artificial intelligence marketing thesis

Artificial Intelligence in Marketing Thesis Paper

Key takeaways.

Artificial Intelligence (AI) in Marketing - read the research paper (PDF):

How does Artificial Intelligence (AI) affect marketing?

Artificial intelligence is a burgeoning technology, industry, and field of study. While interest levels regarding its applications in marketing have not yet translated into widespread adoption, AI and its subcategories like Machine Learning (ML) and Deep Learning (DL) hold tremendous potential for vastly altering how marketing is done. AI, ML and DL offer an improvement to current marketing tactics, as well as entirely new ways of creating and distributing value to customers. For example, programmatic advertising and social media marketing can allow for a more comprehensive view of customer behavior, predictive analytics, and deeper insights through integration with AI. New marketing tools like biometrics, voice, and conversational user interfaces offer novel ways to add value for brands and consumers alike. These innovations all carry similar characteristics of hyper-personalization, efficient spending, scalable experiences, and deep insights.

Adapting to an AI Marketing landscape

There are many important issues that need to be addressed before AI is extensively implemented, including the potential for it to be used maliciously, its effects on job displacement, and the technology itself. The recent progress of AI in marketing is indicative that it will be adopted by a majority of companies soon. The long-term implications of vast implementation are crucial to consider, as an AI-powered industry entails fundamental changes to the skill-sets required to thrive, the way marketers and brands work, and consumer expectations.

Artificial Intelligence in Marketing (Research Paper - Honors Thesis)

Table of Contents

Introduction, scope and methodology.

A brief history and origin story of AI.

Consumer Perceptions

How do people perceive AI currently? What are the brand safety implications of using AI for marketing?

Defining Artificial Intelligence

Key AI terminology and vocabulary to know.

Artificial Intelligence (AI)

Artificial general intelligence (agi), machine learning (ml), deep learning (dl), natural language processing (nlp), signal processing, ai in marketing today.

How are brands currently using AI in marketing today? What are some examples of how AI can be applied to marketing?

1:1 Marketing

How AI enables hyper-personalization, or 1-to-1 marketing at-scale.

Levels of AI Implementation

A framework for analyzing the varying degrees in which companies are implementing AI currently, from off-the-shelf AI products to full-scale deployment.

Programmatic Advertising

How does AI affect programmatic digital advertising? The impact of AI on Facebook Ads, Google Ads, and more.

Transparency , Distrust, and Fraud

Omnichannel marketing, retargeting, organizational structure.

How companies are structuring their teams to enable successful AI projects.

Image Recognition and Computer Vision

A look at various applications of AI image recognition and computer vision models in marketing.

Social Media

Segmentation and targeting, facial recognition, interactive marketing through biometrics.

Applications of AI for creative use cases in marketing.

Personalized Narratives

Localization, audio generation, image curation, augmentation, data synergy.

How AI-powered chatbots can be used in marketing, eCommerce, and customer service.

Customer Service

Personal assistants, chatbot management, personalized ui and ux.

Using AI to deliver personalized digital experiences.

Applications of AI for voice.

Advertising on Voice Assistants

Impact of voice on seo, shopping using voice, automated phone calls, pitfalls of ai and areas of improvement.

What are some of the main weaknesses of AI in its current state? How can AI be improved?

Malicious AI

Job displacement, underlying technology, where marketers should go.

How can marketers adapt to an AI Marketing landscape? How can brands and non-programmers get started with AI?

The Individual's Perspective

The brand's perspective, ai readiness framework, implementation.

What are the steps for implementing AI? What are the prerequisites for a successful AI launch?

Requirements

Long-term implications.

How will AI affect marketing long-term? What changes might we see as AI becomes more widely adopted? How does the relationship between brands and consumers change in an AI Marketing landscape?

Impact of AI on Consumer Behavior

Increased importance of brand purpose, human-centered technology.

Citations for Artificial Intelligence in Marketing (Research Paper)

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Artificial intelligence (AI) offers numerous applications in marketing, but at the same time, there are several limitations to consider in its adoption. After the first part about a general analysis of the applications and negative aspects of AI and chatbots, the thesis focuses on the case of the implementation of a chatbot by the Department of Economics and Management “Marco Fanno” of the University of Padua. The research question turns towards understanding whether the chatbot implemented by the Department was effective in easing and supporting the work of the administrative office and answering students questions. For this purpose, the paper analyses if the number of emails is decreased after the chatbot introduction. In addition, a questionnaire was carried out to evaluate the experience that the students of the Department have had with the university chatbot. The survey also asked students what services they would like the chatbot to add to their current ones. Moreover, an economic analysis on benefits and costs was conducted to estimate whether the chatbot will generate a positive outcome. This study allows evaluating the impact a chatbot could have in the education field. In particular, it can provide insight to universities on whether a chatbot could enhance the engagement with students, offload staff from repetitive tasks and generate net economic benefits in the long period. The questionnaire itself was conducted through a web survey on Google Forms and a chatbot survey. In this way, it could also be verified which of the two methods is the most effective to conduct a survey. Some evidence finds how chatbot surveys can lead to less satisfactory answers by respondents. Comparing the two survey results, I can verify these past findings with a different sample of participants, the students of Economics. The results did not show clear evidence of whether the chatbot allowed reducing the number of emails. But an investigation over a longer period is suggested. Then, findings highlighted a good appreciation of students for the chatbot and suggested the introduction of push notifications that remember university deadlines such as taxes. The estimation of the benefits-cost analysis forecasted a net positive outcome over three years with an ROI of 29%. Also, the chatbot survey partially confirmed the encouraging finding in reducing satisficing by respondents. 

L'intelligenza artificiale (AI) offre numerose applicazioni nel marketing, ma allo stesso tempo ci sono diverse limitazioni da considerare nella sua adozione. Dopo la prima parte di analisi generale delle applicazioni e degli aspetti negativi dell'AI e dei chatbot, la tesi si concentra sul caso dell'implementazione di un chatbot da parte del Dipartimento di Economia e Management “Marco Fanno” dell'Università di Padova. La domanda di ricerca è volta a capire se il chatbot implementato dal Dipartimento sia stato efficace nell'alleggerire e supportare il lavoro dell'ufficio amministrativo e nel rispondere alle domande degli studenti. A tal fine, il documento analizza se il numero di email è diminuito dopo l'introduzione del chatbot. Inoltre è stato svolto un questionario per valutare l'esperienza che gli studenti del Dipartimento hanno avuto con il chatbot di ateneo. Il sondaggio ha anche chiesto agli studenti quali servizi vorrebbero che il chatbot aggiungesse a quelli attuali. Inoltre, è stata condotta un'analisi economica su benefici e costi per valutare se il chatbot genererà un risultato economico positivo. Questo studio consente di valutare l'impatto che un chatbot potrebbe avere nel campo dell'istruzione. In particolare, può fornire informazioni alle università sul fatto che un chatbot possa migliorare il coinvolgimento con gli studenti, liberare il personale da compiti ripetitivi e generare benefici economici netti nel lungo periodo. Il questionario stesso è stato condotto attraverso un sondaggio web su Google Forms e un sondaggio attraverso un chatbot. In questo modo ho anche analizzato quale dei due metodi sia il più efficace per condurre un'indagine. Alcune prove rivelano come i sondaggi condotti attraverso un chatbot possano portare a risposte più accurate da parte degli intervistati. Confrontando i risultati ottenuti della due modalità di sondaggio ho potuto verificare queste evidenze con un nuovo campione di partecipanti, gli studenti di Economia. I risultati della tesi non hanno mostrato prove chiare del fatto che il chatbot consentisse di ridurre il numero di e-mail. Ma si suggerisce un'indagine su un periodo più lungo. Successivamente i risultati hanno evidenziato un buon apprezzamento degli studenti per il chatbot e hanno suggerito l'introduzione di notifiche push che ricordano delle scadenze universitarie come le tasse. La stima dell'analisi costi-benefici prevedeva un risultato netto positivo su tre anni con un ROI del 29%. Inoltre, il sondaggio chatbot ha parzialmente confermato la tendenza ad ottenere risposte più accurate rispetto ad un classico sondaggio web.

Artificial intelligence applications in marketing: the chatbot of the Department of Economics and Management "Marco Fanno”

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Artificial intelligence (ai) sales could soar 820% by 2030: 2 amazing ai stocks to buy now and hold long-term.

Artificial intelligence (AI) sale across hardware, software, and services totaled about $196 billion last year, according to Grand View Research. But analysts expect that figure to surge 820% by 2030, such that annual AI spending exceeds $1.8 trillion at the end of the decade. Investors hoping to benefit should be positioning their portfolios today, and they should be diversifying beyond Nvidia .

With that in mind, Alphabet (NASDAQ: GOOGL) (NASDAQ: GOOG) and Datadog (NASDAQ: DDOG) stand out as worthwhile long-term investments. The companies have strong footholds in relevant markets, and the stocks trade reasonable valuations compared to future growth prospects.

Here's what you should know.

1. Alphabet

Alphabet reported first-quarter financial results that beat expectations on the top and bottom lines. Revenue rose 15% to $80.5 billion on especially strong momentum in Google Cloud, the segment comprising cloud infrastructure and platform services, as well as the Google Workspace applications. Meanwhile, GAAP net income soared 61% to $1.89 per diluted share because of disciplined cost control, something management continues to prioritize.

The investment thesis for Alphabet is simple. Its Google subsidiary has a strong presence in two growing markets, digital advertising and cloud computing , and the company is using its artificial intelligence (AI) expertise to create new monetization opportunities within those markets.

To elaborate, Google is the largest digital advertiser in the world. Its market share is declining because of competition from Amazon and others ad tech companies. However, Google is still expected to account for 27% of digital ad spending next year, and it could recapture lost market share with help from artificial intelligence. For instance, Google is using generative AI to improve the search experience for internet users and streamline the creation of advertising campaigns for media buyers. Management says the initial feedback is positive.

Meanwhile, Google Cloud Platform is the third-largest cloud services provider. Here, the company has gained 3 points of market share over the last four years, such that it accounted for 11% of cloud infrastructure and platform services revenue in the fourth quarter. Investments in AI product development (like Gemini) could help the company maintain that momentum. CEO Sundar Pichai hinted at that during the latest earnings call, noting that 90% of generative AI unicorns are Google Cloud customers.

Going forward, the online advertising and cloud computing markets are forecasted to expand at annual rates of 16% and 14%, respectively, through 2030. Wall Street expects Alphabet to grow sales at 10.5% annually over the next five years, but I think the company could surprise analysts to the upside given the secular tailwinds behind its business. In that context, its current valuation of 7 times sales appears reasonable.

Datadog reported fourth-quarter results that beat what Wall Street expected. Revenue rose 26% to $590 million and non-GAAP net income more than doubled to reach $0.44 per diluted share. But the stock still slipped following the report because of a disappointing outlook. Specifically, management estimated that non-GAAP earnings would increase 21% in the first quarter, but Wall Street expected 39% growth. Even so, the investment thesis remains intact.

Datadog sells observability and security software that helps businesses monitor the performance of IT infrastructure and applications. Its platform incorporates artificial intelligence to hasten problem resolution, and it integrates 19 products to support vendor consolidation. In other words, businesses need not deploy point products from different vendors; they can choose the more efficient path of getting their observability software through one platform.

Datadog's platform strategy and product innovation have won praise from analysts. The company has been recognized as a leader in several observability software verticals, including application performance monitoring and AI for IT operations. It also has a strong position in the server monitoring, cloud infrastructure monitoring, and database monitoring markets. Finally, Datadog ranked second on the 2023 Fortune Future 50 list, an annual report that scores the largest companies in the world based on long-term growth prospects.

Going forward, demand for observability software should increase as IT environments become more complex. That means technology shifts like cloud computing and artificial intelligence should be major tailwinds for Datadog. Indeed, the company said adoption of AI integrations jumped 75% sequentially in the fourth quarter, meaning customers are rapidly deploying Datadog software to monitor large language models and generative AI applications.

Last year, Alex Zukin of Wolfe Research said the rise of generative AI could make Datadog "the fastest growing software company," and Sanjit Singh at Morgan Stanley selected Datadog as one of the software companies best positioned to monetize generative AI. As for the rest of Wall Street, analysts expect Datadog to grow sales at 25% annually over the next five years. That consensus estimate makes its current valuation of 21.6 times sales look reasonable. Patient investors should feel confident in buying a small position in this growth stock today.

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Artificial Intelligence (AI) Sales Could Soar 820% by 2030: 2 Amazing AI Stocks to Buy Now and Hold Long-Term was originally published by The Motley Fool

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3 Stocks Poised to Profit from the Rise of Artificial Intelligence

Take the path less traveled

Advertisement

• Check out these underappreciated AI stocks to buy.
• Deere ( DE ): Deere’s automated tractors fill a critical need.
• Kroger ( KR ): Kroger aims to utilize AI across multiple business functionalities.
• Nerdy ( NRDY ): Nerdy is using computer-generated intelligence to close the education gap.

Source: shutterstock.com/Tex vector

While artificial intelligence may be all the rage, the usual suspects in the space have largely flourished handsomely, which then incentivizes the case for underappreciated AI stocks to buy.

Rather than simply focusing on technology firms that have a direct link to digital intelligence, it’s useful to consider companies – whether they’re tech enterprises or not – that are using AI in their businesses. Yes, the semiconductor space is exciting but AI is so much more than that.

These less-appreciated ideas just might surprise Wall Street. With that, below are intriguing AI stocks to buy that don’t always get the spotlight.

At first glance, agricultural equipment specialist Deere (NYSE: DE ) doesn’t seem a particularly relevant idea for AI stocks to buy. Technically, you’d be right. After all, this is an enterprise that as roots going back to 1837. That said, an old dog can still learn new tricks.

With so much talk about autonomous mobility, Deere took a page out of the playbook and has invested in an automated tractor . Featuring 360-degree cameras, a high-speed processor and a neural network that sorts through images and determines which objects are safe to drive over or not, Deere’s invention is the perfect marriage between a traditional industry and innovative methodologies.

Perhaps most importantly, Deere is meeting a critical need. Unsurprisingly, fewer young people are interested in an agriculture-oriented career. Therefore, these automated tractors are entering the market at the right time.

Lastly, DE trades at a modest price/earnings-to-growth (PEG) ratio of 0.54X. That’s lower than the sector median 0.82X. It’s a little bit out there but Deere is one of the underappreciated AI stocks to buy.

Kroger (KR)

While it’s just my opinion, grocery store giant Kroger (NYSE: KR ) sells itself. No, the grocery industry is hardly the most exciting arena available. At the same time, people have to eat. Further, the company benefits from the trade-down effect. If economic conditions become even more challenging, people will eschew eating out for cooking in. Overall, that would be a huge plus for KR stock.

With that baseline bullish thesis out of the way, Kroger is also an enticing idea for hidden-gem AI stocks to buy. Earlier this year, the company announced that it will use AI technology for content management and product descriptions for marketplace sellers. Last year, Kroger’s head executive mentioned AI eight times during an earnings call.

Fundamentally, Kroger should benefit from revenue predictability. While the consensus sales target calls for a 1% decline in the current fiscal year, the high-side estimate is aiming for $152.74 billion. Last year, the print came out to just over $150 billion. With shares trading at only 0.27X trailing-year sales, KR could be a steal.

Nerdy (NRDY)

Billed as a platform for live online learning, Nerdy (NYSE: NRDY ) represents a legitimate tech play for AI stocks to buy. Indeed, its corporate profile states that its purpose-built proprietary platform leverages myriad innovations – including AI – to connect students, users and parents/guardians to tutors, instructors and subject matter experts.

Fundamentally, Nerdy should benefit from two key factors. Number one, the Covid-19 crisis disrupted education , particularly for young students. That could have a cascading effect down the line, making it all the more vital to play catchup. Nerdy can help in that department.

Number two, U.S. students have continued to fall behind in international tests . It’s imperative for social growth and stability for students to get caught up, especially in the digital age. Therefore, NRDY is especially attractive.

Finally, analysts anticipate fiscal 2024 revenue to hit $237.81 million, up 23% from last year’s tally of $193.4 million. And in fiscal 2025, experts are projecting sales to rise to $293.17 million. That’s up more than 23% from forecasted 2024 sales. Therefore, it’s one of the top underappreciated AI stocks to buy.

On the date of publication, Josh Enomoto did not have (either directly or indirectly) any positions in the securities mentioned in this article.  The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com  Publishing Guidelines .

A former senior business analyst for Sony Electronics, Josh Enomoto has helped broker major contracts with Fortune Global 500 companies. Over the past several years, he has delivered unique, critical insights for the investment markets, as well as various other industries including legal, construction management, and healthcare. Tweet him at @EnomotoMedia.

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INTEGRATION OF ARTIFICIAL INTELLIGENCE AND HUMAN FACTORS IN MOBILE WORK ZONES AND ROUNDABOUTS FOR SAFETY AND PERFORMANCE MONITORING

The transportation system is facing serious safety concerns at work zones and intersections, which are two major areas where accidents and fatalities occur. In addition, slow improvement in transportation industry workers’ performance is also a bottleneck to overall productivity. This dissertation aims to integrate artificial intelligence and human factors to improve the safety of mobile work zones and unsignalized intersections and monitor real-time worker’s performance.

To improve work zone safety, the Autonomous Truck Mounted Attenuator (ATMA) technology is explored with support from the Indiana Department of Transportation (INDOT). The ATMA can be driven automatically which removes drivers from the TMA truck to improve their safety. In this study, the ATMA system was tested under four mobile work zone operations, including trash pickup, crack sealing, Raised Pavement Marking (RPM) inspection, and drainage inspection with several roadway types, including interstate, trunk highway, and state road. During the testing, video, motion, and physiological data from the workers is collected. The data is used to develop models for transportation construction workers’ activity classification and physical fatigue level monitoring using various machine learning techniques. In addition, workers’ perception of the ATMA system is collected by a survey and the results found that more training or exposure to the ATMA system improved their evaluation of the system.

To improve unsignalized intersection safety, an in-vehicle warning system is developed and evaluated under various levels of aggressive vehicle behaviors across different warning conditions through a driving simulator study. A customized driving simulator is developed to support human driving experiment, which integrates SUMO and Webots. A real-world roundabout is built and calibrated in the simulator and both driving performance and eye movement data are collected from the experiments. The results indicate that advanced warnings can effectively influence vehicle speed, steering wheel control, and drivers’ attention on different areas of interests (AOIs). It is found that a proper warning time is critical to improve drivers’ safety and comfort. Gender differences are also identified from both types of data. Interestingly, although male drivers and female drivers demonstrate different driving behaviors, their safety performance in terms of minimum time to collision (TTC) is similar. Finally, to better facilitate the design of the advanced warning systems, two machine learning models are developed to predict minimum TTC and classify drivers’ perceived risk.

The contributions of this dissertation are summarized from the following four perspectives. First, this dissertation contributes to the body of knowledge by using a Deep Learning (DL)-based model for mobile work zone workers’ activity classification. The dissertation also innovatively integrates domain knowledge to refine the DL-based model’s performance. Second, this dissertation advances the application of feature-level data fusion in monitoring transportation construction workers. Specifically, the feature-level data fusion between kinematic and physiological data is found effective in improving model accuracy. Third, to improve mobile work zone safety, the ATMA system is tested with various road maintenance activities. This is the first ATMA test with a focus on mobile work zone operations with human workers working on the ground. The testing results are valuable for the future ATMA design and implementation. Fourth, this dissertation discloses the positive impacts of in-vehicle warning systems in roundabout merging scenarios. Furthermore, a customized driving simulator is developed to support human driving simulation experiments and is open-sourced for public use.

Degree Type

• Doctor of Philosophy
• Construction Management Technology

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Advisor/supervisor/committee co-chair, additional committee member 2, additional committee member 3, usage metrics.

• Construction engineering
• Transport engineering

Tech giants' market cap falls on AI doubts, high rates; Alphabet, Tesla gain

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Stay alert: Quake warning app demand surges in earthquake-rattled Taiwan

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