How to use AI in marketing, beyond content creation

If you are a marketer or a business owner managing your own promotions, chances are that you probably started using AI for writing an ad copy, social media caption, or blog post.

But AI's capabilities go far beyond generating generic marketing content. From helping customers discover your brand to influencing their decision to convert, AI can support almost every stage of the marketing process.

In fact, around 67% of SMBs already use AI in marketing, not only because of its efficiency, but also because it enables lean teams to do more with fewer resources.

In this guide, you will learn more about how to use AI in marketing for different use cases, apart from generating content.

What is AI in marketing?

AI in marketing refers to using artificial intelligence, machine learning, or natural language processing to handle marketing tasks such as writing an email, personalizing campaigns, or identifying new marketing opportunities.

Different use cases of AI in marketing

Here are some of the most common use cases of AI marketing:

How to use AI in marketing: Use cases

Market research and customer segmentation

Manually analyzing and grouping customers is a time-consuming process. This will become difficult as your customer base grows.

With AI, this process is both simplified and elevated. AI makes it possible to analyze large volumes of customer data and identify patterns in customer interests, behaviors, or traits that may be otherwise difficult to detect.

For businesses that invest in marketing, proper audience segmentation also serves as the foundation for various marketing activities, such as personalized email campaigns, product recommendations, targeted advertising, or tailored offers.

Businesses that use customer segmentation can see almost two to three times higher conversion rates. However, this also depends upon the effectiveness of AI-powered segmentation, which again hinges heavily on the quality and volume of customer data available.

Personalization and email marketing

Email marketing is the second most common application of AI in marketing, after content creation. AI-driven personalization helps improve the click rates of emails by 26% and conversion by 20%.

Here are some ways to use AI in personalized email marketing.

• Personalize email content: The most obvious use case is to tailor messaging, offers, and product recommendations within emails using customer data and past interactions.

• Optimize send times: AI can be used to identify when individual customers are most likely to open their emails, based on past data, and optimize send times for better engagement.

• Automate customer journeys: You can set up workflows that trigger relevant emails based on actions such as sign-ups, purchases, cart abandonment, or inactivity.

• Optimize campaign performance: One of the best ways to utilize AI is to use it for analyzing email engagement data. This can be taken as feedback to improve email content, timing, targeting, or messaging.

Advertising and social media marketing

Ad optimization is one of the three leading AI use cases in marketing, alongside content creation and email, used by 52% of marketers.

AI bidding and targeting is found to cut cost-per-acquisition by 25 to 35% across channels, as per Google and Meta's own reporting.

AI is now built into most major advertising platforms, including:

• Google Ads

• Meta Ads

• LinkedIn Ads

• TikTok Ads

• Amazon Ads

Among these platforms, here are some features that use AI in marketing.

Google Performance Max

Performance Max uses AI to optimize campaigns across Google's advertising ecosystem, including Search, YouTube, Display, Discover, Gmail, and Maps. Based on your campaign objective, audience signals, creative assets, and conversion data, Google can automatically determine where and when to show your ads to maximize results.

Meta Advantage+

Meta's Advantage+ suite depends on AI to automate certain key aspects of ad campaign management, such as audience targeting, ad placements, budget allocation, and creative optimization.

LinkedIn Predictive Audiences

LinkedIn's Predictive Audience is an AI-powered targeting tool that can identify users who are more likely to take a desired action. This is done by connecting your first-party data, for example, CRM or lead form submission, with LinkedIn's platform engagement signals.

Amazon Ads dynamic bidding

Amazon's AI-powered bidding tools adjust bids based on the likelihood of a conversion. By analyzing shopping intent and real-time signals, these tools can help advertisers allocate their budget more effectively across high-potential opportunities.

Customer service

Personalized customer service has become a basic standard of ecommerce platforms, and AI is highly useful in delivering tailored experiences.

The top use cases of AI in customer service include:

• AI-powered chatbots and virtual assistants

• Sentiment analysis to identify customer frustration or intent

• Intelligent ticket routing and prioritization

• Personalized recommendations and support

• Assistance for human support agents and response suggestions

While this significantly cuts down the workload for businesses, studies consistently show that customers still prefer speaking to a human when dealing with issues.

In fact, 56% of people report negative feelings about companies using AI as part of the customer experience, while 89% believe businesses should always offer the option to speak with a human.

Analytics and forecasting

One of the best places to use AI is in analytics and forecasting. AI can go beyond simply reporting to helping businesses understand why something happened and what is likely to happen next.

By analyzing large volumes of data across customer behavior, campaigns, sales, and market trends, AI can easily identify even minute patterns.

Marketers can use AI to:

• Forecast demand – Predict future sales, customer demand, and market trends.

• Predict customer behavior – Identify customers who are likely to convert, churn, or make another purchase.

• Analyze campaign performance – Identify which channels, messages, and campaigns are driving the strongest results.

• Identify trends – Detect emerging customer preferences and market shifts earlier.

• Optimize marketing budgets – Predict which channels and campaigns are likely to deliver the highest returns.

• Improve attribution – Analyze customer journeys across multiple touchpoints to understand what contributes to conversions.

Things to keep in mind while using AI in marketing

The benefits of AI in marketing are immense, including reduced operational costs, better targeting, improved customer engagement, and greater scalability for teams working with fewer resources.

However, it's not without challenges, limitations, and risks. Here are some of the most important ones to consider.

AI still needs human oversight

AI can process vast amounts of data, but it does not replace human judgment. AI-generated outputs can be inaccurate, lack context, or fail to reflect the nuances of a brand and its audience, so marketers should always review and validate everything before using them.

Data privacy and security concerns

AI systems often rely on large volumes of customer data to generate insights and deliver personalized experiences. Providing sensitive customer data to AI has to be done only with consent and should follow strict protocols set internally.

AI can produce inaccurate or biased outputs

AI is only as reliable as the data and systems it is trained on. If data is incomplete, inaccurate, or insufficient, AI usually makes assumptions on its own. Regular monitoring and human review are essential to identify such issues early.

Full integration is better than fragmented usage

Integrating AI across your marketing workflows drastically increases gains. A McKinsey report shows that by embedding AI across an entire workflow, including content, targeting, and reporting, it resulted in a 22% increase in ROI, when compared to using it for a single use case.

Conclusion

Treating AI as just another tool for producing more content is limiting its potential. Businesses should put its capabilities to good use, starting with finding out where your marketing is constrained.

This can be because of various reasons, like limited resources, slow analysis, poor personalization, inefficient campaigns, or a lack of visibility. Once these causes are identified, slowly introduce AI to address those specific bottlenecks and scale after carefully tracking its results.