Consumer choice prediction : artificial neural networks versus logistic models

Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN) to analyse consumer behaviour and to model the consumer decision-making process. Neural networks are considered as a field of artificial intelligence. The development of the models was inspired by the neural architecture of human brain. Neural networks have been generally applied to two different categories of problems - recognition problems and generalisation problems. Recognition problems include visual applications such as learning to recognize particular words and speak them. Generalization problems include classification and prediction. Recently, ANN have been applied in the business and marketing research areas. Most of the studies have utilised the multi-layer feed-forward neural networks (MLFN) in analysing consumer choice problems. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN), a special class of neural networks, and a MLFN with a logistic model on consumers' choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics, and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income, and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN) exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors

Deep Learning for Online Fashion: A Novel Solution for the Retail E-Commerce Industry

The online shopping experience for clothing can be further enhanced by implementing Deep Learning techniques, such as Computer Vision and personalized recommendation systems. Automation, as a principle, can be applied to solving problems surrounding efficacy, efficiency, and security. It also provides a layer of abstraction for the user during the online shopping experience. This research aims to apply Deep Learning methods and principles of automation to augment the e-commerce fashion market in a novel way. After using these methods, it was found that Convolutional Autoencoders and Item-to-Item Based Recommenders may be used to accurately and precisely recommend articles of clothing based on a users’ styling preferences

Buyer Prediction Through Machine Learning

Targeted marketing has grown in popularity in recent years, as well as recognizing when a consumer will desire a commodity may be extremely important to a business. Predicting this demand, however, is a complex procedure. Businesses, promoters/marketers, and sellers are using machine learning approaches to execute buyer prediction. This study focuses on when a customer would buy fast-moving retail merchandise by evaluating a customer’s purchase history at partner vendors. The projections should be used to customize special discounts for customers who are about to make a purchase. In addition, buying behavior is a set of consumption habits that can be analyzed to help in predicting the needs of specific target audience. Knowing consumption habits, business is much more likely to formulate sales items tailored to the market. Thus, the chances of success and acceptance of products and services increase. Promotional offers can then be supplied to the most relevant clients (with alerts sent directly to buyers’ mobile devices) thus reducing the use of the traditional/general paper-based marketing. More specifically, I will create a machine learning model that predicts potential future buyers based on the supplied market dataset. I will use a data source that gathers clients’ consumer history to establish a solid basis for this approach. The study focuses on consumer groupings rather than individual purchasers to forecast purchasing. After analyzing which of these purchase behaviors fits the consumer\u27s decision-making of a product or service, it will be easy to establish appropriate/focused marketing and sales strategies

Anthropomorphism Appeals: Influencing Consumer Attitudes and Memory through Humanlike Presentation

Advertisements in which a good or service is portrayed as humanlike in one or more ways (i.e., anthropomorphism appeals) are commonplace, yet scarecely investigated. In this state, managers using anthropomophism appeals are left to wager pomotional budgets without knowledge of the impact these appeals have on viewers or the factors most frequently influencing these appeals. The present study addresses this limitation by evaluating the advertising effectiveness of anthropomorphism appeals and the roles of contextual (product type and product category) and individual (loneliness and product knowledge) factors. Using an online panel service, three experiments evaluated the effectiveness of anthropomorphism appeals at enhancing consumer attitudes, intentions, and recall. MANOVA results suggest that the principal benefit of anthropomorphism appeals relates to enhancing recall of advertising information (np2 = .055, p \u3c.001). Spotlight analysis of the study\u27s moderators revealed this effect on recall was substantially enhanced when subjects were relatively lonely (ß = .706, p = .001) or had relatively low self-assessed product knowledge (ß = .690, p = .001). When the product being advertised was hedonic, anthropomorphism appeals had an additional influence on attitude toward the advertisement (np2 = .013, p = .002). When visual elements were included in the advertisement, the anthropomophism condition was associated directly with increased attitude toward the brand (np2 = .022, p = .047), directly with attitude toward the advertisement (np2 = .059, p = .001) and indirectly with purchase intentions. Critically, the positive attitudinal influence of anthropomorphism appeals on consumer attitudes were reversed when the subjects reported relatively high product knowledge. In total, the present study offers a broad, early evaluation of anthropomorphism appeals. Relevant to theorists is evidence of a bridge between psychology and marketing models of anthropomorphism. Of value to marketing practitioners is the early guidance these studies offer in managing anthropomorphism appeals with consideration for the design elements, contextual factors, and individual psychographic variables

IT contribution in the consolidation of organizational competences - A case study on a hotel group

The information economy is based on the net appliance, which decreases the intermediary's participation in the activities of purchase and sale of products and services.The tourism sector has its activities based on information in which its products are considered low touch, where the purchaser acquires a service without a physical approaching with it.However, it can be noticed that there is still a trade off between embracing and wealth, but day-by-day it is reduced by the increasing use of Information Technology and communication through its information system.The advancement of IT allied to business' strategic can establish new models of business to observe in a more efficient way the sectors innovation that works intensively with information, contributing with the competence consolidation aiming the client and connected network attendance.This paper analyses, through a case study in a big Hotel network, some aspects that are related to IT's application for competitiveness increasing. Such aspects are related to the entrepreneur's strategic articulation as the general configuration of Front Office and Back Office, client's communication improvement, the use of web, client fidelity, disintermediation of activities and cultural activities adaptation on the services offering

Impact of Augmented Reality on Purchase Intention of Foreign Products Online

Augmented reality (AR) is a significant technology that holds the promise to transform how consumers interact with products before purchasing. It creates immersive experiences that enable people to engage with digital material in a more intuitive and straightforward manner. When used effectively, AR can be influential in every stage of customer journey including purchase intention stage. Assessing purchase intention of international consumers is critical for organizations because it allows them to plan and make choices about marketing, inventory, and expenses. Purchase intent provides international companies with information on what their global consumers are willing to purchase enabling them to modify their marketing and goods to better fit their customers' demands.  This research examined how augmented reality increase the purchase intention of global customer using the data, which includes data for 810 different overseas visitors of an e-commerce site.  We collected these data from visitors of a global e-commerce shop that integrated augmented reality (AR) into their smartphone app to enable users to imagine how they would appear with various items.  The study performed a Robust Least Squares Method-estimation. Our research's findings provide some early proof that using AR increases the level of purchase intention of foreign products.  The findings also indicate that price, and the number of positive reviews increase the purchase intention of foreign products.  Customers' buying intentions may help firms predict future trends and organize their strategy appropriately. Businesses must also understand the elements that drive purchase intent, such as immersive experience with AR, consumer demographics, nationality, product attributes, pricing, and customer experience. &nbsp

Examining the two-dimensional perceived marketplace influence and the role of financial incentives by SEM and ANN

In recent years, research on sustainable consumption has been particularly relevant, highlighting the importance of the collective over the individual to reduce pollution. This study focuses on the study of the perceived marketplace influence (PMI) concept in its organizational and consumer dimensions, together with the financial incentives that exist in the adoption of electric cars and their effect on green customer engagement. A sample of 382 potential buyers of electric vehicles was obtained. A new hybrid analytical approach was taken structural equation modelling and artificial neural network. The research found the most significant variables affecting purchase intention were financial incentives, followed by PMI Organization and finally PMI Consumer. The results of artificial neural network analysis confirmed all the findings of the structural equation modelling, although the importance of each PMI dimension is different for each technique used. The conclusions point to new business opportunities that can be exploited by companies selling this green technology.Funding for open access charge: Universidad de Granada / CBU

Profiling for profit : a report on target marketing and profiling practices in the credit industry

This report examines how many businesses make significant investments to purchase and develop customer relationship management systems. Given such investments, information about these systems is not widely available, but some publicly available information gives indication of the extent, and purpose, of the use. Recognising that lenders use customer information and highly sophisticated systems to target their marketing strategies, is the first step towards ensuring that these practices are taken into account in the development of consumer policy and law reform. This research was funded by the consumer advisory panel of the Australian Securities and Investment Commission (ASIC)

Online Shoppers’ Priority Attributes in Egypt

E-commerce and online shopping have been among the world\u27s fast-growing trends in the past few years. Each year the number of e-commerce deals grows enormously. Furthermore, the tendency will continue because many people are constricted by work and household duties. Simultaneously, the Internet saves much time and allows having a full shopping experience from the comfort of one\u27s home. With the improvement of technology and the continuous progression in web development, retailers are not only seeking e-commerce to expand their sales, but the trend of full online retailing with no physical existence is becoming widespread. Given the expansion, it is becoming challenging for e-retailers to maintain their customers since consumers can easily compare the platforms and pick to place their orders at the platform that best meets their needs. To prevent this fast customer turnover, it is important to consider the consumers\u27 preferences when online shopping to meet their needs better and locate their investments accordingly. This study holds a new perspective in presenting the service business\u27s packaging by materializing the e-commerce business as an example. The author develops a model that guides in enhancing online platforms\u27 efficiency based on online shoppers\u27 preferences and priority attributes. These attributes are considered the packaging elements that augment the main business aspects summarized in the 7 Ps marketing mix module. In the context of interpreting the marketing orientation theory, the study measures consumers\u27 priority attributes, summarized in the E-SERVPACK Model, in online shopping in four different product categories. Results revealed that the highest and lowest priority attributes are common across all four categories, yet the importance level differs from one category to another. It is advised that e-retailers consider developing their platforms and allocate their budgets based on their target consumers\u27 preferences and the business\u27s product type