Landing pages are often your campaign’s first touchpoint, and AI can make that first impression count by dynamically adapting page content in real time. An adaptive landing page uses AI to tailor itself to each visitor adjusting headlines, images, or offers based on things like the visitor’s source, behaviour, or profile. The goal is a personalised experience that feels hand-crafted for every user.

Why it matters: Relevance drives conversions. Rather than a one-size-fits-all page, an AI-driven landing page might show different product highlights to a returning customer versus a first-time visitor, or adjust the messaging if the visitor came from an email campaign versus a social ad. This level of personalisation has a direct impact on results. Studies show personalised landing pages can boost conversion rates by up to 25%, and even something as simple as tailoring calls-to-action (CTAs) can increase conversion up to 42%. One case study reported that using an AI-driven landing page optimiser (Unbounce’s Smart Traffic) led to a 51% increase in conversions for their campaigns. In another example, BMC Software achieved a 49.5% conversion rate on a campaign by leveraging AI-driven page personalisation – an almost unheard-of performance with traditional methods.

Real-world example: Imagine a mid-sized e-commerce brand selling outdoor gear. With adaptive landing pages, their site could automatically detect if a visitor is browsing from a cold region and showcase winter jackets and boots prominently, while a visitor from a tropical region sees camping tents and hydration packs. If the visitor came via a Facebook ad about hiking, the page might open with hiking imagery and tailored copy matching the ad. This real-time relevancy makes visitors more likely to stay and act, rather than bounce. As one marketing expert put it, “personalisation is critical; 77% of buyers prefer to engage with brands that provide personalised experiences”. Adaptive pages deliver that personalisation instantly.

   AI chatbots have evolved from clunky novelties into powerful marketing allies. For in-house teams, a well-deployed chatbot can handle countless customer interactions from answering FAQs and guiding product selection to qualifying leads, all without requiring a human rep’s constant presence. The result is scalable, always-on engagement that complements your team and improves customer experience.

Why it matters: Today’s consumers value quick responses and personalised assistance. A chatbot allows your brand to be available 24/7, providing instant answers or help at any time. This not only improves customer satisfaction (for instance, 82% of consumers prefer an immediate chatbot interaction over waiting for a human reply) but also frees up your team to focus on high-value tasks. Importantly, chatbots can directly contribute to marketing ROI. According to recent reports, 83% of businesses using AI (including chatbots) have seen positive ROI. Chatbots can even drive revenue in retail e-commerce, automated assistants have been shown to boost revenue by 7–25% by efficiently upselling and preventing drop-offs. They can also reduce customer service costs by ~30% by handling routine queries, making them cost-effective. No wonder 71% of larger businesses plan to implement chatbot technology in their operations.

Real-world example: A growing online education company might use a chatbot on its website to greet visitors and help them find the right course. The bot asks what skills the user wants to learn, then recommends a course and even offers a sign-up discount. Meanwhile, it collects the visitor’s email and learning goals. This not only improves the user’s experience (immediate guidance rather than searching a course catalogue) but also generates a qualified lead for the sales team. In fact, research in the EdTech sector found 62.5% of companies use chatbots specifically to qualify leads, and 25% use them to recommend products or courses to customers. These bots essentially act as tireless junior sales reps. Another example: a small retail brand’s chatbot can handle “Do you have this in stock?” queries, provide shipping info, and suggest related products. If the bot gets a question it can’t handle, it seamlessly hands off to a human rep – but by that point it might have answered 20 other common questions automatically. This offloads repetitive work from your team. And the payoff is clear some companies have reported up to a 70% increase in conversion rate to sign-ups when using chatbots for customer engagement, likely because the immediate interaction keeps customers from leaving in frustration.

   The third strategy and possibly the most transformative is predictive personalisation. This is the apex of AI personalisation marketing: using machine learning and predictive analytics to tailor marketing messages, offers, and experiences to each customer before they even explicitly tell you what they want. In simpler terms, the AI sifts through data to anticipate individual customer needs or behaviours, allowing you to deliver the right content at the right time for each person.

Why it matters: Modern consumers expect brands to know and cater to their preferences. Consider that 71% of consumers expect companies to deliver personalised interactions, and 76% get frustrated when this doesn’t happen. Generic, one-size-fits-all marketing is increasingly ignored; personalisation isn’t a nice-to-have, it’s expected. Predictive AI takes personalisation to the next level by leveraging patterns in data (browsing history, past purchases, demographic info, engagement timing, etc.) to predict what a customer is likely to do or want next – and then automatically acting on that insight. McKinsey research finds that companies who get personalisation right can increase revenue by 10-15% and achieve significantly higher marketing efficiency. Moreover, 90% of leading marketers say personalisation significantly contributes to profitability for their business, and brands that excel at it drive 40% more revenue from personalisation efforts compared to average players. The payoff is not just short-term conversions but long-term loyalty: after a positive personalised shopping experience, 60% of shoppers say they’ll become repeat buyers. In other words, predictive personalisation can boost immediate sales and build customer lifetime value.

Real-world example: Large firms like Amazon and Netflix have long used predictive algorithms. Amazon’s recommendation engine generates a huge portion of its sales by predicting what you might want next. But even a smaller brand can apply the same principles. Imagine a niche fashion ecommerce site that uses AI to analyse each visitor’s browsing and purchase history. The AI might learn that a particular customer often buys running gear every autumn. As summer winds down, the system can automatically send that customer a personalised email with new running shoes and apparel, possibly with a loyalty discount, anticipating their need before they even search. On the website, if that customer visits, the homepage banner might immediately feature running products. If another customer tends to buy when there’s a sale, the AI can show them a personalised offer or promo code. This kind of AI-driven predictive targeting ensures each customer sees the content or deal most likely to resonate with them, rather than a generic homepage or generic email blast. One mid-sized retailer implemented predictive churn modelling their AI-identified customers with declining engagement and triggered a “win-back” campaign with a special offer; this reduced churn in that segment by a significant margin. The common thread: predictive personalisation lets you treat each customer individually at scale, something human marketers alone could never manage across thousands of customers in real time.

   AI is opening new frontiers in marketing, but you don’t need to be a tech giant or have a huge tech budget to benefit. The three approaches discussed: adaptive landing pages, chatbots, and predictive personalisation, are practical how to use AI in marketing examples that any in-house team can start implementing today. Each delivers on the promise of making your marketing more responsive, efficient, and customer-centric:

• Adaptive landing pages ensure your campaigns convert better by automatically showing each visitor what they’re most likely to respond to.
• Chatbots provide scalable, cost-effective customer interactions that improve experience and free your team’s time, all while capturing more leads and driving sales 24/7.
• Predictive personalisation turns data into a competitive advantage, allowing you to anticipate customer needs and delight them with timely, relevant content or offers.

   Crucially, these AI-driven tactics complement your marketing team rather than replace the human touch. Your team sets the strategy and creative direction; the AI amplifies your execution by handling the heavy data lifting and real-time adjustments that humans can’t easily do at scale. When thoughtfully applied, AI in marketing becomes a force multiplier for in-house teams enabling you to punch above your weight and deliver personalised, AI-powered brand experiences that build lasting customer relationships.

   Finally, remember that success with AI is a journey. It’s wise to start small: pilot one chatbot on a key page, or personalize one email funnel with predictive recommendations, or use an AI tool on one of your landing pages. Learn from the results, then expand. The insights and quick wins from these initial steps will build confidence and support for broader AI projects.

Ready to take the next step? Consider diving deeper with resources like our AI-Powered Brand Experiences Blueprint, which offers a step-by-step guide to implementing these strategies in real campaigns. With the right approach, even a small in-house marketing team can harness AI to deliver big results