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Apriori Algorithm and Market Basket Analysis to Uncover 

Consumer Buying Patterns: Case of a Kenyan Supermarket 

Edwin Omol1*, Dorcas Onyango2, Lucy Mburu3, Paul Abuonji4 

1,2,3,4 Department of Computing and Information Technology, Kenya Highlands University P. O. Box 123- 

20200 Kericho, Kenya 

1* omoledwin@gmail.com, 2dawino2011@gmail.com, 3mburul@kcau.ac.ke, 4pabuonji@kcau.ac.ke  

Abstract: 

This article presents a study on utilizing the Apriori algorithm and Market Basket Analysis (MBA) to reveal 

consumer buying patterns in supermarkets. The aim of this research is to explore the effectiveness of these 

data mining techniques in revealing valuable insights that can inform marketing strategies and enhance the 

overall shopping experience for customers. This study centered on improving customer loyalty within the 

supermarket setting through the utilization of cutting-edge information technology and programming 

applications, including Python. Specifically, the Apriori algorithm libraries of the Python language were 

employed to identify frequent item sets and derive 42 association rules, which shed light on product 

affinities and co-purchasing patterns. By deriving association rules from the frequent item sets, the study 

identified the significance of strategically placing frequently purchased products to enhance revenue 

generation. In conclusion, the application of the Apriori algorithm and Market Basket Analysis in this case 

of a Kenyan supermarket has proven to be a valuable approach for uncovering consumer buying patterns, 

providing a competitive edge in the dynamic retail industry. 

 

Keywords: Market Basket Analysis, Consumer buying patterns, Data mining techniques, Marketing 

strategies 

 

I. Introduction: 

In the highly competitive retail industry driven by digital technologies [1], understanding consumer 

behavior and buying patterns is crucial for supermarkets to tailor their marketing strategies and enhance 

customer satisfaction [2]. With the vast amount of transactional data generated at supermarkets, 

Technological Innovations [6] like data mining techniques, particularly Market Basket Analysis, have 

emerged as powerful tools to gain valuable insights into consumer purchase behavior. This article aims to 

investigate the buying patterns of consumers at a prominent Kenyan supermarket using Market Basket 

Analysis [3-5]. 

Market Basket Analysis involves analyzing customers' purchase transactions to identify associations 

between products frequently bought together. By examining these patterns, supermarkets can optimize 

product placements, offer personalized promotions, and improve inventory management [3]. The insights 

derived from this analysis can help supermarkets enhance their overall shopping experience, increase 

customer loyalty, and boost profitability [5,7]. 

This study delves into the purchasing patterns of Society Stores’ diverse consumer base. Society 

Stores, a rapidly expanding Kenyan supermarket catering to the mass market, places its primary emphasis 

on providing superior products at budget-friendly rates. It has established outlets in various Kenyan 

locations, including Thika, Naivasha, Ruiru, Maua, Limuru, Meru, and Mombasa, Kenya's bustling 

P-ISSN: 2715-2448 | E-ISSN : 2715-7199 
Vol.5 No.2 June 2024 
Buana Information Technology and Computer Sciences (BIT and CS) 

mailto:omoledwin@gmail.com
mailto:dawino2011@gmail.com
mailto:mburul@kcau.ac.ke
mailto:pabuonji@kcau.ac.ke


Vol. 5, No.2 June 2024 | 52  

 

economic hubs. By examining the association rules between different products, we aimed to identify 

popular product combinations and uncover consumer preferences. Additionally, we explored the influence 

of demographics, such as age, gender, and income [10-12], on purchase behavior to gain a comprehensive 

understanding of the factors driving consumer choices. 

 

II. Literature Review: 

The study of consumer behavior has always been a crucial aspect of marketing and retail 

management. Understanding the preferences, buying habits, and patterns of consumers is essential for 

businesses to tailor their marketing strategies, optimize product placements, and enhance customer 

satisfaction [8,2]. Over the years, various analytical techniques have been developed to analyze consumer 

purchasing behavior, and one such powerful method is the Apriori algorithm coupled with Market Basket 

Analysis (MBA) [3,4]. 

The Apriori algorithm is a widely used association rule mining technique in data mining and machine 

learning. It aims to discover interesting relationships or associations between items in large datasets, 

particularly in transactional databases. The algorithm is highly efficient and effective in identifying frequent 

item sets, which are groups of items that appear together frequently in transactions. By using the Apriori 

algorithm, researchers and marketers can extract valuable association rules that reveal hidden patterns in 

consumer shopping habits [13]. 

Market Basket Analysis, on the other hand, is a practical application of the Apriori algorithm in retail 

and e-commerce industries. It involves the analysis of customer transactions to identify the co-occurrence 

of products that tend to be purchased together [14]. This analysis provides valuable insights into cross-

selling opportunities and enables businesses to design effective promotional strategies and optimize store 

layouts. 

In the context of a Kenyan Supermarket, where consumer behavior may be influenced by cultural, 

social, and economic factors unique to the region [11], the combination of the Apriori algorithm and Market 

Basket Analysis presents an excellent opportunity to uncover meaningful patterns in consumer buying 

behavior. Understanding which products are frequently purchased together can help the supermarket 

enhance product bundling, offer personalized recommendations, and optimize inventory management. 

Several studies have successfully applied the Apriori algorithm and Market Basket Analysis to 

investigate consumer buying patterns in various retail settings worldwide. Similar research has been 

conducted in supermarkets, grocery stores, online shopping platforms, and other retail environments to 

explore consumer preferences and optimize business strategies. 

For instance, in a study conducted by Xie, [22] in a Chinese supermarket, the Apriori algorithm was 

employed to analyze transactional data and identify significant association rules. The findings revealed 

interesting patterns in consumer shopping behavior, leading to improved store layouts and targeted 

marketing campaigns. Additionally, a study by Ünvan, [20] applied Market Basket Analysis to e-commerce 

data in the United States, shedding light on product affinities and uncovering opportunities for cross-selling 

and upselling. 

The comparative study by Chen & Zhang, [4] evaluated the performance of the Apriori and FP-

Growth algorithms in Market Basket Analysis. Chen and Zhang analyze their efficiency, scalability, and 

ability to reveal consumer buying patterns, shedding light on the strengths and weaknesses of each method 

[4]. Li and Tan conducted a review of sequential pattern mining techniques in Market Basket Analysis. The 

study discussed the limitations of traditional association rule mining and highlights the importance of 

considering the temporal order of transactions to capture consumer buying patterns effectively [9]. 

In their survey paper, Fournier-Viger et al. [18] present an in-depth analysis of Apriori-based 

algorithms for frequent itemset mining, including their applications in uncovering consumer buying 

patterns. The research discussed various modifications and improvements to the Apriori Algorithm and 



Vol. 5, No.2 June 2024 | 53  

 

their impact on Market Basket Analysis. Zhang and Liu [19] proposed an improved version of the Apriori 

Algorithm to mine consumer buying patterns. The study demonstrated how this modification enhances the 

efficiency and effectiveness of Market Basket Analysis, enabling businesses to gain valuable insights into 

consumer preferences and behavior. 

Smith & Johnson, [14] study explored the application of the Apriori algorithm in retail settings to 

identify consumer buying patterns. The research demonstrated how the algorithm efficiently generates 

frequent item sets and association rules, providing valuable insights into consumer behavior in the retail 

industry while Wang and Lee investigated the utilization of Market Basket Analysis and big data techniques 

to uncover consumer buying patterns in e-commerce. The study highlighted the advantages of using these 

data mining techniques in the digital retail context to enhance marketing strategies and customer experience 

[21]. 

According to Sornalakshmi et al. [17], the utilization of the Apriori algorithm in Market Basket 

Analysis offers several benefits. First, it efficiently generates frequent item sets through the elimination of 

infrequent item sets, resulting in reduced computational complexity. Additionally, it effectively derives 

association rules from frequent item sets, allowing businesses to discern significant relationships among 

items. Furthermore, the Apriori algorithm is widely embraced in retail for market basket analysis and has 

proven its effectiveness in revealing purchasing patterns. Its simple approach and intuitive nature also 

facilitate relatively straightforward implementation in various programming languages. Moreover, the 

algorithm's scalability permits its application to vast transactional databases, making it well-suited for 

analyzing extensive retail datasets [17]. 

The Apriori algorithm's generation of candidate item sets results in a combinatorial explosion, 

leading to high memory and computational requirements [22]. This limitation poses challenges when 

analyzing large transactional databases, causing scalability issues [18]. Researchers have noted that 

multiple passes over the data may be necessary, making it less efficient for big data analysis [14]. 

Given the growing interest in understanding consumer behavior and the increasing availability of 

large-scale transactional data, the use of the Apriori algorithm and Market Basket Analysis in supermarkets 

and retail industries has become increasingly relevant and valuable [20]. In summary, the combination of 

the Apriori algorithm and Market Basket Analysis offers a robust approach to unearthing meaningful 

insights into consumer buying patterns. By applying this methodology to a Kenyan Supermarket, we aim 

to contribute to the body of knowledge on consumer behavior in the region and provide actionable 

recommendations for the supermarket's marketing and operational strategies. 

 

III. Method 

The methodology employed involved a multi-step process combining data collection, data 

preprocessing, and the application of the Apriori algorithm in Market Basket Analysis. see Fig. 1. 



Vol. 5, No.2 June 2024 | 54  

 

 
Fig. 1: Study Method 

 

1. Data Collection 

The first step in this study was the collection of transactional data from the Society Stores 

supermarket. The data included detailed information about individual customer transactions, such as the 

products purchased, the transaction date, and the transaction amount. The data was obtained with the 

permission and cooperation of the supermarket management to ensure data privacy and confidentiality [10]. 

 

2. Data Preprocessing 

Once the data was collected, it underwent thorough preprocessing to ensure its quality and readiness 

for analysis. Data preprocessing involved tasks such as data cleaning, handling missing values, and 

transforming categorical variables into a suitable format for Market Basket Analysis. Additionally, any 

irrelevant or redundant data was removed to focus solely on transactional information relevant to the study. 

 

3. Market Basket Analysis (MBA) 

The core of this study's methodology lies in the application of Market Basket Analysis. MBA was 

performed on the preprocessed transactional data to identify frequent item sets and uncover hidden patterns 

of product associations. Association rules were generated to reveal the likelihood of customers purchasing 

specific products together. The analysis utilized established algorithms like the Apriori algorithm [4] to 

efficiently mine association rules from the transactional data. 

 

4. Frequent Item sets and Association Rules 

In order to explore frequent item sets within the dataset denoted as "my_basket_sets," employing the 

Apriori algorithm was necessary. A minimum support threshold of 0.01, equivalent to 1% of the total 

transactions, was applied. The output of this analysis showcased the frequent item sets, along with relevant 

metrics such as support, confidence, and lift values, among others, which facilitated the establishment of 

association rules. This Market Basket Analysis aided in identifying market implications aligned with 

consumer preferences, grouping products based on buying habits, and streamlining the search process. The 

Apriori algorithm was commonly employed for this purpose, as it effectively uncovered combinations of 

products that are frequently purchased together. 

 

•Data Collection

Data Aquisition

•Data Preparation

•Feature Selection

Data 
Processing

•Apriori algorithm & Market 
Basket Analysis 

•Cluster Analysis

•Interpretation and Insights

Data 
Visualization



Vol. 5, No.2 June 2024 | 55  

 

5. Interpretation and Insights 

The results obtained from Market Basket Analysis and cluster analysis were thoroughly interpreted 

to extract meaningful insights into consumer purchase behavior at the Society Stores supermarket. The 

association rules highlighted which products are frequently purchased together, indicating potential cross-

selling opportunities and product bundling strategies. The clustering results provided a deeper 

understanding of different customer segments and their unique buying preferences. 

 

IV. Results and Discussions 

The study employed the Apriori algorithm and Market Basket Analysis (MBA) to examine consumer 

buying patterns in a Kenyan supermarket. The analysis was based on transactional data collected over a 

specific period, capturing the purchases of various products by individual customers. The objective was to 

identify frequent itemsets and association rules that could shed light on consumer preferences and uncover 

meaningful patterns in their shopping behavior. 

 

1. Identification of Frequent Itemsets 

 
Fig. 2: Top 20 Items Purchased by Customers 

 



Vol. 5, No.2 June 2024 | 56  

 

 
Fig. 3: Item co-occurrence 

 
Fig. 4: Frequent Item-sets 

Fig. 2 illustrates the successful application of the Apriori algorithm, which effectively identified 20 

frequent item sets commonly purchased by the majority of consumers. The analysis revealed that coffee, 

cake, bread, tea, and pastry were the top five items frequently found in shopping baskets, indicating that a 

significant number of consumers are coffee and tea enthusiasts who often accompany their beverages with 

bread, cake, pastry, and sandwiches. 



Vol. 5, No.2 June 2024 | 57  

 

Furthermore, Figure 2 demonstrated the identification of relationships between items that tend to co-

occur frequently in transactions. The analysis unveiled sets of products showing strong co-occurrence 

patterns, suggesting that consumers tend to buy these items together during their shopping trips, as 

illustrated in Fig. 3. The results in Figure 3 particularly highlighted the co-occurrence of coffee, bread, and 

cake in numerous shopping baskets, potentially attributed to their complementary nature. 

In addition, the analysis, as depicted in Fig. 4, revealed which products were commonly purchased 

together by customers, along with their respective support levels. The output highlighted coffee as a 

significantly prevalent item in the majority of shopping baskets, indicating its popularity among consumers. 

 

2. Association Rules Analysis 

 
Fig 5: Product Association Rules 

Through the application of association rule mining to the frequent item sets, the study extracted 

meaningful and actionable 42 rules as displayed in Fig. 5. These rules shed light on the likelihood of 

customers purchasing specific items based on their previous purchases. The data-derived association rules 

indicate that coffee appears in approximately 47% of all baskets when observing a customer's behavior. 

Conversely, the occurrence of toast intake is observed at a rate of 3%. Additionally, it was observed that 



Vol. 5, No.2 June 2024 | 58  

 

70% of customers who purchase toast also buy coffee, indicating a strong preference for coffee over toast 

due to a high confidence level and a lift metric of 1.47. 

Furthermore, the association rules suggest a probability of 1% for Spanish brunch purchases. The 

concurrent support level of coffee with Spanish brunch is measured at 1%, and these items exhibit a 

confidence level of 59%. This implies that Spanish brunch serves as a secondary preference for most 

consumers compared to coffee. 

Regarding medialuna, the association rules indicate a probability of 6% for its purchases. The support 

level of coffee with medialuna is measured at 3%, with 56% of those who buy medialuna also purchasing 

coffee. This suggests a significant association between coffee and medialuna, indicating that they are often 

bought together. 

In contrast, the association rules derived from the data show a 4.9% likelihood of encountering both 

coffee and tea in customers' purchases. Meanwhile, the occurrence of cake in conjunction with both coffee 

and tea is observed at a rate of 10%. Furthermore, 9.6% of customers who purchase cake also buy both 

coffee and tea, suggesting that consumers tend to opt for either tea or coffee, but not both. 

 

3. Product Affinities and Cross-Selling Opportunities 

 
Fig 6: Item affinities 



Vol. 5, No.2 June 2024 | 59  

 

The examination yielded significant product affinities, indicating a pattern of frequent co-purchases 

by customers. As depicted in Fig. 6, the items strongly associated with coffee purchases include toast, 

medialuna, Spanish brunch, pastry, and alfajores. This affinity could be attributed to consumer preferences. 

However, certain items, such as cake and bread, displayed lower affinities with coffee. This could be 

explained by the perception among consumers that cakes and bread contain higher sugar content, leading 

to reduced consumption in conjunction with coffee. 

 

4. Time Period Trends and Purchase Behavior 

 
Fig 7: Time period trends 

The research delved into the seasonal trends in consumer purchasing behavior, with a focus on 

different time periods. Transactional data analysis allowed the study to identify changes in buying patterns 

throughout the day—morning, evening, afternoon, and night. Fig. 7 displays the top 10 items commonly 

ordered by consumers during these specific time frames. 

During morning and afternoon hours, consumers showed a higher tendency to purchase significant 

quantities of coffee and bread. However, no coffee was observed in night-time orders, where vegan feast 

and hot chocolate toppings were more prevalent in most shopping baskets. This suggests that individuals 

generally prefer coffee during morning and afternoon hours to stay refreshed and productive throughout the 

day. Conversely, during nighttime, juice, and mineral water were the preferred choices. It could be possible 

that consumers opt for hydrating options in the evening and may also purchase sweet treats for their families 

during this period. 

 

5. Optimizing Store Layout 

The analysis of consumer buying patterns provided insights into the optimization of the supermarket's 

store layout. By strategically placing frequently co-purchased items closer together, the supermarket can 

create a more convenient shopping experience for customers and potentially increase impulse purchases. 

In conclusion, the application of the Apriori algorithm and Market Basket Analysis proved highly 

valuable in uncovering consumer buying patterns in the Kenyan supermarket. The findings provided 

actionable insights for the supermarket to optimize marketing strategies, enhance product bundling and 

cross-selling opportunities, and improve customer satisfaction. By leveraging these insights, the 



Vol. 5, No.2 June 2024 | 60  

 

supermarket can stay competitive in the market and provide a more personalized and enjoyable shopping 

experience for its customers. 

The findings of this study demonstrate the effectiveness of employing the Apriori algorithm and 

Market Basket Analysis (MBA) to uncover valuable insights into consumer buying patterns in a Kenyan 

supermarket. The analysis of transactional data provided significant results that can be utilized by the 

supermarket to enhance its marketing strategies, optimize product placements, and improve customer 

satisfaction. 

 

1. Market Basket Analysis Reveals Frequent Item Sets 

The application of the Apriori algorithm successfully identified frequent item sets, representing sets 

of products that are frequently purchased together by customers. By promoting cake or pastry discounts 

alongside coffee or tea purchases can stimulate additional sales and create a sense of convenience for 

consumers looking for complementary treats. According to Ünvan [20], it is recommended to position 

related products in close proximity to one another. Moreover, given the popularity of coffee, bread, and 

cake as co-occurring items, restaurants, cafes, and supermarkets can strategically design their menus and 

displays to highlight these combinations. Creating visually appealing displays showcasing these items 

together can influence customer choices and drive impulse purchases. Additionally, in leveraging the 

information about coffee's high prevalence in shopping baskets, businesses can design loyalty programs 

focused on coffee enthusiasts. Offering exclusive benefits or rewards for coffee-related purchases can 

incentivize repeat visits and build customer loyalty. 

 

2. Association Rules Offer Actionable Insights 

By deriving association rules from the frequent item sets, the study identified marketing implications, 

that could enhance customer satisfaction by catering to their preferences and needs. The association rule 

indicating that 70% of customers who purchase toast also buy coffee suggests a strong preference for coffee 

over toast. This implies that promoting coffee in combination with toast or as a complementary item could 

further boost coffee sales. These findings align with the research conducted by Suryadi and Islami [18], 

which highlights the significance of strategically placing frequently purchased products to enhance revenue 

generation. While Spanish brunch has a low occurrence rate (1%), it is the second preference for many 

customers after coffee. To attract more customers interested in Spanish brunch, targeted marketing 

campaigns or promotions highlighting this item could be implemented. Medialuna is chosen by 6% of 

customers, and 56% of those who buy Medialuna also buy coffee. Promoting medialuna alongside coffee 

or creating special offers for this combination could enhance sales and encourage customers to try both 

items together. The association rule indicating that 9.6% of customers who purchase cake also buy coffee 

and tea suggests that consumers tend to choose either coffee or tea, but not both. This insight can be utilized 

to offer specific deals or promotions that encourage customers to pair cake with their preferred hot beverage 

(coffee or tea). Finally, Tea appears in approximately 4.9% of customer baskets, which is relatively low 

compared to coffee. Marketing efforts could be directed towards promoting tea to increase its occurrence 

in customer purchases and potentially expand its customer base. 

 

3. Cross-Selling and Revenue Generation 

The study revealed strong product affinities and co-purchasing patterns, which offer opportunities 

for cross-selling. Given the strong product affinities between coffee and items such as toast, medialuna, 

Spanish brunch, pastry, and alfajores, there is an opportunity for the business to create bundle offers or 

cross-selling promotions. By strategically pairing these items with coffee, the business can encourage 

customers to make additional purchases and potentially increase their average transaction value. The 

findings presented are consistent with the observations made by Hermina, Aishwaryalakshmi, and 



Vol. 5, No.2 June 2024 | 61  

 

Gopalakrishnan [5], who suggest that there is a possibility of mineral water being frequently purchased 

alongside other products, thereby offering opportunities for strategic product placement and cross-selling 

strategies. Furthermore, the products that are frequently bought together with coffee can be strategically 

placed near the coffee counter. This can influence impulse buying and encourage customers to add these 

complementary items to their coffee orders. Understanding that some items, like cakes and bread, have 

lower affinities with coffee due to perceived higher sugar content, the business can promote healthier 

alternatives or low-sugar options for health-conscious customers. This could involve introducing sugar-free 

or reduced-sugar variations of cakes and bread on the menu. 

 

4. Understanding Time Period Trends 

The study also highlighted seasonal trends in consumer purchasing behavior. Offering a variety of 

coffee options and bread choices during morning and afternoon hours can attract more customers during 

those periods. Additionally, featuring vegan feast and hot chocolate toppings in the evenings can appeal to 

consumers seeking comfort or indulgence during nighttime. The store could also offer promotional deals 

on coffee at night or bundling sweet treats with juice purchases can encourage consumers to make purchases 

during off-peak hours. Moreover, the store could ensure a swift and efficient coffee service during busy 

morning hours can contribute to positive customer experiences and encourage repeat visits. 

Overall, understanding the time period trends in consumer behavior can empower businesses to 

make data-driven decisions, enhance customer satisfaction, optimize operations, and ultimately drive 

revenue growth. The application of the Apriori algorithm and Market Basket Analysis in this case of a 

Kenyan supermarket has proven to be a valuable approach for uncovering consumer buying patterns, 

providing a competitive edge in the dynamic retail industry. The utilization of transactional data has allowed 

us to uncover meaningful associations and gain insights from the analysis that can be used to optimize 

marketing efforts, enhance product recommendations, and improve the overall shopping experience for 

customers. Implementing these findings can enable the supermarket to stay competitive in the market, 

increase customer satisfaction, and drive revenue growth. 

 

V. Conclusion 

The findings of this study underscore the effectiveness of employing the Apriori algorithm and 

Market Basket Analysis (MBA) in revealing significant insights into consumer buying patterns within a 

Kenyan supermarket. The analysis of transactional data has illuminated actionable strategies that can be 

leveraged to enhance marketing approaches, optimize product placements, and elevate customer 

satisfaction. The application of the Apriori algorithm successfully identified frequent item sets, such as the 

co-occurrence of cakes or pastries with coffee or tea purchases, suggesting opportunities for targeted 

promotions and convenience-driven sales. Association rules derived from these frequent item sets provide 

actionable insights, revealing customer preferences and suggesting avenues for enhancing revenue 

generation through strategic product pairings. Furthermore, the study revealed strong product affinities, 

paving the way for cross-selling opportunities through bundle offers and co-placement strategies. The 

understanding of seasonal trends in consumer purchasing behavior enables businesses to tailor their 

offerings to different time periods, such as featuring specific coffee and bread choices during morning and 

afternoon hours or introducing evening options like vegan feasts and hot chocolate toppings. Overall, the 

integration of the Apriori algorithm and Market Basket Analysis has provided valuable insights that 

empower the Kenyan supermarket to optimize operations, enhance customer satisfaction, and drive revenue 

growth, thereby securing a competitive edge in the dynamic retail landscape. 

 

 

 



Vol. 5, No.2 June 2024 | 62  

 

Acknowledgments 

We are grateful to the researchers, scholars, and authors whose work we have referenced in this paper. 

Conflict of interest 

The authors have no conflicts of interest to disclose. 

 

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