Digitizing Offline Shopping Behavior Towards Mobile Marketing

The proliferation of mobile technologies makes it possible for mobile advertisers to go beyond the real-time snapshot of the static location and contextual information about consumers. In this study, we propose a novel mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To evaluate the effectiveness of this strategy, we design a large-scale randomized field experiment in a large shopping mall in Asia based on 83,370 unique user responses for two weeks in 2014. We found the new mobile trajectory-based advertising is significantly more effective for focal advertising store compared to several existing baselines. It is especially effective in attracting high-income consumers. Interestingly, it becomes less effective during the weekend. This indicates closely targeted mobile ads may constrict consumer focus and significantly reduce the impulsive purchase behavior. Our finding suggests marketers should carefully design mobile advertising strategy, depending on different business contexts

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Customer habits in a B2B context : impacts on cash flow level and volatility

Hábitos estão presentes em uma grande parte do dia-a-dia das pessoas. À medida que são repetidas ações com resultados satisfatórios em contextos estáveis, as respostas para ações futuras começam a ser ativadas automaticamente na memória de um indivíduo. Com o tempo, as decisões tornam-se menos impulsionadas por objetivos e intenções e, desta forma, um hábito é formado. Medidas empíricas de hábitos baseadas em dados de transações de clientes foram desenvolvidas pela área de marketing e vincularam comportamentos habituais de pessoas na hora da compra e o impacto financeiro nas empresas. Esta dissertação tem como objetivo analisar o impacto de comportamentos habituais no contexto B2B de transações entre fabricantes e varejistas. O responsável por efetuar uma compra em uma empresa pode comparar especificações, preços e avaliar os concorrentes antes de fazer um pedido. No entanto, é praticamente impossível avaliar todos os produtos sempre que for necessária uma compra para reabastecer estoques ou para solicitar um item vendido no catálogo por um vendedor dentro da loja. Portanto, espera-se que com o tempo, uma parte das transações que são realizadas começam a ser conduzidas por comportamentos habituais de alguém envolvido no processo de compra. Esta dissertação propõe medir os hábitos de compra e promoção em um banco de dados de transações e aplicar análises quantitativas para avaliar como os hábitos impactam os níveis de fluxo de caixa e a volatilidade dos mesmos. Uma análise posterior é proposta para comparar como os clientes habituais se relacionam com os clientes mais valiosos da empresa e uma simulação é proposta para analisar o impacto de uma eventual aquisição de clientes. Os resultados mostram que os hábitos mais fortes de compra aumentam os níveis de fluxo de caixa, mas também afetam positivamente a volatilidade do fluxo de caixa. Em contrapartida, os hábitos de promoção, com o passar do tempo, tendem a gerar fluxos de caixa menos voláteis que os hábitos de compra, mas com a desvantagem de diminuir os níveis dos mesmos.Habits are widespread in most of life. As people repeat actions with satisfactory outcomes in stable contexts, responses start to become automatically retrieved in memory. Over time decisions become less driven by goals and intentions, and therefore, a habitual behavior is formed. Empirical measures of habits based on customer transactions data were developed by marketing scholars and have linked habitual behaviors of people when purchasing and their impact on firms’ performance. This dissertation aims to analyze the impact of habitual behaviors in the context of business-to-business transactions with manufacturers and retailers. The responsible for buying in a firm may compare specifications, prices and assess competitors before making a purchase. However, it is unfeasible to evaluate all products every time it is required a purchase to replenish stocks or to order a sold item in a catalog by a sales employee. Therefore, it is expected that over time, a portion of repeat transactions start to be driven by habitual behaviors of someone involved in the process of buying. This dissertation proposes to measure the Purchase and Promotion Habits in a database of transactions and to apply quantitative analyzes to evaluate how habits affect cash flow levels and their volatility. A later analysis is proposed to compare how regular customers relate to the company's most valuable customers and a simulation is proposed to analyze the impact of eventual customer acquisition. The results show that stronger Purchase Habits increase cash flow levels, but also positively affect cash flow volatility. On the other hand, Promotion Habits, over time, tend to generate less volatile cash flows than Purchase Habits, but with the disadvantage of reducing their levels

The need to use data mining techniques in E-Business

Abstract. The number of Internet users rose from 400 million in 2000 to just over 2 billion in early 2011. This means that approximately one third of the world's population uses the internet. Taking  these conditions into consideration, we can say that businesses have changed their way. Many companies that, over the last century could not even dream that could have a certain volume of activity or they could face competition with industry giants, have succeeded in giving to enjoy great success.  For example: Amazon.com, founded in 1995, had in 1999 a turnover of at least 13 times higher than other prestigious names in the U.S., such as Barnes & Noble and Borders Books & Music. E-business is the key to make life easier for the people. Knowledge of e-business environment is essential for doing business in this century. More must be understood and new technologies applied to extract knowledge from data

Two Wing Models of Sales Promotion: Theorization and Examination

Sales promotion is an important and frequently used marketing tool to influence the consumers’ purchase intentions directly. This research study introduces a two-wing model of sales promotion to show the comparative impact of different formats and benefit levels of instant price discounts and next off purchase promotions on the consumer perceptions and purchase intentions of the promoted products. The uniqueness of this model is that it separately tests the impact of different types and formats of instant price discounts and next off purchase promotions on the consumers’ overall perceptions and purchase intentions of the products under sale. The empirical testing of this model reveals some important findings that can help marketers to develop result oriented sales promotion techniques in Asian markets. Since, this model has been tested in an Asian context only. To know its multi-cultural application it needs to be tested in diverse cultural contexts

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

Online engagement for a healthier you: A Case Study of Web-based Supermarket Health Program

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Obesity is a growing problem affecting millions of people. Various behavior change programs have been designed to reduce its prevalence. An Australian supermarket has recently run a web-based health program to motivate people to eat healthily and do more physical activity. The program offered discounts on fresh products and a website, HealthierU, providing interactive support tools for participants. The stakeholders desire to evaluate if the program is effective and if the supporting website is useful to facilitate behavior changes. To answer these questions, in this work we propose a method to: (1) model individual purchase rate from sparse recorded transactions through a mixture of Non-Homogeneous Poisson Processes (NHPP), (2) design criteria for partitioning participants based on their interactions with the HealthierU website, (3) evaluate the program impact by comparing behavior changes across different groups of participants. Our case study shows that during the program the participants significantly increased their purchases of some fresh products. Both the distribution of behavior patterns and impact scores show that the program imposed relatively strong impact on the participants who logged activities and tracked weights. Our method can facilitate the enhancement of personalized health programs, especially aiming to maximize the program impact and targeting participants through web or mobile applications

DATA MINING: A SEGMENTATION ANALYSIS OF U.S. GROCERY SHOPPERS

Consumers make choices about where to shop based on their preferences for a shopping environment and experience as well as the selection of products at a particular store. This study illustrates how retail firms and marketing analysts can utilize data mining techniques to better understand customer profiles and behavior. Among the key areas where data mining can produce new knowledge is the segmentation of customer data bases according to demographics, buying patterns, geographics, attitudes, and other variables. This paper builds profiles of grocery shoppers based on their preferences for 33 retail grocery store characteristics. The data are from a representative, nationwide sample of 900 supermarket shoppers collected in 1999. Six customer profiles are found to exist, including (1) "Time Pressed Meat Eaters", (2) "Back to Nature Shoppers", (3) "Discriminating Leisure Shoppers", (4) "No Nonsense Shoppers", (5) "The One Stop Socialites", and (6) "Middle of the Road Shoppers". Each of the customer profiles is described with respect to the underlying demographics and income. Consumer shopping segments cut across most demographic groups but are somewhat correlated with income. Hierarchical lists of preferences reveal that low price is not among the top five most important store characteristics. Experience and preferences for internet shopping shows that of the 44% who have access to the internet, only 3% had used it to order food.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

Exploring the decision-making process of men's branded underwear consumers

"The purpose of this paper is to explore the role of involvement, brand loyalty, and gender in the purchase of men's branded underwear, and specifically during the evaluation of alternatives and product choice stages of the decision-making process. Interviews were conducted with fifteen department store shoppers to explore their use of evaluative criteria and the impact of these criteria on product choice. Interview data revealed four main consumer profiles: high involvement /brand loyal, high involvement not brand loyal, low involvement/brand loyal, and low involvement not brand loyal consumers. The majority of participants were either high involvement/brand loyal, or low involvement/not loyal. Results of this study point to the need for marketers to better understand the men's branded underwear consumer in order to successfully market new products in an increasingly diversified apparel product category. Similarly, manufacturers could better cater to consumers' needs and wants by understanding consumer perceptions of brand value. Further research is needed to more fully explore the implications of such considerations as channel type, consumer demographics, and lifestyle marketing for the purchase of men's branded underwear."--Abstract from author supplied metadata

Discovering temporal regularities in retail customers’ shopping behavior

In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customerâ\u80\u99s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity