What is AI Marketing and How to Implement It? A Comprehensive Guide

Companies are rapidly embracing AI-powered technologies for business profit – no one wants to be left behind in the AI revolution. According to Forrester, 84% of AI decision-makers report that their executives are eager to adopt generative AI.

Marketing is one of the areas most suitable for AI-powered transformation thanks to its consistent reliance on digital marketing data, which makes it easy to adopt data-trained AI tools. A recent report by PwC indicates that AI-driven marketing is projected to drive 45% of the total global economy by 2030.

Every day, new AI marketing tools emerge, promising to help marketers work faster, smarter, and more efficiently. But how can companies distinguish between mere gimmicks trying to capitalize on the AI craze and genuinely useful technology?

In this article, we will guide you through everything you need to know about AI in marketing, from its benefits to the technologies used and their applications, and equip you with the knowledge needed to develop your own AI marketing strategy.

What is AI Marketing?

Artificial intelligence (AI) marketing involves using advanced AI software and tools — like data models, algorithms, and machine learning — to gain customer insights, design and execute marketing campaigns, create custom marketing content, and personalize the customer journey. The goal of AI marketing is to enhance a company’s marketing performance, boost efficiency, and reduce costs by automating manual tasks and speeding up data analytics.

Some popular examples of artificial intelligence in digital marketing include:

• Chatbots: Automated assistants that handle customer inquiries in real-time.
• Recommendation engines: Systems that suggest products or content based on user behavior.
• Targeted advertising: Ads tailored to your search history and online behavior.
• Dynamic pricing: Flexible pricing strategies on e-commerce sites that adjust based on demand and other factors.

Overall, AI marketing tools use advanced technologies and data to perform marketing tasks that would either take a lot of time for humans or be completely impossible, such as analyzing large data sets to find patterns.

Types of AI Marketing Solutions

In the business world, the abbreviation “AI” is often used to describe a plethora of technologies that can be trained on vast amounts of data to make independent decisions. Here’s a brief list of the actual artificial intelligence tools most commonly used in marketing.

Machine Learning and Large Language Models

Machine learning (ML) allows systems to learn and improve autonomously using neural networks. By feeding large amounts of data into ML algorithms, financial institutions can train models to solve specific problems and gain insights for ongoing improvement. In marketing, machine learning enhances various aspects, such as customer segmentation, personalized marketing, and predictive analytics.

For example, ML algorithms can be used as an AI marketing tool to identify distinct customer segments based on behavior, preferences, and demographics. This helps marketers tailor their strategies to target specific groups more effectively.

Large language models (LLMs), such as GPT-4, can help marketing teams with content creation, customer service, and sentiment analysis. LLMs can generate high-quality content for web pages, social media, and email marketing. They can write product descriptions, craft engaging headlines, create entire articles or customer-oriented guides. LLM also powers customer service chat bots that provide instant responses to customer queries.

Natural Language Processing

Natural language processing powers your AI marketing tool so it can understand texts written in a natural language i.e. any text written by a human for another human(s). It's used to analyze social listening data, emails and text conversations, marketing materials created for previous campaigns, etc.

Brands can use sentiment analysis to monitor social media and gauge public opinion about their products and campaigns. By analyzing customer feedback, they can identify trends, understand customer emotions, and make data-driven decisions to improve their digital marketing strategies.

Marketing teams can also use NLP to analyze large volumes of text data from various sources, including competitor websites, product reviews, and industry reports. This helps them understand market trends, competitor strategies, and consumer preferences, which informs their marketing tactics and product development.

Semantic search

Semantic search algorithms are an essential part of NLP. They extract and categorize relevant keywords into semantic clusters and reduce duplicates in text mining, leading to more accurate analysis of texts. This technology is vital for AI writing tools and search engine optimization.

For example, marketing teams using semantic search can analyze customer reviews to understand common themes and sentiments about their product and customer experience. If customers frequently mention "fast delivery" and "excellent customer service," the algorithm groups these phrases, helping the team highlight these strengths in their marketing campaigns.

Computer vision

Computer vision enables AI marketing tools to gain insights from non-text digital data, such as images. It powers optical character recognition (OCR) to analyze information and signatures in checks, recognize brand logos in videos, and extract text from images for accessibility.

A retailer could use computer vision to analyze customer photos on social media. By recognizing and tracking their products in these images, the retailer can gauge product popularity and customer preferences, helping them tailor their marketing strategies and product offerings more effectively.

Named entity recognition (NER) and neural networks

Named Entity Recognition is an NLP technique that identifies and categorizes specific entities within text, such as names of people, organizations, locations, dates, and more. NER involves parsing text to recognize these predefined categories and assign them appropriate labels.

For example, NER can identify personal names and preferences in customer data, allowing digital marketers to create personalized email campaigns or product recommendations. Recognizing that a customer often mentions “New York” and “vegan restaurants” can help tailor offers related to these interests.

Neural networks provide powerful models capable of understanding and processing natural language with high accuracy. They remember interconnected data points and keep adding them to their knowledge database. This is what enables ML models to provide more precise results and analyze consumer data better with time through deep learning.

Generative AI

Generative AI tools leverage AI technologies such as neural networks, machine learning, and natural language processing to create new and original content. These tools help marketing teams by automating content creation for various marketing channels, enhancing personalization, and optimizing campaign strategies.

For instance, an e-commerce company could deploy generative AI for its digital marketing campaigns. They could send personalized discount offers and product recommendations to customers, ensuring that these marketing emails feel unique and relevant to the recipient’s interests and buying patterns.

How to Use AI in Marketing?

Let's review the primary use cases for AI marketing and explore how the technologies listed in the section above actually benefit marketers.

Marketing data analysis

AI is extensively utilized to analyze marketing data, providing valuable insights into both customer behavior and the effectiveness of marketing campaigns. AI algorithms can analyze big data to identify patterns in behavior, preferences, and purchasing habits. This helps in understanding what drives customer decisions and how they interact with your brand.

Additionally, AI analyzes historical data from marketing campaigns to assess their performance. This includes tracking digital marketing metrics such as click-through rates, conversion rates, and return on investment (ROI). AI helps in identifying which elements of a campaign need adjustment and uses performance data to optimize ongoing campaigns in real-time. For instance, machine learning algorithms can adjust ad targeting, bid strategies, and content based on current performance to maximize results.

Apart from that, AI can identify emerging trends and shifts in consumer behavior by analyzing publicly available big data, such as social listening data and market research. This helps marketers stay ahead of the curve and adapt their strategies to changing market conditions.

Audience segmentation

AI marketing tools can quickly cluster customers based on their behavior, demographics, and preferences, which leads to more precise market segmentation. This allows for tailored marketing strategies that resonate with different customer groups and target audiences.

In a technique known as lookalike modeling, the technology identifies the common traits and characteristics of your top customers based on their preferences and purchase history. This allows you to target new customers who share similar attributes and are likely to be interested in your products or services.

Content generation

Generative AI tools are used in marketing for all kinds of content generation tasks, from planning out complex marketing campaigns to designing and writing copies for emails and landing pages.

GenAI helps improve marketing campaigns by generating engaging communications tailored to each stage of the customer journey. It can craft compelling email subject lines and personalized content for buyer personas and drive intent-based conversations, enhancing customer experience and boosting sales. Moreover, modern AI marketing software can generate audio, images, and video to enrich your campaigns with audio-visual assets.

Customer personalization

AI can help map out the customer journey by analyzing brand interactions across various touchpoints. This provides a comprehensive view of how customers engage with the brand, allowing you to build marketing campaigns that tailor to specific touch points of specific customer segments.

AI can also utilize customer data insights to create highly personalized content and offers. For example, AI marketing tools can help you create content that reflects different social perspectives, making your messaging inclusive and accessible to a more diverse target audience. This could include personalized email campaigns, product recommendations, and targeted advertising, all tailored to individual preferences and behaviors.

Marketing automation

AI marketing tools are used to automate various digital marketing tasks and processes:

In email marketing, AI can segment email lists based on user behavior and preferences, automating the creation of personalized email content for different segments. AI algorithms analyze past email interactions to determine the optimal times to send emails to each recipient, increasing open rates and engagement. AI-driven chatbots and automated email responders handle common customer queries and provide immediate assistance, improving customer service efficiency.

Automated digital marketing campaigns empowered by AI deliver personalized content dynamically based on user behavior, preferences, and real-time interactions. Moreover, AI automates the process of A/B testing different campaign elements, such as headlines and calls to action, and analyzes the results to determine the most effective variations.

Finally, AI helps automate aspects of lead generation and nurturing by assessing and scoring leads based on their behavior and interactions, prioritizing high-potential prospects for follow-up. AI creates and delivers targeted content and offers based on lead behavior and engagement, guiding them through the sales funnel with minimal manual intervention.

Media buying and advertising

AI supports media buying by automating the media placement process through programmatic advertising platforms. This technology optimizes ad spend and targeting in real-time, using customer history, preferences, and context to deliver more relevant ads and improve conversion rates. If you use Google Ads, you’ve likely encountered similar AI features that assist with the auction process.

Not only can AI adjust targeting and bidding strategies but also fine-tune your ad creatives based on performance data to enhance ROI.

Additionally, AI excels in retargeting by continuously learning from each customer interaction or conversion. This enables the system to optimize future content and marketing strategies, ensuring more effective engagement and higher conversion rates.

Reputation management

AI aids in reputation management by monitoring online mentions of a brand across various platforms, including social media, reviews, and news sites. It uses sentiment analysis to understand public perception and identify potential issues or opportunities.

AI marketing tools can then send real-time alerts for negative sentiments or emerging trends, enabling timely responses and proactive reputation management. Additionally, AI can assist in crafting and disseminating positive content to bolster the brand's image.

Competitive intelligence

AI can help analyze your competitors’ digital marketing strategies and evaluate their performance. By analyzing their digital presence, including social media activity, ad campaigns, and content performance, AI identifies trends, successful tactics, and gaps in competitors' strategies.

For example, AI might reveal that a competitor's ads focusing on sustainability are performing exceptionally well, prompting you to incorporate similar themes into your own campaigns to stay competitive.

Benefits of AI in Marketing

One of the analyst reports on AI in marketing from 2023 shared to following quote by a business leader: “Clients are not viewing genAI as a technology solution. They are seeking smarter, more efficient, more precise ways of accomplishing tasks, which AI can provide.”

Indeed, the benefits AI marketing provides are the main point of interest for many digital marketers and business leaders. In this section, we provide a short roundup of the many advantages of AI marketing for businesses.

Effective marketing decisions supported by data

AI excels at analyzing data and extracting valuable insights from unstructured data, transforming it into actionable intelligence that can drive your digital marketing strategy. This capability allows marketers to analyze activities across diverse marketing channels, such as social media posts, customer reviews, emails, and website interactions, which traditional methods might struggle to process efficiently.

This data-driven approach ensures that marketing strategies are not only based on accurate, up-to-date information but also finely tuned to meet customer needs and expectations, ultimately leading to more successful campaigns and higher customer satisfaction.

Higher ROI for your campaigns

AI marketing tools enable marketers to quickly identify actionable insights from campaign data in near real-time. These tools can also determine the best channels for media buys and the optimal ad placements based on customer behavior.

As a part of this process, AI attribution modeling assigns credit to various marketing touchpoints, revealing the effectiveness of different channels and campaigns. Moreover, predictive analytics powered by AI can forecast customer behaviors like churn and purchase likelihood, suggesting personalized engagement and retention strategies.

All in all, these functionalities help you save budgets by making your campaigns more efficient.

Increased customer engagement and loyalty

Forrester’s Marketing Survey, 2023 shows that 77% of global B2B marketing decision-makers agree that buyers and customers expect a personalized experience across sales and marketing Interactions.

AI tools enable marketers to enhance personalization, allowing them to customize, reuse, and update content to meet customer needs better. These tools help marketers quickly create content variations and use real-time, AI-powered search to find assets or content components via an LLM fine-tuned on their internal data.

The resulting personalized content and interactions help build stronger relationships with customers and increase loyalty over time.

Improved productivity through automation

AI in marketing boosts business productivity by automating repetitive tasks, such as email marketing, social media posting, and ad management. This allows marketers to focus on more strategic activities, improving overall efficiency.

Better transparency into your marketing activities

AI provides greater transparency in marketing activities by offering detailed insights and real-time analytics. AI agents can swiftly gather and analyze vast amounts of data, identifying key metrics and performance indicators. This rapid data is then used for the creation of comprehensive reports that highlight campaign effectiveness, customer behavior, and ROI.

With AI, marketers can easily track and understand the impact of their strategies, ensuring informed decision-making and clear visibility into all marketing efforts.

Increased profit

Excellent campaigns, driven by state-of-the-art data and enhanced customer engagement, contribute to higher profits. The combination of precise targeting, improved engagement, and operational efficiency ultimately leads to greater revenue growth and profitability for businesses.

9 Strategies for Implementing AI in Marketing

1. Define your goals

Before starting out, determine what goal or objective you want to reach. Do you want to make your campaigns more effective? Do you want to accelerate certain marketing activities and increase the number of operations?

You can also start by prioritizing a certain area of marketing to try AI in, be it email marketing, social media, or marketing analytics. By focusing on one direction, you make sure AI marketing implementation doesn't feel overwhelming and give yourself a trial period to see how exactly AI can benefit your marketing team.

2. Be aware of generative AI’s limitations

Many marketers praise AI for its ability to create personalized content, such as text and video. However, it's important to recognize the current limitations of these tools, especially when it comes to content quality, for example GenAI's difficulties with realistic human fingers in images.

Be selective in how you apply AI, ensuring you use it in areas where it can deliver high-quality results, and avoid relying on it where it falls short.

3. Audit your infrastructure and ensure robust data management

Before using an AI marketing platform, evaluate your current tools and infrastructure for AI adoption opportunities.

Create a report outlining potential implementation areas, expected outcomes, and required resources. Since AI capabilities primarily rely on the data it’s trained on, it’s vital to assess your data’s quality, quantity, and accessibility. High-quality data ensures AI provides valuable insights and strategic recommendations.

4. Adhere to data privacy laws

Adhering to data protection laws is crucial when using AI in marketing, as the use of customer data for training and implementation must not violate privacy regulations. One of the biggest challenges for AI marketing solutions is ensuring that customer data is handled securely and ethically. Organizations must prioritize maintaining customer privacy throughout the AI training process to avoid heavy fines and legal repercussions.

To address this challenge, it is essential to foster a culture of ethical AI usage within the organization. This involves updating guidelines and processes to ensure transparent data practices and compliance with data privacy laws. Implementing clear opt-in/opt-out mechanisms and effectively communicating data usage policies are vital steps in protecting customer information.

5. Prioritize AI governance

Before implementing AI for marketing, brands should prioritize governance to mitigate risks and costs. Effective governance includes ensuring consumer data privacy, copyright compliance, and incorporating human intervention to ensure quality output.

By establishing strong governance, brands can safeguard data privacy, avoid legal issues, and maintain the integrity and effectiveness of their AI-generated content.

6. Select the right AI marketing tools

Choose an AI marketing tool that aligns with your business objectives, enhances your marketing efforts, and ensures compliance with data privacy regulations. When browsing the vendors, here's what to look out for:

• Functionality: Ensure the tool offers essential features like predictive analytics, personalization, content generation, customer segmentation, and campaign automation.
• Customization: Look for tools that allow customization to fit your specific marketing needs and workflows. For extensive customization options, consider purchasing a license for an AI no-code platform. For example, Creatio Copilot allows you to create custom AI use cases to automate your marketing processes without the need for software development skills.
• Integration: Check if the tool integrates seamlessly with your existing marketing platforms, CRM systems, and other tools from your tech stack.
• Ease of use: A user-friendly interface that is intuitive and easy to navigate.
• Scalability: It should be adaptable to future technological advancements and changes in your marketing strategies.
• Pricing structure: Understand the pricing model and ensure it fits within your budget.
• Industry relevance: Consider if the tool has been successfully used in your industry.

7. Train your staff

When implementing AI marketing in your organization, it is crucial to ensure your marketing team has the necessary training and knowledge to effectively utilize these advanced tools. Assessing your staff's current capabilities is the first step. You may need to invest in training for your existing team, hire a consultant, or even create a new position dedicated to driving your AI initiatives forward.

Position this as an opportunity for your team to reskill, learn new technologies, and become better marketers. As a VP at a generative AI company told Forrester, “You need your current team — just in different ways. With generative AI, you shift the focus from just producing words to brainstorming, conducting interviews, refining, and editing. It’s about creating quality content with diverse skills.”

Providing comprehensive training on how AI-driven natural language generation tools work will also inspire your team to identify new AI use cases, fostering wider adoption and integration of AI within your organization.

8. Test your AI tools

Finally, it's time to test your AI marketing initiatives. Choose your top areas for implementation and launch your programs. Be sure to set a timeframe and target KPIs to evaluate results after the trial period.

For example, if testing AI-written and AI-placed social media ads, run a trial for a month. Monitor and edit the content throughout, documenting the process.

After the trial, compare the performance of AI-generated, human-generated, and AI-assisted content to evaluate effectiveness and use these insights to create a plan for moving forward.

9. Train brand-specific models

If your AI marketing platform allows it, train custom models based on your own data. Use a comprehensive dataset that includes past campaigns, emails, messages, website copy, and internal branding to enable tailored recommendations that reflect your brand's style.

A robust data infrastructure is essential for successful model implementation and content generation, especially as brands start deploying exclusive language model instances.

Challenges in Implementing AI in Marketing

Training AI solutions:

Custom AI models need extensive training to perform specific tasks. For instance, if you want an AI to engage customers effectively, you'll need a substantial amount of customer data and possibly data scientists to train it.

Data quality and accuracy:

AI's effectiveness depends on the quality of the data it's trained on. Poor data leads to inaccurate or low-quality outputs. Generative AI, while advanced, often produces content with factual errors. Regular human oversight is necessary to ensure accuracy and maintain brand consistency.

Compliance with privacy laws:

AI relies on personal data, making strict adherence to privacy laws essential. Non-compliance can result in fines and reputational damage. It's crucial to follow regulations like GDPR and invest in AI governance to protect consumer information.

Copyright and legal concerns:

Generative AI can produce content similar to existing works relying on proprietary data analysis, raising copyright issues. As AI-generated content's legal status evolves, it's essential to understand how copyright laws apply and ensure your content remains original.

The Future of AI Marketing

In the future, marketers will increasingly rely on AI to make predictions from unstructured data and use first-party data to guide generative AI in creating customer-focused content that aligns with their brand.

AI marketing will evolve to address the imperfections in current AI models. To enhance business insights, AI tools must be fair, secure, reliable, inclusive, and transparent. This requires thoughtful development and training with diverse data to eliminate biases.

Expect more regulations around data privacy, copyright, and AI governance to ensure ethical use of AI technology. As AI advances, companies must focus on security, with 68% of customers emphasizing the need for trustworthiness. Brands will need to implement robust privacy measures and secure their data effectively.

Looking ahead, AI could transform the entire marketing process, automating campaign briefs, content creation, and performance analysis, all while keeping humans in control.

Harnessing the Power of AI with Creatio

You may think that AI marketing implementation requires a lot of technical resources. According to Computer Weekly, 20% of tech execs might turn to shadow HR to upgrade talent acquisition due to the lack of skills like AI, which are harder and more expensive to find. One way to mitigate this issue is to utilize no-code AI-powered platforms.

Creatio stands out as a user-friendly and infinitely customizable marketing automation solution powered by AI. Its unique composable no-code platform allows users to create and implement pre-packaged and custom AI use cases without any need to hire professional developers.

Creatio's product portfolio feature CRM Creatio and the comprehensive Marketing Creatio platform. The latter is focused on marketing automation and covers the entire customer lifecycle, from the initial contact and hand-off to sales to ongoing customer engagement and marketing efforts post-sale.

Creatio's primary AI offering, Creatio Copilot, is a sophisticated AI assistant designed to interpret natural language queries and serve as a dependable resource for all your marketing requirements, including:

• Audience segmentation
• Campaign flow design
• Rich content generation
• Lead scoring
• Lead management assistant
• Content localization and translation
• Content personalization
• Customer intent and behavior analysis
• Digital ad assistance
• Customer insights
• Document management and intelligent search
• .. and other use cases

For example, you can simply provide a narrative of your desired marketing campaign, and Copilot will generate a well-structured campaign flow based on previous successful campaigns, available playbooks, and best practices. It takes into account user preferences, suggesting communication channels, promo materials, and engagement strategies.

An example of Creatio Copilot used to create a digital marketing campaign

For easy audience segmentation, marketers can simply brief Copilot on specific requirements for segmentation, and it will create required rules and filters. It can also apply a lookalike approach and select a target audience with a higher response potential.

An example of audience segmentation with Creatio Copilot

In addition to these turn-key capabilities, Creatio Copilot's workplace allows you to build new AI use cases – you can tell the assistant what process you want to automate or what data to analyze, and Creatio Copilot will build the new model for you.

The platform also provides several pre-packaged machine learning models that you can train on your own data for enhanced analytics and workflow automation. It also integrates with OpenAI and ChatGPT, facilitating routine tasks such as drafting emails and blog post generation.

Creatio was recognized as a leader in the 2023 Gartner® Magic Quadrant™ for B2B Marketing Automation Platforms Report and was recently included in the Everest Group's Innovation Watch Assessment for Generative AI Applications.

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