How to Use AI in Marketing Including Best Practices

Marketing tools – and the technologies that power them – are everywhere. It’s not an understatement to say they’ve changed how most marketing departments operate, particularly in the past decade or so. Tools that manage the endless marketing workload are continually evolving, but there’s nothing more promising than what we’ve seen lately with artificial intelligence (AI) and machine learning (ML). 

AI, combined with an absolute firehose of data now available, facilitates AI marketing and enables marketers to achieve things that were impossible just a decade ago. AI improves marketing in a few different ways that we’ll delve into in this post. Capabilities like predictive analytics, natural language processing, and generative AI play in role in helping retailers understand customers and create relevant, personalized content and offers. 

In this post, we provide some guidance on how AI and marketing work together to help marketers boost customer experience through personalization, improve sales, and allow marketing teams to offload some of the more repetitive time-consuming tasks involved with campaign management. We’ll cover the types of AI marketing tools available today, explain how marketers can benefit from them, and provide a list of ways to use AI in marketing (plus some challenges inherent in the process).

What is AI Marketing?

AI marketing is marketing that leverages artificial intelligence and machine learning capabilities to complete marketing-focused tasks. These include (but aren’t limited to) understanding and segmenting customers, generating content, analyzing data, and optimizing campaign performance. AI marketing also helps marketers understand campaign performance and predict future outcomes by analyzing data – buying history, website traffic, in-session behavior, and more – to produce actionable insights.

Many marketing tasks and functions that were once performed by humans can now be delegated to AI-powered tools. The technology aims to replicate human ways of comprehending and reproducing language, recognizing patterns and anomalies in data sets, and adapting approaches based on new information (e.g., it learns). A bonus is that it can do this at unprecedented scale and speed.

AI in Marketing Examples

AI has already made an appearance in many marketing departments across just about every industry from entertainment to retail to healthcare. These tools often work together to transform the customer experience with personalization, automation, and optimization. Here are some real-world examples of what AI for marketers looks like in 2024: 

• Personalized entertainment – Video and music streaming platforms use AI algorithms to analyze user data, viewing, and listening history. This manifests as personalized playlists, movie, TV and song recommendations, and personalized offers.   
• Peering into the future – Predictive analytics is a form of AI used by many retailers and ecommerce websites. It uses AI to analyze data like past shopping behavior and browsing history to predict what a customer might like, inventory shifts, and potential opportunities. Retailers use this information to better understand what items to stock, create personalized recommendations, and tailor offers to customer segments.
• Robots for customer support – AI-powered chatbots are widely used for customer support, lead generation, and sales support. Chatbots help businesses from Verizon to Chase to your local doctor’s office handle common customer queries, recommend content or products, and provide 24/7 support and automated assistance. 
• Advertising and marketing optimization – Intelligent marketing platforms and tools use AI to automate a host of tasks inherent with campaign planning and managing. For example, AI can analyze data in real-time to determine the best performing combination of images, copy, and placements for a target audience. It can also automatically create audience segments based on customer and behavior data.
• Content creation – Generative AI tools rose to prominence in 2023 with the launch of Open AI’s ChatGPT, but there are many more tools available to help marketers write copy, generate images, write emails, and generate videos.

AI Marketing Tools

There are so many different AI marketing tools available, that it can be overwhelming for businesses to know which ones to implement, how to integrate them into an existing marketing strategy, and which ones will ultimately be embraced by marketing teams. Before you begin shopping for a tool, it’s helpful to understand the types of tools available based on use case/capabilities.

Without drilling down to specific product names, here is a list of AI marketing technologies grouped categorically by capabilities: 

Personalization Tools

Personalization tools use AI to serve up customized content to online visitors across websites, apps, and external websites (as ads). These AI-powered platforms use machine learning algorithms to analyze customer data and serve up automated personalized product recommendations, deals, and messaging that matches individual preferences. 

Other use cases include personalized product groupings (e.g., “dynamic product bundling”, personalized search, and social proof that drives demand as people shop (e.g., “30 people have this item in their cart right now”)

Testing & Experimentation Tools

Sophisticated experimentation suites allow marketers to improve website and ad campaign performance by testing different versions of copy, images, offers, and messaging. AI-powered testing tools automatically analyze performance as data comes in, determining the optimal combination for your goals based on historical data and real-time feedback. This allows marketers to improve campaigns as they’re happening.

Segmentation & Targeting Tools

Robust targeting platforms like Monetate analyze customer data across multiple touchpoints, then segment and target audiences with common traits and interests. This makes the customer journey for a given segment a much more personalized and relevant experience.  

Audience Discovery

AI-powered audience discovery automates the identification of new customer segments based on visitor behaviors and preferences. In Monetate’s case, easy-to-use dashboards provide visibility into these insights across historical tests and experiences which enables real-time personalization, uncovers valuable targets, and informs data-driven campaign strategies.

Journey Analytics

Journey mapping suites analyze the end-to-end path customers take from awareness to purchase. They make it easy for marketers to discover drop-off points where consumers may be struggling. Journey analytics also makes it much easier to find opportunities to refine the shopping journey and improve the overall buying experience. All of this means that you can fix leaks in the funnel and discover new opportunities you otherwise would have missed.

How to Use AI in Marketing to Improve Efficiency

The AI marketing tools and examples covered above can boost marketing efficiency in a few big ways. First, through automation. Automating repetitive tasks like writing copy, segmenting customers, and analyzing campaign metrics allows marketing teams to focus on big-picture strategy. AI makes this scalable – the technology grows as your company or catalog grows.

AI also takes on the enormous burden of data analysis. Surfacing insights with the help of AI gives marketers an opportunity to focus on meaningful work like strategy, analysis, and planning. It helps uncover opportunities more quickly so that changes can be implemented before the opportunity vanishes. It’s easy to miss important insights in the barrage of data coming at us. AI helps marketers prioritize where to focus for the biggest impact. 

Finally, predictive analytics and advanced ML algorithms power a constant process A/B testing. Since the bulk of this work is automated, marketing campaigns and strategies can be continuously refined – as can web page content, offers, and messaging. And while AI may be doing most of the heavy lifting, marketers are providing oversight and direction without being stretched so thin.

Challenges of AI in Marketing

AI in marketing has incredible promise and it’s already proven itself to be effective for many different marketing use cases. But it’s not perfect. There are four challenges you need to consider which include:  

Challenge 1

Integrating artificial intelligence marketing tools into your existing marketing tech stack can be tricky. New tools require vetting to ensure they’re a fit for your marketing approach and infrastructure. You’ll also need to provide training and support to your team so AI is integrated and used properly. This could require hiring new employees with expertise in AI-driven tools.  

Challenge 2

While AI Chatbots offer the benefit of immediacy – they’re always available and respond instantly to customer questions – they require thorough testing to avoid causing more harm than good. You’ll need to make sure they’re actually helping people versus creating an added layer of frustration for your customers. 

Challenge 3

AI relies on customer data, so transparency around data collection and usage is important. Proper data governance is important because it reduces the chance of data breaches and helps maintain customer trust. As you integrate and use more AI-driven solutions, it’s important to prioritize ethical data practices and familiarize yourself with data privacy laws like GDPR and CCPA.

Challenge 4

Despite rapid advances, AI hasn’t been around that long. It’s constantly changing and it’s far from perfect. It won’t solve all your marketing problems. It can’t, for example, automate entire marketing strategies or processes. This why human expertise and oversite are so important. The need for humans in marketing is as strong as ever. 

Whether it’s used to streamline marketing optimization, crunch your data, or personalize content – AI isn’t a quick fix. It’s an investment that requires time, money, and the right expertise. AI in marketing shows incredible promise, but it’s important to set your expectations appropriately.