Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2

Gauge the Effects of Targeted Advertising along the Consumer Funnel

Targeted display advertising for individual consumers has become pervasive on social media platform and other online websites (traditional platform). Yet, the effectiveness of targeted advertising across online platforms is not well understood. Moreover, such advertising effect may be different for different types of consumers, i.e. consumers in the early stage and those in the late stage, relative to the final purchase stage. This paper aims at assessing the effectiveness of targeted advertising across online platforms on consumers\u27 final conversion (purchase). In addition, we measure the complementarity and substitutability of online platforms for targeted advertising for upper funnel (early-stage) consumers and lower funnel (late-stage) consumers. We use machine learning techniques to form case-control designs analyzed employing regularized discrete choice models to select relevant features explaining the final conversion. The empirical analysis shows that (1) targeting across platforms is positively associated with the final conversion for the lower funnel consumers, but there is no measurable synergistic effect for the upper funnel consumers; (2) the main effect of targeting on social media is positively related to the final conversion for consumers in the upper funnel but has no significant impact for lower funnel consumers. We leverage upon these findings to discuss actionable managerial prescriptions

How Digital Nudges Influence Consumers – Experimental Investigation in the Context of Retargeting

Retargeting is an innovative online marketing technique in the modern age. Although this advertising form offers great opportunities of bringing back customers who have left an online store without a complete purchase, retargeting is risky because the necessary data collection leads to strong privacy concerns which, in turn, trigger consumer reactance and decreasing trust. Digital nudges – small design modifications in digital choice environments which guide peoples’ behaviour – present a promising concept to bypass these negative consequences of retargeting. In order to prove the positive effects of digital nudges, we aim to conduct an online experiment with a subsequent survey by testing the impacts of social nudges and information nudges in retargeting banners. Our expected contribution to theory includes an extension of existing research of nudging in context of retargeting by investigating the effects of different nudges in retargeting banners on consumers’ behaviour. In addition, we aim to provide practical contributions by the provision of design guidelines for practitioners to build more trustworthy IT artefacts and enhance retargeting strategy of marketing practitioners

Audience Prospecting for Dynamic-Product-Ads in Native Advertising

With yearly revenue exceeding one billion USD, Yahoo Gemini native advertising marketplace serves more than two billion impressions daily to hundreds of millions of unique users. One of the fastest growing segments of Gemini native is dynamic-product-ads (DPA), where major advertisers, such as Amazon and Walmart, provide catalogs with millions of products for the system to choose from and present to users. The subject of this work is finding and expanding the right audience for each DPA ad, which is one of the many challenges DPA presents. Approaches such as targeting various user groups, e.g., users who already visited the advertisers' websites (Retargeting), users that searched for certain products (Search-Prospecting), or users that reside in preferred locations (Location-Prospecting), have limited audience expansion capabilities. In this work we present two new approaches for audience expansion that also maintain predefined performance goals. The Conversion-Prospecting approach predicts DPA conversion rates based on Gemini native logged data, and calculates the expected cost-per-action (CPA) for determining users' eligibility to products and optimizing DPA bids in Gemini native auctions. To support new advertisers and products, the Trending-Prospecting approach matches trending products to users by learning their tendency towards products from advertisers' sites logged events. The tendency scores indicate the popularity of the product and the similarity of the user to those who have previously engaged with this product. The two new prospecting approaches were tested online, serving real Gemini native traffic, demonstrating impressive DPA delivery and DPA revenue lifts while maintaining most traffic within the acceptable CPA range (i.e., performance goal). After a successful testing phase, the proposed approaches are currently in production and serve all Gemini native traffic.Comment: In Proc. IeeeBigData'2023 (Industry and Government Program

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Exploring the Acceptance for Pixel Technology Implementation in Facebook Ads among Advertisers in Indonesia

The business competition in the digital era is tighter and drives the entrepreneurs to optimize their efforts to win the game. Facebook ads as one of the biggest social marketing media provided a new technology called pixel which replacing conversion tracking pixel on February 15, 2017, for advertisers' advantages. The purpose of this study was to examine the factors influencing the usage of the pixel by the advertisers. This study adopted technology acceptance model (TAM) as a research framework and test it using structural equation modeling. One hundred and eighteen Facebook advertisers from Indonesia which are targeted by custom Facebook ads participated in this study. The findings of this study suggest that the attitude and perceived usefulness of the pixel significantly influence the behavioral intention of the advertisers on using the pixel. The research revealed that the perceived usefulness of the pixel is significantly influenced by the perceived ease of using the pixel. The results of this study will be useful for the Facebook as the provider to improve the technology usefulness and its user interfaces for its effective and efficient use for the Indonesian advertisers.Keywords: pixel, Facebook ads, technology acceptance mode

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

The role of attention and emotional responses on online retargeting campaigns

Retargeting consists of communicating towards consumers that have already been in contact with a brand - because they visited the website or clicked on an advert, for example. Although nowadays people tend to avoid advertising, retargeting has proven to be a very successful method for bringing back consumers that did not conclude a purchase or simply people that showed previous interest in a brand. Also, it is known that attention and emotions play a big role in how people react to advertising and how they perceive the brands that communicate with them. Bearing this in mind, this study hypothesizes that retargeted advertising gets higher levels of attention than either generic or targeted advertising. In the same way, it is proposed that retargeted advertising induces higher levels of positive emotions than the other types of advertising. In order to study such topic, a two-day experiment was created to simulate a decision-making process. Participants were exposed to products but did not finish a purchase of their choice on day one, only to see it advertised on a blog a few days later, among other types of advertising. This way, it was possible to study participant’s reactions to different types of advertising - retargeted, targeted and generic - on a longitudinal study and how retargeted adverts impact their intention to revisit the website, purchase and recommend. This study shows that retargeted advertising gets higher levels of attention than the other two types of ads. Also, it was possible to understand that retargeted advertising has a positive direct relationship with intention to revisit, and a positive indirect relationship with intention to purchase and intention to recommend, both mediated by intention to revisit.O retargeting consiste em comunicar directamente com consumidores que já tenham estado em contacto com a marca - porque visitaram o website anteriormente ou porque clicaram num anúncio da marca. Apesar de se saber que as pessoas tendem a evitar os anúncios, o retargeting tem provado ser um método muito bem-sucedido para trazer de volta consumidores que não chegaram a finalizar uma compra, ou que simplesmente mostraram interesse na marca anteriormente. É também sabido que a atenção e as emoções têm um papel muito importante na definição da maneira como as pessoas reagem à publicidade e do modo como percepcionam as marcas que comunicam consigo. Tendo isto em consideração, o presente estudo lança a hipótese de que anúncios retargeted recebem níveis mais elevados de atenção que anúncios targeted ou genéricos. Da mesma forma, é proposta a hipótese de que os anúncios retargeted induzem níveis mais positivos de emoções, quando comparados com os restantes tipos. Uma experiência de dois dias foi criada de modo a simular um processo de decisão de compra incompleto. Os participantes não finalizavam a compra de um produto que escolhiam como o seu desejo, de modo a que alguns dias depois esse mesmo produto aparecesse num anúncio num blog, entre os outros tipos de anúncios. Desta forma, foi possível estudar as reações dos participantes aos diferentes tipos de publicidade - retargeted, targeted e genérico - mas também estudar o modo como os anúncios retargeted influenciam a intenção de compra, intenção de revisita e intenção de recomendação. Este estudo permitiu concluir que os anúncios retargeted têm genericamente melhores níveis de atenção que os restantes tipos de anúncio. Também foi possível perceber que a publicidade retargeted tem uma relação direta positiva com a intenção de revisita, e uma relação indirecta positiva com a intenção de compra e de recomendação - ambas mediadas pela intenção de revisita

Lookalike Targeting on Others\u27 Journeys: Brand Versus Performance Marketing

Lookalike targeting is a widely used model-based ad targeting approach that uses a seed database of individuals to identify matching “lookalikes” for targeted customer acquisition. An advertiser has to make two key choices: (1) who to seed on and (2) seed-match rank range. First, we find that seeding on others’ journey stage can be effective in new customer acquisition; despite the cold start nature of customer acquisition using Lookalike audiences, third parties can indeed identify factors unobserved to the advertiser that move individuals along the journey and can be correlated with the lookalikes. Further, while journey-based seeding adds no incremental value for brand marketing (click-through), seeding on more downstream stages improves performance marketing (donation) outcomes. Second, we evaluate audience expansion strategies by lowering match ranks between the seed and lookalikes to increase acquisition reach. The drop in effectiveness with lower match rank range is much greater for performance marketing than for brand marketing. Performance marketers can alleviate the problem by making the ad targeting explicit, and thus increase perceived relevance; however, it has no incremental impact for higher match lookalikes. Increasing perceived targeting relevance makes acquisition cost comparable for both high and low match ranks