AI and the Effects On the Marketing Industry

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What is Artificial Intelligence (AI)

Artificial Intelligence (AI)

AI vs. Machine Learning vs. Deep Learning

Artificial Intelligence (AI) refers to computer machines capable of performing tasks that simulate human intelligence. The processes of creating AI include developing a computer system which can process information, learn from it, make decisions and solve problems closely to what a human would do. The evolution of AI has naturally led to many industries and functions utilizing its ability such as in marketing. Specific forms of AI include machine learning and deep learning. [1].

Machine learning is primarily based on algorithms that are trained using mass data sets. This allows the algorithms to detect patterns and learn how to make predictive recommendations and decisions. The algorithms have the ability to adjust and enhance their effectiveness over time by learning from new data and experiences. [1]

Deep learning is a type of machine learning that has the capability to process a larger range of data types. This includes images and videos, in addition to text. Deep learning uses neural networks inspired by the human brain to analyze data and learn from it. It iteratively processes the data, understanding complex patterns. The neural network can then make decisions about the data and use its knowledge to make decisions about new data.[1]


AI and Marketing

The core of marketing revolves around understanding customer needs and communicating the right products and services to persuade them to purchase. By integrating AI it can drastically improve and enhance how marketing activities are done. AI marketing is when marketers use artificial intelligence to collect customer insights and produce better marketing strategies.[2].

Many firms have been using AI to handle specific activities like digital ad placement (also known as “programmatic buying”) to broader activities like predicting sales forecasts. When AI is given highly detailed data on customers it can help streamline the sales process by creating personalized products, services and offerings to drive sales and retention. AI also has the ability to augment human efforts in structured tasks like customer service. [2]


Types of AI Commonly Used in Marketing

Task Automation

Structured, repetitive tasks that require a low level of intelligence. Designed to follow a specific set of rules and a predetermined sequence of operations of low complexity. For example, a system that sends an automatic welcome email to a new customer.[2]

Machine Learning

Uses a large quantity of data to make complex decisions and predictions. Models can recognize images and decipher text to determine how a human will respond to different initiatives. Machine learning is the driver of programmatic buying of online advertisements and personalized e-commerce recommendations.[2]

Generative AI

Focused on creating or generating new content such as images, text, music and videos. This model learns from data and captures statistical patterns, allowing it to generate content that resembles the training examples. Generative AI can enable content creation for companies to produce engaging, relevant, and well-structured content for the specifically targeted audience.[3]


Types of AI Applications for Marketing

Example of an AI Chatbot

Well-established AI Applications in marketing include[2]:

  • Chatbots for customer service
  • Call centre management and analysis
  • Marketing campaign automation
  • Marketing mix analysis
  • Online product merchandising
  • Dynamic pricing tactics
  • Hyper-personalized recommendations
  • Programmatic digital ad buying
  • Social media planning and execution
  • Website optimization


AI Integration Strategy

AI applications in marketing can be categorized into four distinct types based on their complexity and level of integration. Starting with simple stand-alone applications is a prudent approach for marketers, as it allows for easier implementation and management. As companies acquire more data and expertise in AI, they can gradually progress to integrating machine learning, which offers the highest value. [4]

The Four Kinds of Marketing AI

Stand-Alone Task-Automation Apps

  • Clearly demarcated, or isolated AI programs
  • Examples: Facebook Messenger chatbots, email automation systems

Stand-Alone Machine-Learning Apps

  • Clearly demarcated, or isolated AI programs
  • Examples: interactive smartphone applications

Integrated Task-Automation Apps

  • Embedded seamlessly within existing systems
  • Examples: Inbound customer call routing, CRM-linked marketing automation

Integrated Machine-Learning Apps

  • Embedded seamlessly within existing systems
  • Examples: Predictive sales lead scoring, e-commerce recommendation, programmatic digital ad buying

This categorization process plays a crucial role in assisting marketers with their digital strategy. Although these initial apps stand-alone test-automated apps may have limited capabilities, it is best to take a gradual approach for the best results. By comprehending the different categories of AI applications, marketers can make informed decisions about prioritizing and integrating the right technologies into their strategies, leading to more effective and data-driven campaigns.[4]


Evolution of AI in Marketing

1950s and 1960s

During the 1950s and 1960s, initial endeavours to incorporate AI into marketing were undertaken by researchers who utilized methods like linear programming, game theory, and decision trees to enhance marketing mix and pricing strategies [5]. These quantitative techniques work as they rely on a scientific and statistical approach which enables managers to make decisions objectively and efficiently.

1970s and 1980s

In the 1970s and 1980s, the introduction of expert systems and neural networks enabled marketers to model customer behaviour and buying preferences, thus facilitating the creation of personalized recommendations and offers [5]. An expert system is a computer program leveraging AI functionalities to replicate the decision-making and actions of a human [6]. Neural networks are a subset of machine learning which form the basis for machine learning. They attempt to mimic the human brain's structure and function, replicating how biological neurons communicate with each other [7].

1990s and 2000s

Google Analytics in 2005

Entering the 1990s and 2000s come the rise of the internet and e-commerce, presenting new opportunities for data collection, analysis, and online advertising. AI tools such as web analytics, search engine optimization, and email marketing became instrumental in reaching and engaging customers online. During this time, notable tools like Google Ads launched, revolutionizing online advertising. Shortly after in 2005, Google Analytics arrived, becoming one of the most recognizable and essential AI analytic tools marketers have today. [5]

2010s and 2020s

Lastly, in the 2010s and 2020s, the evolution of big data, cloud computing, natural language processing, computer vision, and deep learning has opened up a new horizon of AI applications for marketing. Marketers can now harness AI to generate content, design campaigns, optimize conversions, predict outcomes, and elevate the overall customer experience [5]. In the present day, more than 20% of marketers currently use AI-based applications, with an additional 57% planning to use them in the near future [8].


Major Industry Players

According to Inkwood Research, in 2020 the global artificial intelligence in the marketing market has been dominated by Google, Oracle, Microsoft, Facebook and Intel as the key majour players.[9]


Dynamic 365 Marketing Platform

Google Marketing Platform and Google Ads

Google offers various AI-driven marketing tools, such as Google Marketing Platform and Google Ads. These platforms incorporate machine learning and AI techniques for ad targeting, optimization, and measurement. It allows for integration with essential data sources such as Google Ads, Google Analytics 360 and Campaign Manager. First-party data like Customer Relationship Management (CRM), sales, product, customer service, and social media data to obtain an interconnected platform for marketing analytics. By consolidating all this data, it can then create AI models using built-in machine learning tools on the platform. [10]


Oracle Eloqua Advanced Intelligence

Oracle Eloqua Marketing Automation introduces advanced intelligence capabilities for personalized marketing messages to customers. With fatigue analysis, account intelligence, send time optimization, and subject line optimization innovations, create personalized campaigns while managing email frequency for contacts. Using data science to power exceptional customer experiences. Contacts are classified into nine email fatigue levels, optimizing marketing campaigns based on individual fatigue. The platform supports account-based marketing (ABM), allowing monitoring and actions at the account level. [11]


Microsoft Dynamic 365

Microsoft Dynamics 365 facilitates personalized one-on-one interactions with customers through tailored journeys triggered by interactions. It offers pre-built event triggers or custom ones, orchestrates real-time interactions across channels (email, push notifications, SMS, WhatsApp), and implements frequency capping to prevent messaging fatigue. The platform creates compelling experiences across marketing, sales, and service, engaging customers on preferred channels using AI-driven suggestions. Encouraging actions, reminders and repeated messages are sent until qualifying events occur. Streamlined email delivery simplifies the process without advanced segment builders, enhancing customer engagement. [12]


Facebook Ads

Facebook Ads leverage machine learning to enhance ad delivery for consumers and business. As ads are viewed, and users provide feedback or engage with the content, machine learning models improve in predicting estimated action rates and ad quality. Given the vast user base and the extensive interaction with ads on a daily basis, the system receives abundant data to refine its calculations continually, with the ultimate aim of maximizing value for both users and businesses.[13]


Intel IT Advanced Analytics

Intel IT Advanced Analytics developed an AI system that mines millions of public business web pages to extract actionable segmentation for both current and potential customers. The system focuses on two key classification aspects: industry segments, ranging from broad verticals like "healthcare" to specific fields such as "video analytics," and functional roles like "manufacturer" or "retailer," which help identify potential sales and marketing opportunities.[14]


Economic Value of AI in Marketing

Market Size

Global Generative AI in Marketing Market By Component
AI's significance in the marketing industry is projected to have a substantial impact on economic gains, with research from PwC suggesting that by 2030, 45% of the total economic gains will result from product enhancements, effectively stimulating consumer demand. This is primarily due to AI's ability to facilitate greater product variety, leading to increased personalization, attractiveness, and affordability over time. [15]

The market for Generative AI in Marketing has already shown promising growth, with a valuation of USD 1.9 Billion in 2022. Even more impressive, between 2023 and 2032, this market is expected to achieve the highest Compound Annual Growth Rate (CAGR) of 28.6%, reaching an estimated value of USD 22.1 billion during the forecast period. [16].

AI's potential in the marketing sector is further emphasized by the remarkable global corporate investment of approximately $94 billion in AI-powered marketing automation in 2021 As businesses continue to embrace digital transformation, the marketing automation market is predicted to surge, aiming to reach nearly $15 million by 2030, showcasing a CAGR of 12.3%.[17].


Positive Impacts of AI in Marketing

Improved Data Analysis and Customer Insights

In the AI world, reminiscent of the wild west, untamed potential awaits. AI marketing, in its nascent phase, allows marketers to explore uncharted domains, uncover captivating strategies, and enhance their efforts. This transformative force offers unparalleled capabilities in data analysis and customer insight. With the ability to process and analyze massive amounts of data swiftly and accurately, AI revolutionizes marketing strategies, boosting efficiency and effectiveness.


AI algorithms, powered by machine learning techniques, can sift through large data sets to identify patterns, trends, and customer preferences. Unlike traditional data analysis methods, these algorithms learn from the data they analyze, improving their performance over time. This ability to learn and adapt is a defining characteristic of AI that sets it apart from traditional data analysis techniques.
When AI analyzes data, it can spot trends

For example, consider an AI algorithm that is tasked with analyzing customer data. This data could include a wide range of information, such as purchase history, browsing behavior, and social media interactions. By analyzing this data, the AI algorithm can identify patterns and trends that might otherwise go unnoticed. These insights can then be used to predict future customer behavior and preferences, enabling marketers to anticipate customer needs and tailor their offerings accordingly. This predictive capability of AI can significantly increase the effectiveness of marketing strategies by ensuring that they are aligned with customer needs and preferences [18].

In addition to identifying patterns and trends, AI-powered analytics can also improve customer segmentation and targeting, leading to more personalized marketing campaigns. Customer segmentation involves dividing a company's customer base into distinct groups that share similar characteristics. By analyzing customer data and identifying meaningful segments based on factors such as demographics, behavior, and purchasing patterns, AI can automate this process. Once these segments are identified, marketers can create personalized marketing campaigns that address the specific needs and preferences of each segment. This level of personalization can significantly improve customer engagement and conversion rates [19].

Moreover, AI can provide real-time customer insights, allowing marketers to respond quickly to changes in customer behavior. With traditional data analysis methods, there is often a time lag between data collection and insight generation. However, AI can analyze data in real time, providing marketers with up-to-the-minute insights. This ability can be particularly useful in dynamic markets, where customer preferences can change rapidly [20].

Furthermore, AI can also help marketers optimize their marketing mix by providing insights into the effectiveness of different marketing channels and tactics. By analyzing data on customer responses to different marketing initiatives, AI can identify which channels and tactics are most effective and which need improvement. This information can help marketers allocate resources more effectively, maximizing the return on their marketing investment.


In summary, AI is having a transformative impact on marketing by enabling better data analysis and providing deeper customer insights. It increases the efficiency and effectiveness of marketing strategies by identifying patterns and trends in customer behavior, improving customer segmentation and targeting, and providing real-time insights.


Enhanced Customer Experience

As we delve deeper into the transformative impact of AI in marketing, the second subchapter illuminates the realm of Enhanced Customer Experience. The advent of AI has not only revolutionized the way businesses analyze data and predict customer behavior, but it has also redefined the customer journey by enabling instant, personalized, and intuitive interactions, thereby creating a new paradigm of customer engagement.


AI-powered chatbots and virtual assistants have emerged as indispensable tools in the customer service landscape. These intelligent systems, capable of handling a multitude of customer queries simultaneously, have significantly reduced response times, thereby setting a new benchmark for customer service efficiency. They provide accurate and consistent responses, which not only resolve customer queries but also build trust and credibility. Available round the clock, they ensure that customers can receive support whenever they need it, thereby fostering a sense of reliability and assurance. This level of instant and dependable support can significantly enhance the customer experience, fostering a sense of loyalty and deepening customer relationships [21].
Customers leave the company when they don't feel understood

Transitioning to the realm of AI-powered chat analytics, we find another powerful tool for enhancing the customer experience. By meticulously analyzing customer interactions during chat sessions, these systems can unearth valuable insights into customer behavior and preferences. These insights, when used to personalize future conversations, can transform ordinary interactions into engaging and satisfying experiences for customers. Furthermore, by anticipating customer needs, businesses can proactively address them, creating a customer experience that is not just satisfying, but also delightfully surprising [22].

Voice assistants, powered by AI, are revolutionizing the way customers interact with brands. These systems, with their ability to engage with the brand and access information or support through voice commands, are redefining customer interaction by providing a hands-free and intuitive experience. This seamless interaction, akin to having a personal assistant, can significantly enhance the customer experience, making it not just effortless, but also enjoyable and memorable [23].

AI's role in recommendation systems is another testament to its transformative impact. By analyzing customer behavior and preferences, these systems can suggest relevant products or content to customers. This personalized approach not only enhances the shopping experience but also makes customers feel understood and valued. This sense of personalization not only increases the likelihood of purchase but also fosters a sense of connection between the customer and the brand, thereby driving customer satisfaction [24].

Sentiment analysis, powered by AI, equips businesses with a powerful tool for understanding customer emotions and feedback. By analyzing customer reviews, comments, and social media posts, these systems can gauge customer sentiment towards the brand, products, or services. This information can help businesses proactively address any concerns or issues, thereby not just enhancing customer satisfaction, but also building a reputation of a brand that listens and cares [25].

AI-powered analytics tools can provide businesses with valuable insights from customer interactions and feedback. These insights can help companies identify areas for improvement in their products, services, or customer interactions, or they can be used to personalize future conversations, transforming ordinary interactions into engaging and satisfying experiences for customers. Furthermore, by anticipating customer needs, businesses can proactively address them, creating a customer experience that is also delightfully surprising [26].

Finally, AI-powered virtual reality (VR) or augmented reality (AR) technologies offer a unique and immersive experience for customers. These technologies allow customers to visualize products or services in real-world contexts, thereby enhancing their decision-making process. This immersive experience can significantly improve customer satisfaction, transforming the shopping experience into an exciting journey of discovery, leading to increased sales and customer loyalty [27].


In summary, AI has a profound impact on enhancing the customer experience by enabling instant, personalized, and intuitive interactions. It empowers businesses to understand and anticipate customer needs, personalize interactions, and proactively address concerns, thereby significantly improving customer satisfaction and loyalty. It is not just about making the customer journey more efficient, but also about making it more meaningful and memorable, thereby creating a customer experience that resonates and endures.


Automation and Efficiency

As we progress further into the transformative impact of AI in marketing, the final subchapter focuses on the pivotal role of AI in Automation and Efficiency. Building on the previous positive impacts of AI in Marketing, it becomes evident that AI not only enhances customer experience and provides valuable insights but also significantly improves operational efficiency by automating repetitive tasks and optimizing marketing campaigns.


AI has the potential to automate a wide range of repetitive tasks, such as data entry, content generation, and ad optimization. These tasks, while essential, can be time-consuming and prone to human error. AI-powered systems, however, can perform these tasks with speed and accuracy that far surpasses human capabilities. For instance, AI can automate data entry by extracting information from various sources and inputting it into the relevant databases.

Similarly, AI can generate content such as social media posts, blog articles, and product descriptions, based on predefined templates and data inputs. Moreover, AI can automate the optimization of ads by analyzing performance data and adjusting parameters such as bidding, targeting, and placements. By automating these tasks, AI not only improves efficiency but also frees up marketers' time, allowing them to focus on more strategic activities [28].
An AI helps the marketer in the background

AI-powered tools can also optimize marketing campaigns in real-time. Traditional marketing campaign optimization often involves manual analysis and adjustments, which can be time-consuming and may not always yield optimal results. However, AI-powered tools can analyze campaign performance data in real-time and make adjustments to budgets, targeting, and ad placements to improve performance. For example, if an ad is not performing well in a particular demographic segment, the AI system can redirect the ad budget to other more promising segments. Similarly, if an ad placement is not yielding the expected results, the AI system can identify better-performing placements and shift the ads accordingly. This real-time optimization can significantly improve the performance of marketing campaigns, leading to better return on investment [29].


In conclusion, AI plays a crucial role in enhancing the efficiency and effectiveness of marketing operations. By automating repetitive tasks and optimizing marketing campaigns in real-time, AI not only improves operational efficiency but also enables marketers to focus on strategic activities. As AI continues to evolve and improve, it is likely to play an increasingly central role in shaping the future of marketing operations, driving efficiency, and delivering superior results.


Negative Impacts of AI in Marketing

Whenever an individual uses the internet, their personal data becomes a subject of interest for artificial intelligence (AI) algorithms. These advancements in AI have brought many benefits to the marketing industry, but they have also brought negative impacts that cannot be ignored. This section will explore the negative impacts of AI in marketing, focusing on privacy concerns, limited human personalisation, and discriminatory targeting.


Privacy Concerns

One of the most significant concerns associated with AI is the potential for privacy breaches and data misuse. AI algorithms require substantial amounts of data to learn and make predictions, leading to questions about how this data should be collected, stored, and used. Consequently, AI systems handling sensitive customer data can become targets for hackers, putting personal information at risk. Since AI systems are programmable, this presents a cybersecurity challenge, as they can be manipulated and exploited. One noteworthy constraint of artificial intelligence (AI) is its dependence on computer code, which is programmed to adhere to protocols and adapt when necessary [30]. While this may appear advantageous at first, the fact that AI systems are entirely programmable introduces the risk of easy manipulation. Just a few lines of code can turn this protective technology into a weapon, demanding thorough understanding from developers [30].

Instances of unauthorized access have occurred frequently, especially given the rising advancements in AI-Driven Marketing. Disturbing cases, like Cambridge Analytica's misuse of personal data from over 80 million Facebook users in 2018, were used to target American voters during the 2016 presidential election [31]. In the past, Google-owned YouTube faced a $170 million fine for extracting personal information from children on the platform without parental consent and tailoring advertisements [32]. Therefore, robust security measures are imperative for marketers to protect customer data and mitigate potential negative consequences.

Mitigation

To mitigate these risks, companies must be discerning in their data collection, storing only what they genuinely need and preserving it securely. Transparent communication about data usage empowers consumers to make informed decisions on sharing their personal information. Furthermore, legislation and regulations have been passed to mitigate privacy concerns. For example, the General Data Protection Regulation (GDPR) in Europe was implemented to protect consumers’ privacy rights and ensure that companies are transparent about their data collection & usage. This helps ensure that users' privacy rights are protected and safely stored [33]. According to the General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA), which are among the strictest data protection laws in the world, companies must obtain user consent before collecting and using their data [34]. In conclusion, while AI brings numerous advantages to companies seeking to optimize their marketing endeavors, its negative impacts must not be overlooked. Addressing privacy concerns and data misuse, navigating the intricacies of human personalization, and eradicating bias and discrimination are essential tasks towards responsible AI-integrated marketing.


Limited Human Personalization

While AI excels in automating tasks and boosting efficiency, its Achilles' heel lies in customer interactions. AI-powered chatbots and virtual assistants offer unparalleled scalability and efficiency but have downsides in customer interactions. The potential for customers to experience frustration or dissatisfaction arises when automated systems fail to empathize and address their unique needs and emotions. Chatbots, in particular, may lack the human touch required for personalized engagement such as limited empathy, emotional understanding, and the ability to provide a personalized human touch [35]. Maintaining a balance between automation and human interaction is crucial to ensure that customers feel valued and heard.

Mitigation

One way companies can address this issue is by using AI to augment human interactions rather than replace them entirely. For example, AI can be used to provide customer service representatives with real-time information about a customer’s history with a company so that they can provide more personalized service [36]. Another approach is to use natural language processing (NLP) algorithms that can understand the nuances of human language and respond appropriately [36]. Achieving equilibrium is pivotal in marketing - while AI streamlines processes and boosts efficiency, the human touch adds a unique blend of empathy, emotional intelligence, and creativity to customer interactions. Relying excessively on AI may result in a less satisfying customer experience, as nuanced situations and complex emotions demand a human adaptation. Therefore, it is essential to achieve a balance between AI-integrated marketing and human interaction.


Discriminatory Targeting

AI algorithms are trained on large datasets, and if those datasets contain biased or discriminatory information, the algorithms can reinforce and perpetuate harmful discrimination. For example, AI systems may inadvertently target or exclude certain demographics, reinforcing gender, racial, or socio-economic biases [37]. Such practices not only harm individuals and communities but also have the potential to damage a brand's reputation [37].

From a marketing perspective, failure to address bias can lead to a restricted customer base, hindering a company's ability to foster inclusivity and diversity - vital aspects in today's global marketplace. To mitigate these risks, companies must ensure that their algorithms are free from bias, employing diverse datasets during training and conducting regular algorithm audits. Moreover, transparency in the decision-making process aids consumers in understanding how choices are made [37]. Moreover, transparency in the algorithms used by companies would help consumers make informed choices about data collection [37].
Facebook, now known as Meta

One prominent example of bias is Facebook’s advertising algorithm. Facebook uses machine learning to create targeted ads which promoted harmful stereotypes even when advertisers tried to reach a diverse demographic. For example, the US Department of Housing and Urban Development sued Facebook in 2019 for breaking discrimination laws, alleging that Facebook allowed advertisers to exclude people from housing, credit and job opportunities based on race, religion, and other criteria. This lawsuit prompted Facebook to address discriminatory practices and was settled with Facebook agreeing to overhaul its ad targeting system [31]. Another example is how British consulting firm Cambridge Analytica gained unauthorized access to personal data from over 80 million Facebook users in 2018, using it to build an AI-based system for the Trump Campaign that delivered targeted advertising to American voters during the 2016 presidential election [38]. The Trump campaign was found to target 3.5 million African American voters in Ohio and other swing states to get them to sit out the 2016 election [39].

Mitigation

To mitigate the risks of bias and discrimination in AI-powered marketing, companies must prioritize diversity and inclusivity throughout the development processes. This includes ensuring diverse representation in the teams responsible for designing, training, and evaluating AI systems. Additionally, thorough testing of algorithms should be conducted to address potential biases while regular audits and continuous monitoring can help detect discriminatory outcomes.


Business Applications

AI is revolutionizing the way businesses interact with their customers and market their products or services. In this exploration, we will delve into some current applications and use cases of AI in marketing, shedding light on how various companies are leveraging this technology to enhance their marketing processes and outcomes.


Positive Examples

Mercedes-Benz: Mercedes-Benz is undergoing a significant transformation to become a more customer-centric company. The focus is on delivering a true Mercedes experience to customers at every touchpoint, not just in the car itself. The company has partnered with Salesforce to achieve this goal, adopting the V2MOM (Vision, Values, Methods, Obstacles, and Measures) management process and shared guiding principles.
Traditional German automobile manufacturer sets the course for the future

One key highlight is the connected customer journey, recognizing that 80% of customers now use online sources during their car-buying journey. By implementing personalized communication and using data to understand customer motivations, Mercedes-Benz has seen a remarkable 160% year-over-year increase in email click-through rates. This demonstrates the power of personalization in building brand loyalty and enhancing marketing efficiency.

Another highlight is the integration of digital innovation to bridge the gap between the online and offline worlds. Mercedes-Benz uses shared car data to anticipate customer needs, enabling timely service recommendations and personalized interactions. By creating a seamless experience between the online research phase and the dealership visit, the company increases customer satisfaction and ensures a more tailored conversation during the drive.

The latest coup for the luxury manufacturer is the integration of ChatGPT into the voice control of its vehicles. With the help of OpenAI's model, voice control by calling "Hey Mercedes" from the MBUX voice assistant is to become even more intuitive. In the future, anyone who asks their voice control to tell them interesting facts about their destination, suggest new recipe ideas or clarify knowledge questions will also receive a response. Even without ChatGPT, the car is already able to control your smart home or answer questions about sports results, the weather or your surroundings. The vehicle provides all the information by voice, so the driver can always keep his or her eyes on the traffic situation. The hands remain on the steering wheel.

Overall, Mercedes-Benz's customer-centric approach demonstrates a commitment to delivering exceptional experiences beyond the car itself, ultimately building stronger customer relationships and driving brand loyalty.[40][41]

Watch the accompanying corporate video!


Starbucks:
Popular North American coffee shop chain
This global coffee company also leverages Big Data and AI to enhance customer experience and drive business performance. One of the ways it does this is by using a business intelligence platform called Atlas, developed by Esri. Atlas analyzes a plethora of data, such as traffic patterns, demographics, and proximity to other Starbucks locations, to identify potential new store locations.

In addition, Starbucks uses data from its stores and other industries to create its grocery store product line and decide which products to offer in different areas. The company even tailors its menu based on local tastes and weather conditions. For instance, during a heatwave in Memphis, Tennessee, Starbucks launched a Frappuccino promotion to appeal to customers.[42]


Netflix:
The algorithm learns from customer behavior
Predictive analytics, also known as advanced analytics, uses machine learning and business intelligence to forecast future outcomes. It is a powerful tool that businesses use to build better products, identify market opportunities, and reduce operational costs.

Netflix, a leading entertainment company, uses predictive analytics in its recommendation engine to predict user behavior and suggest TV shows and movies.

The engine is capable of predicting accurate preferences based on the user's past watching habits. This not only enhances the user experience but also helps Netflix retain its customers and increase viewership. The recommendation engine uses algorithms to analyze various factors such as genre, keywords, and ratings. The use of predictive analytics enables Netflix to provide personalized content recommendations, thereby giving it a competitive edge in the entertainment industry.[43]


VMWare:
Marketing also benefits from AI in content creation
Generative AI models are revolutionizing the realm of content creation right now, with profound implications for various sectors including marketing, software, design, entertainment, and interpersonal communications. These models are capable of generating text and images, such as blog posts, program code, poetry, and artwork, by predicting the next word or image based on previous sequences.

Jasper, for example, a marketing-focused version of GPT-3, is at the forefront of this transformation. At the cloud computing company VMWare, for example, writers use Jasper as they generate original content for marketing. It is being used to provide first drafts of various forms of content including emails, articles, reports, blog posts, presentations, and videos. This not only expedites the content creation process but also enhances the quality of the output.

The tool leverages complex machine learning models to make these predictions, thereby adding significant value to the content creation process. Businesses need to comprehend how these tools function and how they can be utilized to their advantage.[44]


Ada:
ChatGPT is changing many areas, including marketing
The Toronto-based company has automated 4.5 billion customer service interactions and has recently partnered with OpenAI to utilize GPT-3.5, the large language model that forms the basis of ChatGPT.

Ada's chatbots are programmed to perform a variety of tasks, such as booking a flight for an AirAsia customer or tracking orders and returns for Meta's virtual reality products. While about 30% of customer queries are repetitive and can be handled by existing automation, Ada aims to leverage OpenAI's large language models to address the remaining 70% of customer service communication that is not straightforward and can benefit from a conversational, intelligent bot.

The partnership with OpenAI is expected to enhance Ada's chatbot training, enabling it to better understand a customer's intent, formulate answers independently, and improve the accuracy and quality of responses. Ada's chatbots are available on multiple platforms, including WeChat, WhatsApp, and Facebook Messenger, as well as companies' websites.

However, the use of AI in customer service is not without challenges. The generative nature of ChatGPT, while fostering creativity, can also lead to unpredictability. There are also concerns about potential misuse of the technology, such as introducing customers to scams. To mitigate these issues, Ada has built preemptive questions into their chatbots to keep the conversation structured and on topic.[45]


Albert:
Disruptive start-ups are changing the industry
AI in marketing and advertising has significantly transformed how companies sell their products and services. It has enhanced ad campaigns, increased customer engagement, conversion rates, and EBITDA margins.

Albert Technologies, a New York-based company, has developed AI solutions for brands and agencies. Albert, the company's autonomous digital marketer, operates along entire customer lifecycles across various channels like Google Ads, Facebook, Instagram, YouTube, and Gmail. It executes campaigns from start to finish and delivers insights from cross-channel data to help marketers improve future campaigns.

The use of AI in campaign optimization allows marketers to tailor their strategies based on real-time data and insights. It enables them to target the right audience, select relevant creative content, and engage target audiences effectively. As a result, businesses can optimize their marketing budgets while shaping ad campaigns around audiences' interests.

However, it's important to note that the success of AI in campaign optimization depends on the quality and quantity of data. Therefore, continuous data collection, analysis, and updating are crucial for the effective implementation of AI in marketing.[46]


Negative Examples

Target:
Pregnant woman fell victim to data-driven marketing
Transitioning from the optimistic applications of AI to a more cautionary tale, let's reflect on Target's data-driven marketing strategy from years ago. The retail giant employed an AI-driven approach that deduced a woman's pregnancy and its stage based on her buying patterns. By monitoring product purchases, such as unscented lotions and certain supplements, Target could assign a "pregnancy prediction" score to shoppers.

They would then send precisely timed coupons for baby products to potential mothers-to-be. However, this predictive accuracy led to an unintended consequence: the erosion of consumer trust. In one notable instance, a father discovered his teenage daughter's pregnancy through the unsolicited coupons she received, much to his shock. The main issue here wasn't the technology's proficiency, but rather the implications on privacy and the invasive nature of such predictions. This serves as a poignant reminder that while AI can unearth deep insights from data, it's essential for businesses to wield this power responsibly and ensure that consumer trust isn't compromised in the pursuit of precision marketing.[47]


Tay:
AI also reflects the dark side of society
Drawing upon another instance where technology intersected unwisely with public sentiments, Microsoft's AI chatbot, Tay, serves as a salient example. Launched as a conversational tool on Twitter, Tay was engineered to learn from its interactions, enhancing its communication abilities with each tweet. However, instead of innocent chatter, Tay quickly absorbed and echoed back the internet's darker currents.

While many of Tay's misguided comments were mere repetitions of what was fed to it, some of its unsolicited statements were alarmingly off-base, demonstrating how unfiltered information can warp AI understanding. The most significant takeaway here isn't Tay's missteps, but the realization that when training AI with vast public data, measures must be taken to prevent the assimilation of harmful biases. If not, there's a risk of creating technologies that, inadvertently, magnify and perpetuate society's flaws, challenging the ethical implications of AI in contemporary marketing and beyond.[48]



The integration of artificial intelligence in marketing offers both commendable advancements and cautionary tales. On the positive side, Mercedes-Benz's intelligent application in enhancing customer relations showcases the immense potential AI holds in elevating consumer experience, fostering brand loyalty, and driving growth. However, pitfalls are evident. Target's intrusive data mining strategies underline the challenges of safeguarding consumer privacy while utilizing personal data. Simultaneously, Microsoft's Tay exemplifies the unpredictability and potential harm AI can cause when left unchecked. As businesses venture deeper into AI-powered marketing, they must strike a delicate balance between harnessing its capabilities and upholding ethical, consumer-centric practices.


Ethical Considerations

Transparency

Transparency plays a pivotal role in navigating the realm of ethics. Challenges stem from companies’ reluctance to disclose internal workings due to trade secrets concerns, and the opacity of "black-box" algorithms, commonly used in deep learning [49]; [50]. While the inputs and outputs of these algorithms may be observable and theoretically explainable, their internal workings often remain undisclosed. Even the creators of complex machine learning algorithms may not fully comprehend their functioning in some cases [51]; [52]. Therefore, obtaining meaningful informed consent becomes essential especially when users have a legal right to explanations regarding data usage, as provided by the EU's GDPR.
DeepMind Technologies Ltd, a Google subsidiary

One example was in 2019, the British parenting club, Bounty, was fined £400,000 for sharing data from over 14 million users with third parties for marketing purposes [53]. Another notable case involved the collaboration between DeepMind Technologies Ltd, a Google subsidiary, and the Royal Free London NHS Foundation, where machine-learning algorithms were developed to aid in acute kidney injury management.The lack of transparency in these cases raised pressing questions about privacy protection, data sharing regulations, and challenges associated with obtaining meaningful individual consent and control amidst increasing transfers of population-derived datasets to large private companies [54].


Data Misuse

A significant ethical issue lies in the potential use of user data to make decisions about individuals without their awareness or consent, which has been shown to decrease their trust in AI-reliant marketing products and services [36]. In fact, a recent survey revealed that over 30% of Canadian businesses have experienced data breaches, a risk compounded by increased data collection in AI [55]. Moreover, data breaches and unauthorized access to personal information pose a serious threat. As personal data accumulates in the hands of corporations and governments, the risk of identity theft, financial fraud, and cybercrime increases [55]. This can lead to identity theft, financial fraud, and other forms of cybercrime [55]. These risks are significant and can damage consumer trust in products and services relying on AI.


The AI "Black Box"

One major ethical consideration when using AI in any context is the concept of the AI “Black Box”. AI black boxes are AI systems that have internal workings hidden from the user, making it impossible to access their code or understand the logic behind their output. Despite this lack of transparency, there is a middle ground between black boxes and fully transparent systems known as AI glass boxes, where all the algorithms, training data, and models are visible. [56]

This can cause implications for marketers, as using a black box model will make it difficult to assess decisions the AI makes that could be biased or discriminatory. Compliance with data protection and privacy laws may also become challenging due to the limited transparency in black box algorithms.


The European Artificial Intelligence Act

The European Union Artificial Intelligence Act is a proposed law that aims to regulate AI applications in the European Union. It is the first act of its kind by a major regulator. It categorizes AI applications into three risk levels. Applications creating unacceptable risks, such as government social scoring, are banned. High-risk applications, like CV-scanning tools for job applicants, are subject to specific legal requirements. Meanwhile, applications not explicitly banned or considered high-risk remain largely unregulated. Similar to the EU's General Data Protection Regulation (GDPR) becoming a global standard, the AI Act could also set a precedent for positive AI effects worldwide.[57]

The EU AI Act significantly impacts AI's use in the marketing industry by introducing a structured regulatory framework. High-risk marketing applications, such as advanced customer profiling tools, will be subject to stringent legal requirements to ensure transparency, fairness, and data protection. On the other hand, marketing applications that do not fall under the banned or high-risk categories will face relatively less regulation, allowing for more flexibility in their implementation. Marketing professionals will need to carefully assess the compliance of their AI-powered strategies and tools with the new regulations to avoid penalties and ensure ethical and responsible AI use.


Future Trends and Opportunities

The rapid advancements in artificial intelligence (AI) have revolutionized various aspects of marketing. In particular, the integration of AI technologies such as Natural Language Processing (NLP), Virtual Reality (VR)/Augmented Reality (AR), and Voice Search has significantly transformed the way companies engage with their target audience.


The Future of Marketers

According to a survey conducted by IAB Europe and Xaxis among digital marketing agencies, the implementation of AI in various aspects of digital strategy is not expected to replace human workers significantly. Only 6% of respondents stated that replacing humans was the most common AI application in their agencies.[58]

Instead, the objective of the majority of applications of AI in digital marketing agencies was optimizing business processes and targeting specific audience segments. Nearly 6 % of marketers were using AI systems to enhance targeting capabilities, aiming to deliver more effective advertising. Additionally, 55% of respondents reported utilizing AI to better identify users and their audiences.[58]


Advancements in Natural Language Processing, VR/AR, and Voice Search

One of the most significant breakthroughs in AI is the development of Natural Language Processing (NLP) techniques. It is a subfield of AI that focuses on the interaction between computers and humans using natural language. NLP enables machines to understand, interpret, and generate human language, facilitating effective communication between businesses and consumers. With NLP, companies can automate customer interactions, enhance customer service, and personalize marketing messages. For instance, chatbots powered by NLP algorithms enable automated customer interactions, enhancing customer service and delivering personalized marketing messages promptly [59].

NLP can be used to analyze customer feedback and sentiment analysis. Virtual Reality (VR) and Augmented Reality (AR) are also being used in marketing to create immersive experiences for customers [59]. Voice search is another area where AI is being used in marketing. It is becoming more popular with the rise of smart speakers such as Amazon Echo and Google Home [59].


AI-driven Customer Segmentation and Targeting and 360° Customer View

AI can be used for customer segmentation and targeting. AI can analyze customer data to identify patterns and preferences which can be used to create personalized marketing campaigns [60]. AI can also be used to create a 360° customer view, providing a comprehensive understanding of customers' interactions with the brand across various touchpoints. This holistic view allows marketers to deliver more targeted and relevant marketing messages, resulting in improved customer engagement and higher conversion rates [60].


AI and Influencer Marketing

AI can be used in influencer marketing to identify the right influencers for a brand. AI can analyze social media data to identify influencers who have the right audience demographics and interests. Influencers play a vital role in promoting products and services to their dedicated followers. AI can assist in identifying the right influencers for a brand by analyzing social media data. By analyzing audience demographics, interests, and engagement metrics, AI can help marketers find influencers who align with their target audience, ensuring more effective influencer partnerships and campaigns [59].

While the advancements in NLP, VR/AR, and voice search offer significant benefits, there are also potential challenges that marketers need to address. Privacy concerns, data security, and ethical considerations must be carefully managed to ensure consumer trust. Implementing transparent data practices, obtaining informed consent, and adhering to regulatory guidelines are essential mitigation strategies for marketers leveraging these technologies [61].


Final Thoughts

In conclusion, AI has emerged as a game-changer in the marketing landscape, revolutionizing data analysis, customer insights, and enhancing customer experiences. It has automated repetitive tasks, freeing marketers for strategic activities, and optimized campaigns in real-time. AI's predictive capabilities have enabled businesses to anticipate customer needs, personalize interactions, and proactively address concerns, thereby significantly improving customer satisfaction and loyalty. However, the rapid expansion of AI in marketing raises significant privacy concerns due to excessive data collection, consent issues, and security vulnerabilities. To mitigate these data privacy risks, it is crucial for marketers to prevent excessive data collection, strengthen security measures, and maintain transparent data usage.

From AI-powered chatbots and voice assistants to sentiment analysis and recommendation systems, AI is transforming the way businesses interact with their customers and market their products or services. As AI continues to evolve, it is expected to bring about even more groundbreaking changes in the field of marketing, making it an indispensable tool for businesses aiming to stay competitive in the digital age.

The exploration of current applications and use cases of AI in marketing has shed light on the immense potential of this technology in enhancing marketing processes and outcomes.


Authors

Amber Sun Jeremie Joyeaux Nicole David
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada 		Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada 		Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada


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