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What is Artificial Intelligence (AI) Marketing?

Many businesses, as well as the marketing teams that support them, are rapidly implementing intelligent technology solutions to increase operational efficiency while improving the customer experience. Marketers can gain a more nuanced, comprehensive understanding of their target audiences by using these platforms. The insights gained from this process can then be used to increase conversions while also reducing the workload on marketing teams.

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1 What is Artificial Intelligence (AI) Marketing?

What is Artificial Intelligence (AI) Marketing?

Artificial intelligence marketing makes automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is frequently used in marketing efforts where speed is critical. AI tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without human intervention, ensuring maximum efficiency. Many modern marketers use AI to supplement marketing teams or to perform more tactical tasks that require less human nuance.

Artificial intelligence (AI) is an umbrella term that refers to a wide range of technologies that allow computers to not just mimic but also improve upon human performance in certain tasks.

Artificial Intelligence (AI) refers to the creation of computer programs and devices for simulating brain functions and activity. It also refers to the research program aimed at designing and building intelligent artifacts. Learn more in: The Emerging Field of Technoethics

Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Although there are no AIs that can perform the wide variety of tasks an ordinary human can do, some AIs can match humans in specific tasks.

The term “artificial intelligence” generally refers to the ability of a computer to perform functions and reasoning typical of the human mind. It covers the theory and techniques for the development of algorithms that allow computers to show an ability and/or intelligent activity, at least in specific domains. Learn more in: Artificial Neural Networks Tutorial

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.[a]

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Learn more in: Methods and Techniques of Effective Management of Complexity in Aviation

Artificial intelligence (AI) is the mimicking of human thought and cognitive processes to solve complex problems automatically. AI uses techniques for writing computer code to represent and manipulate knowledge. Different techniques mimic the different ways that people think and reason. AI applications can be either stand-alone software, such as decision support software, or embedded within larger software or hardware systems. Learn more in: Electrocardiographic Signal Processing Applications in Telemedicine

Also abbreviated as AI, this term refers to the ability of a computer, an IT system, or a robot to complete either ordinary or complex tasks that are previously only possible for intelligent beings (such as human beings). Some manifestations of intelligence include the ability to learn through information acquisition and interpretation, to comprehend a brand-new language, to solve problems, to reason, to develop a possible conclusion, and to generate expert insights. Learn more in: Reality-Creating Technologies as a Global Phenomenon

Artificial intelligence is a name of the whole technological sector where computers are programmed to learn, solve problems, and mimic the actions of humans.Machine learning is a field of AI that addresses a wide range of practical issues without providing a definitive computational algorithm. In machine learning, the algorithm learns itself from the initial data provided, meaning that it provides possible solutions, and when the data is updated, the computational model is updated also, and improved solutions are provided.

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Learn more in: IoT Applications in Smart Home Security: Addressing Safety and Security Threats

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

AI marketing is the use of AI technologies to create, deliver, and measurement of marketing campaigns.

AI in marketing (also referred to asartificial intelligence marketing) is a strategy of leveraging data and machine learning to deliver campaigns that help achieve a brand’s goals more effectively. Most marketers utilize AI in market research, data science, and real-time campaign analysis.

Simply put, AI marketing is a method of leveraging intelligence technologies to collect data, customer insights, anticipate customers’ next moves, and make automated decisions that impact marketing efforts. In marketing, AI is usually used in which speed is essential. AI, actually, can boost the return of investment (ROI) of marketing.

AI marketing is the process of utilizing artificial intelligence to automate data collection and analysis, empowering marketing teams to make more effective data-driven decision making. AI increases the speed at which consumer data can be processed, with insights delivered in real-time, and communication with customers optimized to be more impactful. Messaging can be tailored to the individual consumer, delivered at the optimal time, and without the direct intervention of marketers.

AI marketing is a method of leveraging technology to improve the customer journey. It can also be used to boost the return on investment (ROI) of marketing campaigns. This is accomplished by using big data analytics, machine learning, and other processes to gain insight into your target audience. With these insights, you can create more effective customer touch points. Whether you’re engaging in email marketing or providing customer support, AI eliminates much of the guesswork involved in customer interactions.

Artificial intelligence (AI) in marketing is the process of utilizing data models, mathematics and algorithms to generate insights that can be used by marketers. Marketers will use AI-derived insights to guide future decisions about campaign spending, strategy and content topics. AI in marketing can be used in planning, production personalization, promotion, and performance stages of marketing. In addition, AI can be used in an unattended manner, to directly instrument and optimize campaigns without human intervention.

Intelligent AI marketing, however, takes advantage of machine learning — a type of AI that can become more accurate over time, learning as it goes, essentially. Intelligent AI runs large quantities of data through its pre-programmedalgorithms to make complex predictions and decisions.

Better data management. AI marketing tools also help to significantly reduce the risk of improper data interpretation, support optimal data integration, and eliminate data silos. An AI marketing tool is software that leverages AI technology to automate decisions based on collected data.

Marketing AI can be categorized according to two dimensions: intelligence level and whether it’s stand-alone or part of a broader platform. Some technologies, such as chatbots or recommendation engines, can fall into any of the categories; it’s how they’re implemented within a specific application that determines their classification.

AI digital marketing will coexist with artificial marketing in terms of further narrowing users’ intent and understanding. Neuromarketing, which is about examining the brain’s response to external factors and then creating more user-centered data, is one of the most appropriate examples of this subject.

The concept of PPC marketing, to begin with, is fundamentally based on machine learning and artificial intelligence. Marketers feed the AI with a prompt, description, or parameters, and then it’s up to the algorithm to make the most of your money. For example, Facebook has recently rolled out Automated Rules, an AI-based tool that continuously monitors the conditions of your ads and makes necessary adjustments in real-time.

AI technology is being used to automate marketing processes, allowing marketers to spend more time developing creative strategies and targeting customers.

Using AI in marketing alleviates the performance of tasks that require less human nuance. Such tasks are, for example, collecting prospective clients’ quotes and handling customer support tickets. And managing these rather repetitive and time-consuming tasks works smoothly – thanks to AI.

AI marketing empowers marketing teams to deliver powerful and compelling messaging to consumers. Big data can be processed at speed, utilizing data sets from across multiple digital channels. This empowers marketers to think and act more strategically, increasing the effectiveness and ROI of marketing initiatives.

AI marketing tools can provide marketers with a deeper understanding of their customers’ behaviors and thus help map out the best strategy for each customer. Namely, the AI-based software utilizes data to learn about the customer’s journey and craft a tailored message to serve the customer at the perfect time to convert them.

AI is helping today’s marketers analyze data and engage customers better than ever before. AI is so quickly becoming so indispensable that marketers may not even realize that the technology they use to do their jobs today was the stuff of wild-eyed imagination just a few short years ago.

AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in marketing efforts where speed is essential. AI tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without intervention from marketing team members, ensuring maximum efficiency. For many of today’s marketers, AI is used to augment marketing teams or to perform more tactical tasks that require less human nuance.

Another key use case for AI in marketing is to increase efficiency across various processes. AI can help to automate tactical processes such as the sorting of marketing data, answering common customer questions, and conducting security authorizations. This allows marketing teams more time to work on strategic and analytical work.

AI is being used in marketing initiatives in a multitude of use cases, across a broad array of industries including financial services, government, entertainment, healthcare, retail, and more. Each use case offers different results, from improvements to campaign performance, to enhanced customer experience, or greater efficiency in marketing operations.

The amount of data produced in the digital marketing world is enormous. It’s a meta to sell and buy, own, and protect. The solution to the challenge of the management and analysis of this big data lies in AI technologies so that companies can benefit from it to improve their marketing performance.

Today, AI can analyze and interpret large quantities of data in microseconds and offer up hyper-personalized insights. As a result, marketers can craft campaigns that are highly relevant to each customer, meaning less wasted money andgreater return on investment.

Beyond identifying target audiences and analyzing their behavior, artificial intelligence is the secret hero of the digital world’s marketing strategies to reach them with real-time personalized content. Also, check our brand new digital marketing agencies in the USA and digital marketing agencies in the UK directories if you’re looking for one which uses AI in their services.

Components of AI in Marketing

Brainstorming: Brainstorm with your team to come up with ideas for how AI can be used in marketing.

AI marketing empowers marketing teams to deliver powerful and compelling messaging to consumers. Big data can be processed at speed, utilizing data sets from across multiple digital channels. This empowers marketers to think and act more strategically, increasing the effectiveness and ROI of marketing initiatives.

AI marketing is the process of utilizing artificial intelligence to automate data collection and analysis, empowering marketing teams to make more effective data-driven decision making. AI increases the speed at which consumer data can be processed, with insights delivered in real-time, and communication with customers optimized to be more impactful. Messaging can be tailored to the individual consumer, delivered at the optimal time, and without the direct intervention of marketers.

Marketing teams will be put under increased pressure to demonstrate marketing value and ROI to executive stakeholders. Teams will leverage AI solutions to drive these targets and better allocate funds towards successful campaigns and provide the marketing metrics that demonstrate the value of campaigns.

From infographics and e-books to videos and interactives, we’ve brainstormed simple pieces and massive campaigns to help our clients connect with their audiences. In the process, we’ve also learned what does and doesn’t work when you’re trying to come up with fresh ideas.

With so much data coming, marketing teams are having a hard time actually deriving insights from it. AI allows marketing teams to make the most of this data using predictive analytics, which leverages an assortment of machine learning, algorithms, models, and datasets to predict future behavior. This can help marketing teams understand the types of products a consumer will be looking for and when – allowing them to position campaigns more accurately.

Many firms now use AI to handle narrow tasks, such as digital ad placement (also known as “programmatic buying”); assist with broad tasks, like enhancing the accuracy of predictions (think sales forecasts); and augment human efforts in structured tasks, such as customer service. (See the sidebar “Well-Established AI Applications in Marketing” for a list of some common activities AI can support.)

If leveraged correctly, marketers can use AI to transform their entire marketing program by extracting the most valuable insights from their datasets and acting on them in real time. AI platforms can make fast decisions on how to best allocate funds across media channels or analyze the most effective ad placements to more consistently engage customers, getting the most value out of campaigns.

Modern marketing relies on an in-depth understanding of customer needs and preferences, and then the ability to act on that knowledge quickly and effectively. The ability to make real-time, data-driven decisions has brought AI to the forefront for marketing stakeholders. However, marketing teams must be discerning when deciding how to best integrate AI into their campaigns and operations. The development and use of AI tools are still in early stages. Therefore, there are a few challenges to be aware of when implementing AI in marketing.

AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in marketing efforts where speed is essential. AI tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without intervention from marketing team members, ensuring maximum efficiency. For many of today’s marketers, AI is used to augment marketing teams or to perform more tactical tasks that require less human nuance.

Analyzing and implementing decisions based on data requires significant time and resources. AI can be used to create data models that combine the analytics and decision-making process, improving the efficiency of marketing operations. This facilitates more personalized marketing efforts, targeting consumers with the products they need when they need them. Not a scattergun approach of bombarding consumers with offers that are not relevant.

Data Collection: Use tools like Amazon Mechanical Turk to collect data that can be used for marketing purposes.

Diversification and scale of personnel of Mechanical Turk allow collecting an amount of information that would be difficult outside of a crowd platform. Mechanical Turk allows Requesters to amass a large number of responses to various types of surveys, from basic demographics to academic research. Other uses include writing comments, descriptions and blog entries to websites and searching data elements or specific fields in large government and legal documents.

Every day Amazon Mechanical Turk (MTurk) helps Requester customers solve a range of data processing, analysis, and moderation challenges. This is made possible by the contributions from Worker customers around the world that power the MTurk marketplace.

Mechanical Turk (MTurk), an online labor market created by Amazon, has recently become popular among social scientists as a source of survey and experimental data. The workers who populate this market have been assessed on dimensions that are universally relevant to understanding whether, why, and when they should be recruited as research participants. We discuss the characteristics of MTurk as a participant pool for psychology and other social sciences, highlighting the traits of the MTurk samples, why people become MTurk workers and research participants, and how data quality on MTurk compares to that from other pools and depends on controllable and uncontrollable factors.

Amazon.com’s Mechanical Turk (MTurk) is an online, web-based platform that started in 2005 as a service to allow researchers to “crowdsource” labor-intensive tasks for workers registered on the site to complete for compensation.1,2 MTurk has rapidly become a source of subjects for experimental research and survey data for academic work, as its representativeness, speed, and low cost appeal to researchers.2,3 Researchers post links to surveys and experiments and use MTurk to crowdsource the survey, collect the data, and compensate workers.4 A Google Scholar search of “Amazon Mechanical Turk” revealed 15,000 results published between 2006 and 20143 and 17,400 results by mid-2017. MTurk is the largest online crowdsourcing platform,4 with about one-third of the tasks related to academic tasks.5 The growing popularity of MTurk has led to questions about its soundness as a subject pool; MTurk is the most studied nonprobability sample available to researchers.3

One of the most expensive and time-consuming aspects of building your machine learning (ML) model is probably generating a high-quality dataset. Many times, all you have is a big bucket of raw, unlabeled data. Furthermore, the process of manually annotating massive datasets might be the most painful phase of your ML workflow. Crowdsourcing can be a great way to minimize the costs and the time it takes to collect and annotate data. Amazon Mechanical Turk makes accessing human intelligence simple, scalable, and cost-effective. In this workshop, learn how to use crowdsourcing to find still images to best represent scenes from a hit TV series, The Marvelous Mrs. Maisel, and identify and label items in those images to train an ML model.

In the Facebook–Cambridge Analytica data scandal, Mechanical Turk was one of the means of covertly gathering private information for a massive database. The system paid persons a dollar or two to install a Facebook connected app and answer personal questions. The survey task, as a work for hire, was not used for a demographic or psychological research project as it might have seemed. The purpose was instead to bait the worker to reveal personal information about the worker’s identity that was not already collected by Facebook or Mechanical Turk.

Amazon Mechanical Turk (MTurk) is a crowdsourcing website for businesses (known as Requesters) to hire remotely located “crowdworkers” to perform discrete on-demand tasks that computers are currently unable to do. It is operated under Amazon Web Services, and is owned by Amazon. Employers post jobs known as Human Intelligence Tasks (HITs), such as identifying specific content in an image or video, writing product descriptions, or answering questions, among others. Workers, colloquially known as Turkers or crowdworkers, browse among existing jobs and complete them in exchange for a rate set by the employer. To place jobs, the requesting programs use an open application programming interface (API), or the more limited MTurk Requester site. As of April 2019, Requesters could register from only 49 approved countries.

Amazon Mechanical Turk provides a platform for processing images, a task well-suited to human intelligence. Requesters have created tasks asking workers to label objects found in an image, select the most relevant picture in a group of pictures, screen inappropriate content, and classify objects in satellite images. Also, crowdworkers have completed tasks of digitizing text from images such as scanned forms filled out by hand.

Machine learning helps marketers to speed up the process of analyzing vast data sets. Trends and insights into consumer behavior can be highlighted, with machine learning helping to identify changes in consumer behavior and predict responses to messaging. This empowers marketing teams with a deep understanding of their customers and accurate predictions of their behavior.

Companies with large online catalogues use Mechanical Turk to identify duplicates and verify details of item entries. Some examples of fixing duplicates are identifying and removing duplicates in yellow pages directory listings and online product catalog entries. Examples of verifying details include checking restaurant details (e.g. phone number and hours) and finding contact information from web pages (e.g. author name and email).

Analysis: Use machine learning techniques to analyze the data collected in step 2 and make decisions based on that information.

To deep analyse our findings, for each recipe we gathered information regarding the required data for the implementation, the main algorithms used, the KPIs taken into account, the predominance of the recipe, and finally two or more practical use cases. The predominance is based on the relative presence of the recipe in the literature we have collected in our research (● = infrequent, ●●● = very frequent). Table 1 summarizes the recipes discussed in the previous section.

Machine learning is driven by artificial intelligence, and it involves computer algorithms that can analyze information and improve automatically through experience. Devices that leverage machine learning analyze new information in the context of relevant historical data that can inform decisions based on what has or hasn’t worked in the past.

The paper is organized as follows: Sect. 2 provides some theoretical background on Big Data, Machine Learning, and their applications in Marketing. Section 3 illustrates the methodology we adopted for conveying the information gathered in business and scholarly research into a structured taxonomy. Section 4 presents the findings and describes each portion of the taxonomy, providing examples of real-world application of each use case. Finally, the last section discusses conclusions and acknowledges the limitations of the study, highlighting opportunities for further research.

By framing the right question to be asked, this platform allows the program to model several data organizations to highlight anomalies. Further, the association over this type of learning could be applied to know more about tendencies based on newly discovered relationships among variables over a vast database.

During May 2021 we have collected 75 use cases of ML and AI in Marketing and removed 35 cases that failed to meet one or more of the four selection criteria described above. By applying the SCA methodology as described in Sect. 3, we obtained 11 activation recipes in a taxonomy organized on three levels. We linked each of the 40 different real-life implementations found in the literature to a recipe, which represents the most appropriate ML area of application, at the lowest level of the taxonomy. We have then grouped the 11 recipes into four categories, which correspond to the second level of the hierarchy, to give a clear framework of ML applications from a strategic marketing perspective. On the consumer-facing side we classified the recipes into (1) improve shopping fundamentals, and (2) improve consumption experience, while on the business-facing side, we classified them into (3) improve decision making, and (4) improve financial applications. Figure 2 shows a visual tree rendering of the resulting taxonomy where branches correspond to the split in conceptual classes while leaves correspond with the identified recipes. In this section, we are going to describe the essential features of each class of recipes (highlighted in the text in italic) and provide a selection of use cases as an illustration.

Finally, we recognize that this work displays some limitations to be solved in future studies. Firstly, while SCA is suitable for qualitative exploration and categorization of documents, we envision the possibility to achieve deeper and less subjective findings using quantitative methodologies such as NLP and, particularly, topic modelling. Secondly, our findings could be further expanded by encompassing a larger number of use cases, which might be obtained through a wide surveying activity or a broader review of literature based on more sources. Lastly, this study did not attempt to numerically quantify the impact of leveraging ML on marketing performance indicators, which would greatly help firms prioritize their investment choices.

Using this technique can help you identify advantages and disadvantages much earlier than you normally would, which puts you at an upper hand with your idea. Once you’ve figured out your weaknesses and threats, you can immediately start figuring out a way to get around them.

Failure to get the right data to the right app or point-of-analysis in real time Your machine learning training is only as good as the data you feed into your AI/ML frameworks and intelligent applications. If the data is bad, old or incomplete, the training will be poor and the answers and results generated will be (at best) equal to the quality of the data — and perhaps flat out wrong.

We believe that our study offers three significant contributions to the practice of ML usage in marketing and its conceptual development as a research area. First, we offer a definition and a structured description of ML applications in marketing and provide a theoretical justification of business strategies enabled by ML from a strategic marketing perspective. Second, the taxonomy presented in this work can be used by business managers to assess the completeness of their ML programs by checking the extent of marketing applications that are currently enabled. We believe that this exercise can help firms identify which routes are still unexplored, resulting in further opportunities for leveraging data and ML algorithms at best. Lastly, we provide scholars with a framework that systematizes the broad set of business applications of ML and offer a structured view of the current related literature, enabling the identification of gaps to be filled through further research.

Challenges for Artificial Itelligence Marketing

identify needs and wants.

Feeling AI can be used to understand existing and potential customer needs and wants, for example, who they are, what they want, and what their current solutions are. The major distinction between market analysis and customer understanding is that the latter often involves emotional data about customer sentiments, feelings, preferences, and attitudes. Thus, feeling AI can do a better job of understanding customers than mechanical AI and thinking AI, due to its capability of analyzing emotional data.

Task type refers to whether the AI application analyzes numbers versus non-numeric data (e.g., text, voice, images, or facial expressions). These different data types all provide inputs for decision making, but analyzing numbers is substantially easier than analyzing other data forms. Practitioners, such as senior managers from Infinia ML, formulate this categorization slightly differently, noting that data that can be organized into tabular formats are significantly easier to analyze than those data that cannot. In our discussions with employees of Stitch Fix, we gained further clarity on this point. Stitch Fix elicits data from customers using both direct questions about their preferences (which can be put in tabular formats) and indirect elicitations from customers’ Pinterest pages and likes. Stitch Fix uses proprietary AI algorithms to analyze the latter, non-numeric data and regards these data as very useful, because it has learned that customers cannot always articulate their preferences on numeric scales.

Segmentation is to slice a market into pieces, with customers in each piece having unique needs and wants, for example, using gender to slice the shoe market into male and female shoes segments; and using price and quality to slice the air travel market into budget and premium airlines segments. Mechanical AI, especially the various mining and grouping techniques, has the strength of identifying novel patterns from data.

In academic research, existing studies have shown various approaches of using feeling AI to understand customers. For example, the sentiment expressed by consumers in social media (e.g., online reviews, tweets), including explicit and implicit language and discourse patterns, can be analyzed to understand consumer responses using their own language (Hewett et al. 2016; Humphreys and Wang 2018; Ordenes et al. 2017), the interaction between conversational AI and customers can be enhanced by applying analytical mapping to script appropriate response sequences that make customers feel that they have a “conversation” with AI (Avery and Steenburgh 2018), consumer consideration heuristics can be understood by machine learning (Dzyabura and Hauser 2011), and customer needs can be identified from user-generated content using convolutional neural network machine learning (Timoshenko and Hauser 2019).

The Stitch Fix’s business model offers another example. As we noted, Stitch Fix delivers apparel directly to customers (Wilson et al. 2016), without requiring the customers to actually engage in a formal shopping task. No Stitch Fix retail location exists. Instead, customers fill out style surveys, provide their physical measurements, evaluate sample styles, create links to their Pinterest boards, and send in personal notes. As may be expected, customers have trouble explicating their exact style preferences using words and numbers, but their pins and likes can be (better) indicators of their preferences. Stitch Fix’s proprietary machine learning algorithms examine numbers, words, and Pinterest pins, then summarize the findings for the company’s fashion stylists, who in turn select suitable clothing to send to each customer. The above example illustrates the need to suitably balance AI input and human input; senior managers from Stitch Fix told us that—in their experience—their AI works best when it augments the (human) stylists’ capabilities.

First, to automate business processes, AI algorithms perform well-defined tasks with little or no human intervention, such as transferring data from email or call centers into recordkeeping systems (updating customer files), replacing lost ATM cards, implementing simple market transactions, or “reading” documents to extract key provisions using natural language processing. Second, AI can gain insights from vast volumes of customer and transaction data, involving not just numeric but also text, voice, image, and facial expression data. Using AI-enabled analytics, firms then can predict what a customer is likely to buy, anticipate credit fraud before it happens, or deploy targeted digital advertising in real time. For example, stylists working at Stitch Fix, a clothing and styling service, use AI to identify which clothing styles will best suit different customers. The underlying AI integrates data provided by customers’ expressed preferences, their Pinterest boards, handwritten notes, similar customers’ preferences, and general style trends. Finally, AI can engage customers, before and after the sale. The Conversica AI bot works to move customer transactions along the marketing pipeline, and the AI bot used by 1–800-Flowers provides both sales and customer service support. AI bots offer advantages beyond just 24/7 availability. Not only do these AI bots have lower error rates, but also they free up human agents to deal with more complex cases. Further, AI bot deployment can be scaled up or down as needed, when demand ebbs or flows.

Feeling AI provides relationalization benefits (i.e., personalizes relationships), due to its capability to recognize and respond to emotions. Any marketing functions or activities that require interaction and communication, with the goal of relational benefits (e.g., when customer lifetime value is high) should consider feeling AI—one example being customer service. A broad range of marketing functions involves feelings, for example, customer satisfaction, customer complaints, customer moods, and emotions in advertising, etc., and can make use of feeling AI.

The current practice relies on self-service to automate routine delivery and labor-intensive physical distribution for ordering processing, materials handling, and delivery; unskilled labor at the frontline to offer homogeneous assistance, and frontline employees for emotional labor.

Distribution, logistics, and delivery can mostly be automated with mechanical AI, and is a fast-growing emerging practice, for example, product tracking systems for firms to track where the product is in the supply chain, and for customers to track when they can expect to receive the product. Thinking AI, such as personal shopping assistants, has been used to assist customers about where to find the product. Feeling AI, such as conversational bots, can be used to display emotions in service interaction without the need to actually experience emotions (Wirtz et al. 2018).

infer preferences.

“Let us consider trying to personalize the image we use to depict the movie Good Will Hunting. Here we might personalize this decision based on how much a member prefers different genres and themes. Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian.”

Task characteristics also influence AI adoption. To the extent a task appears subjective, involving intuition or affect, customers likely are even less comfortable with AI (Castelo 2019). Research confirms that customers are less willing to use AI for tasks involving subjectivity, intuition, and affect, because they perceive AI as lacking the affective capability or empathy needed to perform such tasks (Castelo et al. 2018).

Across channels, different consumers respond to different messages – some may resonate with an emotional appeal, some humor, others logic. Machine learning and AI can track which messaging consumers have responded to and create a more complete user profile. From there, marketing teams can serve more customized messages to users based on their preferences. For example, Netflix uses machine learning to understand the genres a certain user is interested in. It then customizes the artwork that user sees to match up with these interests. On the Netflix Tech Blog, they explain how they use algorithms to determine which artwork will most entice a viewer to watch a certain title, saying:

By contrast, data about customers’ feelings, moods, and emotions can be obtained directly from customers’ interaction with AI (e.g., conversational bots), rather than inferred from psychometrics, using conversational bots and analyzed using feeling analytics (e.g., posts on social media, voice recordings of customer interactions, and chat transcripts). Feeling analytics can identify customer insights with scale and cost-efficiently. Given that emotional data are personal and in context, understanding customers in context provides richer insights about who they are and what they like.

As a result, campaign managers can now make more intelligent decisions about where they spend their time and money – knowing how individual voters feel about particular issues is vital for crafting persuasive messages that resonate with the right people.

Finally, we consider the privacy–personalization paradox (Aguirre et al. 2015). Customers must balance privacy concerns against the benefits of personalized recommendations and offers. Important questions relate to how customers determine the optimal trade-off, including which individual difference variables and state variables might moderate their choices. Does the trade-off depend on the product category or the level of the customer’s trust in the firm, for example? Also, how would this trade-off shift over time?

On the customer level, AI applications can shape decision-making processes. Personalization, psychological targeting, and recommender systems can serve as adaptive, structural, or informational nudges (Burr et al., 2018; Floridi, 2016; Milano et al., 2020; Sunstein, 2016). These kind of interventions influence customers’ choice sets—the choice architecture (Thaler & Sunstein, 2008) -, or information related to choices and eventually preferences and decisions. Customers’ autonomy is impacted in such a way that decisions are delegated to AI systems at the information collection stage of the decision-making process, particularly, (pre-)filtering of information and options customers are exposed to. That can be beneficial due to resource efficiency (e.g., time, cognitive resources) and tailored content (Burr et al., 2018), but also detrimental to customers in case of aversion of or overreliance on AI systems’ recommendations (e.g., Banker & Khetani, 2019; Dietvorst et al., 2015, 2018) or due to manipulated or deceptive content (e.g., Burr et al., 2018; Milano et al., 2020). An informed and conscious decision of humans (as customers or company representatives) of whether to delegate their decision-making power to AI systems and applications would again demand a certain degree of understanding of AI functionalities (i.e., intelligibility).

Finally, if customers’ ideal preferences actually differ from their past behaviors (e.g., customers trying to stop eating unhealthy foods), AI might make it harder for them to find and move toward their preferred options, by only presenting them with choices reflecting their past behaviors. The widespread “retargeting” of digital ads is one example of this phenomenon. How to train AI to best manage this issue?

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As most of you are probably aware AI systems are driven and developed by leveraging quality data. This is why the process of AI implementation should start with the use of the right set of data. It can be quite hard to determine which data to use, because different types of data will be flowing across organizations.

craft persuasive messages.

Abstract: The ability to persuade others is critical to professional and personal success. However, crafting persuasive messages is demanding and poses various challenges. We conducted nine exploratory case studies to identify adaptations that professional and non-professional writers make in written scenarios to increase their subjective persuasiveness. Furthermore, we identified challenges that those writers faced and identified strategies to resolve them with persuasive natural language generation, i.e., artificial intelligence. Our findings show that humans can achieve high degrees of persuasiveness (more so for professional-level writers), and artificial intelligence can complement them to achieve increased celerity and alignment in the process.

In essence, ethical principles should not pursue the objective of inhibiting actions or (technological) progress; they should rather amplify the scope of action, autonomy, freedom, and self-responsibility (Hagendorff, 2020). We follow this path and provide ideas of how to leverage AI applications in marketing to promote social and environmental good. Kaplan and Haenlein (2020, p. 44) noted that “AI can be major game changer” to address climate change. We concur with this thought and attempted to show how to add the fuel of AI to the fire of sustainability efforts in the marketing context. To achieve a dual advantage for society (Floridi et al., 2018), this beneficence-inspired view is complemented by cautioning against misuse of AI, particularly, when directed at vulnerable consumers. We hope that some of our suggestions motivate marketing researchers and practitioners to further investigate how the AI-powered promotion of well-being can be refined, advanced, and effectively put into practice.

With context and consent at the heart of the debate around ubiquitous and seamless uses of AI in the creative industries, the panel discussion here attempts to untangle the numerous threads deriving from the questions above. Ultimately, it asks for pause and reflection when considering the design of new interfaces and iteration of technologies capable of reading and responding to the subtleties of human behaviour.

Promotion (communication) is the marketing communications between the consumer and the marketer. It can include personal selling, traditional mass media advertising, and more commonly nowadays direct marketing, database marketing, and digital marketing (social media marketing, mobile marketing, search engine optimization, etc.). All these can benefit from AI intelligences.

Some successful positioning statements help brands to occupy a unique position in customers’ minds and thus succeed in the market for a long time. For example, Nike’s “Just do it,” Apple computer’s “Be different,” and McDonalds’ “I’m loving it” all communicate with customers by speaking to their hearts. Feeling AI, such as feeling analytics, is ideal for this strategic decision to help develop compelling slogans by understanding what resonates with target customers.

Feeling AI can be used to track real-time customer response to promotional messages (like, dislike, disgusted, funny, etc.) and then adjust what to deliver and what to emphasize in both media and content. At the feeling level, more real-time and accurate emotion sensing from posted messages can better engage customers and provide a better interaction experience (Hartmann et al. 2019; Lee et al. 2018).

The remainder of our study is structured as follows. After delineating our methodological approach and briefly illustrating the role and uses of AI in marketing, we present an overview of the rapidly expanding research on AI ethics. Afterward, we consolidate both perspectives by applying selected ethical principles to AI applications in marketing. We conclude our investigation with suggestions of how to harness AI in marketing for promoting societal and environmental well-being and with directions for future research.

Politicians have been using data analytics for years, but the current trend is that they see the benefits of reaching out to potential constituents by utilizing artificial intelligence. A study from Stanford University found that “AI can do a better job than humans when it comes to finding people’s persuasive arguments on social media posts.”

How should marketers and AI collaborate, to resonate with customers? Positioning requires creativity and empathy about the preferred way that a customer would like to see a product. AI can optimize a positioning recommendation, but may not be mature enough to resonate with customers, given that we do not have true feeling AI yet. Thus, for the time being, it is important to explore the best approaches to marketer-AI collaboration for a positioning that resonates with customers. This is a question that involves exploring the role of AI intelligences in creativity; for example, to what extent should marketers let machines be creative on their own (e.g., the Lexus commercial example), or use them as creative support? What will be consumers’ attitudes toward machine-generated creativity? What will decide the acceptance or rejection of machine creativity?

Existing studies show that various thinking AI can be used for this purpose. Examples include targeting customers using a combination of statistical and data-mining techniques (Drew et al. 2001), screening and targeting cancer outreach marketing using machine learning and causal forests (Chen et al. 2020), optimizing promotion targeting for new customers using various machine learning methods (Simester et al. 2020), identifying the best targets for proactive churn programs from field experimental data using machine learning techniques (Ascarza 2018), and profiling digital consumers for targeting using online browsing data (Neumann et al. 2019).

How to Use AI in Marketing

Understand how AI can be used in marketing.

Human brains can only interpret and analyze so much. You need marketing tools to sift through mountains of data and drive your decision-making. AI-powered systems generate helpful marketing insights and reach the right customers at the right time. You can craft your messaging based on these insights and make it meaningful and engaging to convert.

Using AI helps marketers understand their audience better and target them accordingly. This helps marketers make use of the correct marketing channel, target specific users with customized ads, and also make use of appropriate content for different users.

Analyzing and implementing decisions based on data requires significant time and resources. AI can be used to create data models that combine the analytics and decision-making process, improving the efficiency of marketing operations. This facilitates more personalized marketing efforts, targeting consumers with the products they need when they need them. Not a scattergun approach of bombarding consumers with offers that are not relevant.

AI marketing empowers marketing teams to deliver powerful and compelling messaging to consumers. Big data can be processed at speed, utilizing data sets from across multiple digital channels. This empowers marketers to think and act more strategically, increasing the effectiveness and ROI of marketing initiatives.

AI is used in marketing to generate meaningful insights and help make data-driven decisions. AI automates day-to-day marketing efforts, saving time and money while empowering marketers to learn more about their customers and provide the right solutions.

The amount of data produced in the digital marketing world is enormous. It’s a meta to sell and buy, own, and protect. The solution to the challenge of the management and analysis of this big data lies in AI technologies so that companies can benefit from it to improve their marketing performance.

AI marketing is the process of utilizing artificial intelligence to automate data collection and analysis, empowering marketing teams to make more effective data-driven decision making. AI increases the speed at which consumer data can be processed, with insights delivered in real-time, and communication with customers optimized to be more impactful. Messaging can be tailored to the individual consumer, delivered at the optimal time, and without the direct intervention of marketers.

A quick example of using AI in marketing comes from Gmail and Google Docs that use AI in Smart Compose to read what you are typing, then understand it and suggest what to type next. I will also compile some examples of AI marketing use cases from big brands in the following section. It would be fun to see what others are doing with this powerful technology, so keep reading!

AI is used in marketing to provide actionable recommendations for managing relationships. By integrating multiple data sources and systems, AI can generate a profile for each consumer, so that decision-makers can understand what drives their customers’ choices and guide them to their next best action.

Knowing what to do next and doing it right is what every business should aim to meet customers’ expectations and earn more sales. The application of AI in marketing makes it easier for marketers to understand customers and participate in their actions based on the data collected on their contacts and past purchases.

use AI to target customers.

While there are plenty of ways marketers and businesses can use AI to improve their online marketing efforts, the targeting aspect seems like the most promising. By better understanding your audience based on the data available to you, you can garner a much clearer picture of where your business needs to go in order to better reach them, which can ultimately mean more sales and higher profits.

To begin, choose AI marketing software that has a customizable chatbot feature. Then, create prompts based on behavior like reading-specific content or interacting with page elements. Tailor the conversation and questions to your buyer personas to maximize engagement. For instance, if customers use certain keywords, automate the chatbot to send links to relevant product pages, demos or forms.

Once segmentation of the audience is done, AI-led machines target segmented groups of customers in order to reach out to them separately. Targeting involves creating separate advertisements and campaigning products in distinct ways to market a product or a service in a better way.

Proper segmentation and audience targeting lead to more relevant advertising, which is reflected in a recent survey by McKinsey Analytics that found 14%* of users adopted AI for customer segmentation purposes. By delivering messaging that relates to the end-user, advertisers can expect higher engagement rates and more conversions.

This AI is often referred to as personalized pricing. It’s a pricing strategy wherein a product’s price is determined by demand and/or supply. A good example is the prices of ride-sharing apps that increase as demand rises or when you can’t find a discount when you need to purchase a product online.

Contextualize your value proposition in relation to your customers’ pain points. Take ROSS Intelligence, for example. ROSS is a legal research platform powered by artificial intelligence, which helps users to quickly determine the answers to legal research questions.

There are many ways to segment your target market. A common approach is to use demographic characteristics (education, country of origin, income and so on) to divide your target market into distinct groups. However, this can be very time-consuming, especially if you have a large number of target customers.

For firms with limited AI experience, a good way to begin is by building or buying simple rule-based applications. Many firms pursue a “crawl-walk-run” approach, starting with a stand-alone non-customer-facing task-automation app, such as one that guides human service agents who engage with customers.

Built on ML algorithms, chatbots can be triggered by customers’ specific behavior, such as reading certain types of content or interacting with web page elements. Or when directly contacted in any type of messaging app, chatbots can perform the tasks of human customer support managers. Algorithms can be programmed according to a business’s specific buyer personas, and lead conversations in a specific tone of voice to maximize engagement with those personas.

That’s why we rely on AI to uncover patterns that aren’t always intuitive to human perception. Digital Alchemy’s uDiscover provides marketers with a refined list of target customers that are highly likely to respond positively to marketing communications aimed at changing their behaviours. uDiscover will synthesize available data and identify relevant behavioural patterns and traits that marketers will be able to target.

use AI to measure the success of marketing campaigns.

One way that businesses do this with AI is to use predictive marketing analytics. By having AI analyze data of past events, it can reasonably and accurately infer how performance will look in the future based on a variety of factors. More importantly, analyzing what users like most can be useful when looking to suggest products to them.

The ability to use artificial intelligence to predict the success of marketing campaigns and to better personalize experiences for users is a powerful technological trend that will continue for years to come. Adaptation to include this tool in your arsenal is critical for relevancy at scale.

Measuring campaign success is also an important component of any advertising strategy. Being able to measure how a campaign performs allows advertisers to put their dollars where it matters most. In Deloitte’s State of AI in the Enterprise, 3rd Edition,* AI success has proven easy to measure, with 26% of all respondents and 45% of seasoned AI adopters saying that AI technologies have enabled them to establish a significant lead over their competitors.

That’s no surprise, since using AI in marketing allows specialists to identify which ads generate more revenue than others, spot declining CTRs on time, and even anticipate changes in customer behavior. Then they can properly look into each case based on changes identified in the data.

The first step to adopting AI software for this purpose is ensuring that your current and past campaigns are integrated. Include customer data from tools like Google Analytics as well. This gives the software as much data as possible to find the highest converting audiences.

While looking into the future to see how campaigns perform seems like something out of a sci-fi movie, it’s available right now. AI marketing software has the ability to use predictive analytics to forecast sales. This allows teams to work as usual while getting a glimpse of what their activities will return to them. This also prevents spending weeks to months on something before you determine that it is (or isn’t) worth scaling.

While advertisers have traditionally struggled with measuring the success of their campaigns, analytics helps companies determine what’s working and what’s not. Therefore, in the future, advertisers can be proactive in taking steps that will positively impact their campaigns. According to research by Deloitte,* 73% of AI adopters believe AI is “very” or “critically” important to their business today and 64% said AI technologies enable them to establish a lead over competitors. Targeting the right audiences, with the right message, every time, helps ensure a reduction in advertising waste and greater ROI.

It can be difficult for marketing teams to demonstrate the value of AI investments to business stakeholders. While KPIs such as ROI and efficiency are easily quantifiable, showing how AI has improved customer experience or brand reputation is less obvious. With this in mind, marketing teams need to ensure they have the measurement abilities to attribute these qualitative gains to AI investments.

As with any marketing program, it is important that clear goals and marketing analytics are established from the outset. Start by identifying areas within campaigns or operations that AI could stand to improve, such as segmentation. Then establish clear KPIs that will help illuminate how successful the AI augmented campaign has been – this is especially important for qualitative goals such as “improve customer experience.”

According to Markus, “AI-powered systems can help advertisers test out more ad platforms and optimize targeting. That’s exactly what Facebook is doing with their ad delivery optimization. However, this approach could also be applied to omnichannel PPC campaign data (held by a single company) by using third-party or in-house AI tools.”

Benefits of Leveraging Artificial Intelligence in Marketing

using AI in marketing can improve results.

There are many incredible ways to take advantage of AI in your digital marketing. With the available tools, you can improve customer experience and employee engagement while driving sales. Plus, with the powerful algorithms doing the learning, it’s not so arduous for you to get up to speed on the latest technology.

Using AI to drive personalized customer experiences helps you use the power of your data, and using ML to predict the behavior of users and quickly optimize advertising offers at scale and speed, will result in maximizing your marketing budget and being able to save up time and have better accuracy.

In digital marketing, AI can be also helpful in analyzing data, spotting patterns, and making accurate predictions. Human analysis can be sometimes based on limited past experiences or arbitrary decisions, but the AI-based machines process data and calculate results based on prediction analysis instead of intuition. Hence, you can expect flawless digital marketing strategies with the influence of AI in it.

Another benefit of leveraging AI in your marketing efforts is that AI carries out data analysis incredibly fast, which allows for swift decision-making. For example, with AI tools, marketers are able to make better media choices sooner rather than later by leveraging real-time analytics.

AI in marketing has many uses. In addition to fine-tuning content, it can learn customer preferences and offer them relevant recommendations. It can predict customer spending patterns, perform market research, and assist customers in real-time when live agents are unavailable. Artificial intelligence and marketing is a partnership that makes sense, given the thousands of data points marketing teams could use to improve their content.

On the strategy side, AI has the potential to help marketers map out an end-to-end content strategy. Some marketing tools are already providing this feature. I predict it will also be able to generate comprehensive reporting on content initiatives, with little to no human labor involved.

AI also uses machine learning to reach objective, data-based conclusions based on each customers’ context and situation. This gives marketing teams invaluable insights to boost conversions, as well as enough time to implement strategic solutions and market responses.

By now, I am sure you would have got a clear idea of the different benefits of using Artificial Intelligence in Marketing. It’s the right time for marketers to understand how AI-driven strategies can help create highly personalized experiences for their customers.

Whether your goal is to study consumer behavior on your website or develop tailored advertising campaigns, AI can play a pivotal role. Many digital marketing strategies can benefit significantly from these technologies, ranging from social media to SEO.

AI is especially beneficial in marketing where objectives and goals need to be achieved within a particular time frame. Custom deep learning, for example, helps predict customer behavior and build a successful full-funnel marketing strategy. It also enables companies to reach out to their present and potential customers in a more personalized and timely way.

improving the effectiveness of marketing campaigns.

From then, customer segmentation can be done more effectively. Marketers can identify more clearly which customers should be targeted and included or excluded from the campaign, better match customers to the products they’re likely to buy, and prevent promoting irrelevant or out-of-stock products to customers.

In today’s competitive marketing industry, personalization is quite valuable. A report by Statista shows that 78-96% of marketing professionals in the U.S. use personalization. This is important because consumers are more likely to interact with personalized marketing messages.

Global digital marketing spend grew 17% last year to reach over $389 Billion. Customers are already overwhelmed with the growing digital communications overload and as a result 71% expect brands to personalize their experiences. McKinsey refers to digital personalization at scale as “Marketing’s Holy Grail”.

Today’s marketing teams need a targeted, personalized approach for email subject line optimization that is more captivating than: “Hi, we noticed you left something in your cart.” They need to know the words they choose are the exact right words so their customers take notice and don’t instantly hit the delete button.

85% of CMOs know their organizations’ future business success hinges upon creativity and big ideas that build the brand and create an emotional connection, according to a survey from Dentsu Aegis Network. Only 54% of these same CMOs believe they deliver on that promise. Another 84% said data collection, management, and analytics that drive consumer insight are important for their success, yet only 49% believe they deliver well in this area.

Aesthetics are an important part of digital marketing – it’s crucial to leave a memorable first impression. And because the human brain processes images 60,000 times faster than it does text, it’s clear that your digital marketing efforts have to be visually appealing.

Michael has been working in marketing for almost a decade and has worked with a huge range of clients, which has made him knowledgeable on many different subjects. He has recently rediscovered a passion for writing and hopes to make it a daily habit. You can read more of Michael’s work at Qeedle.

Danni White draws on over 15 years of experience as a marketer, writer, and content strategist in both B2B and B2C industries. Over the past decade, she has worked with agencies, startups, and digital publications to create content that matters to audiences and converts. She is the founder of DW Creative Consulting Agency where she works with clients to create, manage, and optimize content for optimal business impact.

Entrepreneur Naomi Simson, a host on Shark Tank Australia, owns a company called RedBalloon, which sells gifts and experiences online. She was spending $45,000 per month on ad agencies alone to run digital advertising for the brand that resulted in great losses.

Unilever, parent company of Ben & Jerry’s, gathered insights from multiple AI data centers and discovered that at least 50 songs in the public domain mention “ice cream for breakfast.” Based on this finding, Unilever launched several cereal-flavored ice cream varieties.

boosting customer engagement.

Your goal is to automate routine tasks that marketers normally perform. And that reduces the time required to complete those tasks, leveraging time to generate value and eliminating the need for marketers to perform tedious, repetitive, and time-consuming tasks.

For instance, eCommerce business doers are getting more positive results by making push notifications mobile-friendly. When done well, push notifications delivered via mobile gain more attention from customers because it creates a sense of personalization.

It’s crucial to offer your prospects answers to any questions they might have regarding your product or services as that can be a make-or-break factor to purchase. Similarly, once they decide to opt for your product, they’d like to start using it right away. Meaning they’ll be frustrated if they don’t understand how a certain feature works.

When personalization-at-scale is done right, businesses achieve a notorious increase in sales revenue, in order frequency, and a higher ranking in average order value. The benefits transcend marketing alone and drive higher customer satisfaction metrics as well.

Sales forecasting plays an important role in determining long-term business growth. Though employees of the sales team invest a large portion of their time and focus in this process, unfortunately, most of the businesses fail to derive desired profits through their sales forecasting techniques.

New fitting experience: This concept enables customers to quickly get the best look at what they wear by adding suggested garments and accessories brought to them by the store’s staff after they try out an item. FashionAI also carries increased omnichannel capability with a “Virtual Wardrobe” on its mobile Taobao app. This feature allows customers to view the clothes they are trying on along with other recommended items that would complement their outfits.

Michaels, the largest arts and crafts retailer in the United States, partnered with Persado in search of a better way to personalize messaging. The company had been collecting data on its users but didn’t have a strategy about how to use that information to form emotional connections with its artist and “maker” customers.

In fact, Starbucks has successfully built its relationships with customers through this Rewards Program. For example, the company gives its customers free coffee on their birthdays. Via its mobile app, customers can talk with the barista via voice assistance to order their coffee.

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“Let us consider trying to personalize the image we use to depict the movie Good Will Hunting. Here we might personalize this decision based on how much a member prefers different genres and themes. Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian.”

References

Marketing Evolution

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What is Artificial Intelligence (AI) Marketing?