An intelligent simulation model of online consumer behavior (original) (raw)
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Simulation of customer behavior using artificial neural network techniques
Elite Hall Publishing House, 2013
In recent years, service industries and companies have faced big customer data which its analysis takes a lot of time. Hence, there is an urgent need for systems able to extract useful information from a mass of information. One technique to do this is data mining. Today, data mining and neural network techniques can be used to analyze customer behavior and find hidden information in that behavior. Prediction of behavior of loyal and new customers of a service company has a significant impact on that company's marketing techniques and the profit earned by that. In this paper we discuss a data mining technique for identification of customers' favorite products based on their purchase and analyze the results. Finally the aim of this research paper is to predict customer behavior using artificial neural networks and data mining techniques.
Effective Factors on Iranian Consumers Behavior in Internet Shopping: A Soft Computing Approach
Journal of Computer Science, 2009
Problem statement: Nowadays, the Internet is increasingly becoming the fastest growing shopping channel in these days. Moreover, it has been predicted that the city of Isfahan in Iran will experience a sharp increase in the Internet and the Web usage in the next decade. However, the factors affect the shopping of different products via the Internet have received a little direct research attention so far. Thus, there is an inherent need to investigate the nature and perceptions of consumers and the suitability of different types of products and services and also the role of each factor which impacts consumers' behavior in choosing between buying from the Internet or traditional stores. Our case study is Isfahan Iran. Approach: The present study aimed to consider the influencing factors on consumer eshopping behavior for different types of products. The data were obtained from 412 volunteers who had the Internet shopping experience and were analyzed using MLP neural networks and logistic regression for each types of product. Then, after comparing the accuracy of these methods, the most important factors which motivate the consumers to buy online were determined by the trained neural network. Results: Compared to the logistic regression, the neural network method showed a better performance in predicting the factors which affect on consumer online shopping behavior with the accuracy of at around 93% for all types of products included in this study. Conclusion: The results showed that companies should invest on different factors for different types of products to motivate consumers to shop online from them. Again, for each sort of products some factors are more important than the others. This study also suggested the merits of ANNs as non-linear predictors in commercial studies which can be used in reverse engineering as well.
Analysis of Online Consumer Behavior - Design of CRISP-DM Process Model
Agris on-line Papers in Economics and Informatics, 2020
The basis of the modern marketing of a business entity is to know the behavior of its customers. Advanced artificial intelligence methods, such as data mining and machine learning methods, penetrate data analysis. The application of these methods is most appropriate in the case of online sales of any goods in large quantities and various industries. They are very often used in the sale of electronics, PCs or clothes. However, it is also possible to apply them to the agricultural industry, not only in B2C, but also in B2B in the sale of seeds, agricultural products, or agricultural machinery. Appropriate combinations of offers and knowledge of customers can bring the selling entity higher profits or competitive advantages. The main goal of our study is to design a CRISP-DM process model that will enable small businesses to analyze online customers' behavior. To reach the main goal we perform a data analysis of the online sales data by using machine learning methods as clustering, decision tree and association rules mining. After evaluating the proposed model, we discuss its use of the proposed model in the field of internet sales in the agricultural sector.
Development and performance evaluation of neural network classifiers for Indian internet shoppers
Expert Systems with Applications, 2012
The rapid growth of usage of internet has paved the way towards the use of online shopping. Consumers' behavior is one of the significant aspects that is considered by the service providers for the improvement of various services. Consumers are generally satisfied if their needs are fulfilled. In this paper an in depth investigation is made on the behavior of Indian consumers towards online shopping. Factor analysis is carried out to extract significant factors that affect online shopping of Indian consumers and these consumers are clustered based on their behavior, towards online shopping using hierarchical clustering. Employing the results of clustering in training of multilayer perceptron (MLP), functional link artificial neural network (FLANN) and radial basis function (RBF) networks efficient classifier models are developed. The performance of these classifiers are evaluated and compared with those obtained by conventional statistical based discriminant analysis. The simulation study demonstrates that the RBF network provides best classification performance of internet shoppers compared to those given by the FLANN, MLP and discriminant analysis based methods. The simulation study on the impact of different combination of inputs demonstrates that demographic input has least effect on classification performance. On the other hand the combination of psychological and cultural inputs play the most significant role in classification followed by psychological and then cultural inputs alone.
From Real Purchase to Realistic Populations of Simulated Customers
2013
The use of multiagent-based simulations in marketing is quite recent, but is growing quickly as the result of the ability of such modeling methods to provide not only forecasts, but also a deep understanding of complex interactions that account for purchase decisions. However, the confidence in simulation predictions and explanations is also tightly dependent on the ability of the model to integrate statistical knowledge and the situatedness of a retail store. In this paper, we propose a method for automatically retrieving prototypes of consumer behaviors from statistical measures based on real data (receipts). After preliminary experiments to validate this data mining process, we show how to populate a multiagent simulation with realistic agents, by initializing some of their goals with those prototypes. Endowed with the same overall behavior, validated in earlier experiments, those agents are put into a spatially realistic store. During the simulation, their actual actions reflect the diversity of real customers, and finally their purchase reproduce the original clusters. Besides, we explain how such statistically realistic simulation may be used to support decision in retail, and be extended to other application domains.
Role of Data mining in analyzing consumer ’ s online buying behavior
2017
Online shopping is still in its nascent stage in India but growing at a fast pace. To continue its growth it is significant to understand the user’s preferences. Analysis of consumer’s behavior with respect to online shopping consists of detailed information about consumers past purchases as well as prediction of future purchases. This growing need for refined information can’t be met with simple database software. Data Mining is used for finding the hidden information from the pool of data. It has also been called as data analysis, knowledge discovery and deductive learning. The ability to recognize and track patterns in data help businesses sift through layers of seemingly unrelated data for meaningful relationships. Through this analysis it becomes easy for the online retailers to determine the dimensions that influence the uptake of online shopping and plan effective marketing strategies. This paper builds a roadmap for analyzing consumer’s online buying behavior with the help o...
Customer Attitude Towards E-Commerce using Naive Bayes Algorithm : Machine Learning Approach
International Journal of Mathematics Trends and Technology, 2018
In the era of the e-commerce transmission and its services provided to the customer over the Internet, the Internet is commonly used by both customers and business to buy and sell their goods and services across the world. This study focuses on the factors influencing customer decision and attitudes towards adopting online shopping in India. This study is mainly emphasizes on how demographic variable (age, income and occupation) & Psychological factors (Motivation, Perception, Learning, Belief and attitudes) affect customer buying behavior towards E-commerce. The research questionnaire and hypotheses were developed on review of the literature. Based on the research objectives, a structured questionnaire with 30variables, mainly with a 5-point Likert scale was used. The survey was carried out on selected 1500 customers using E-Commerce applications in India. For data collection, random sampling was adopted. Data mining Tools and Techniques i.e. WEKA, R programming, R Rattle and R Cmdr. have been used to analyze the data. Multinomial Logistics model is applied for predicting customer buying behaviours. Data mining techniques like Principle Component Analysis and factor analysis is applied for Data Dimension Reduction. Originally there were 30 factors influencing attributes/variables which were reduced to 7 after the PCA technique is applied. This research result represent that customer region is the variable that is highly influencing the customer buying behavior and the E-Commerce. The second reason is the customer income which is also influencing the customer buying behavior and E-Commerce next to the customer Region to some extent. The rest of the demographic variables are not showing significant influence.
A Review on Consumer Behavior Towards Online Shopping using Machine Learning
International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence
Study of consumer behavior in online shopping, as a rule, manages identification of consumers and their purchasing behavior. The purpose of such studies is to verify who purchases where, what, when, and how. The analysis of such consumer behavior is useful to get the buyer's prerequisites and requirements for their future aims towards the product. Through this review, E-commerce organizations can follow the utilization and sentiments appended to their items and adopt suitable promoting strategies to give a customized shopping experience to their buyers, consequently expanding their hierarchical benefit. This paper purpose to utilize information-driven promoting models, for example, information perception, natural language processing, and AI models that help in getting the demographics of an association. Additionally, make recommender frameworks through cooperative filtering, sentiment analysis, and neural networks.
2019
There are matters to pay attention to in running a business, specifically marketing that will determine the right types of consumers. We can determine the right customer segments for a product or service by observing their behavior. If the target consumers are in line with the product or service offered, it makes it easier to sell it. However,choosing the right consumer segment is not an easy task. There are aspects need considering to comprehend the behavior of consumers. By benefiting from artificial intelligence and its ability to process multiple factors simultaneously. This study uses a behavioral approach. The three phases conducted in this study are 1) preliminarystudy and initial data collection, 2) initial data analysis and designing optimization algorithm, and 3) application of optimization algorithm and its resulting behavioral analysis. There are three main results: 1) a 1 click/day increase in the number of clicks (from 7 to 8 clicks/ day), 2) a 76 posts/day drop in the...