AI in Marketing
25 minutes read

A Complete Guide to AI marketing in 2023. Now with 5+ FREE use cases

What is AI marketing?

Intelligent technology solutions are being quickly adopted by many businesses to promote operational efficiency and enhance the customer experience. Artificial intelligence marketing platforms are a common form of these clever solutions.

These platforms allow marketers to develop a more detailed, all-encompassing understanding of their target audiences. In this optimization process, artificial intelligence-driven insights can then be used to increase conversions while lightening the marketing team’s workload.

What is the use of artificial intelligence (AI) in marketing?

In marketing, artificial intelligence (AI) technologies are used to make automated decisions based on data gathering, data analysis, and further observations of audience or economic trends that may affect marketing efforts.

In digital marketing campaigns where speed is crucial, AI is frequently used. To ensure maximum efficiency, AI marketing solutions learn how to communicate with customers by analyzing data and customer profiles.

They then serve them tailored messages at the appropriate time without help from marketing team members. Today’s digital marketers frequently use AI to support marketing teams or carry out more tactical tasks that don’t require as much human nuance.

Use Cases for AI in Marketing:

  • Data analytics: Data analysis is gathering and sorting through massive amounts of marketing data from various programs and campaigns that would otherwise need to be done by hand.
  • Natural language processing: Developing human-like language for customer service bots, experience personalization, and other uses are known as natural language processing (NLP).
  • Media Buying: Predict the best ad and media placements for a company to reach its target audience and maximize the return on investment from its marketing strategy.
  • Automated decision making: AI marketing tools assist businesses in selecting the best marketing or business growth strategy based on historical or external data inputs.
  • Content generation: The process of producing both short and long forms of content for a marketing plan, such as video captions, email subject lines, internet copy, blogs, and more, is known as a content generation.
  • Real-time personalization: Modifying a user’s interaction with a marketing resource, such as a web page, social media post, or email, to match the user’s previous preferences to nudge them into taking a particular action, like clicking a link, signing up for something, or making a purchase.

Available solutions for AI Marketing

The most effective current solutions for bridging the gap between the enormous customer data being collected and the practical next steps digital marketers can take in their future campaigns include the following elements of AI marketing:

Machine Learning

Artificial intelligence, which consists of computer algorithms that can analyze data and automatically improve digital marketing campaigns through experience, powers machine learning.

Machine learning-enabled devices examine new data in the context of pertinent historical data, which can help digital marketing campaigns by informing them on what worked and what didn’t.

Big data & Analytics

As “big data” has risen due to the emergence of digital media, it has become possible for digital marketers to understand their efforts and precisely attribute value across channels. As a result, there is now an abundance of data, and many digital marketers need help deciding which data sets are worthwhile to collect.

AI marketing can assist in quickly sifting through all that data, reducing it to the essentials, analyzing it, and suggesting the most effective components for upcoming digital marketing campaigns.

AI Marketing Tools & Platforms

Digital marketers have a central platform for managing the enormous amounts of data collected thanks to efficient AI-powered marketing solutions. With the help of these AI marketing platforms, you can gather insightful marketing intelligence about your target market and use it to inform your data-driven choices about how to best reach them.

For instance, frameworks like Bayesian Learning and Forgetting can assist marketers in better understanding a customer’s level of receptivity to a particular digital marketing effort.

Current challenges of AI in marketing

In-depth knowledge of customer needs and preferences and the capacity to act swiftly and effectively on that knowledge are essential components of modern marketing. AI marketing solutions are now in the spotlight for marketing stakeholders thanks to the ability to make real-time, data-driven decisions.

When deciding how to integrate AI into their campaigns and operations, marketing teams must exercise caution. The creation and application of AI marketing tools are still in their infancy. As a result, when incorporating AI into your digital marketing strategy, there are a few obstacles to be aware of.

Quality of Data and Training Time

Artificial intelligence (AI) marketing tools need to learn what to do to accomplish marketing objectives. Just like humans, they need time and instruction to learn about organizational objectives, client preferences, historic trends, the broader context, and how to become an expert.

Data quality assurances are also necessary for this learning process. Your brand-new AI marketing tool will become nothing more than a toy if it is not trained with high-quality data that is accurate, timely, and representative.

As a result, you’ll end up with wrong data decisions that must accurately reflect consumer desires.


The way businesses use their data is coming under increasing scrutiny from both consumers and regulatory bodies. While implementing an AI marketing strategy, digital marketing teams must ensure they are using consumer data ethically and in accordance with regulations like GDPR, or they run the risk of facing harsh fines and reputational damage.

Your AI marketing tools may go beyond what is considered appropriate in using consumer data for marketing personalization unless they are specifically programmed to follow specific legal guidelines.

Scribbleminds Privacy Policy

Getting Support from stakeholders

It can be challenging for digital marketing teams to convince business stakeholders of the value of AI marketing investments. While KPIs like ROI and efficiency are simple to measure, it can be more challenging to demonstrate how AI marketing has improved customer experience or brand reputation.

In light of this, digital marketing teams should ensure they have the appropriate measurement tools to link AI investments to these qualitative benefits.

Best practices for deployment

Since AI marketing is a more recent tool in the arsenal of digital marketers, there still needs to be transparent best practices to direct marketing teams’ initial deployments. So, there aren’t any established best practices yet.

Every digital marketing team should consider more than the potential short-term gains when implementing an AI marketing strategy.

Dynamics of the Marketing Landscape 

The emergence of AI marketing disrupts the daily operations of marketing. Marketers must assess the jobs that will be eliminated and those that will be created. According to one study, marketing technology will eventually replace almost six out of ten current marketing specialists’ and analysts’ jobs.

How to Marketing with AI?

When implementing AI in marketing campaigns and business operations, starting with a well-thought-out plan is critical. This will guarantee that marketing teams quickly avoid expensive problems and get the most out of their AI investment.

There are a few essential factors for digital marketers to think about before implementing any AI marketing tools:

Establishing KPIs & goals

Your AI marketing program should start with clearly defined goals and marketing analytics, just like any other marketing initiative. Start by identifying areas within campaigns that can be improved. For e.g., segmentation could be improved.

Establishing clear KPIs is then crucial, especially for qualitative goals like “improve customer experience by a chatbot,” which will help show how effective the AI-augmented marketing campaign has been.

Data Privacy Guidelines

Make sure your AI marketing platform will stay within what is considered appropriate data use at the outset of your new marketing program in the name of data personalization.

To maintain compliance and customer trust, ensure privacy standards are established and programmed into your AI marketing platforms as necessary.

Quantity and Sources of Data

Digital marketers typically need access to a large amount of data before using AI marketing. Customer preferences, outside trends, and other elements are the data points that affect the success of AI-enabled marketing campaigns. We will train marketing AI algorithms using this data. This information can be gleaned from the company’s CRM, prior marketing initiatives, and website data.

Marketers may also add second and third-party data to this, such as location data, weather data, and other extraneous elements that might affect a consumer’s purchase choice.

Develop Your Data Science Talent

Working with enormous amounts of data and delivering insights can be challenging when marketing teams need more staff members with the necessary data science and AI expertise.

Organizations should collaborate with outside groups that can help with data collection and analysis to train their tools for the best performance and to make ongoing maintenance easier to get AI marketing programs off the ground. 

I do believe every marketer must learn machine learning and code on his/her own.

Maintaining data quality

Machine learning and AI marketing programs will develop decision-making skills as they process more data. The insights gained from your AI marketing program will only be helpful if the data it receives is standardized and error-free, and they may even lead to choices that worsen rather than improve your bottom line.

Marketing teams must collaborate with data management teams and other lines of business to establish data cleansing and maintenance processes before implementing any AI marketing programs.

When doing so, keep in mind the following seven crucial data dimensions:

  • Promptness
  • Completeness of data
  • Resilience and consistency
  • Pertinence or relevance
  • Openness and transparency
  • Precision

Devise an all-around AI marketing strategy.

Most digital marketers find their AI marketing tools to be especially effective when used in conjunction with an existing marketing strategy rather than as a stand-alone tactic. With the help of AI marketing tools, it is possible to streamline processes that are currently labor-intensive or subject to error, like data analysis or attribution.

Digital marketers can use these powerful, forward-thinking strategies to elevate their marketing by incorporating AI to fill in these “blind spots,” They can also build on the foundation of successful digital marketing strategies they have already employed.

Picking a Platform for AI

The first step in starting an AI marketing program is choosing the right platform or platforms. Marketing professionals need to be astute in spotting the holes that the platform is attempting to fill and wise in choosing solutions based on capabilities.

This will depend on the objective that marketers are attempting to fulfill. For instance, speed and productivity objectives will call for a different functionality than those for increasing customer satisfaction with AI in general.

When choosing a tool, remember the level of transparency you’ll need to understand why an AI marketing platform made a particular choice. Depending on the algorithm, digital marketing teams may receive a detailed report on why a particular decision was made and which data influenced the decision.

In contrast, algorithms working at a more advanced level with deep learning may need to be able to provide conclusive reasoning.

AI platforms (source: topbots)

Advantages of Using Artificial Intelligence (AI) in Marketing

Your digital marketing programs can benefit from various use cases for integrating AI marketing, and each use case has its advantages. AI marketing, for instance, can benefit businesses in several ways, including lowering risk, speeding up processes, increasing customer satisfaction, and generating more revenue. Benefits may be quantifiable (based on sales volume) or not quantifiable (user satisfaction).

There are a few general advantages that apply to all AI marketing use cases:

A higher campaign ROI

By drawing the most critical insights from their datasets and acting on them in real time, marketers can use AI marketing to transform their entire marketing program if it is used properly. To maximize the effectiveness of campaigns and consistently engage customers, AI platforms can quickly decide how to allocate funds across media channels or analyze the best ad placements.

Real-time personalization and improved customer relationships

AI marketing can assist you in sending customers personalized messages at the correct times in the consumer lifecycle. Additionally, it can assist digital marketers in identifying at-risk clients so they can send messages designed to re-engage them with the brand.

Improvements in Marketing Measurement

Since many businesses need help to keep up with the volume of data that digital marketing campaigns generate, it can be challenging to attribute success to particular campaigns.

Dashboards that use AI marketing enable a more thorough understanding of what is practical, allowing it to be replicated across channels and budgets allocated appropriately.

Speed up decision-making

Using machine learning, AI marketing uses tactical data analysis to make decisions more quickly based on campaign and customer context than its human counterparts.

Team members now have more time to concentrate on strategic projects that will eventually guide campaigns with AI support.

With AI marketing, digital marketers can use real-time analytics to choose the best media instead of waiting until the end of a campaign to make decisions.

7 Marketing AI (Artificial Intelligence) Case Studies

In numerous use cases across various industries, AI marketing is used in digital marketing initiatives. Financial services, government, entertainment, healthcare, retail, and other sectors use AI marketing and its optimization capabilities.

Each AI marketing use case offers different outcomes, such as increased campaign performance, improved customer experience, or improved marketing operations efficiency.

Businesses can benefit from AI marketing and machine learning in various ways to develop a more thorough marketing strategy. Here are a few use cases – 

1. Programmatic media buying bidding: 

Making decisions about placing advertisements and messaging is a challenge that marketing teams frequently face. Marketing teams can develop well-informed plans based on user preferences.

Still, these teams need to be more adaptable and agile to change the plan immediately in response to the most recent consumer data. Digital marketers are utilizing AI marketing through programmatic advertising to address this issue.

Programmatic media buying platforms use machine learning to make real-time bids on relevant ad space to target audiences. Data on interests, location, past purchases, buyer intent, and other factors inform the bid.

This makes it possible for digital marketing teams to use AI marketing to target the appropriate channels at the appropriate time and for a reasonable price. Programmatic purchasing is an example of how machine learning can increase marketing flexibility to accommodate changing consumer needs and interests.

2. Right messaging at the right time

Consumers react differently to messages across channels; an emotional appeal may move some, others by humor, and others by logic. AI marketing and machine learning can track which messages customers have interacted with and build a more thorough user profile.

From there, based on the users’ preferences, marketing teams can deliver more tailored messages to them.

For instance, Netflix uses machine learning to identify the genres that a particular user is drawn to. The artwork the user sees is then tailored to reflect their interests.

The author of the Netflix Tech Blog describes how the company uses AI tools and algorithms to determine which visuals will most persuade viewers to watch a particular title, saying:

“Let’s try to make the image we use to describe the movie Good Will Hunting more distinctive. Here, we might tailor this choice based on how much a particular member enjoys a particular genre or set of themes. If we display the artwork featuring Matt Damon and Minnie Driver, someone who has watched a lot of romantic comedies might be interested in Good Will Hunting. However, if we display the artwork featuring Robin Williams, a well-known comedian, someone who has watched a lot of comedies might be drawn to the film.

from Artwork Personalization at Netflix. Written by Ashok Chandrashekar, Fernando Amat, Justin Basilico and Tony Jebara

Utilizing machine learning and AI marketing tools, these platforms can compile insightful customer data that enables marketing teams to boost conversion rates and enhance the customer experience.

Then, using all this data, marketing teams can develop a more nuanced understanding of the customer, taking into account extra elements like whether a user would have watched a title regardless of the image and how that might affect upcoming messaging.

3. Personalization at a granular level

Consumers of today demand an excellent level of personalization. A user’s interests, past purchases, location, previous brand interactions, and a variety of other data points should all be considered when crafting marketing messages.

Marketing teams can now learn more about consumer preferences on a detailed, individual-level thanks to AI, which goes beyond traditional demographic data. This assists brands in curating experiences based on a customer’s particular preferences.

For instance, Spotify uses AI marketing tools to generate personalized playlists based on what a user has previously listened to, the hottest songs right now across genres, and what music is trending.

These datasets build user-specific playlists and genre playlists based on artists that pop up in conversations, articles, etc. This information has assisted Spotify in elevating the customer experience through personalization and helping it become a leading streaming service.

Atomic content is a further trend based on AI-enabled personalization. When creating a customized email or offer for a client that includes pertinent images, videos, or articles, AI considers the client’s preferences and selects content from a content library.

4. Conversational experiences and chat-bots

Chatbots are now being used to supplement customer service representatives thanks to the advancement of natural language processing through AI. Customers with straightforward questions can turn to chatbots for prompt, precise responses.

They can deliver personalized results by utilizing historical data and previous questions. This gives customer service representatives more time to work on complex requests that require more human nuance.

5. Predictive analytics for marketing

Marketing teams need help to derive insights from their organization’s massive daily data. Using predictive analytics, which uses a combination of machine learning, algorithms, models, and datasets to predict future behavior, AI marketing tools enable marketing teams to make the most of this data.

As a result, digital marketing teams will be better able to position their campaigns by knowing the kinds of products consumers will be looking for and when.

For instance, Amazon makes product recommendations to customers based on their past purchases and behavior, which boosts conversions and customer satisfaction.

By using AI marketing tools to track attribution more precisely, marketing teams can see which campaigns had the most significant impacts on ROI.

6. Marketing Ops

Increasing efficiency across various processes is another essential application for AI marketing tools. AI can assist in automating tactical procedures like classifying marketing data, responding to frequent customer inquiries, and performing security authorizations. Marketing teams now have more time to focus on strategic and analytical tasks.

7. Adaptive Pricing

By enabling dynamic pricing, AI marketing can aid in increasing brand competitiveness. By analyzing vast amounts of historical and competitive data, AI platforms can provide real-time pricing recommendations for products.

The retail industry & airlines have seen particular success with this tactic. It enables businesses to raise sales, differentiates themselves from rivals, and adjust prices to reflect the demand for particular products.

AI Marketing Trends and Predictions

Although AI marketing tools are still primarily recent additions to the marketing field, they are expected to become increasingly popular. The following are a few AI trends that marketers will experience over the coming years and should start preparing for:

AI is still in the nascent phase.

  • According to Gartner, by 2022, AI will replace about 33% of marketing data analysts.
  • Tech firms are aware of the advantages and potential of AI. They were already spending, on average, $20 to $30 billion in 2016. This budget was allocated to deployment and research at a 90% clip.
  • Additionally, Gartner forecasts that by 2020, automation will account for more than 40% of data science tasks.

Scaling up teams using AI

  • Marketing teams will face more pressure to convince executive stakeholders of the value and ROI of marketing. Teams will be able to use AI marketing solutions to achieve these goals, better allocate resources to effective campaigns, and provide the marketing metrics needed to prove the effectiveness of campaigns.

Those who use AI to leverage marketing will replace marketing leaders who don’t.

According to Gartner, those in charge of marketing insights will be less competitive in this shifting marketing landscape. Most respondents to Gartner’s survey either use or plan to use AI solutions in their marketing strategies. In the next three years, only 13 percent of people do not foresee a use for it.


Regardless of your marketing team size, machine learning and AI marketing tools can help boost efficiency, ROI, and productivity while also processing the mountains of data that your team might not otherwise have the time to deal with.

Every little step towards an AI-powered future can help your brand and exceed revenue targets all year long, even if it’s just using a machine learning program to help you write the email subject lines for your next digital marketing campaign.

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