent_itemsets = {k: v / n_transactions for k, v in itemsets.items() if v / n_transactions >= min_support}

    return frequent_itemsets

# Generate frequent itemsets
frequent_itemsets = eclat(transactions, min_support=0.01)
print(frequent_itemsets)

# Generate association rules
# Note: Custom implementation may be needed to derive rules from frequent itemsets.

Final Steps

After running the chosen algorithm and generating frequent itemsets and association rules, review the output to ensure it meets your business objectives. Key metrics to consider include:

• Support: The proportion of transactions that include the itemset.
• Confidence: The likelihood that a transaction containing the antecedent also contains the consequent.
• Lift: The ratio of observed support to expected support if the items were independent.

By following these steps and using the provided code snippets, you can effectively apply Market Basket Analysis algorithms to uncover valuable insights into customer purchasing patterns.

Step 5: Analysis and Interpretation

After applying the algorithm and generating the frequent itemsets and association rules, the next crucial step is to analyze and interpret the results. This involves understanding the types of rules generated, evaluating their significance, and making actionable recommendations based on the insights derived. Here’s a detailed guide on how to approach this step:

Types of Rules

1. Frequent Itemsets: These are combinations of items that appear together frequently in transactions. Each itemset is associated with a support value, indicating the proportion of transactions that include the itemset.
2. Association Rules: These rules are derived from frequent itemsets and indicate patterns in customer purchasing behavior. An association rule is usually in the form of “If A, then B,” where A and B are itemsets.
   • Antecedent (A): The item or set of items found in transactions.
   • Consequent (B): The item or set of items that are likely to be found in the same transactions as the antecedent.

Key Metrics

1. Support: The support of an itemset is the proportion of transactions in the dataset that contain the itemset. It helps to identify how frequently an item or itemset appears in the transaction dataset.
2. Confidence: Confidence measures the likelihood that the consequent (B) is purchased when the antecedent (A) is purchased. It is calculated as the ratio of the support of the combined itemset (A and B) to the support of the antecedent (A).
3. Lift: Lift is the ratio of the observed support to that expected if A and B were independent. A lift greater than 1 indicates a positive correlation between A and B; they occur together more often than would be expected by chance.

How to Analyze the Results

1. Identify Strong Rules: Focus on rules with high support, confidence, and lift values. High support indicates that the rule is relevant to a large number of transactions. High confidence shows a strong relationship between the antecedent and consequent, and a high lift value confirms that this relationship is more significant than random chance.
2. Filter Rules: Depending on the business context, you might want to filter rules based on specific thresholds for support, confidence, and lift. This helps to focus on the most impactful rules.
3. Interpret Rules in Context: Consider the business implications of the rules. For example, a rule like “If a customer buys bread, they are also likely to buy butter” can inform cross-selling strategies. Contextualize each rule to understand its potential impact on marketing, inventory management, and customer satisfaction.

Recommendations and Tips

1. Actionable Insights: Translate the rules into actionable business strategies. For instance, if a rule indicates that customers who buy chips also frequently buy soda, consider placing these items near each other in the store or offering bundled promotions.
2. Customer Segmentation: Use the rules to segment customers based on purchasing patterns. Tailor marketing campaigns to different segments to increase engagement and sales.
3. Optimize Store Layout: Rearrange products in physical stores or on e-commerce platforms based on the discovered associations. Products that are often bought together should be placed in proximity to encourage additional purchases.
4. Promotional Strategies: Design targeted promotions and discounts based on association rules. For example, offer a discount on a related product when a customer purchases a specific item.
5. Inventory Management: Adjust inventory levels based on the insights to ensure that frequently associated items are always in stock together, reducing the risk of stockouts and improving customer satisfaction.
6. Continuous Monitoring: Market Basket Analysis is not a one-time task. Continuously monitor transaction data and update the analysis periodically to capture evolving customer preferences and trends.

Practical Tips

• Visualize Results: Use visualization tools to better understand the relationships between items. Heatmaps, network diagrams, and scatter plots can make it easier to interpret complex data.
• Collaborate with Stakeholders: Share findings with other departments such as marketing, sales, and operations to ensure that insights are effectively translated into business actions.
• Validate Findings: Test the recommended strategies on a small scale before full implementation to ensure they yield the expected results.

By thoroughly analyzing and interpreting the results of your Market Basket Analysis, you can derive valuable insights that drive strategic decisions, enhance customer experiences, and ultimately improve your business performance.

Tools for Market Basket Analysis

Market Basket Analysis requires robust tools to efficiently handle data preprocessing, algorithm implementation, and result interpretation. Various software and platforms are available to facilitate this process, each offering unique features and capabilities. Here, we’ll explore some of the most widely used tools for Market Basket Analysis.

Python Libraries

1. Pandas
   • Overview: A powerful data manipulation and analysis library.
   • Usage: Ideal for data cleaning, transformation, and preprocessing.
   • Example: pandas.read_csv() for loading transaction data.
2. mlxtend
   • Overview: A library that provides machine learning extensions, including implementations of Apriori and FP-Growth algorithms.
   • Usage: Used for generating frequent itemsets and association rules.
   • Example: mlxtend.frequent_patterns.apriori() for applying the Apriori algorithm.
3. SciPy and NumPy
   • Overview: Libraries for numerical computations and data manipulation.
   • Usage: Useful for handling large datasets and performing complex calculations.
   • Example: numpy.array() for creating data arrays.

R Libraries

1. arules
   • Overview: A comprehensive package for association rule mining in R.
   • Usage: Provides functions for the Apriori algorithm, as well as tools for visualization and analysis of association rules.
   • Example: arules::apriori() for running the Apriori algorithm.
2. arulesViz
   • Overview: A visualization package that complements the arules package.
   • Usage: Used for visualizing itemsets and association rules.
   • Example: arulesViz::plot() for plotting association rules.

Data Mining and Business Intelligence Tools

1. RapidMiner
   • Overview: An integrated data science platform for machine learning, data mining, and predictive analytics.
   • Usage: Provides a user-friendly interface for performing Market Basket Analysis with drag-and-drop functionality.
   • Example: Use pre-built templates for association rule mining.
2. KNIME
   • Overview: An open-source platform for data analytics, reporting, and integration.
   • Usage: Allows users to build workflows for data preprocessing, algorithm application, and result visualization.
   • Example: Implement Market Basket Analysis using KNIME nodes.
3. Tableau
   • Overview: A leading data visualization tool.
   • Usage: Excellent for visualizing the results of Market Basket Analysis and creating interactive dashboards.
   • Example: Use Tableau’s data connectors to integrate with data sources and visualize association rules.

Specialized Software

1. IBM SPSS Modeler
   • Overview: A data mining and predictive analytics software.
   • Usage: Offers advanced features for Market Basket Analysis, including the Apriori and GRI algorithms.
   • Example: Use SPSS Modeler’s graphical interface to design and execute data mining processes.
2. SAS Enterprise Miner
   • Overview: A comprehensive analytics and data mining solution.
   • Usage: Provides tools for association analysis, including the generation of frequent itemsets and rules.
   • Example: Utilize SAS’s powerful analytical capabilities for large-scale Market Basket Analysis.

Cloud-Based Platforms

1. Google Cloud Platform (GCP)
   • Overview: Offers a suite of cloud computing services.
   • Usage: Use Google BigQuery for handling large datasets and applying machine learning algorithms for Market Basket Analysis.
   • Example: Implement association rule mining using SQL queries in BigQuery.
2. Amazon Web Services (AWS)
   • Overview: A comprehensive cloud computing platform.
   • Usage: Use AWS Glue for data preprocessing and Amazon SageMaker for deploying machine learning models for Market Basket Analysis.
   • Example: Leverage SageMaker’s built-in algorithms for scalable analysis.

Practical Tips for Tool Selection

1. Assess Your Needs: Determine the size of your dataset, the complexity of the analysis, and your budget to choose the most appropriate tool.
2. Ease of Use: Consider tools with user-friendly interfaces if you have limited technical expertise.
3. Scalability: Ensure the tool can handle your data volume and scale as your business grows.
4. Integration: Choose tools that integrate well with your existing data sources and systems.

Use Case Examples

Market Basket Analysis is a versatile tool used across various industries to uncover valuable insights from transactional data. Here are some real-world use case examples demonstrating how different sectors can benefit from Market Basket Analysis:

Retail

Product Placement and Cross-Selling

A large supermarket chain utilized Market Basket Analysis to understand which products were frequently bought together. The analysis revealed that customers who purchased bread often bought butter as well. By placing these items closer together, the store saw a significant increase in the sales of both products. Additionally, they implemented cross-selling promotions, such as discounts on butter with the purchase of bread, further boosting sales.

E-Commerce

Personalized Recommendations

An online retailer used Market Basket Analysis to enhance its recommendation engine. By analyzing customer purchasing patterns, the retailer identified common item pairings and used this information to suggest additional products to customers during their shopping experience. For example, customers who bought a laptop were frequently shown laptop bags and accessories as recommended items. This personalization led to higher average order values and improved customer satisfaction.

Banking and Financial Services

Fraud Detection

A bank applied Market Basket Analysis to transaction data to identify unusual patterns that could indicate fraudulent activity. By analyzing the typical spending behavior of customers, the bank was able to flag transactions that deviated significantly from the norm. For instance, if a customer who usually made small, local purchases suddenly made a large international transaction, it would trigger an alert for further investigation. This proactive approach helped the bank reduce fraud and protect customer accounts.

Telecommunications

Churn Prediction

A telecommunications company used Market Basket Analysis to predict customer churn. By examining the service usage patterns and purchase behavior of customers, the company identified key indicators of potential churn, such as a decrease in data usage or frequent calls to customer service. With this information, the company could target at-risk customers with retention offers and personalized communication, thereby reducing churn rates and improving customer loyalty.

Healthcare

Treatment Pathways

A hospital implemented Market Basket Analysis to study treatment pathways for patients with chronic conditions. By analyzing the sequences of treatments and medications administered, the hospital identified common treatment patterns that led to better patient outcomes. This information was used to develop standardized treatment protocols, improving the quality of care and ensuring that patients received the most effective treatments.

Manufacturing

Inventory Management

A manufacturing company used Market Basket Analysis to optimize its inventory management. By analyzing the co-occurrence of raw materials in production orders, the company identified which materials were frequently used together. This insight allowed the company to streamline its inventory processes, ensuring that commonly paired materials were stocked together and reducing the likelihood of production delays due to missing components.

Hospitality

Menu Optimization

A restaurant chain applied Market Basket Analysis to its sales data to optimize its menu. By examining which dishes and beverages were frequently ordered together, the chain identified popular combinations and created combo deals to promote them. For example, customers who ordered a specific type of pizza were often likely to order a particular soda. By offering a discounted combo meal featuring these items, the restaurant increased its sales and enhanced the dining experience for customers.

Conclusion

Market Basket Analysis is a powerful tool for uncovering hidden patterns in transactional data, enabling businesses to make data-driven decisions. By following a systematic approach and leveraging appropriate tools, companies can optimize product placement, enhance marketing strategies, and ultimately increase sales and customer satisfaction.