Retail Analytics: The Quest for Actionable Insights from Big Data on Consumer Behavior and Operational Execution

2021 ◽  
Author(s):  
Robert P. Rooderkerk ◽  
Nicole DeHoratius ◽  
Andres Musalem
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Retail Analytics

An Effective Model for Consumer Need Prediction Using Big Data Analytics

2021 ◽  
Author(s):  
Yihao Tian
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Customer Behavior ◽  
Data Set ◽  
Customer Data ◽  
Demand Prediction ◽  
Management Ratio ◽  
The Veil

Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).

Volunteered Surveillance

2019 ◽  
pp. 2053-2071
Author(s):  
Subhi Can Sarıgöllü ◽  
Erdem Aksakal ◽  
Mine Galip Koca ◽  
Ece Akten ◽  
Yonca Aslanbay
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Historical Data ◽  
Front End ◽  
Commercial World ◽  
Asymmetric Power ◽  
Ownership Control

As the front end of the digitized commercial world, corporations, marketers, and advertisers are under the spotlight for taking advantage of some part of the big data provided by consumers via their digital presence and digital advertising. Now, collectors and users of that data have escalated the level of their asymmetric power with scope and depth of the instant and historical data on consumers. Since consumers have lost the ownership (control) over their own data, their reaction ranges from complete opposition to voluntary submission. This chapter investigates psychological and societal reasons for this variety in consumer behavior and proposes that a contractual solution could promote a beneficial end to all parties through transparency and mutual power.

Teach an I-O To Fish: Integrating Data Science Into I-O Graduate Education

2015 ◽  
Vol 8 (4) ◽  
pp. 539-544 ◽  
Author(s):  
Juliet R. Aiken ◽  
Paul J. Hanges
Keyword(s):  
Big Data ◽  
Informed Consent ◽  
Consumer Behavior ◽  
Best Practices ◽  
National Security ◽  
Graduate Education ◽  
Data Science ◽  
National Security Agency ◽  
Internet Searches ◽  
Corporate Language

Big data is becoming a buzzword in today's corporate language and lay discussions. From individually targeting advertising based on previous consumer behavior or Internet searches to debates by Congress concerning National Security Agency (NSA) access to phone metadata, the era of big data has arrived. Thus, the Guzzo, Fink, King, Tonidandel, and Landis (2015) discussion of the challenges (e.g., confidentiality, informed consent) that big data projects present to industrial and organizational (I-O) psychologists is timely. If the hype associated with these techniques is warranted, then our field has a clear imperative to debate the ethics and best practices surrounding use of these techniques. We believe that Guzzo et al. have done our field a service by starting this discussion.

Big Data and consumer behavior: imminent opportunities

2016 ◽  
Vol 33 (2) ◽  
pp. 89-97 ◽  
Author(s):  
Charles F. Hofacker ◽  
Edward Carl Malthouse ◽  
Fareena Sultan
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
A Priori ◽  
Secondary Data ◽  
Poor Quality ◽  
Consumer Decision Making ◽  
Marketing Practice ◽  
Content Type ◽  
Skill Sets ◽  
Data Culture

Purpose – The purpose of this paper is to assess how the study of consumer behavior can benefit from the presence of Big Data. Design/methodology/approach – This paper offers a conceptual overview of potential opportunities and changes to the study of consumer behavior that Big Data will likely bring. Findings – Big Data have the potential to further our understanding of each stage in the consumer decision-making process. While the field has traditionally moved forward using a priori theory followed by experimentation, it now seems that the nature of the feedback loop between theory and results may shift under the weight of Big Data. Research limitations/implications – A new data culture is now represented in marketing practice. The new group advocates inductive data mining and A/B testing rather than human intuition harnessed for deduction. The group brings with it interest in numerous secondary data sources. However, Big Data may be limited by poor quality, unrepresentativeness and volatility, among other problems. Practical implications – Managers who need to understand consumer behavior will need a workforce with different skill sets than in the past, such as Big Data consumer analytics. Originality/value – To the authors ' knowledge, this is one of the first articles to assess how the study of consumer behavior can evolve in the context of the Big Data revolution.

Data Breach on Consumer Behavior

2021 ◽  
pp. 1861-1879
Author(s):  
Ruimin Chen
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Health Services ◽  
Online Shopping ◽  
Payment System ◽  
Security Risks ◽  
Data Breach ◽  
Daily Lives ◽  
Ethical Concerns

Big data is now affecting the daily lives in many different areas, such as payment system, online shopping, health services and so forth. There has no doubt that big data is able to make the lives of people more convenient to a certain extent, but it can also threaten privacy security in the meantime. In order to explore the hazardous effects of data breach on consumer behavior and understand how netizens act and feel when experiencing it, a questionnaire was completed by 110 participants. This article will demonstrate the primary issues on potential security risks on big data, especially the effects of data breach on consumer behavior by discussing the causes, solutions and ethical concerns.

Identifying Buying Patterns from Consumers’ Purchase History Using Big Data and Cloud Computing

2021 ◽  
Author(s):  
Hui Zhang
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Big Data ◽  
Consumer Behavior ◽  
Global Marketplace ◽  
Customer Preference ◽  
Second Stage ◽  
Two Stages ◽  
Brand Consistency ◽  
Research Analysis

Consumers can select their goods and resources in several ways, significantly affecting customer preference in the online world and raising network customers’ demands to anticipate their purchasing pattern. The current work aims to identify buying patterns from consumers’ purchase history (IBP-CPH) framework for analyzing the evolving trend of customer decision-making in the global marketplace. The project is carried out in two stages to achieve the goals. A comprehensive research analysis is conducted to evaluate the latest consumer behavior trends in the digital economy in this first stage. In the second stage, identifying buying patterns from consumers’ purchase history (IBP-CPH) framework identifies the finalized factor’s preference amounts (s). The concept of a fugitive setting requires the incoherence of information to be recorded. The results achieved in this research stated that buyers are very aware of new and sophisticated brands and brand consistency so that internet companies can keep their customers on their web platforms.

Ethical Dilemmas Associated With Social Network Advertisements

2021 ◽  
pp. 1833-1865
Author(s):  
Alan D. Smith ◽  
Onyebuchi Felix Offodile
Keyword(s):  
Big Data ◽  
Social Network ◽  
Consumer Behavior ◽  
Social Networking ◽  
Ethical Dilemmas ◽  
Online Social Networking ◽  
Uses And Gratification ◽  
Uses And Gratification Theory ◽  
Gender And Age ◽  
Age Trends

A significant amount information can be relayed on Facebook, MySpace, and Twitter, but the question remains whether or not organizations are using this to their advantage, especially in the era of big data. The present study used a sample of working professionals that were knowledgeable in the various options of social networking to test these assumptions. The three hypotheses dealt with the interplay of online social networking, advertising effectiveness, gender and age trends, and remaining the interplay with positive comments of the use of the “like” function and its impacts on consumer behavior, as derived from the review of relevant operations literature and from applying the basic tenants of uses and gratification theory. All three specific research hypotheses were accepted in the null form.

Ethical Dilemmas Associated With Social Network Advertisements

2019 ◽  
pp. 337-369
Author(s):  
Alan D. Smith ◽  
Onyebuchi Felix Offodile
Keyword(s):  
Big Data ◽  
Social Network ◽  
Consumer Behavior ◽  
Social Networking ◽  
Ethical Dilemmas ◽  
Online Social Networking ◽  
Uses And Gratification ◽  
Uses And Gratification Theory ◽  
Gender And Age ◽  
Age Trends

A significant amount information can be relayed on Facebook, MySpace, and Twitter, but the question remains whether or not organizations are using this to their advantage, especially in the era of big data. The present study used a sample of working professionals that were knowledgeable in the various options of social networking to test these assumptions. The three hypotheses dealt with the interplay of online social networking, advertising effectiveness, gender and age trends, and remaining the interplay with positive comments of the use of the “like” function and its impacts on consumer behavior, as derived from the review of relevant operations literature and from applying the basic tenants of uses and gratification theory. All three specific research hypotheses were accepted in the null form.

Volunteered Surveillance

2018 ◽  
pp. 133-150
Author(s):  
Subhi Can Sarıgöllü ◽  
Erdem Aksakal ◽  
Mine Galip Koca ◽  
Ece Akten ◽  
Yonca Aslanbay
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Historical Data ◽  
Front End ◽  
Commercial World ◽  
Asymmetric Power ◽  
Ownership Control

As the front end of the digitized commercial world, corporations, marketers, and advertisers are under the spotlight for taking advantage of some part of the big data provided by consumers via their digital presence and digital advertising. Now, collectors and users of that data have escalated the level of their asymmetric power with scope and depth of the instant and historical data on consumers. Since consumers have lost the ownership (control) over their own data, their reaction ranges from complete opposition to voluntary submission. This chapter investigates psychological and societal reasons for this variety in consumer behavior and proposes that a contractual solution could promote a beneficial end to all parties through transparency and mutual power.

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