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AI in marketing: A complete guide

This guide covers everything you need to know about how to use AI in marketing—including best practices for getting started.

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What is AI in marketing?

AI in marketing has been around for decades, but the release of ChatGPT in November 2022 dramatically increased public awareness of this rapidly evolving technology. ChatGPT demonstrated how generative AI can comprehend and produce natural-sounding text, opening up new possibilities for customer engagement, content creation, and more.

But let’s back up for a moment. What exactly is artificial intelligence, anyway? Think of it like this: AI is a super-smart assistant that can sift through marketing data, spot patterns, and suggest the next best action. It works by combining a few related technologies:

Machine learning (ML): Machine learning allows computers to learn from data and improve over time without being explicitly programmed to do so. When it comes to AI for marketing, ML can predict customer preferences, improve ad targeting, and analyse campaign performance.

Natural language processing (NLP): NLP enables a computer to understand, interpret, and respond to human language. This technology powers advanced virtual assistants, making them capable of holding natural conversations with customers.

Large language models (LLMs): These are advanced AI models that can generate human-like responses based on the data they’ve been trained on. LLMs can be used in AI for marketing to create personalised content, draft emails, and write blog posts.

What are the benefits of AI in marketing?

Maybe you’re saying to yourself: “This all sounds fine—in theory. But what does it actually look like in practice?” Here are some real-world examples of AI in marketing:

Chatbots and virtual assistants

AI-powered chatbots and virtual assistants are becoming increasingly common on websites and apps. For example, companies like Sephora use AI chatbots to provide personalised product recommendations based on customer preferences and past purchases.

Predictive analytics

Predictive analytics uses AI to examine historical data and forecast future outcomes. With AI for marketing, this can be used to forecast customer behaviour, such as identifying which customers are likely to make a purchase or which are at risk of churning. Retailers like Target use predictive analytics to send personalised offers to customers, anticipating their needs based on past shopping habits.

Dynamic pricing

AI-driven dynamic pricing allows businesses to adjust prices in real time based on factors such as demand, competition, and even weather conditions. Airlines and ride-sharing companies like Uber often use dynamic pricing to maximise profits. For instance, during periods of high demand, prices might increase to reflect the scarcity of available options, whilst during slower periods, prices might be lowered to attract more customers.

AI-generated content

Content creation is another area where generative AI in marketing is making significant progress. For example, The Washington Post uses an AI tool called Heliograf to generate short news reports and updates during events such as the Olympics.

Social media listening and sentiment analysis

AI marketing tools can monitor social media platforms to track mentions of a brand, product, or service, and analyse the sentiment behind these mentions. This process, known as sentiment analysis, helps companies understand how customers feel about their brand in real time. Brands like Starbucks use AI-driven social listening tools to gauge customer sentiment, identify trending topics, and even respond to customer feedback.

Programmatic advertising

Programmatic advertising uses AI to automate the buying and placement of adverts in real time, targeting specific audiences with precision. For example, a company like Audi might use programmatic advertising to target ads for luxury vehicles at users who have shown interest in high-end products.

Voice search optimisation

With the rise of voice-activated devices like Amazon’s Alexa and Google Home, optimising for voice search has become increasingly important. For instance, Domino’s Pizza uses AI to enable customers to order pizza through voice commands on their smart speakers.

What are the challenges of AI in marketing?

While AI in marketing offers many benefits, it also comes with its own set of challenges. It’s important to be aware of these potential obstacles so you can find the right way forward.

AI bias

AI bias occurs when the data used to train an AI system reflects existing prejudices, leading the AI to produce skewed or unfair outcomes. For example, an AI-powered customer segmentation tool may inadvertently group users based on superficial characteristics rather than meaningful behavioural patterns. This oversimplification could lead to less effective marketing campaigns and missed opportunities to engage certain customer groups.

Data privacy

AI in digital marketing often relies on customers’ personal information such as browsing history, purchasing behaviour, location, and even social media activity. This type of data can reveal a lot about an individual, making it both valuable and extraordinarily sensitive. If that data is mishandled, it could lead to a costly breach—and a loss of customer trust.

Complexity and skills gap

AI systems can be highly complex, requiring specialised knowledge to set up and maintain. Many marketing teams may not have the necessary expertise in-house, so they may need to invest in training or hire new talent. (One way around this obstacle is to choose AI tools that are user-friendly.)

Integration with existing systems

Many organisations have legacy systems that are not designed to work seamlessly with modern AI technologies. This can lead to compatibility issues, data silos, and inefficiencies.

Transparency and explainability

AI algorithms can sometimes be opaque—in other words, they make decisions without clear explanations. This lack of transparency can be problematic, especially in marketing, where understanding why a particular decision was made (for example, targeting a specific customer segment) is important for refining strategies and maintaining trust.

How to use AI in marketing: Nine tips for putting your best foot forward

Like any technology, AI in marketing tends to be most effective when it’s applied according to proven principles. These guidelines can help you take your AI marketing strategy to the next level.

Start with clear goals: Before diving into AI, you need to know what you want. Are you looking to improve customer engagement? Increase sales? Enhance the customer experience? Clear, measurable goals will guide your AI marketing strategy and help you evaluate how it’s performing. Try starting small by targeting specific areas where AI might have the most impact, then expand as you see results.
Invest in quality data: Remember, AI models learn from the data they’re fed—rubbish in, rubbish out. Investing in high-quality data is crucial for AI to deliver meaningful insights and results.
Choose the right AI marketing tools: The enterprise AI landscape is vast, with a wide range of tools and platforms on the market. It’s important to find the right marketing solutions with built-in AI that align with your goals and work with your CRM system. Ensure you consider factors such as ease of use, scalability, and support when choosing which solution to use. Don't rush the selection process; thoroughly research and test marketing solutions to find the best fit.
Keep humans in the loop: AI should enhance, not replace, human interaction. For example, AI can help you tailor your messaging, predict customer needs, and provide instant support through chatbots. But many customers still value the human element in customer service—so ensure to strike the right balance between AI and human involvement.
Monitor and adjust: Artificial intelligence marketing isn’t a set-it-and-forget-it solution. Continuous improvement is the secret to long-term success. Keep a close eye on the performance of your AI initiatives to ensure they’re meeting your goals. Gather feedback from customers and stakeholders to understand the impact of AI on your marketing efforts, then make adjustments so you can keep improving.
Make it collaborative: AI in marketing often requires working closely with IT, data science, and customer service. Encourage regular communication between teams to align goals, share insights, and work through challenges together.
Educate and empower your team: For AI to be successful, your marketing team needs to understand how to use these technologies effectively. Invest in training and education to give your team the skills they need to make the most of AI.
Test and experiment: One of the strengths of AI is its ability to run experiments and improve in real time. Take advantage of this by continuously testing different AI-driven strategies and tactics, whether it's A/B testing email subject lines or experimenting with different customer segments.
Consider environmental impact: Look for AI tools for marketing that are designed with energy efficiency in mind. By making sustainability part of your AI marketing strategy, you can contribute to broader environmental goals.

Examples of AI in marketing

Maybe you’re saying to yourself: “This all sounds fine—in theory. But what does it actually look like in practice?” Here are some real-world examples of AI in marketing:

Chatbots and virtual assistants

Predictive analytics

Dynamic pricing

AI-generated content

Social media listening and sentiment analysis

Programmatic advertising

Voice search optimisation

AI and the future of marketing

As technology continues to evolve, AI will become even more deeply embedded into nearly every aspect of digital marketing—offering new strategies on a once-unthinkable scale. Here’s a look at some of the emerging trends that are already changing the future of marketing:

Hyper-personalisation

AI has the potential to create custom adverts that resonate on a deeply personal level. For example, generative AI in marketing could eventually create unique video adverts for each viewer based on hyper-specific behavioural and contextual data.

AI-enhanced augmented reality (AR) and virtual reality (VR)

AR and VR are already transforming the way consumers interact with products, but the integration of AI will take these technologies to new heights. AI can enhance AR and VR experiences by making them more interactive and contextually relevant. For example, an AI-powered AR app could allow customers to visualise how a piece of furniture would look in their living room by adjusting the lighting, colours, and placement based on personal preferences and room dimensions.

Emotion AI

Emotion AI, also known as affective computing, is an emerging field that involves AI systems capable of recognising, interpreting, and responding to human emotions. In marketing, emotion AI could be used to enhance customer interactions by tailoring messages and experiences based on the emotional state of the user. For example, AI could analyse a customer’s facial expressions, tone of voice, or text inputs to determine their mood and adjust marketing content accordingly.

Blockchain and AI integration

The combination of blockchain technology and AI holds huge potential for the future of marketing. Blockchain can provide a secure, transparent, and decentralised way to store and manage data, while AI can analyse and utilise that data for marketing purposes. That could revolutionise digital marketing by creating a more trustworthy and efficient ecosystem. For example, blockchain could be used to verify the authenticity of ad impressions, ensuring that marketers only pay for genuine engagements.

Sustainable AI in marketing

The future of marketing will likely see a focus on reducing the environmental impact of AI technologies. This might include the development of more energy-efficient AI algorithms, the use of renewable energy sources to power data centres, and a greater emphasis on sustainable data management practices.

AI-driven market research

Traditional market research methods can be time-consuming and expensive, but AI is set to revolutionise this field by enabling faster, more accurate insights. In the future, AI will be able to conduct real-time market research by analysing vast amounts of unstructured data from social media, forums, and other online platforms. That will allow businesses to stay ahead of market trends, understand consumer sentiment, and identify new opportunities more quickly.

Autonomous marketing systems

The ultimate future of AI tools for marketing could be the development of fully autonomous marketing systems. These systems would be capable of managing entire marketing campaigns from start to finish with minimal human intervention. They could set goals, develop strategies, create content, deploy adverts, and optimise performance in real time, all while learning and adapting to changing market conditions. While human oversight will always be important, these autonomous systems could significantly reduce the manual effort required in marketing—allowing teams to focus on higher-level strategic tasks.