AI in Marketing: 18 Innovative Approaches for Customer Engagement

One of the most innovative ways to use AI in marketing right now is not to respond to who the customer is, but how they feel in the moment.

The best thing is that you already have the tools to accomplish this. GA4, Hotjar, your CRM of choice, and many other analytics platforms already quietly collect signals that reveal customer intent in real time: how long someone lingers on a pricing page, what time of day they browse, how many times they’ve visited without buying. That data shows you where someone is emotionally when researching solutions.

You can export this data and feed it into an AI tool like Claude or ChatGPT via a simple automation (Zapier works just fine), which can then generate messaging tailored to the specific moment of a buyer’s journey. A late-night customer who keeps returning but never converts gets reassurance-led copy. A fast, decisive daytime buyer gets social proof. Same product, same audience — totally different message logic.

Of course, there are dedicated tools that handle this entire cycle end-to-end, but they require a substantial upfront investment, making them more of an enterprise-level solution. For SMBs, a CRM + Zapier + AI API stack can get you most of the way there.

Either way, the engagement impact is tangible, for several reasons. First, customers stop feeling marketed at, and start feeling understood. Second, hesitant buyers get friction-reducing messages immediately, instead of being followed around by annoying retargeting ads, when the moment has already passed. Third, open and click rates improve because the message tone matches the customer’s immediate emotional state. Finally, in the long term, trust compounds — and continues to drive LTV more reliably than any discount.

In short, moment-based AI personalization enables you to leverage impulse — not just intent.

We run paid social for e-commerce brands and the biggest unlock for us has been using AI to sharpen ICP callouts at scale. We pull context from what’s actually performing in the ad accounts — winning hooks, customer language from reviews and comments, offer angles that are converting — and feed that into our creative development process. What comes out is ad copy and concepts that speak directly to specific buyer profiles rather than a generic audience. Someone who’s pain-aware gets a different message than someone who’s just entering the category, and AI lets us build and test those variations faster than any manual process would allow.

The second piece we’ve been building out is landing page personalization to match. The ad and the landing page need to be congruent — same language, same angle, same ICP signal — and AI makes it possible to produce those variations at scale without rebuilding pages from scratch for every audience segment. When the message a person sees in their feed flows directly into what they land on, conversion rates reflect it. The drop-off that typically happens between click and purchase tightens considerably.

The throughline in both cases is that AI isn’t doing the strategy — it’s accelerating our ability to execute that strategy across more profiles, more creatives, and more moments than we could otherwise reach.

When we talk about AI in marketing, most folks immediately think about automation, copywriting or, worst of all, graphic design. But we decided to look at it through a different lens asking ourselves: How can we use this technology to make a stranger feel seen and understood?

In the B2B growth space, decision-makers are drowning in noise. They don’t want another generic pitch. They want to know you understand their specific, local challenges.

Instead of using AI to write copy, we used an advanced AI scraping and synthesis workflow to build Hyper-Localized Market Briefings for prospective clients.

We fed our AI tool specific parameters: a prospect’s zip code, local competitor, and recent local news in their industry.

Rather than just writing an email, the AI synthesized all that data into a 3-point tactical brief that pinpointed the specific operational blind spots that company was probably facing that week.

With that brief, I record a 60-second personalized video saying, “Hey Sarah, I noticed the new regional competitor just opened two miles down the road from you, and with the current state of your industry, I imagine your team is feeling the squeeze. Here is one thing you can do on your website to protect your census.”

We let AI do the heavy lifting of data aggregation so that I could step in and do what humans do best: deliver empathy, strategy, and genuine care.

The results completely blew our traditional marketing benchmarks out of the water because we shifted the metric from volume to depth.

Cold video open rates jumped to +78%. Because this didn’t look like marketing, it looked like a peer reaching out to help.

Booking rates for discovery calls increased by 4x. Instead of receiving a “No thanks,” it was: “Peter, how did you know we were struggling with this?”

Sales cycle shortened by nearly 30%. Because the very first interaction was rooted in deep, localized context rather than a generic sales deck, we went straight into solving real problems.

AI shouldn’t replace the marketer; it should amplify their abilities. When you use data to respect someone’s time and acknowledge their specific reality, engagement isn’t a metric you have to force, it’s a natural byproduct of being helpful.

One way we were able to use AI to enhance personalization in marketing was through using a method called intent-based segmentation. We used this method to identify user interests in real time. In addition to focusing on interest, we also focused on specific behaviors, which included: page depth, content consumption behavior, email interaction behavior, and repeat visit behavior.

Using these behavioral data points, we created an AI model that predicted what each individual was looking for at that exact time. The major benefit occurred when the AI model identified micro-intent groups that our team did not previously consider. For instance, two individuals may have downloaded the same resource; however, one was considering their options and the other was ready to make a purchase. Once the AI identified the difference in the micro-intent group, it would automatically adjust the messaging and sequence of emails and recommendations of content based on the micro-intent differences versus treating both individuals equally.

The results of this methodology were positive. Click-through rates for email campaigns increased, the length of sessions on websites increased, and follow-up conversations became more relevant as prospects believed that the brand was aware of their needs prior to asking for additional information. Moreover, we transitioned from a reactive form of marketing to a proactive (predictive) form of marketing as we were delivering the correct message to the prospect prior to the prospect requesting the information.

One innovative way I have used AI is to scout for assets rather than having to do it myself.

When I started building display assets for our launch, we needed a professional-looking video that matched our messaging and quality, but sourcing the right stock footage was laborious and becoming incredibly time consuming. Traditional stock libraries rely heavily on keyword matching, which often misses the emotional context we were trying to create. I wanted to find a fast way around, and instead of manually searching through hundreds of pages of stock assets, I used an AI-powered video editing tool to identify footage based on a detailed prompt that described the mood, audience, and narrative we wanted to convey — not just keywords. The AI surfaced assets that better matched the emotional intent of the campaign, allowing us to build a more personalized story for our target audience.

I would then review the AI-selected assets, validate them through Google reverse image searches when needed, and source the footage directly or wait for this to be remarketed as discounted licensing opportunities. While the process wasn’t fully scalable due to stock library limitations, it dramatically reduced the manual effort involved. Previously, I would sift through hundreds of pages of footage; with AI, I could focus on refining the story and selecting from a much more relevant set of options.

The impact was that we were able to produce more relevant creative much faster, which improved engagement with the launch content. More importantly, it shifted our effort away from repetitive asset hunting and toward crafting messaging and visuals that resonated with the audience, resulting in stronger viewer retention and interaction with the campaign.

Out of the 131 organic impressions from the 73 Unique viewers, we are at an Impressions click-through rate of 6.3% (This measures how often viewers watched a video after seeing an impression) with a Watch time of 11.7 hrs. and an Average view duration of 5.21. YouTube also indicates 322% of viewers are still watching at around the 0:30 mark, which is typically past the hook.

Most people get this wrong. They think AI personalization means showing different products to different people. That is the obvious use, and it is also the one that burns budget fast.

We ran a split test for a furniture retailer where the AI did not change the product at all. It changed the reason to buy. One visitor saw, “Free delivery this week.” Another saw, “Delivered in 3 days, or your sofa is free.” A third saw, “47 customers in Berlin bought this model last month.” Same sofa. Same price. Three entirely different psychological triggers.

The variant with social proof won. 23% lift in add-to-cart rate. 11 days to significance, 8,400 visitors per arm. The coupon-code variant actually underperformed the control by 4%, which told us something useful: their audience does not trust discounts.

The lesson is that AI is not a product engine. It is a persuasion engine. Feed it behavioral data, not demographic tags. Let it test why people buy, not just what they buy.

We built our platform around the idea that “personalization” in marketing has been stuck at the email-merge-tag layer for fifteen years. You change a {{first_name}}, swap a product image, A/B a subject line, and call it personalized. What we are seeing change in 2026 is personalization happening one layer deeper, inside the actual conversation, not the message wrapper.

Concrete example: brands running virtual influencers on our platform hold ongoing 1:1 chats with fans, and the persona keeps persistent memory of every prior exchange. If a fan mentioned last week that she is studying for the bar exam, the persona asks how it went. If a fan said his dog is sick, the persona checks in. When a sponsorship moment comes up (a coffee brand drop, a skincare release), the persona surfaces it inside a relationship the fan is already in, with a callback to something real about that fan. The “campaign” is not a creative artifact pushed at an audience. It is a slow-built memory across thousands of one-to-one threads.

Engagement metrics shift accordingly. We do not optimize for opens or click-through. We track minutes of conversation per fan per week and retention curves that look more like a friendship product than a marketing funnel. The fan side of the platform monetizes via Points spent per minute of chat, which means engagement is a literal revenue signal, not a proxy.

The honest tradeoff: this only works when the persona is set up to be a companion first and a marketing surface second. Brands that try to bolt a virtual influencer onto a normal paid-acquisition funnel get the worst of both. The unlock is treating the persona as infrastructure for an ongoing relationship, then letting commerce flow downstream of that relationship.

One approach we’ve been using involves automating the first touch of our outbound prospecting in a way that leads with insight rather than sales.

Using n8n for the workflow, we pull live SEO performance data on prospects via the DataForSEO API before a single email is sent. The data: visibility scores, keyword rankings, traffic estimates — is used to generate a personalized SEO snapshot for every prospect. So instead of a cold email saying “we help businesses with SEO,” the opening message arrives with something specific to them: here’s roughly how your site is performing in organic search, here’s where the opportunities are.

The AI layer sits in the copywriting — taking the raw stats and shaping it into a readable (humanistic) summary that feels like it came from someone who’d actually looked at their site.

The impact on engagement has been measurable. Leading with a deliverable rather than a claim tends to shift the dynamic — recipients are responding to something useful, not evaluating a sales pitch. Reply rates have improved compared to our previous cold outreach, and the quality of replies is better too; people are engaging with the specifics rather than asking who we are.

The broader lesson is straightforward — personalization only works when it’s based on something real. Generic personalization — using someone’s first name, referencing their industry — is less effective that it once was. Using real world data is a different approach.

Our users are mostly restaurant owners and small business operators who aren’t running formal “campaigns” in the traditional sense. They’re just trying to make sure the link in their bio actually does something useful. So when we started looking at how AI could help personalize that experience, we kept the bar low on purpose: no dashboards, no setup, no learning curve.

What we built is a page that auto-generates from a business’s existing Google Reviews. The AI reads the review content and surfaces the themes that come up most, things like “great for date night” or “best tacos in the city,” and uses those signals to shape how the page presents the business. The result is a link page that feels like it was written by someone who actually knows the place, because in a way it was. It was written by the customers.

The impact on engagement has been subtle but meaningful. Business owners tell us the page feels more “theirs” than a generic Linktree ever did, even though they didn’t write a single word of it. And because the page reflects what real customers are already saying, visitors arriving from Instagram or TikTok see social proof immediately, not a blank list of links.

The bigger insight for us was that personalization doesn’t have to mean collecting user data or running A/B tests. Sometimes it means starting with the data that already exists and getting out of the way.