market basket analysis case study

Market Basket Analysis: Case Study

• October 15, 2023

Download the dataset below to solve this Data Science case study on Market Basket Analysis.

Market Basket Analysis is a data-driven technique used in retail and e-commerce to uncover patterns of co-occurring items in transactions. It aims to identify relationships between products that are frequently purchased together, providing valuable insights for product placement, cross-selling, and marketing strategies.

The dataset provided contains transactional data, with columns representing:

• BillNo: A unique identifier for each customer’s bill.
• Itemname: The name of the purchased item.
• Quantity: The quantity of the item purchased.
• Price: The price of each item.
• CustomerID: A unique identifier for each customer.

Perform a Market Basket Analysis on the given dataset to discover associations between purchased items. Specifically, the goal is to identify:

• Which items are frequently purchased together by customers.
• The strength of these associations (e.g., support, confidence, lift).

References to Solve this Data Science Case Study

• Market Basket Analysis by Aman Kharwal

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Table of Contents

What is market basket analysis, purpose of market basket analysis, types of market basket analysis, algorithms associated with market basket analysis, how does market basket analysis work, metrics for market basket analysis in data mining, benefits of market basket analysis , examples of market basket analysis, choose the right program, what is market basket analysis overview, uses, types, and examples.

The retail sector is especially benefiting from machine learning. It aids the retail industry in every way, from identifying customers to forecasting sales performance. One such prominent retail use of machine learning is market basket analysis (MBA). Knowing which goods customers frequently buy together enables merchants to organize their stores and websites consistently. It is mostly accomplished by looking at their prior purchase behavior. Businesses use it as a cross-sell tool for their itheon their web platform. But it's not just employed in the retail industry—false credit card transactions and insurance claims also use it. 

Retailers utilize market basket analysis, a data mining approach, to boost sales by better understanding client buying habits. Identifying product groups and items that are most likely to be bought together, includes evaluating big data sets, such as purchase history.

Finding items that buyers desire to buy is the major goal of market basket analysis. Market basket analysis may help sales and marketing teams develop more effective product placement, pricing, cross-sell, and up-sell tactics. 

● Predictive Market Basket Analysis

This kind employs supervised learning methods like regression and classification. In essence, it seeks to imitate the market to examine what factors influence events. In essence, it determines cross-selling by taking into account things bought in a particular order.

● Differential Market Basket Analysis

For competition analysis, this kind of analysis is useful. To identify intriguing patterns in consumer behavior, it compares purchase histories across brands, periods, seasons, days of the week, etc.

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The market study definition is based on Association Mining rules, as was already explained. Association mining is a technique used by the AIS, SETM, and Apriori algorithms. The Apriori Algorithm is the MBA algorithm that is used the most frequently.

The IF, THEN construct is used in association rule mining to replicate market basket analysis. When a customer buys bread, he is likely to also buy butter. Examples of association rules include the following: "Bread" -> "Butter"

Learn the following definitions to better understand market basket analysis:

● Antecedent

The entities or "itemsets" produced from the data are called antecedents. To put it another way, it's the IF element on the left. In the situation before, bread serves as the antecedent.

● Consequent

The term "consequent" refers to an item or group of items that are encountered along with the antecedent. The THEN part of the sentence is displayed on the right-hand side. The result in the aforementioned case is butter.

You can put a lot of interesting controls on your association rules. These consist of

Consider the following scenario: A well-known e-commerce site handled 4000 transactions. They are trying to determine how many transactions, how much lift, trust, and support there is for the two things, a phone, and a phone cover, out of 5000. The phone has 500 transactions, the phone case has 800 transactions, and the two together have 1000 transactions.

• Gaining market share: Once a business reaches its peak growth, finding new ways to do so might be difficult. Market basket analysis may be used to integrate gentrification and demographic data to locate the sites of new businesses or geo-targeted marketing.
• Campaigns and promotions: MBA is used to identify the goods that work well together as well as the products that serve as the cornerstones of their product range.
• Behavior analysis: A fundamental tenet of marketing is comprehending consumer behavior patterns. MBA may be used for anything, including UI/UX and basic catalog designs.
• Optimization of in-store activities: MBA is useful in deciding what goes on the shelves as well as at the back of the shop. Because geographic patterns are a major factor in determining the strength or popularity of particular products, MBA is increasingly used to manage inventory for each store or warehouse. 

The most well-known case study using market basket analysis is probably Amazon.com. As soon as you visit Amazon to look at a product, the product description will suggest "Items purchased together frequently." It is the clearest and most straightforward example of Market Basket Analysis cross-selling tactics.

Along with e-commerce methods, consumer in-store retailers also greatly benefit from BA. For grocery stores, visual merchandising and shelf optimization is crucial. For instance, shower gel is almost usually kept close to one another at the grocery store.

Examining credit or debit card history is a highly advantageous MBA opportunity for IBFS companies. For instance, Citibank frequently sends sales representatives to large malls to tempt potential customers with enticing on-the-go discounts.

Additionally, they collaborate with services like Swiggy and Zomato to provide customers with a selection of offers that they may use their credit cards to redeem.

Due to the intense competition in the telecom sector, businesses are paying close attention to the advantages that customers frequently utilize. For instance, telecom has started to combine TV and Internet bundles with other affordable internet platforms to reduce migration.

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Market basket analysis may be used by more and more businesses to get relevant information about associations and unspoken linkages. A predictive form of market basket analysis is gaining traction across various industries in an effort to pinpoint sequential purchases as industry leaders continue to investigate the technique's use.

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1. What is market basket analysis in data mining?

Answer: Market Basket Analysis is a type of data mining that identifies patterns of consumer behavior in any retail environment. Simply said, market basket analysis in data mining examines the assortment of items that have been purchased together.

2. What is market basket analysis used for?

Answer: Retailers use analytics methods like market basket analysis (MBA) to comprehend the purchasing patterns of their customers. It is used to find out which products customers usually buy together or put in the same basket. This purchasing data is used to increase the efficiency of sales and marketing .

3. What are the benefits of using market basket analysis?

Answer: Data from point of sale (PoS) systems that pertain to customers can be used in market basket analysis (MBA). Retailers benefit from its:

•  Increasing sales and return on investment
•  Boosts consumer engagement
•  Increasing client satisfaction
•  Aid in improving customer comprehension
•  Identifies patterns and behavior of customers
•  Improves marketing initiatives and strategies

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Market Basket Analysis 101: Anticipating Customer Behavior

In the business world, “market basket analysis” helps retailers better understand – and ultimately serve – their customers by predicting their purchasing behaviors. applied to consumers as recommendation systems, market basket analysis is the most common form of artificial intelligence consumers encounter. we’ll explain how market basket analysis works and what it takes to deploy a market analysis solution..

Market basket analysis (MBA) is a data mining technique that is used to uncover purchase patterns in any retail setting. MBA is a set of statistical affinity calculations that help business leaders better understand – and ultimately serve – their customers by highlighting purchasing patterns. In simplest terms, MBA looks for what combinations of products most frequently occur together in transactions. These relationships can be used to increase profitability through cross-selling, recommendations, promotions, or even the placement of items on a menu or in a store.

Explore more insights for restaurants

The approach is based on the theory that customers who buy a certain item (or group of items) are more likely to buy another specific item (or group of items). For example: while at a quick-serve restaurant (QSR), if someone buys a sandwich and cookies, they are more likely to buy a drink than someone who did not buy a sandwich. This correlation becomes more valuable if it is shown to be stronger than that between the sandwich and drink without the cookies.

MBA can be used to recommend a purchase based on the absence of a common pairing, such as when a customer at a QSR orders only a small sandwich. They might be more likely to buy a dessert or a second sandwich than someone who bought a large sandwich. Staff trained to recognize these situations can offer their customers the additional items, possibly with a discount to make the option more attractive.

Applied more deeply, MBA allows companies to identify the keystone products , those that differentiate them in the market and could potentially hurt business if they were unavailable or more expensive. Gourmet or other specialty items in a grocery store might have limited appeal, but the customers they attract (and their subsequent spending) could justify high-visibility placement. Customers ordering through the company’s app could be interested in items or combinations that offer extra loyalty points.

Generating reliable insights from MBA typically requires large volumes of transactional data . Large data sets are difficult to process without highly-scalable storage and compute resources. Modern cloud-based architectures allow for more agile data mining and analytics with the capability to test a number of theories of customer behavior or to examine the effectiveness of a recent marketing campaign. Smartbridge clients are using Microsoft Azure with Microsoft Synapse or Snowflake as a data lake with analytic tools like Power BI , integrated with Python or specialized R statistical packages, to solve these problems.

Market Basket Analysis, Explained

MBA aims to find relationships and establish patterns across purchases. The relationship is modeled in the form of a conditional algorithm:

IF {sandwich, cookies} THEN {drink}

In the shorthand notation, which translates to “the items on the right are likely to be ordered with the items on the left:”

A collection of items purchased by a customer is an itemset . The set of items on the left-hand side (sandwich, cookies in the example above) is the antecedent of the rule, while the one to the right (drink) is the consequent . The probability that the antecedent event will occur, i.e., a customer will buy a sandwich and cookies, is the support of the rule. That simply refers to the relative frequency that an itemset appears in transactions. In a QSR, the support of an item or item combination helps to identify keystone products. Hence, if a sandwich and cookies have high support, then they can be priced to attract people to the store.

The probability that a customer will purchase a drink on the condition of purchasing a sandwich and cookies is referred to as the confidence of the rule. Confidence can be used for product placement strategy and increasing profitability. Placing high margin items near associated high confidence (driver) items can increase the overall margin on purchases.

The lift of the rule is the ratio of the support of the left-hand side of the rule (sandwich, cookies) co-occurring with the right-hand side (drink), divided by the probability that the left-hand side and right-hand side co-occur if the two are independent.

A lift greater than 1 suggests that the presence of the antecedent increases the chances that the consequent will occur in a given transaction

Lift below 1 indicates that purchasing the antecedent reduces the chances of purchasing the consequent in the same transaction. Note: This could indicate that the items are seen by customers as alternatives to each other

When the lift is 1, then purchasing the antecedent makes no difference on the chances of purchasing the consequent

Market basket analysts search for rules with lift that are greater than 1 backed with high confidence values and often, high support.

The Apriori algorithm is a commonly-applied technique in computational statistics that identifies itemsets that occur with a support greater than a pre-defined value (frequency) and calculates the confidence of all possible rules based on those itemsets.

Market Basket Analysis Example

The Apriori algorithm is implemented in the arules package , which can be installed and run in R . Data is loaded into the engine in the following format:

The first column is the order/transaction number and the second is the item name or, more often, the item ID. The next steps usually involve aggregating each transaction across records into a single record as an array and converting the dataset to an R transaction. The result of that aggregation is as shown below:

Finally, the Apriori logic is applied to the transactions with the following  resultset:

Overall, we see a number of strong consequent (RHS) combinations with soda, which could indicate that soda is a keystone product category. A manager could decide to keep the price and margins low on soda to drive sales volume. Bottled water is the exception, but we can assume that those would be considered alternatives to each other.

Perhaps a more interesting result is that all of the rules with ice cream show significant lift with a confidence of 1. (Associations in the other direction aren’t nearly as likely.) This could lead us to further promote ice cream with the expectation that customers will purchase other items at the same time.

Other Uses for Market Basket Analysis

In addition its popularity as a retailer’s technique, MBA is applicable in many other areas:

Manufacturing: predictive analysis of equipment failure

Pharmaceutical/Bioinformatics: discovery of co-occurrence relationships among diagnosis and pharmaceutical active ingredients prescribed to different patient groups

Financial/Criminology: fraud detection based on credit card usage data

Customer Behavior: associating purchases with demographic and socio-economic data

More and more organizations are discovering ways of using market basket analysis to gain useful insights into associations and hidden relationships. As industry leaders continue to explore the technique’s value, a predictive version of market basket analysis is making in-roads across many sectors in an effort to identify sequential purchases.

Understanding Customer Purchasing Behavior with Market Basket Analysis

Uncover associations between what your customers are purchasing by identifying combinations of products or menu items that frequently co-occur in transactions. Retailers can identify relationships between the products that people buy and use this information to create new products or pricing models to generate new revenue.

Retailers can use the insights gained from MBA in a number of ways, including:

• Cross Sell:   Group products that customers purchase frequently together in the store’s product placement
• Webstores:   Recommend associated products that are frequently bought together. “Customers who purchased this product also viewed this product…”
• Marketing Promotions: Target marketing campaigns to customers and entice them to purchase related products for items they purchased recently

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Market Basket Analysis

What is market basket analysis .

It is a mathematical modelling technique based upon the theory that if a customer buy a certain group of items, they are likely to buy another group of items.

What is the use of Market Basket Analysis ?

It is used to analyse the customer purchasing behavior & helps in increasing the sales and maintain inventory by focusing on the point of sales transaction data.

• Allows us to identify patterns in customer purchases.
• Identify who customers are (not by name)
• Understand why they make certain purchases
• Fast and slow movers
• Products which are purchased together
• Products which might benefit from promotion
• Store layouts
• Which products to put on specials, promote, coupons…
• What products tend to be bought together?
• What products may benefit from promotion?
• What are the best cross‐selling opportunities?
• One basket tells about what one customer purchased at one time.
• A loyalty card makes it possible to tie together purchases by a single customer (or household) over time.

Apriori Algorithm:

It is a level wise, breadth first algorithm which counts transaction to find frequent item sets & them derive association rules from them.

The following measures are used to evaluate the strength of association. Suppose, we are interested in the association between two events A and B:

• Support   = ( Number of Rows having both A & B )/(Total Number of Rows)
• Confidence = (Number of Rows having both A & B)/( Total Number of Rows with A )
• Expected Confidence = (Number of Rows having B)/(Total Number of Rows)
• Lift =  Confidence /( Expected Confidence )

Analysis done for given data using Market Basket Analysis:

We had a data containing the purchasing behavior of the customer in which we did the following analysis:

Analysis 1: Top 5 related product :

Here we can know customers buying lhs products may also buy rhs products for example customers buying Instant food products & soda also buy Hamburger meat, people buying flour & baking powder also buy sugar, etc.

Analysis 2: Top 25 most frequently purchased items :

Analysis 3: rules that lead to buying specific product(milk):.

• By this we can analyse that if a customer is buying Other Vegetables, rolls/buns, yogurt, root vegetables & tropical fruits then with that they prefer buying Milk mostly.

Analysis 4: Those who took particular product ( ‘milk’) also took:

• By this we can analyse that if a customer is buying Milk with that they prefer Other Vegetables, rolls/buns, yogurt, root vegetables & tropical fruits.

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Resource Center > Articles > The Walmart beer and diapers legend: how market basket analysis paints human stories

The Walmart beer and diapers legend: how market basket analysis paints human stories

• Convenience Stores

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An urban legend with a powerful message

In the realm of data analytics and market basket analysis, few stories capture the imagination quite like the legendary tale of the correlation between beer and diapers at Walmart. Despite some skepticism surrounding its authenticity, this urban legend continues to be told, serving as a powerful reminder of the potential insights that data analytics and basket analysis can uncover.

The legend begins in the 1990s when Walmart, a retail behemoth renowned for its data-driven approach, delved into the world of market basket analysis. As Walmart’s analysts sifted through the vast amounts of transactional data, they stumbled upon an intriguing pattern—an association between beer and diapers in customers’ shopping baskets. This discovery sparked curiosity and ignited a wave of fascination, leading to the birth of a legend.

According to the legend, exhausted parents, often fathers, would make late-night diaper runs to Walmart. In their sleep-deprived state, they would impulsively grab a six-pack of beer to reward themselves for enduring the rigors of parenthood. The story spread like wildfire, captivating audiences and serving as an amusing anecdote that highlighted the unexpected quirks of consumer behavior.

However, as the legend gained traction, skepticism arose. Some questioned the reliability of the data, while others considered it to be more of an urban myth than a factual representation of consumer habits. Nonetheless, the enduring power of the Walmart beer and diapers trope lies not in its veracity, but in the narrative it weaves and the insights it symbolizes.

While the specific correlation between beer and diapers at Walmart may remain shrouded in uncertainty, the legend serves as a compelling metaphor for the potential insights that can be gleaned from data analytics. It reminds us that even in the most mundane of transactions, valuable patterns and associations can be found. It encourages us to dig deeper, challenge assumptions, and uncover the hidden stories that data holds.

How beer and diapers humanizes data

Furthermore, this urban legend serves as a cautionary tale about the dangers of blindly accepting data insights without critical evaluation. It underscores the need for robust analytics methodologies, thorough data validation and sound statistical analysis to draw meaningful conclusions. In a world inundated with data, it reminds us to approach analyses with a healthy dose of skepticism and intellectual curiosity.

The legend of the Walmart beer and diapers market basket analysis captivates our imagination because it highlights the power of storytelling and the role it plays in data interpretation. It reminds us that behind every data point lies a human story waiting to be unraveled. Whether the legend itself is rooted in reality or not, its enduring popularity demonstrates the allure and fascination that data-driven narratives hold.

Ultimately, the Walmart beer and diapers urban legend is a testament to the enduring power of storytelling and the captivating nature of data analytics. It continues to be shared and discussed, sparking conversations about the possibilities and challenges inherent in analyzing consumer behavior. Whether rooted in truth or fiction, the legend serves as a reminder of the transformative potential of data analytics, urging us to explore, question, and uncover the hidden narratives within the vast sea of data.

The Walmart beer and diapers market basket analysis urban legend, while debated in terms of its accuracy, remains a captivating and thought-provoking tale, providing a symbolic reminder of the power of data analytics and the stories that data can tell. Whether real or mythical, the legend continues to inspire us to dive into the world of data, challenge assumptions, and extract meaningful insights that shape our understanding of consumer behavior and drive innovation in the field of analytics.

To learn more how basket analysis can support your retail strategies, check out these articles: Harnessing basket analysis to evaluate buy-now-pay-later (BNPL) services How basket analysis empowers merchandisers Causation vs. correlation: Basket analysis in the retail industry

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Market Basket Supermarket Chain’s Issues Case Study

Background/situation, key individuals, issues/problems, decisions/alternatives, conclusions/recommendations.

The case study under analysis reveals the situation of the Market Basket chain of supermarkets. Initially, Athanasios and Efrosine Demoulas opened a small store and called it DeMoulas. Within 15 years, it was bought and reorganized by their sons – George and Telemachus in a supermarket chain. However, George died in 1971 making Telemachus the only head of the business. Telemachus opened several shops giving them different names. In reply, Telemachus was accused and sued for defrauding Telemachus’s wife and children out of their shares. As a result, they won the case and were awarded 50.5 percent of the company stock.

• Athanasios and Efrosine Demoulas were founders of the company.
• Telemachus, their son, expanded the company from 14 stores to 51 stores between 1971 and 1994.
• Arthur, son of Telemachus, enhanced annual sales from $ 3 to $ 4 billion and expanded the number of employees from 14,000 to 25,000 (Ton, Kochan, & Reavis, 2015). According to the shareholder’s decision, he was fired which caused employees to strike.

Described individuals belonged to one family but had a feud. In order to save the business, they should act appropriately without emotions and personal interest.

The first problem concerned customer service. In particular, there were no loyalty cards, no website, and no self-checkout lines. Second, employee management was focused on duration rather than on the quality of operation. Third, shareholders, not the company had to pay taxes, and some of them accused Arthur of excessive costs and fired him. It caused employees’ discontent. As a result, employees were striking. The race for power is a subliminal problem as some members of the family wanted to win neglecting business interests. Among other issues that might occur, there are competitors’ strengthening or obsolescence of the company’s policy. The problems above are of great importance as they might lead even to the bankruptcy of the company.

An inappropriate organization was the key determinant of problems. Family members along with development managers caused the above issues. To support that causes are real, it seems useful to consider credible sources such as documents of the company.

To eliminate issues concerning employees, it is necessary for the company management to initiate paid sick leaves and retirement funds putting the staff before profits. In relation to customers, the company should reduce average prices by 20 percent. It also would place Market Basket in a pole position against competitors. All in all, the company should create a policy of customer and employees’ loyalty. I would recommend criteria of regular examination when the situation would be under permanent control. There are no alternatives to these decisions as they are the most suitable in that case. I would implement these decisions gradually without any procrastination with the help of top managers as the success of the whole business is at stake.

In conclusion, it should be stressed that the case reflects a family business case where different feuds appear that might lead to the decrease of employees’ loyalty and the company’s prosperity in general. In order to avoid such issues, I would appoint a top head of the company while others would be shareholders with limited rights.

In my point of view, Bridges Transition Model is the model that best relates to the case. It explains how to improve the performance of the company in detail overcoming negative aspects such as stress, ambivalence, and neutral zone and achieving acceptance and enthusiasm.

Ton, Z., Kochan, T. A., & Reavis, C. (2015). We Are Market Basket . Cambridge, MA: MIT Sloan School of Management.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2021, May 29). Market Basket Supermarket Chain's Issues. https://ivypanda.com/essays/market-basket-supermarket-chains-issues/

"Market Basket Supermarket Chain's Issues." IvyPanda , 29 May 2021, ivypanda.com/essays/market-basket-supermarket-chains-issues/.

IvyPanda . (2021) 'Market Basket Supermarket Chain's Issues'. 29 May.

IvyPanda . 2021. "Market Basket Supermarket Chain's Issues." May 29, 2021. https://ivypanda.com/essays/market-basket-supermarket-chains-issues/.

1. IvyPanda . "Market Basket Supermarket Chain's Issues." May 29, 2021. https://ivypanda.com/essays/market-basket-supermarket-chains-issues/.

Bibliography

IvyPanda . "Market Basket Supermarket Chain's Issues." May 29, 2021. https://ivypanda.com/essays/market-basket-supermarket-chains-issues/.

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