Search results

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

In the era of Big Data, larger volumes of data arrive in various forms at an increasing pace but of questionable quality and value. The abundant information (that emanates from these 5Vs – volume, variety, velocity, veracity, and value) taxes the bounded capacity of managers. This chapter introduces a taxonomy of approaches available for strategic decision making in an information-rich environment, several of which showcase that automation can help to augment (not supplant) managerial decision making. This taxonomy is then applied to an innovation context. Mapping a stylized version of the phases of the innovation process (i.e., front-end innovation, new product development, commercialization) onto the four decision-making approaches yields an organizing framework for understanding strategic decision making in the realm of innovation. The chapter concludes by identifying promising areas for future research.

This conceptual chapter: (1) explicates the foundational terminology regarding strategic decision making in a marketing context; (2) provides a primer on the era of Big Data and making strategic decisions in an information-rich environment; (3) introduces a taxonomy, which features approaches to decision making in an information-rich environment; and (4) applies the taxonomy in an innovation context to yield an organizing framework.

This chapter focuses on the nascent field that is emerging at the intersection of innovation, marketing strategy, and information-rich environments, and breaks new ground by exploring automation available to aid managerial decision making in this realm.

The main practical implication is to elucidate that managers can apply different approaches to decision making in today’s information-rich environment. Tables 2–4 provide to managers 12 examples of the types of decision making in an innovation context.

This chapter introduces a new taxonomy to classify four approaches for making strategic decisions in an information-rich environment, and extends that framework to the innovation realm. This framework aims to prompt researchers to explore important topics that exist at the intersection of innovation, marketing strategy, and managerial decision making in an information-rich environment.

In today’s dynamic business environment, it’s crucial for companies to employ robust strategies and decision-making tools to remain competitive. This chapter explains the key comprehensive policies that can be applied by any company to maximise the effectiveness of the APPNIE framework. These policies include the following key elements for effective use of the APPNIE model: appointing an APPNIE manager responsible for maintaining and updating the model, collaborating with top executives, and formulating action plans; raising awareness of the APPNIE manager’s role across the organisation to encourage sharing valuable information (establishing clear procedures and channels for data collection); encouraging the communication of valuable data by offering recognition or rewards, ensuring a steady flow of filtered information; monitoring the eight quadrants using qualitative and quantitative data for up-to-date assessments of key APPNIE’s factors; organising regular meetings between APPNIE managers and directors from different functions to share perspectives, discuss action plans, and address challenges and opportunities. The chapter shows that by adopting these practices, companies can navigate ‘complexity’, make informed decisions, and enhance their overall success and competitiveness in the market.

While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don’t go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.

This chapter provides an overview of the marketing strategy development process in the commercialization of breakthrough technologies. Important concepts and elements that are considered critical when developing market applications are presented with emphasis on three key decisions: target market selection, segmentation, and positioning. These strategic decisions will guide the more tactical considerations relating to the specific elements, or marketing mix, of the product’s marketing strategy. Marketing strategy development is a dynamic process that is impacted by many factors. This chapter highlights the dynamic nature of this process as well as providing insight as to the fundamental considerations in strategy formulation.

This chapter provides an overview of the marketing strategy development process in the commercialization of breakthrough technologies. Important concepts and elements that are considered critical when developing market applications are presented with emphasis on three key decisions: target market selection, segmentation, and positioning. These strategic decisions will guide the more tactical considerations relating to the specific elements, or marketing mix, of the product's marketing strategy. Marketing strategy development is a dynamic process impacted by many factors. This chapter highlights the dynamic nature of this process as well as provides insight into the fundamental considerations in strategy formulation.

This chapter points out that the use of a wide range of theoretical paradigms in marketing research requires researchers to use a broad range of methodologies. As an aid in doing so, the chapter argues for the use of case study research (CSR), defines CSR, and describes several CSR theories and methods that are useful for describing, explaining, and forecasting processes occurring in business-to-business (B2B) contexts. The discussion includes summaries of six B2B case studies spanning more than 60 years of research. This chapter advocates embracing the view that learning and reporting objective realities of B2B processes is possible using CSR methods. CSR methods in the chapter include using multiple interviews (2 + ) separately of multiple persons participating in B2B processes, direct research and participant observation, decision systems analysis, degrees-of-freedom analysis, ethnographic-decision-tree-modeling, content analysis, and fuzzy-set qualitative comparative analysis (fs/QCA.com). The discussion advocates rejecting the dominant logic of attempting to describe and explain B2B processes by arms-length fixed-point surveys that usually involve responses from one executive per firm with no data-matching of firms in specific B2B relationships – such surveys lack details and accuracy necessary for understanding, describing, and forecasting B2B processes.

In the academic field of business management, several potential theories were established during the last decades to explain companies' decisions, organizational behavior, consumer patterns, and internationalization, among others. As a result, businesses and scholars were able to analyze and decide based on theoretical approaches to explain the current conditions of the market. Secondary research was conducted to collect more than 36 management theories. This chapter aims to develop the most famous theories related to business applied in the international field. The novelty of this chapter relies on the compilation of recognized previous research studies from the academic literature and evidence in international business.