Data Mining Techniques: A Source for Consumer Behavior Analysis

Raorane, Abhijit; Kulkarni, R. V.

doi:10.5121/ijdms.2011.3304

Cited by 25 publications

(14 citation statements)

References 5 publications

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“…Data mining is one of the methods that use to analyzing the data to get useful knowledge, which can help the company to increase its revenue, it allows to analyze the data from different views, also classified the data, and identified the relationships [7]. Technically, the data mining process is to find the patterns from huge data, it sued on many sectors and the more used is in consumer retail that enables the companies to know the relationships between internal and external factors such as product, price, customer and the economic indicator.…”

Section: Technology -Data Miningmentioning

confidence: 99%

“…Now a day many researchers are interested in data mining -association rules-that mining the organization database to get the association rules. The knowledge association from huge customer purchasing orders can help in decision making such as marketing analysis and rearrange items position [7]. Our association rule method is shown in picture [1].…”

Section: Association Rules 6 / 10mentioning

confidence: 99%

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Analyze and Enhance Sales in Lulu Supermarket using Data Mining Technology

Koofan

Kaleem

2020

J Stud Res

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-Data mining is a powerful technology for analyzing huge data, it has many techniques such as; classification, clustering, prediction and association rules etc., In this research Association rule will be used for analyzing data, which will help to extract the data related to combinations of items. Numerous customers tends to purchase items regularly, each time they visit supermarket, customer’s need to move around from shelf to shelf for the product of their interest which is time consuming. This research will help to minimize the time consumption for customers by analyzing the customer’s invoices and letting know the supermarket about the patterns of customer's orientations. In this work python tool will be used for data mining, by using association rule to analyze the customer’s purchases and retrieve the relevant information which will help to determine the customer’s pattern and know the association between products. In this rationale, the data of customer’s purchases were collected from Lulu hypermarket for data analysis and the outcomes of the analysis is to know the customer’s patterns and making the shopping easy by reorganizing the related items and the most buying items together on same shelf.

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Section: Technology -Data Miningmentioning

confidence: 99%

Section: Association Rules 6 / 10mentioning

confidence: 99%

Analyze and Enhance Sales in Lulu Supermarket using Data Mining Technology

Koofan

Kaleem

2020

J Stud Res

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“…This research helps companies improve marketing strategies and reduce problems such as: how consumer spending patterns are influenced by the environment, the reason consumers buy different products, customer interest in products, how management increases marketing and marketing ideas that effectively reach consumers. The association rule method in data mining is used to extract data for very large industries (Raorane & Kulkarni, 2011).…”

Section: Introductionmentioning

confidence: 99%

Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores

Irfiani

2019

SinkrOn

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A good sales strategy has an effect on increasing the number of sales of goods. Problems that often occur in outdoor sports equipment stores are the difficulty in determining sales strategies because there is not much interest in outdoor sports in the community. In addition, the amount of inventory in the store is excessive, which affects the sales cycle of goods. One way to help determine strategy is to use apriori algorithm. In this method can determine consumer shopping behavior patterns. Apriori algorithms are part of the data mining analysis association. This algorithm is used to determine association rules. In the study, a combination of sports equipment purchased by consumers will be determined. Determination of the combination starts from 1 itemset to 3 itemsets, the combination of rule association produces different sales transaction patterns. The results of the study in the form of a combination of consumer shopping behavior patterns that will be used as recommendations for shop owners in determining the sales strategy. The resulting Rule association will help sales promotions and add the amount of inventory that many customers buy.

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“…One of the important disciplines where TAR mining can be applicable is customer buying behavior analysis. Buying behavior is a combination of various factors [8] like customer's preferences, income, taste, product features, brand, marketing strategies, etc. The discovered TARs facilitate retailers to adopt marketing strategies by gaining insight into association rules among items and their time of association.…”

Section: Introductionmentioning

confidence: 99%

Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

Shalini*¹,

Lal²

2019

IJITEE

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In retail business, customers’ behavior analytics is a study of customers’ buying behavior for a better understanding of customer needs to be able to provide service accordingly. The buying behavior is majorly influenced by the preferences of a customer. However, preferences of a customer change over a period of time due to various factors like change in income, taste, culture or newer products, etc. Understanding these changes in customer behavior is a very challenging task especially in a dynamic, ever-changing environment. There are various customer behavior mining models and techniques available in the data mining domain that are designed to work on static and dynamic databases. The traditional incremental mining techniques consider all the previous datasets in order to update the patterns. However, in a dynamic database, the size of the database grows with every update. To mine customers’ behavior in a time-variant database, the re-mining of the updated database is required that further increases processing cost in terms of execution time and memory space with every update. The purpose of this paper is to propose a method that can analyze the changes in customers’ behavior in time-variant databases without mining all the transactions. In this paper, an optimized incremental technique is proposed that utilizes temporal association rule mining in a time-variant database for mining customer behavioral patterns in an updated database. The proposed algorithm named ‘Autoregressive Moving Average model-based Incremental Temporal Association Rules Mining (ARMA-ITARM)’ utilizes the ARMA model to substantially reduce the database and maintains temporal frequent patterns in the updated database. Inspired by sliding window and pre-large concepts, the algorithm utilizes past frequent itemsets and probable frequent itemsets from customers’ purchased history along with frequent itemsets and probable frequent itemsets that reduce search space. Consequently, the entire database is scanned only once to count the frequency of occurrence of a few candidate itemsets. In effect, execution time memory need of the algorithm is very small. Experimental results demonstrate that our proposed technique performs better over recent techniques like ITARM, SWF, etc

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Data Mining Techniques: A Source for Consumer Behavior Analysis

Cited by 25 publications

References 5 publications

Analyze and Enhance Sales in Lulu Supermarket using Data Mining Technology

Analyze and Enhance Sales in Lulu Supermarket using Data Mining Technology

Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores

Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

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