Identifying Consumer Preferences From User-generated Content On Amazon.com By Leveraging Machine Learning

Inexperienced consumers may have high uncertainty about experience goods that require technical knowledge and skills to operate effectively; therefore, experienced consumers\u27 prior reviews can be useful for inexperienced consumers. However, one-sided review systems (e.g., Amazon) only provide the opportunity for consumers to write a review as a buyer and contain no feedback from the seller\u27s side, so the information displayed about individual buyers is limited. Therefore, this study analyzes consumers\u27 digital footprints (DFs) for programmable thermostats to identify and predict unobserved consumer preferences, using a dataset of 141 million Amazon reviews. This paper proposes novel approaches (1) to identify unobserved consumer characteristics and preferences by analyzing the target consumers\u27 and other prior reviewers\u27 DFs; (2) to extract product-specific product content dimensions (PCDs) from review text data; (3) to predict individual consumers\u27 sentiment before they make a purchase or write a review; (4) to classify consumers\u27 sentiment toward a specific PCD by using context-based word embedding and deep learning models. Overall, this approach developed in this paper is applicable, scalable, and interpretable for distinguishing important drivers of consumer reviews for different goods in a specific industry and can be used by industry to design customer-oriented marketing strategies

Essays on Customer Engagement Strategies and Tactics in Business and Consumer Markets

In the last decade, customer engagement has become a key topic for both practitioners and researchers. Classically, customer engagement deals with customer behavior beyond purchase and thus non-monetary contributions by the customer, such as Word-of-Mouth (WOM), feedback and online reviews, or participation in the innovation process. While previous literature largely focused on the conceptualization of customer engagement itself, only a few studies have investigated how managers can actually stimulate and/or facilitate customer engagement. However, the latter is of high importance since only a few customers are truly engaged and it is often left to the firm to take the initiative to engage the customer. Thus, marketers need to understand how to design and successfully implement customer engagement initiatives. Accordingly, this dissertation investigates customer engagement strategies and tactics. While customer engagement strategy pertains to the overarching plan to leverage customer engagement to achieve the firm’s goals, customer engagement tactics deal with single actions taken by the firm to facilitate customer engagement across the various touchpoints in the customer journey. Specifically, this dissertation includes three essays, each addressing distinct questions with respect to customer engagement over the customer journey. Specifically, the first essay is conceptual in nature and provides an analysis of the strategic relevance of customer engagement in business-to-business (B2B) markets. The second essay explores how industrial firms can leverage service touchpoints as opportunities to engage their B2B customers in the post-purchase phase by employing the field service force for cross- and up-selling. Finally, the third essay investigates how marketers can use executional content cues in their TV advertisings (e.g., informativeness, creativity, or branding) to engage consumers and mitigate zapping behavior. Both empirical studies are based on unique datasets of real-world engagement tactics and related customer behavior obtained from co-operating companies

Artificial Intelligence Enabled Solutions in Marketing: Case Ekokompassi

Artificial Intelligence is a relevant field in computer science which is emerging into businesses. Due to the complexity of the concept itself, it is important to understand what AI is and how it can be integrated into the marketing operations in a business. The objective of this thesis was to utilize the information and insights gathered from experts in the field of computer science, business and marketing to gain a holistic view of the current and future capabilities of artificial intelligence in marketing so that recommendations could be provided to the commissioning company Ekokompassi Oy. The research questions are how to use artificial intelligence in marketing, secondly what are the future predictions in the field of marketing and AI and finally what are the potential AI enabled solutions in marketing for Ekokompassi. Qualitative tools, more precisely in-depth-interviews, were used to gather the main data in this research. The main data was analysed using content analysis methods from which four main categories were extracted for further examination. The main conclusions of this study answered the initial research questions reinforcing the knowledge gained from the theory. The conclusions indicated that companies which leverage technology in their business strategies can gain an advantage over their competitors who remain to work in traditional ways. AI can predict, analyse and personalize one to one marketing messages to consumers at scale and with precision that humans are incapable of. Companies should not fear technology but embrace it throughout the core functions of the business bearing in mind the issues around ethics and data privacy. The best time to begin gathering business data is today

Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach

Aesthetics are critically important to market acceptance in many product categories. In the automotive industry in particular, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing new product aesthetics. A single automotive "theme clinic" costs between \$100,000 and \$1,000,000, and hundreds are conducted annually. We use machine learning to augment human judgment when designing and testing new product aesthetics. The model combines a probabilistic variational autoencoder (VAE) and adversarial components from generative adversarial networks (GAN), along with modeling assumptions that address managerial requirements for firm adoption. We train our model with data from an automotive partner-7,000 images evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs-38% improvement relative to a baseline and substantial improvement over both conventional machine learning models and pretrained deep learning models. New automotive designs are generated in a controllable manner for the design team to consider, which we also empirically verify are appealing to consumers. These results, combining human and machine inputs for practical managerial usage, suggest that machine learning offers significant opportunity to augment aesthetic design

Monopoly Power in the Electronic Information Industry: Why, and So What?

This law and economics article diagnoses why monopoly power infects so many markets in the electronic media, communications, and information technology industries (collectively the Industry ),and recommends changes to prevailing intellectual property and antitrust doctrines to remedy this problem. The analysis focuses on a single norm -- the maximization of economic value, as defined by standard welfare economic theory. Identifying three distinct functions that operate throughout this otherwise diverse Industry -- authoring, publishing, and distribution -- the article notes that two economic peculiarities characterize most Industry markets: the technical feasibility of non-rivalrous use of digitized information products, and the frequent creative destruction of Industry markets by new technologies and business methods. Using these concepts, the article argues that, while concern surrounding media megamergers is overwrought, certain public policies do significantly constrain economic value creation in the Industry. The article proposes reforming several major legal doctrines and public policies to loosen these constraints, e.g.: (a) reduce the over recognition of copyrights and patents, (b) cease the over enclosure: of the radio spectrum, (c) challenge more frequently nationalizing mergers among local/regional distribution network monopolies, (d) aggressively promote open standards for interconnecting networks and software platforms, and (e) simplify antitrust rules against the cross-market leveraging of monopoly power, including a ban on dominant distribution companies engaging in preferential self-dealing in related markets. The article concedes that such reforms, though also satisfying many non-economic norms, would meet stout political resistance from established Industry firms