According to the BBC, AI could replace up to 300 million jobs, highlighting its transformative impact on businesses. These investments will help businesses reduce costs, boost revenue, and maintain a competitive edge—particularly in marketing. As competitors adopt AI, their costs drop, enabling them to provide the same quality of service at a lower price.
Using AI tools like ChatGPT for tasks such as writing subject lines or email bodies is no longer enough. The companies that leverage AI at scale to automate once labor-intensive tasks will appear ahead. AI can automate tasks such as customer research, data categorization, and personalized messaging, replicating human logic at scale. This trend of integrating AI into workflows is driven by AI agents.
This article explores the most impactful use cases of AI in marketing so you can stay ahead of the competition.
How AI agents power impactful marketing use cases
AI agents are software systems that independently gather information, make decisions, take actions, and learn from feedback to achieve a specific goal. These tools deliver significant benefits by automating tasks once reliant on manual effort, improving productivity and impact.
- Scaling without adding headcount: AI agents can now execute what once required hours of human effort at scale. These systems can simultaneously handle hundreds—or even thousands—of tasks, making growth possible without expanding your team.
- Maintaining consistency of your customer experience: AI agents can be trained with your team's expertise and processes, ensuring tasks are executed uniformly and at scale. This consistency reinforces your brand identity while minimizing errors.
- Focusing on more high-value tasks: By automating repetitive tasks, AI agents free your team to focus on strategic initiatives and creative problem-solving, where human expertise is invaluable.
- Responding to customers anytime: Unlike humans, AI agents operate 24/7, enabling seamless customer support and task execution across time zones. This ensures no opportunity is missed, regardless of when it arises.
- Enhancing process with continuous experimentation:: AI agents continuously learn and adapt, refining their processes without constant retraining. Their ability to uncover inefficiencies and optimize workflows often leads to innovations humans might overlook.
AI Marketing use cases
Here are some real-world examples of using AI to carry out marketing use cases so you can focus on higher-value tasks.
1. Cross-sell promotion
Cross-sell promotions are a powerful way to boost revenue and increase customer lifetime value. Yet, many marketers rely on generic product suggestions, popular items, or loosely related goods that often fall flat. This approach misses the opportunity to connect customers with items they genuinely want, reducing the effectiveness of cross-selling efforts.
AI transform this process by analyzing detailed customer histories and broader behavioral patterns, such as transaction data, product preferences, and shared traits among similar buyers. Instead of relying on guesswork, these agents use continuous learning to recommend products highly likely to resonate with each individual. The AI can then deliver personalized prompts—via email or app notifications—inviting the customer to add these items to their order.
For instance, a customer with a history of purchasing red sneakers might receive a recommendation for performance socks or a fitness tracker based on the buying habits of similar shoppers. This tailored strategy increases the likelihood of an immediate upsell, drives the average order value (AOV), and enhances customer satisfaction by providing relevant suggestions.
2. Content recommendations
Providing customers with personalized content increases engagement, enhances satisfaction, and drives conversions. By delivering the right content at the right time, customers get more value from your business and become more loyal. Without AI, content recommendations often depend on static rules or broad segmentation, like suggesting popular shows. This approach overlooks individual preferences, resulting in irrelevant suggestions and reduced engagement.
AI analyzes user behavior, preferences, and interactions to recommend personalized content continuously learns from interactions, and refines recommendations to meet user needs and interests.
For instance, a video streaming platform powered by AI can identify a user's preference for science fiction and recent interest in time-travel themes. The AI can then recommend time-travel films and series—popular, obscure, or upcoming—or curated playlists in the same genre. By dynamically personalizing recommendations, the platform keeps users engaged, encourages longer watch times, and increases the likelihood of users renewing their subscriptions. A by-product of higher engagement means that the streaming platforms can sell more ads, increasing their average revenue per user (ARPU).
3. Website personalization
Personalized customer experiences are key to increasing conversion rates and enhancing customer satisfaction. When your users are shown products that match their interests, they're far more likely to engage and make a purchase.
Unfortunately, many websites rely on static designs or broad user segments, often resulting in irrelevant recommendations. This lack of precision can frustrate users, hampers conversion opportunities, and leave potential revenue untapped.
AI solve this issue by analyzing purchase histories and real-time interactions. They use this data to deliver a dynamic customer experience, so every time a customer visits your site, they get a different experience depending on how they behaved previously and what resonates with similar customers.
For example, if a customer shows interest in hiking gear, the AI might highlight related items like trekking poles or weatherproof jackets on their next visit. This targeted approach boosts conversions, elevates the customer experience, and builds long-term loyalty.
4. Win back campaigns
Re-engaging inactive customers through win-back campaigns can boost revenue and extend customer lifetime value. Successfully re-engaging customers who have drifted away is often more cost-effective than acquiring new ones. However, traditional win-back strategies often rely on a one-size-fits-all approach—sending generic discount offers to all inactive customers. This lack of personalization leads to low conversion rates and wasted marketing efforts.
AI analyze the customer’s purchase history, interaction patterns, and feature usage and create personalized messages designed to resonate. These messages, delivered via email or targeted social media ads, highlight relevant feature updates or offer enticing discounts to encourage re-engagement.
Consider an e-commerce business with customers who haven’t purchased in six months. AI can identify patterns, such as frequent seasonal clothing purchases, and craft a targeted email showcasing a new collection of seasonal items with a limited-time discount. This approach delivers personalized, timely content that boosts re-engagement rates, recovers lost customers, and generates incremental revenue.
5. Dynamic advertising content
Delivering personalized advertising content is one of the most effective ways to enhance campaign performance, boost engagement, and maximize return on ad spend. Personalizing messages to individual users can capture attention more effectively and drive meaningful results.
Without AI, advertisers face significant challenges. Refreshing ad content and setting up campaigns manually often requires grouping customers into broad audiences because of time constraints. This process is inefficient and overlooks individual preferences and behaviors, which leads to lower engagement and wasted ad spending. Static campaigns miss opportunities to connect personally, diminishing their overall impact.
AI revolutionize this process by enabling the creation of dynamic, personalized ad content at scale. By analyzing user data, such as past purchases, browsing history, and conversion patterns, AI can auto-generate tailored creatives and ad copy for each user and then seamlessly launch campaigns on paid media platforms. These agents also adapt the content in real-time, ensuring ongoing relevance and optimized performance.
For instance, an e-commerce brand could leverage AI to dynamically adjust ad content. A first-time visitor might see a promotional offer for jackets, while a returning customer is shown a new sneaker collection. This targeted approach leads to higher click-through rates, improved efficiency, and greater revenue from paid campaigns.
6. Customer onboarding
Each customer’s needs vary, making it challenging to design a single onboarding flow that works for everyone. While many companies use segmented approaches based on job role or industry, this broad strategy often overlooks key features that could significantly benefit individual users. As a result, activation rates may suffer.
AI can transform the onboarding process by leveraging data collected during sign-up and subsequent user interactions, such as skipped steps or clicks on specific features. AI can use this information to create a personalized onboarding experience that highlights features relevant to each user and dynamically adapts to their behavior.
For example, a SaaS company might use an AI agent to personalize onboarding by asking targeted questions like, 'What are your main goals?' or 'What challenges are you trying to solve?' The AI then analyzes these responses, compares them with similar users, and creates an onboarding flow that adapts to future customer interactions. This level of personalization will help increase activation rates, create loyal customers, and increase customer lifetime value.
7. Churn identification and prevention
Reducing churn, or predicting it before it occurs, is critical. After investing in customer acquisition, you don't want those efforts wasted, and losing customers impacts revenue and reduces the lifetime value of your user base. Traditionally, companies track user data, spot warning signs (e.g., no logins for 10 days), assign a churn score, and then segment users into standardized 'save' campaigns, highlighting features or offering timely discounts. However, this generic approach may not provide the individualized interventions that meet each customer's needs.
AI are transforming churn prevention with personalized retention strategies. AI identifies subtle disengagement indicators by analyzing historical data from churned customers, current user behavior, and peak interaction patterns. Using this insight, AI creates tailored "save" plans, including customized content, optimal communication channels, and specific action prompts, such as increasing product usage or exploring additional features.
For example, if users stop engaging with a feature they previously relied on, AI can recommend targeted tutorials or promote new, related functionalities. This proactive, data-driven approach enhances customer retention and continuously refines itself by learning which strategies work best for similar user profiles.
8. Automated sales outbound
Outbound sales is a powerful way to generate high-quality leads and close deals, especially with high-value accounts. Personalizing outreach based on deep insights about potential customers ensures your messaging resonates, leading to stronger engagement and increased conversion rates.
However, traditional outbound sales processes are time-intensive and resource-draining. Sales teams often spend countless hours researching accounts, collecting data, and crafting outreach messages—only to see generic efforts fall flat. Without actionable insights, outreach lacks relevance, leading to missed opportunities and stagnant pipelines. As businesses scale, these inefficiencies grow, becoming untenable.
AI are transforming outbound sales by automating research, crafting hyper-personalized messages, and continuously learning from past successes to improve outcomes. They analyze data on visiting companies—such as industry, size, and behavior—and scan public information like filings or news to uncover challenges and opportunities. This enables AI to deliver precisely targeted, data-driven outreach that is aligned with your sales goals at the optimal time.
For example, an AI agent might identify a company frequently visiting your pricing page. Analyzing their industry trends and potential pain points, such as scaling challenges or customer retention issues, crafts a personalized email showing how your solution addresses these needs. The agent then ensures delivery at the perfect moment to maximize engagement.
9. Sentiment analysis
Understanding how customers feel about your brand is crucial for identifying areas for improvement and addressing negative feedback before it escalates. However, without the right tools, this process can be overwhelming. Traditionally, businesses had to manually review social media posts, customer reviews, and feedback—a time-consuming and resource-intensive task, especially for larger organizations.
AI equipped with natural language processing (NLP) simplify this process by automatically analyzing vast amounts of text. They can detect positive, negative, or neutral sentiments and identify specific emotions behind customer feedback. This highlights areas where improvements are needed and allows the AI to draft tailored responses, helping brands engage with their audience more effectively and efficiently.
For example, an e-commerce company might use an AI agent to analyze reviews of a new product launch. If the AI identifies recurring mentions of a specific flaw, it can flag the issue for the team to address and suggest a personalized apology to the customers affected. This proactive approach improves brand perception and builds stronger customer trust.
How to implement AI in marketing
You can create AI agents in two ways. First, you can build them in-house, leveraging tools like Botpress, AutoGen by Microsoft, or LangChain. This option gives you full customization and integration flexibility but demands technical expertise and ongoing maintenance.
Alternatively, you can adopt emerging platforms that offer ready-made AI Decisioning. These solutions require no setup, simplify user interaction, and often cater to specific use cases, making them accessible to non-technical teams. Here are some examples of AI tools:
- Hightouch AI Decisioning focuses on marketing outcomes (engagement, revenue, LTV), using machine learning to recommend decisions that drive results.
- Unifygtm specializes in sales outbound, automating repetitive outreach tasks, identifying ideal buyers, running workflows on autopilot, and crafting personalized messages.
- Cognigy streamlines customer service through automated, human-like conversations across chat and voice channels. It integrates generative AI for empathetic dialogues and hyper-realistic voices.
AI agents will grow increasingly sophisticated as the technology evolves, so adopting them early can give you a lasting competitive edge. If you’d like to learn more about Hightouch AI Decisioning, schedule a demo with one of our solutions engineers.
