17 Ways to Use AI to Personalize Content and Messaging for Better Engagement

December 10, 2025

Artificial intelligence is transforming how businesses connect with their audiences through personalized content and messaging. This article explores 17 practical ways to leverage AI for better engagement, backed by insights from industry experts. From context-aware support bots to messaging that responds to individual user behavior, these strategies offer actionable approaches to create more relevant customer experiences.

  • Craft Hooks From Viewer Pains

  • Customize Homepages For Repeat Visitors

  • Target Actions Over Demographics

  • Lead Outreach With Personal Public Themes

  • Trigger Emails From Live App Events

  • Respond To Visit Cues With Precision

  • Mirror Prospect Language And Priorities

  • Match Recommendations To Local Weather

  • Align Blog Openers With Searches

  • Update Copy To Reflect Current Regulations

  • Tailor Messages To Individual Interaction Histories

  • Deploy Context-Aware Support Bot

  • Personalize Content By Health Symptom Patterns

  • Design Templates From Usage Insights

  • Rewrite Intros From Engagement Signals

  • Recommend Products From Real-Time Preferences

  • Address Specific Incentive Pain Points

Craft Hooks From Viewer Pains

I used AI as a kind of script doctor for our YouTube intros, and it made a big difference in how long people stuck around.

We started with a simple problem. Retention in the first 30 seconds was awful. One video had only 46 percent of viewers still watching at the 30-second mark, and the average view duration sat at 2 minutes 14 seconds. I pulled the retention graphs, transcripts, and a big chunk of viewer comments, then asked AI to do two jobs. First, cluster the comments into a few clear viewer "pains." Second, rewrite our opening 15 seconds so each hook spoke directly to one of those pains in plain language.

Out of that came a modular intro format. In the first 15 seconds we now hit three pieces. A hook that names a real problem in the viewer's words. A quick credibility line so they know why they should listen. A specific payoff, not a vague "we'll cover some tips." The AI helps generate several versions of that intro for different pains, then I pick and edit the one that feels most like how I would say it.

After that change, the next video jumped to 57 percent retention at 30 seconds, and average view duration moved to 3 minutes 50 seconds. It worked because the intro stopped sounding like "content" and started sounding like a one-to-one conversation. The title, thumbnail, and first lines all mirrored the exact situation viewers had described in their comments, so they felt like the video was made for them, not for an algorithm.

David Uebergang, Head Creative & Video Editor, Digital Darts

Customize Homepages For Repeat Visitors

One of the most effective ways I've used AI to personalize content was by analyzing the behavior patterns of returning website visitors and automatically tailoring the homepage message based on their past interactions. For example, if someone had repeatedly viewed our case studies, the AI would surface a more insight-driven message instead of the generic welcome banner. This small shift increased click-through rates because the content suddenly felt relevant instead of broad. It worked because the personalization wasn't superficial — it aligned the message with the visitor's intent, removing guesswork and making the user feel understood.

Target Actions Over Demographics

The biggest jump I saw came from tailoring stuff by behavior, not demographics. I had AI group users based on what they actually did, like pages they viewed, actions taken, past responses; then it suggested different email flows and site messages for each group. People comparing pricing got totally different follow-ups than first-timers. Because messages spoke to where people actually were, my clicks and replies shot up. Also made my content updates way more deliberate since I could see which paths different groups followed and refine those pages for what they really needed.

Lead Outreach With Personal Public Themes

Here is an example of what you are asking about: I've used AI (and specifically Gemini deep research) to personalize content that really made an impact during outreach to clients in an attempt to drive more business (i.e., client projects). Instead of sending generic messages, I used AI to scan the client person's public interviews, articles, and online activity, then summarize what they consistently talk about. The AI pulled out themes like their leadership style, the problems they talk about most, and the language they naturally use.

From there, I wrote emails that matched their interests. For example, if someone talked a lot about building high performing teams, the outreach led with that. If someone cared about AI productivity or design thinking, the message opened with that angle instead of a generic pitch.

Engagement jumped because people could feel the email was written for them, not blasted to a list. AI did the research part quickly, but the final voice and intention of the emails were mine. It worked because it respected their time and showed that I understood what mattered to them before asking for anything.

Matthew Mead, Chief Technology Officer, SPR

Trigger Emails From Live App Events

One practical way I've used AI is to personalize outbound emails based on real product behavior, not just static personas. We fed event data into an OpenAI API layer, then had it draft different intros and CTAs in HubSpot depending on what the user actually did last week. For example, “created 3 reports but never invited a teammate” triggered a collaboration-focused message, while “hit usage limits twice” triggered an expansion-focused message. Across a few SaaS campaigns we saw roughly a 15-20% lift in reply and click rates. That worked because the copy reacted to their context, not a generic segment label.

Pratik Singh Raguwanshi, Team Leader Digital Experience, CISIN

Respond To Visit Cues With Precision

One of the most effective ways I've used AI for personalization was creating behavior-based email variations for users who visited the same page multiple times but didn't convert. Instead of sending a generic follow-up, AI analyzed their browsing patterns: what section they paused on, which comparison tables they checked, and whether they returned from mobile or desktop, and generated a tailored message addressing the exact friction points they seemed stuck on. For example, users lingering on pricing sections received a value-focused breakdown, while those revisiting feature pages got a short benefits summary in plainer language. Engagement rates jumped by over 40% because the content finally felt like it understood them, not like a mass blast. It worked because AI didn't just personalize by name; it personalized by intent, which is the real driver behind meaningful engagement.

Mirror Prospect Language And Priorities

One approach that worked well for us involved using AI to map audience intent at a far more nuanced level. Instead of relying on broad segments, we trained a model on real conversations from prospects and clients. This allowed us to understand the patterns in how founders speak about their challenges, their timelines, and their priorities.

When we applied those insights to our messaging, something shifted. The content started mirroring the specific language people used when they reached out to us. Posts felt familiar to them. Emails felt aligned with their stage of growth. Founders mentioned that the content felt written with them in mind rather than written for a general industry audience.

It worked because relevance builds trust. People pay attention when they feel understood, and AI helped us scale that feeling without losing authenticity.

Sahil Gandhi, Brand Strategist, Brand Professor

Match Recommendations To Local Weather

Our team assisted an outdoor gear company in creating AI-driven product suggestions that took into account current weather conditions in specific locations. The newsletter would display waterproof equipment when it was raining in Oregon but show hiking shorts when the weather was sunny in Arizona. After implementing this change, newsletter engagement rates increased by 72%. The reason for this success became clear: customers received recommendations that made them feel understood, rather than treated like generic potential buyers.

Align Blog Openers With Searches

One time I used AI to tweak my blog intros based on what readers usually search for. I fed the AI my past posts, comments, and search queries, and it started generating openings that matched the exact tone and curiosity of my audience. The engagement jumped because the content felt instantly relevant. Readers saw their questions reflected in the first few lines, so they stayed longer and interacted more. It worked because the message wasn't generic anymore; it felt like I was speaking directly to them.

MUDASSAR SALEEM, Founder & Editor, Learning Breeze

Update Copy To Reflect Current Regulations

AI made updating SEO content for our own website stop feeling like starting from scratch every time. We're a crypto OTC desk and liquidity provider, and regulation changes happen constantly in this space. Updating our website messaging used to mean rewriting everything manually whenever new compliance rules dropped. Now AI helps us adapt our core content to reflect the latest regulatory shifts while keeping our positioning intact.

The personalization works because the AI understands the nuances well enough to update compliance language and examples without losing our voice. Our engagement metrics improved significantly once prospects started seeing content that showed we actually stay current with regulations instead of outdated fintech talk.

Tailor Messages To Individual Interaction Histories

One practical way I've used AI to personalise content — and saw an immediate lift in engagement — was by tailoring outreach messages based on behavioural data rather than broad audience segments. Instead of sending the same update to everyone, I used an AI model to analyse how different clients interacted with previous content: what they clicked on, how quickly they responded, and which topics held their attention the longest.

From there, the system generated short message variations optimised for each person's pattern. The tone stayed human — I edited everything myself — but the framing aligned with what that individual consistently cared about.

It worked because it respected attention. People engage more when the message feels written for them, not for a category they happen to fall into. The AI didn't replace judgment; it simply surfaced insights I would never have had the time to parse manually.

Deploy Context-Aware Support Bot

One of the most effective ways we've used AI to personalize messaging is through our AI-powered chatbot. It handles about 70% of customer questions, and the reason it works so well is because it responds based on the exact situation the customer is in, not generic scripts.

For example, if someone is stuck comparing policies or doesn't understand a coverage detail, the bot doesn't send a broad "Need help?" message. It gives the specific answer they need in that moment and guides them to the next step. That relevance is what lifts engagement.

It worked because it wasn't "personalization" in the superficial sense; it was helping people at the exact friction point they were experiencing. When the support feels immediate and tailored, people stay in the flow instead of abandoning it.

Louis Ducruet, Founder and CEO, Eprezto

Personalize Content By Health Symptom Patterns

The method that impressed me most involved using AI to identify customer feedback patterns based on symptoms and life stages of pregnancy and menopause, allowing us to create targeted email content for each group. The system delivered specific content to users based on their search history, rather than relying on generic messaging about women's health. Because the content aligned with user search behavior, it led to higher engagement rates — it gave people what they were actually looking for, rather than what we assumed they needed.

The personalization strategy succeeded because it was built on actual health information and symptom patterns identified by the customer insights team. People are much more likely to engage with learning activities when they feel understood in the context of their unique health situations. That sense of being seen and understood becomes the foundation for building trust.

Hans Graubard, COO & Cofounder, Happy V

Design Templates From Usage Insights

We analyzed customer behavior by tracking video template searches and usage patterns to understand what resonated most with our users. Based on these insights, we created new template variations aligned with high-performing use cases and developed seasonal templates ahead of upcoming holidays. This approach increased customer loyalty because it helped users find relevant, timely content that matched their specific needs. By being proactive rather than reactive, we ensured our users had the right tools at the right time.

Yessy Abolila, Marketing Project Manager, Animoto

Rewrite Intros From Engagement Signals

I used AI to rewrite email intros based on the reader's past behavior — what they clicked, what they ignored, and how often they opened messages. The core content stayed the same, but the tone and angle shifted slightly depending on their interests. Engagement jumped because people felt like the message spoke to them instead of everyone at once. It worked mainly because the personalization was subtle, not forced or overly complicated.

Heinz Klemann, Senior Marketing Consultant, BeastBI GmbH

Recommend Products From Real-Time Preferences

We implemented an AI-driven personalized recommendation engine on a client's e-commerce website that analyzed user behaviors in real-time. Within just a few weeks, we saw increased conversion rates, higher average order values, and improved overall sales. It worked because the AI could quickly understand individual user preferences and present relevant product recommendations at the right moment, creating a more personalized shopping experience that resonated with each visitor.

Steve Dune, Digital Marketing Manager, Koderhive

Address Specific Incentive Pain Points

We used AI to group customers by the exact incentive problems they faced, then tailored messages to each group. One segment struggled with rebate visibility, so we sent examples showing how real-time tracking solved that issue. Engagement jumped because the message spoke directly to their pain. It worked by meeting people where they were instead of pushing a broad pitch.

Hillel Zafir, CEO and Co-founder, incentX