AI in Marketing: Strategies for Success in 2025

Today, a lot of companies are using AI for successful marketing strategies. It optimizes campaigns in real time through dynamic pricing, predictive analytics, and personalized ad experiences. 

In this article, we discuss the core strategies you need to boost your return on investment (ROI), drive customer engagement, and stay ahead of the competition. We explore how automation, data-driven decision-making, and  personalization are transforming the marketing landscape.

The landscape of AI in marketing today

Just like internet speed in the 1990s and 2000s, the journey of AI and marketing hasn’t been easy. What started as simple automation tools has evolved into systems that can access various touchpoints and crunch a ton of data.

Some of them are in this graph below:  

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Let’s look at some factors you can focus on in 2025 to get you closer to that winning edge:

1. Efficiency
   The more you can free yourself from the tiny changes to focus on higher-level marketing goals, the more you can focus on actual growth. 
   
   
2. Return on investment (ROI) 
   Most of the time, this doesn’t mean a cheaper CPC (cost per click) or more leads. It means more customers knocking on your door, ready to spend more and stay longer at your “establishment.”
   
   
3. Customer experience
   A higher ROI means nothing if you end up with many more chargebacks or refund requests. 

Personalization at scale

It’s not the 1900s anymore. According to PR Newswire, digital marketing alone is growing at a compounding rate of 13.9% each year. That’s a lot! 

The more it grows, the more your audience is exposed to ads, email campaigns, landing pages, and social media posts. Without personalization, these start to sound and feel the same as everyone else’s.

Do you think consumers don’t care about personalization? Not according to several studies, look:

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And the last thing you want is for all your efforts to go to waste simply because everyone else is doing the same thing.

That’s where personalization at scale comes in. 

So how can you implement this? 

Let’s talk music. We all know how difficult it can be to find music similar to the artists you really like, right? Spotify uses machine-learning algorithms to build custom playlists for its customers. This information isn’t public, but Spotify probably uses data such as the artists you follow, which songs you listen to on repeat, and the most common genres you search for. The advantage is that each customer gets personalized recommendations.

Now, imagine applying this to your marketing efforts. You could have an ad with the same angle and call-to-action (CTA) but a custom-tailored hook and transitions. It can talk to each member of your audience using their interests without feeling overly intrusive. The result? Higher engagement and a more memorable experience for your customers.

AI platforms can now pull and analyze data from multiple sources that once required months of coding to integrate.

Expanded demographics

Age and gender are too basic nowadays. AI allows you to pull cultural context, language, voice (for those targeting B2B), and regional behavioral nuances.

User behavior patterns

This involves analyzing browsing history, click patterns, and social media interactions.To improve user experience, leveraging data analysis allows businesses to better understand customer preferences and behaviors.

There are tools like Magic Feedback that can use sentiment analysis on voice and text to gauge customer moods. Imagine adapting your response 1:1 based on the mood of your lead or customer without having to physically take part in the conversation.

Purchase history and beyond

In most markets, the best customer is a customer who has already bought something from you. That’s why it’s much easier to sell to an already hot audience than it is to a cold one.

With AI, you can now examine previous purchases, average spend, and seasonal trends. Depending on privacy settings, you can also tap into affiliate networks or loyalty programs to understand lifestyle patterns. 

If you don’t want to deal with potential data privacy concerns, you can also use AI to see what your competitors are doing and then model and improve on that. 

For example, in the fashion industry, Unspun is using AI to create custom jeans via 3D body scans. Not only are they reducing waste, but they’re differentiating themselves from typical jeans stores. 

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They also ask their potential customers to complete their welcome survey before proceeding to the 3D body scan:

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Real-time customization in ad campaigns

Imagine launching an ad that includes messaging, imagery, or offers based on who is viewing it at that very moment. In 2025, this isn’t science fiction—it’s reality:

Here are some ways you can implement real-time customization in your ad campaigns.

Dynamic pricing

With AI in marketing today, you no longer have to set fixed pricing or manually update product systems and Excel sheets every time you want to run a campaign. Here are some dynamic pricing features AI helps you elevate things you can do with AI:

• Demand fluctuations 
  Prices can drop during slow periods to attract buyers or increase when demand is high.
  
  
• Competitive analysis
  AI monitors competitor prices and market trends, allowing businesses to stay competitive. One key element of this is keyword research—understanding which search terms drive traffic to competitors can help refine your own ad copy and content strategy. You can use AI to identify weaknesses in your competitors’ messaging and leverage this insight so your own ads and sales pages stand out.
  
  
• Customer behavior
  You can have systems automatically personalize pricing based on a customer’s interaction history. You can reward loyal customers with exclusive offers or identify lurkers who follow your content religiously but haven’t bought yet.

Real-time decision making

One of the best improvements in AI is the ability to make split-second decisions. Let’s face it: Using actionable insights, maths, and heuristics to make decisions in seconds compared to hours battling it out in meetings can be exhausting. 

Here’s what you can do:

• Adjust campaigns on the fly
  If an ad isn’t performing well, AI can instantly tweak its elements—this could be the headline, image, or CTA—to improve performance.
  
  
• React to market changes
  For example, if a trending topic emerges on social media, AI can pivot your ad strategy to capitalize on the moment. 
  
  
• Dynamically react to expanded interests
  Not all ad platforms allow this, and it might be easier to achieve in B2B than B2C. If you have the right data, you can use AI to learn more about what your leads or customers like. This allows you to change your images or videos to fit their personalities or online behaviors. For example, in event sponsorships, AI can find the best events based on the age and interests of the target audience and what past attendees thought about those events.

Here’s an example from Amazon on how they use a Large Language Model (LLM) to dynamically change a product title to better match what the customer wants and their search history:

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Budget decisions and bid optimization

• Predictive analytics
  AI can help you move your budget to the best-performing ad channels by predicting trends and finding new patterns.
  
  
• Automated reallocation
  As real-time data comes in, funds can shift between campaigns, so high-performing ads get maximum exposure while underperformers are paused or adjusted. You can stop wondering about budgets and allocations. Instead, you can focus on what truly matters: your revenue metrics and what to improve next.
  
  
• Maximized return on ad spend (ROAS)
  By keeping a close eye on important numbers, like how many people click on your ads and how many actually buy something, you can use AI to adjust your bids. This helps you get the best return on your advertising investment.

This is an example of how Facebook Meta uses AI to improve ads on the fly:

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Chatbots and enhanced customer service

AI-powered chatbots have evolved far beyond simple scripted responses. Today, they serve as the frontline of customer service, providing not only efficiency but also a human-like touch.

Some key features of AI chatbots include:

24/7 availability

They’re always available, even if you’re sleeping or your customer support team is on a break. They ensure that visitors and customers receive immediate assistance. They can give a first answer, respond to simple questions, or fix easy problems and send harder ones to your support team. This reduces wait times, keeps your customers engaged, and reduces bounce rates.

Screenshot provided by the author

Instant query resolution 

Advanced natural language processing (NLP) allows chatbots to understand and resolve common inquiries. An AI chatbot that uses sentiment analysis can change how it sounds depending on how a customer feels. This helps make the conversation more understanding and caring. They can also redirect to specific FAQs or video tutorials you’ve already made.

Improved engagement

Chatbots can guide customers through the buying process. They can make recommendations and suggest add-ons or upsells based on browsing history. They can even offer live assistance when they detect frustration or hesitation.

Here’s an example: A luxury online fashion retailer can use its chatbot to suggest outfit combinations. This can lead to faster conversions. Now, imagine using this while partnering with another company that’s in the dating niche, targeting men. They help their clients get dates, and you’ll help their clients get a second date by dressing correctly. It's a win-win for everyone.

Proactive assistance

Some systems now predict customer needs before a question is even asked. Chatbots can use tools from other plugins to watch how people behave in real time, like when they spend too much time on a product page. Then, they can offer discounts or give more information about the product to help them out.

Real-life example of using personalization AI in 2025 

Here’s a real-life example from Henry Meds on how they’re using AI in marketing to design diet plans:

To promote "The Ultimate Guide to Designing a Semaglutide Diet Plan," AI-driven personalized content can tailor your marketing strategy's messages to users based on their browsing habits, interests, and health-related searches, increasing relevance.  

• AI chatbots can be integrated to engage website visitors in real time, offering immediate answers about semaglutide and encouraging them to explore the guide. 
• Machine learning algorithms can optimize paid ads by analyzing performance data to ensure ads are shown to users most likely to convert. 
• Social media platforms can leverage AI-powered tools for audience segmentation, targeting individuals with health, fitness, and weight loss interests to amplify content visibility.

Finally, AI analytics can continuously track user engagement and customer feedback, allowing for campaign adjustments and maximized ROI.

Practical steps to implement AI in your marketing strategy

It’s important to know which AI strategies you should implement and which tools to use. The plan — and execution — that are right for someone else may not work for you. 

So, let’s take a brief look at how you can make the most of AI, regardless of whether you’re a solopreneur or a larger brand.

1. Assess

Go over your customer data, such as your CRM and website analytics. 

Make sure everything is clean and integrated. Check if your ad campaigns are already performing well and which could benefit from AI-driven optimization.

Unless you’re a developer, tech is probably boring for you. But try to fight the urge to start integrating AI before knowing your systems, apps, and tools. Otherwise, you’re in for a long haul of trial-and-error to fit everything together — and that’s going to cost you a lot of time and money.

2. Choose

Now, it’s time to choose the right tools. Whatever you choose, consider the complexity. The longer it’s going to take you to integrate and set up, the more future-proofing you want. So, even if you’re not running Facebook or Google ads at the moment, it wouldn’t hurt to choose tools that can integrate with those platforms, too.

 Examples include AdEspresso and WordStream.

DataFeedWatch is another you can use, an AI product feed management solution that provides instant results. With the click of a button you can:

• Assign categories to your products
• Generate titles and descriptions, so you don’t have to spend more time optimizing your product feeds
• Get accurate information on your product’s color and size filled in

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If you’re a solopreneur, you’ll probably want something that’s straightforward to use. For larger companies, apps with API endpoints are typically the way to go for maximum flexibility.

Don’t forget that with most tools, you’ll need to think about analytics, logs, and scalability. The last thing you want when dealing with banking, customers, or advertising problems at scale is realizing you don’t have the necessary data to make decisions or adjustments because you don’t have access to analytics and logs that give you a complete picture.

3. Cost vs benefit

Using AI in marketing can incur additional costs, especially if these tools charge based on usage. Like any other marketing change, it can also be blinding by boosting specific metrics without considering the long-term effects.

So consider asking yourself things like:

• “How much can AI reduce my cost per lead or increase my sales conversions?”
• “Our ROAS has suddenly increased to 50X. Is this actually good, or are we suddenly converting cheap deal seekers and bringing down our overall Customer Lifetime Value (CLTV) and Maximum Lifetime Value (MLV)?”
• “Does this mean I should fire our PPC specialist? Or train them to leverage AI and manage more campaigns, bringing in more revenue for us and giving them a higher commission in the process?”

4. Key performance indicators (KPIs):

KPIs and their overall insight can help you track which AI strategies are actually working.

Here are some essential KPIs to monitor:

• Click-through rates (CTR) on your ads and emails
• ROAS using tools like Hyros and Google Analytics
• Customer lifetime value (CLTV) to understand buying patterns and how much they value your brand
• Average order value (AOV). If you have sales funnels with upsells, bumps, or downsells, a higher AOV with a higher CLTV is typically what you’ll want to aim for, even if you get a higher CPC

Here’s a screenshot of Hyros’ dashboard:

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Final thoughts on AI in marketing

AI and marketing are no longer a “someday maybe” thought process. They’re here, and together, they can make us better marketers while creating more and happier customers. They can boost efficiency, improve customer satisfaction, and increase ROI. 

While there are challenges with data quality and ethical considerations, it is up to you and us to use these tools responsibly. This way, we can stay ahead while mixing AI with human creativity and feelings.