Innovative technological solutions are being swiftly adopted by many firms to increase operational effectiveness and improve customer satisfaction. By utilising these platforms, marketers may be able to develop a more nuanced and comprehensive image of their target audience. The information obtained through this process may then be used to boost conversions while also lowering the work put forward by marketing teams.

How does artificial intelligence (AI) marketing operate?

Artificial intelligence (AI) marketing makes decisions on an automated basis using data collection, analysis, and additional audience or economic trend observations that may have an impact on marketing activities. When speed is a must, AI is regularly used in marketing initiatives. Based on data and client profiles, AI systems learn how to communicate with customers effectively, then send them personalised messages at the ideal time without the involvement of marketing people, ensuring maximum productivity. Many marketers nowadays are using AI to support marketing teams or to carry out more tactical tasks that don’t require as much human skill.

Examples of applications for artificial intelligence in marketing include:
  • examination of data
  • natural language processing
  • the buying of media
  • automated decision-making
  • content creation
  • instantaneous personalization

What Artificial Intelligence Is and Isn’t in Marketing

Unquestionably, artificial intelligence plays a significant role in helping marketers interact with consumers. The most effective current solutions for bridging the gap between enormous amounts of consumer data obtained and actionable next actions that may be applied to future campaigns include the following AI marketing components:

Machine intelligence (AI)

Machine learning, which uses computer algorithms that can analyse data and get better automatically over time, is powered by artificial intelligence. Machine learning tools evaluate fresh data in light of pertinent historical data, enabling them to draw conclusions about what has and has not worked in the past.

Big Data analytics

Big data has flowed in as a result of the growth of digital media, enabling marketers to more accurately assess their efforts and distribute value across channels. This has led to a glut of data as many marketers struggle to determine which data sets are valuable to collect.

AI Platform Solutions

Marketers can handle the enormous amounts of data they are collecting on a single platform with the help of efficient AI-powered solutions. These platforms could give you useful marketing information about your target market, enabling you to make data-driven choices about how to speak to them in an effective way. Frameworks like Bayesian Learning and Forgetting, for instance, may help marketers more accurately gauge a consumer’s receptivity to a certain marketing strategy.

Obstacles in AI Marketing

The ability to act quickly and efficiently in response to customer preferences and needs is essential for modern marketing. As a result of its ability to make instantaneous, data-driven decisions, AI has become increasingly important to marketing stakeholders. On the other hand, marketing teams must be cautious when deciding how to integrate AI into their campaigns and procedures. The creation and use of AI tools are still in their infancy. As a result, while incorporating AI in marketing, there are a few things to think about.

Training time and data quality

Tools using artificial intelligence (AI) do not always know what actions to take to accomplish marketing goals. They will require time and training to understand corporate objectives, customer preferences, historical trends, comprehend the whole context, and develop expertise. This requires time and reassurance of the quality of the data. The tool will generate poor decisions that do not reflect user preferences if AI technologies are not trained on high-quality data that is accurate, timely, and representative, reducing the instrument’s usefulness.


Both consumers and government authorities are exerting pressure on firms to use personal data more responsibly. Marketing teams must ensure that they are treating client data sensibly and in compliance with laws like the GDPR, or they risk facing harsh fines and reputational damage. This presents a problem for AI. The tools may go beyond what is considered permissible in terms of customization using client data unless they are specifically designed to adhere to specific regulatory restrictions.

How to Get Buy-In

Selling the advantages of AI investments to company stakeholders may be difficult for marketing teams. While KPIs like ROI and efficiency are easy to gauge, it is more challenging to show how AI has improved customer experience or brand reputation. In light of this, marketing teams must make sure they have the resources needed to attribute these qualitative benefits to AI investments.

Optimal Techniques for Deployment

Because AI is a newer marketing technology, there aren’t currently any established concrete best practises to guide marketing teams through their initial implementations.

Changing Marketing Environment: Adaptation

The emergence of AI will constantly alter marketing activity. The jobs that will be destroyed and the jobs that will be created must be decided by marketers. One prediction is that ultimately, nearly six out of ten jobs for marketing analysts and experts will be replaced by technology.

How Can Marketing Use Artificial Intelligence (AI)?

It’s crucial to begin with a well-thought-out plan when implementing AI in marketing campaigns and business processes. This will assist marketing teams in avoiding expensive challenges and maximising the return on their AI investment as quickly as feasible.

Before using AI technology in marketing initiatives, there are a few important factors to take into account:

Establish goals

As with any marketing effort, it is essential to establish specific objectives and marketing metrics early on. Start by discovering potential applications for AI inside campaigns or processes, such as segmentation. Next, specify the KPIs that will help disclose the success of the AI-enhanced campaign for qualitative goals like “improve customer experience.”

Laws governing data protection

When you launch your AI programme, be sure that your AI platform will not go beyond the bounds of authorised data usage in the name of personalization. As appropriate, develop privacy standards and programme them into platforms to maintain compliance and consumer confidence.

Data Sources and Amount

To begin using AI for marketing, marketers need to have access to a lot of data. This will teach the AI tool about consumer preferences, broader trends, and other factors that have an impact on how well AI-powered advertising performs. Through the company’s CRM, marketing campaigns, and website, this information may be discovered. Additionally, marketers may improve this by using second- and third-party data. This might include details like location, the weather, and other environmental factors that affect a buyer’s decision.

Employ data science professionals

It can be difficult to work with enormous amounts of data and develop insights when marketing teams lack individuals who are knowledgeable in data science and artificial intelligence. Companies should work together with other groups that can assist with data collection, analysis, and ongoing maintenance in order to get projects off the ground.

Maintain Data Quality

As they process more data, machine learning algorithms will have the ability to make accurate and efficient decisions. The insights will be meaningless, and AI systems may even make decisions that hurt marketing campaigns, if the data is not standardised and error-free. Before implementing AI marketing, marketing teams must create data cleansing and maintenance procedures in collaboration with data management teams and other business lines. While doing so, take into account the following seven data dimensions:

  • Timeliness
  • Completeness
  • Consistency
  • Relevance
  • Transparency
  • Accuracy
  • Representativeness

Platform Selection for Machine Learning

One of the most important initial steps in starting an AI marketing campaign is selecting the appropriate platform or platforms. Marketers need to be aware of the gaps the platform is aiming to fill and select solutions based on their suitability. The goal that marketers are trying to accomplish will dictate this; for instance, solutions that employ AI to increase overall consumer pleasure would require different capabilities than tools that enhance speed and efficiency. Consider how much openness you’ll need from a tool while trying to comprehend why an AI platform came to a given result. Depending on the algorithm in use, marketing teams may get a clear report on why a certain decision was made and which data contributed to the result, however algorithms working at a more advanced level with deep learning may not be able to offer as definitive an explanation.

Artificial intelligence’s Benefits for Marketing

AI may be applied to marketing in a number of ways, and each of these approaches has its own set of advantages, like less risk, accelerated speed, improved customer satisfaction, greater revenue, etc. Benefits may or may not be quantifiable (in terms of revenue) (user satisfaction). All applications of AI can benefit from a few general advantages:

Return on Investment (ROI) for campaigns has grown

If marketers use AI successfully, drawing the most relevant insights from their datasets and acting on them in real-time, they may use it to change the entire marketing campaign. To guarantee that customers are continually engaged and to increase the value of campaigns, AI systems can make rapid decisions on how to spend money efficiently across media channels or investigate the most successful ad placements.

Better Customer Relationships & Real-Time Personalization

AI can help you reach customers with tailored messaging at the opportune moments in their lives. AI might help marketers spot clients who are at danger and reach out to them with information that would entice them to do business again.

Enhanced Marketing Measures

It can be challenging to credit success to specific projects since many organisations find it difficult to keep up with the volume of data produced by digital endeavours. A more comprehensive view of what is working is offered by AI-powered dashboards, enabling it to be replicated across channels and resources to be allocated correctly.

Make decisions more quickly

AI can analyse tactical data more quickly than humans can, and it uses machine learning to make judgments quickly depending on campaign and consumer context. Team members may now devote more time to key initiatives, which can then be leveraged to direct AI-powered marketing. Thanks to AI, marketers can use real-time data to choose more effective media instead of waiting until the end of a campaign to make decisions.

7 Case Studies of Artificial Intelligence in Marketing

A wide range of businesses, including financial services, government, entertainment, healthcare, retail, and more, are using artificial intelligence (AI) in marketing efforts. Each use case results in a different consequence, such as a better customer experience, more successful campaigns, or more effective marketing operations.

A more comprehensive marketing plan may be built by firms using a number of machine learning techniques. Keep in mind the following:

1. Bidding for programmatic media purchases

A challenge that many marketing teams have is deciding where to show advertisements and content. While marketing teams can make wise choices based on user preferences, they are not adaptable or nimble enough to shift course immediately in response to fresh consumer data. To solve this problem, marketers are using programmatic advertising and AI. Programmatic platforms utilise machine learning to make real-time bids on ad space that is relevant to target audiences. The bid is determined using information such as interests, location, purchasing history, buyer intent, and more. As a result, marketing teams may effectively and affordably target the appropriate channels at the appropriate time. Programmatic purchasing is one example of how machine learning may increase marketing flexibility to meet clients’ shifting needs and interests.

2. Select the Proper Message

Different audiences react to various messages across media; some may be affected by an emotional appeal, others by humour, and yet others by reason. Artificial intelligence (AI) and machine learning can track which messages consumers have responded to and provide a more thorough user profile. Depending on the consumers’ choices, marketing teams may then send them communications that are more tailored. For instance, Netflix uses machine learning to determine the genres that its users are most interested in. The artwork the viewer sees is then customised to suit their preferences. On the Netflix Tech Blog, they explain how they use algorithms to determine which visuals will most effectively persuade a viewer to watch a particular movie, saying:

“Perhaps we might try to tailor the image we use to represent Good Will Hunting. Depending on how much a specific member like a certain genre or subject, we could tailor this choice. If we put up the poster for Good Will Hunting with Matt Damon and Minnie Driver, someone who has watched a lot of romantic movies might be drawn to it, but if we put up the poster with the well-known comic Robin Williams, someone who has seen a lot of comedies may be as well.

When AI and machine learning are used, these systems may gather a lot of consumer data, enabling marketing teams to increase conversion rates and enhance the customer experience. Then, using all of this data, marketing teams may create a more thorough portrait of the customer, including factors like whether a user would have read a headline without the accompanying image and how it would effect next communications.

3. Personalized attention to detail

Modern consumers want a great degree of precise customization. Marketing communications should take into account a user’s interests, purchasing history, location, prior brand interactions, and a range of other data aspects. In addition to the usual demographic information, marketing teams may utilise AI to learn about client preferences on a specific, tailored level. This gives advertisers the ability to create customised experiences based on the interests of their clients. For instance, Spotify uses AI to create customised playlists based on what a user has previously listened to, the most popular songs right now across genres, and what songs are trending. These databases are used to create genre playlists based on artists mentioned in discussions, magazines, and other sources, as well as tailored playlists for users. This has helped Spotify become a well-known streaming service that prioritises user experience through personalization.

Another idea for personalization using AI is atomic content. Here, AI builds a customised email or offer for a client based on consumer preferences and chooses pertinent images, videos, and articles from a content repository.

4. Chatbots and Conversational Experiences

Thanks to AI’s breakthroughs in natural language processing, chatbots are being used more often as a complement to customer service professionals. Customers with simpler inquiries can employ chatbots, which will provide prompt and accurate answers. By utilising past inquiries and historical data, they would be able to provide customised results. This gives customer service agents more time to focus on complicated issues that call for more nuanced human interaction.

5. Predictive Marketing Analytics

Marketing teams are having a hard time gleaning insights from the influx of data. Marketing teams may maximise the use of this data with the use of predictive analytics, which forecasts future behaviour using a combination of machine learning, algorithms, models, and databases. This can help marketing teams place advertising more effectively by helping them understand the kind of items customers will be looking for and when they will be looking for them.

an illustration. Amazon uses predictive analytics to suggest products to consumers based on their past purchases and buying patterns, increasing conversions and customer satisfaction. Using AI, marketing teams can more precisely track attribution and identify the activities that had the most impact on ROI.

6. Sales and marketing operations

Increasing efficiency across a range of tasks is one significant use of AI in marketing. Automation of tactical processes like sorting marketing data, responding to common consumer inquiries, and security authorizations can be helped by AI. This gives marketing teams more time to concentrate on strategic and analytical duties.

7. Adaptive Pricing

AI can help organisations become more competitive by offering dynamic pricing. AI technologies may recommend the best prices for products in real time by evaluating enormous volumes of historical and competitive data. In the retail industry, this strategy has shown to be very effective. It helps businesses to raise sales and gain a competitive edge by changing price to match the demand for particular products.

AI Marketing Trends and Predictions

Even though artificial intelligence (AI) is still a relatively new concept in the marketing industry, its acceptance is anticipated to grow. In the coming years, marketers should start adjusting to the following AI developments:

The use of artificial intelligence is growing: by 2022, 33% of marketing’s data analysts will be replaced by AI, according to Gartner.

The advantages and possibilities of AI have been acknowledged by IT firms. In 2016, they had already spent, on average, $20 to $30 billion. 90% of the entire funding was allocated to deployment and research.

Additionally, according to Gartner, the percentage of automated positions in data science will rise to above 40% by 2020.

AI will cause teams to expand

The demand on marketing teams to prove the worth and return on investment of marketing to executive stakeholders will increase. Teams will leverage AI tools to assist them in achieving these objectives, more effectively distribute funding to successful initiatives, and provide marketing analytics that demonstrate the efficacy of campaigns.

Those that employ AI in their marketing will supplant those who do not.

People in charge of marketing insights won’t be as competitive in this changing marketing environment, according to Gartner. Most of the respondents to Gartner’s survey indicated that they were now utilising or intended to employ AI in their marketing strategy. Only 13% of people do not think it will be helpful in the following three years.

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