Not long ago, I was a skeptic when it came to AI in marketing. I viewed artificial intelligence as just another flashy buzzword—something big brands used to hype their latest campaigns but impractical for everyday marketers like me. Then ChatGPT arrived, and it redefined how I saw AI.
Suddenly, AI transformed from hype to a powerful productivity booster. Tasks that used to consume hours became manageable in minutes, freeing me up to focus on strategy and innovation. But despite this newfound enthusiasm, diving deeper into AI felt overwhelming. Where should I even start? The landscape was vast, complex, and often confusing.
After interviewing dozens of experts on my AI marketing podcast, I started noticing patterns in how marketers used AI effectively. To simplify this complexity, I categorized these insights into five distinct types of AI marketing. Doing so didn’t just help me—it brought clarity to many others seeking to leverage AI strategically.
Let’s first define clearly what AI marketing means and why having a structured approach matters.
What is AI Marketing?
When diving into something new like artificial intelligence, clarity matters—a lot. Without a shared understanding of what we’re actually discussing, we’re bound to talk past each other. Think about it: if you’re picturing AI as a handy robot automating tedious tasks, and I’m imagining it as a supercharged brainstorm buddy, we’re essentially in two different worlds. Getting on the same page about what AI marketing truly is paves the way for meaningful conversations and actionable insights.
I’ve always believed that definitions aren’t just about semantics—they’re foundational. A clear, shared definition ensures we can build upon ideas, test strategies, and achieve real results. So, let’s lay down that critical groundwork right now.
Here’s how I define AI marketing:
What is AI marketing? AI marketing is the application of artificial intelligence technologies to marketing, enabling the automation and augmentation of tasks that traditionally required human intelligence.
Let’s break this down clearly:
- “Application of Artificial Intelligence Technologies“
AI isn’t passive—it’s an active tool applied specifically to marketing problems. This means practical, real-world usage, solving tangible challenges rather than just theoretical applications. - “To Marketing”
This ensures we focus strictly on marketing-related tasks. We’re not talking about AI broadly in healthcare or finance, but specifically in marketing activities like campaign creation, distribution, and customer engagement. - “Automation“
AI takes over repetitive, mundane tasks, giving marketers room to think bigger:- Social media scheduling: AI tools predict ideal posting times (think Buffer, Hootsuite).
- Customer service: AI chatbots handle routine questions effortlessly.
- Ad optimization: AI dynamically adjusts ad bids and placements to maximize ROI.
- “Augmentation“
AI doesn’t replace marketers—it amplifies our capabilities:- Idea generation: Tools like ChatGPT spark fresh ideas and refine rough concepts.
- Data analysis: AI rapidly uncovers insights in complex data, saving hours of tedious manual work.
- Creative collaboration: AI serves as a creative partner, offering suggestions and helping overcome mental roadblocks.
Why This Matters
This dual focus on automation and augmentation captures the true essence of AI marketing. It’s never about sidelining marketers but empowering us to excel where human creativity and intuition matter most, while AI handles the heavy lifting. Tasks traditionally dependent on human intelligence—like data interpretation, audience understanding, or personalized messaging—become quicker, more precise, and more impactful when enhanced by AI.
Why Other Definitions Fall Short
Many industry definitions lean heavily into what AI accomplishes, like improved customer segmentation or enhanced decision-making, rather than clarifying what AI marketing fundamentally is. For instance:
- Salesforce: “AI marketing uses artificial intelligence technology to automate data collection, analysis, and insights that help marketers understand their customers better and create personalized experiences.”
- HubSpot: “AI marketing refers to the use of artificial intelligence in improving customer experiences and driving conversions through targeted campaigns that are optimized and data-driven.”
- Forbes: “AI marketing is the process of leveraging artificial intelligence tools and techniques, such as machine learning and algorithms, to analyze consumer behavior and improve decision-making for campaign targeting and personalization.”
- IBM: “AI marketing is the integration of artificial intelligence technology to enhance and automate data-driven decision-making in marketing activities, enabling businesses to deliver tailored messages at scale.”
- TechTarget: “AI marketing leverages customer data and AI concepts like machine learning to anticipate the customer’s next move and improve the customer journey.”
These definitions aren’t necessarily wrong, but they’re outcomes-focused. They describe results rather than capturing AI marketing’s core nature. For a sustainable and timeless understanding, we need to separate the essence of AI marketing from specific tools, platforms, or outcomes.
By clearly defining AI marketing as the practical application of artificial intelligence technologies for automation and augmentation, we set the stage for deeper understanding, clearer discussions, and smarter strategies moving forward.
The Five Types of AI Marketing (With Examples)
When I first started exploring AI marketing, it felt a bit like drinking from a firehose. The sheer variety of tools and use cases was overwhelming. I needed a simple way to categorize everything, so I broke it down into five distinct types. Creating this framework wasn’t just an academic exercise—it provided practical clarity, helping marketers easily identify where and how to apply AI effectively.
Here’s why organizing AI marketing into five types matters: it brings order to chaos, simplifying discussions and strategy decisions. Whether you’re a seasoned marketer or new to AI, this structure helps you quickly understand, evaluate, and leverage AI’s full potential.
Let’s dive into each of the five types:
1. Internal Copilot
Alright, let’s talk about AI as a copilot. You’re probably thinking, “Hey, I already do that!” And you’re not wrong. Most marketers are indeed using AI to co-drive their workflows, speed up internal processes, and tackle everyday tasks faster.
In fact, I usually put AI as a copilot right at the heart of my workflow diagrams. Why? Because it’s the tool that supercharges everything else you’re doing.
But here’s the deal—AI as a copilot isn’t just about automating customer-facing processes or flashy campaigns. There’s plenty of internal magic happening behind the scenes:
- Idea Generator & Brainstorming Buddy: Need fresh ideas for internal events, meeting agendas, or tricky conversations with your boss? AI has your back.
- Process Automator: You know those tedious tasks you used to hand off to interns—relabeling, reformatting, shuffling data? AI can effortlessly handle these without ever interacting directly with customers.
- Project Planning Pro: Tools like Asana now use AI to instantly map out projects, giving you a solid starting point that you can tweak instead of starting from scratch.
- Documentation Guru: Ever record a quick instructional video because writing it out is too painful? AI easily converts those transcripts into step-by-step standard operating procedures (SOPs).
Think about every tiny, repetitive task you face daily. AI can streamline almost all of them, making you—and your team—significantly more efficient.
AI is more than just a flashy customer-facing tool. It’s your daily copilot for boosting internal efficiency, organizing workflows, and transforming minor tasks into major productivity gains.
So ask yourself: Are you fully leveraging AI as your copilot, or are you leaving opportunities for efficiency on the table?
Internal Copilot Examples
- Ally Financial (B2C banking)—Ally.ai (a proprietary GPT-powered assistant) was rolled out to support Ally’s marketing team with planning and content tasks. In a one-month pilot, it delivered 34% time savings on routine marketing tasks, equivalent to nearly 3,000 work hours saved per year. Talk about efficiency—the team can now reinvest that time into strategy and creative work.
- Influencer Marketing Factory (B2B agency) – This agency’s marketers used an AI outreach copilot (via the platform Reply) to automate their PR and link-building emails. The result was 70+ press mentions in publications like WSJ, Adweek, etc., achieved with minimal manual effort. The AI handled finding contacts and sending follow-ups, acting like an extra team member, and dramatically amplifying their reach.
- Unilever (B2C CPG) – Unilever built a custom GPT-3 assistant called “Alex” to help its consumer care and marketing teams handle incoming emails. Alex reads customer emails, understands sentiment and intent, and then drafts responses in the proper brand voice. It cut response time by 90% for agents – a huge productivity win. Another Unilever AI tool (“Homer”) auto-writes product descriptions, showing how AI copilots can take on copywriting grunt work internally.
- Bloomreach (B2B SaaS)—E-commerce software company Bloomreach armed its four-person content marketing team with an AI writing assistant (Jasper). By offloading first drafts and repetitive writing to AI, the team increased blog output by 113% and organic traffic by 40%. The internal AI helper lets them scale content distribution without adding headcount while freeing the team to focus on high-level SEO strategy and creativity.
- Buzet (Marketing agency) – Small agency Buzet used an AI platform (Scalenut) as an internal research and copy assistant. The AI automated time-consuming SEO research, suggested content outlines, and even generated drafts. As a direct result, Buzet saw a 25% jump in organic traffic for their site, accomplishing more with the same staff and in shorter timelines. This illustrates how even lean teams can leverage an AI copilot to punch above their weight in content marketing.
2. Content Production and Distribution
Let’s dive into one of my favorite topics: AI for content creation. I know what you’re thinking—AI equals writing blogs, tweets, or LinkedIn posts, right? Sure, that’s where many of us started, and let’s be honest, AI makes writing faster and sometimes even better than we can do alone.
But here’s the real magic: AI excels at repurposing content. Imagine taking a podcast and instantly transforming it into a captivating blog post, or turning your blog into bite-sized social media nuggets. Repurposing content is AI’s sweet spot.
But hold on—there’s way more to explore here:
- Visual Content Mastery: Forget stock photos. Tools like DALL-E and Midjourney are enabling marketers to generate unique images—whether you want a sleek corporate photo, a striking oil painting, or a retro sci-fi illustration. If you can describe it, AI can probably create it.
- Video Creation (Getting Better Every Day!): Sure, video AI is still evolving. But right now, you can integrate AI-generated elements into your existing videos or even produce entire clips. Keep your eye on this space; it’s developing faster than your TikTok feed!
- Audio Innovation: Think voiceovers, custom sound effects, and even music. Full disclosure: I released an entire AI-generated album on Spotify and Apple Music, and trust me—I’m no musician. That’s the power of AI!
The Bottom Line
As marketers, the entire content world is now at our fingertips, from text to visuals, video to audio. AI isn’t just making our jobs easier; it’s opening entirely new ways to connect, educate, and entertain our audiences. Are you leveraging AI to its fullest potential—or are you just scratching the surface?
Content Product and Distribution Examples:
- Heinz (B2C food)—To spice up social media, Heinz ran an AI-driven campaign asking, “What does ketchup look like in different scenarios?” using OpenAI’s DALL-E. The AI-generated wild ketchup imagery (ketchup in space, under the sea, etc.), which the brand posted and even asked followers to caption. The result: social engagement shot up 38% higher than their previous campaigns. Heinz garnered tons of buzz and user-generated content, proving AI can fuel creative content that resonates.
- Nike (B2C retail) – For Nike’s 50th anniversary, the brand partnered with agency AKQA on an AI-powered video celebrating Serena Williams. They used machine learning to virtually pit Serena from 1999 vs. Serena from 2017, analyzing her playing style over time to create a realistic “match.” This innovative content racked up 1.7 million YouTube views and achieved a 1082% increase in organic views compared to Nike’s usual content. In Dan’s words, that’s an insane leap in reach – showing how AI-crafted storytelling can captivate audiences.
- JPMorgan Chase (B2C finance) – The banking giant’s marketing team ditched traditional copywriting for some ads and embraced Persado’s AI language platform. In A/B tests, AI-generated ad copy outperformed human copy, delivering up to a 450% higher click-through rate on some campaigns! (Even the “lower” gains were ~50–200% lifts.) Chase was so impressed it signed a 5-year deal for AI-written marketing creative. It’s a prime example of AI boosting content effectiveness, not just efficiency.
- Stick Shift Driving School (B2C services) – This niche driving academy used MarketMuse (AI) to guide its content marketing. The AI analyzed search data to tell them exactly which topics to write about and even generated content briefs. By following the AI’s recommendations, the school achieved a 72% increase in organic traffic, a 110% uptick in form completions (leads), and a 120% increase in inbound calls from new students . In short, AI content guidance translated to real enrollment growth.
- Crabtree & Evelyn (B2C retail) – Heritage beauty brand Crabtree & Evelyn turned to Albert AI, a self-learning marketing platform, to manage and distribute its Facebook ads. Albert autonomously tested countless ad variations and optimized targeting in real-time. In under 2 months, C&E saw a 30% boost in return on ad spend (ROAS) without increasing spend. By letting AI take the wheel on ad distribution, they reached new audiences and squeezed more value from their budget – a content delivery win.
- Novo Nordisk (B2C pharma) – Email marketing can be tough in the pharma world, so Novo Nordisk used Phrasee’s AI to generate better subject lines and email copy for its campaign on diabetes awareness. The AI’s knack for language paid off: email open rates jumped 24% and click rates 14% after implementing the AI-optimized subject lines. That means thousands more patients engaged with life-saving content, all thanks to a few tweaks from AI-driven copy.
- Buzet (B2B/B2C marketing agency) – (Mentioned earlier as an internal copilot example) By using AI tools for content research, templates, and even drafting, this small agency managed to increase organic traffic by 25% to content they published. Essentially, AI helped them produce more and better content, which the agency then successfully distributed to drive significantly higher traffic. It highlights how content outcomes improve when AI lightens the load on creators.
- Coca-Cola (B2C beverage) – Coca-Cola’s “Create Real Magic” platform invited fans to create original artwork with the help of AI (using DALL-E 2 and GPT). In 2023, users generated 120,000+ pieces of content, spending an average of 7+ minutes per visit engaging deeply with the brand. Coke even integrated this into a holiday campaign where consumers made AI-personalized Christmas cards. By distributing AI tools to its audience, Coke turned content consumers into content creators, massively amplifying brand engagement.
3. Hyper-Personalization
Alright, let’s dive into something genuinely exciting—hyper-personalization with AI. Let’s face it, traditional marketing automation has gotten a bit stale. Sure, “insert-first-name-here” emails had their moment, but let’s admit it: People aren’t falling for that trick anymore.
Enter AI. This isn’t just about making the same stuff faster or slightly better—this is about doing things we’ve never been able to do before.
Here’s how AI takes personalization to the next level:
- Emails on Steroids: Imagine emails so tailored that each recipient gets unique content and instructions customized precisely to their role, interests, and even the customers they serve. I’m not talking merge tags; I’m talking genuinely one-to-one interactions.
- Beyond Text: Sure, it starts with hyper-personalized text and email, but imagine custom videos, personalized graphics, or even entire interactive experiences crafted uniquely for each individual. The possibilities are staggering—and they’re already beginning to happen.
- Real-Life Example: I’ve built an entire 5-day email course on AI fundamentals that’s 100% customized to each participant based on their input. Every lesson, every instruction, is tailored to exactly who they are and what they do. (Check it out—it’ll blow your mind!)
You can enroll in my 5-day AI Fundamentals for free to see hyperpersonalization in action.
We’re just scratching the surface of what’s possible with hyper-personalization powered by AI. Imagine where we’ll be in six months—or even a year from now. One thing’s for sure: Generic marketing messages are quickly becoming a relic of the past.
Are you ready to leap into the future of hyper-personalized marketing? Your prospects and customers certainly are.
Hyper-Personalization Examples:
- Netflix (B2C streaming) – Netflix’s famously savvy recommendation engine is powered by AI analyzing each viewer’s habits. The impact? Around 75–80% of all Netflix viewing is driven by personalized recommendations. In other words, the majority of what people watch comes from “Because you watched X” suggestions. This level of hyper-personalization keeps subscribers hooked (and binge-watching), dramatically reducing churn – a fact not lost on Netflix, which credits its recommender system for saving ~$1B per year in retention.
- Amazon (B2C e-commerce) – Similarly, Amazon’s product recommendation AI (“Customers who bought X also bought Y”) is a conversion machine. A McKinsey report attributed 35% of Amazon’s sales to its recommendation algorithms. Considering Amazon did ~$470B in revenue last year, that’s a mind-boggling amount of AI-driven sales. By leveraging browsing and purchase data to personalize suggestions, Amazon’s AI upsells and cross-sells so effectively that it’s like having a super-intelligent salesperson for every shopper.
- Itison (B2C deals marketplace) – Itison sends weekly deals to subscribers, and by using Recombee’s AI to personalize each email with offers suited to the individual, they achieved a 25% increase in e-commerce conversion rate and 20% more traffic. In monetary terms, their AI-personalized emails generated a whopping 2000% ROI. This is hyper-personalization at scale – thousands of unique emails, each hitting the right notes for that recipient, resulting in far more sales.
- Bidwells (B2B services) – UK property consultancy Bidwells overhauled its website with Optimizely’s AI personalization engine. The site now dynamically shows content and calls to action based on each visitor’s behavior and interests (e.g., highlighting relevant properties or insights). The outcome was dramatic: a 93% increase in customer sessions and 300% increase in leads after implementing AI-driven content recommendations and UX improvements. This B2B example shows that personalization isn’t just for consumer streaming or retail – even in commercial real estate, tailoring the digital journey pays off in spades.
- Ivanti (B2B tech) – Ivanti, an IT software firm, faced fragmented customer data across multiple recent acquisitions. They implemented 6Sense (an AI-driven CDP and predictive analytics tool) to unify data and identify high-intent accounts for sales and marketing. The AI helped uncover “ready-to-buy” accounts based on myriad signals (web visits, intent keywords, etc.). In one year, Ivanti saw a 71% increase in new opportunities (totaling a $263M pipeline) and 94% more deals won, generating an extra $18.4M in revenue. That’s hyper-personalized account targeting in action – focusing outreach where the AI says it will hit – massively improving B2B sales outcomes.
- Cosabella (B2C retail) – This luxury lingerie brand used an AI platform to send highly segmented, personalized emails instead of generic blasts. The AI customized email content and product picks for each subscriber based on their browsing and purchase history. In just a year, Cosabella’s email-driven sales jumped 40–60%year-over-year – achieved without any extra discounts. Essentially, personalization alone drove a huge revenue lift. Additionally, open and click rates improved since customers found the content more relevant.
- Sephora (B2C beauty) – Sephora’s Virtual Artist is an AI-powered AR app that lets users “try on” makeup virtually and get shade recommendations. This is hyper-personalization in the product experience: each user sees cosmetics on their own face and gets custom-fit suggestions. Sephora reported this AI tool significantly increased conversion rates and overall sales, as shoppers were more confident in purchases that were tailored to their skin tone and style. By leveraging AI to personalize the try-before-you-buy experience, Sephora boosted both customer satisfaction and revenue.
4. Conversational AI
Look, I have a confession to make—I used to despise chatbots. Remember those clunky Drift bots from around 2017-2019? Yikes. Give me a simple form any day over a frustrating chatbot that couldn’t understand me.
But then something shifted—enter ChatGPT. Suddenly, conversational AI wasn’t just tolerable; it became essential. Why? Because it’s no longer just about website chats. It’s now transforming how we communicate across multiple platforms:
- Social Media DMs: Instantly answer questions, send product recommendations, or even schedule meetings.
- Text and SMS: Automate conversations without losing that personal touch, making each interaction meaningful.
- Inbound Requests: Conversational AI can handle floods of leads gracefully—no more frustrated sales reps yelling, “Stop sending us leads!”
- Customer Service: Forget repetitive FAQ responses; conversational AI answers intelligently, guiding customers with empathy and accuracy.
Here’s the beautiful part: AI-powered conversations feel genuinely human. It answers questions intelligently, books meetings seamlessly, and offers real, actionable solutions. It’s customer service with brains and heart—something we marketers and our prospects truly deserve.
The days of outdated chatbots are over. Today’s conversational AI isn’t just good marketing—it’s essential. Are you ready to embrace it?
Conversational AI Examples:
- Sephora (B2C beauty) – Sephora has been a pioneer in chatbots. Its Sephora Virtual Artist chatbot on platforms like Facebook Messenger gives users makeup tips, product info, and tutorials in a conversational way. Crucially, it also guided shoppers to purchases. Sessions involving the chatbot saw a 25% higher conversion rate compared to those without it . In other words, customers who chatted with Sephora’s AI were significantly more likely to buy – presumably thanks to instant answers and personalized advice.
- TheCultt (B2C resale marketplace) – TheCultt’s small team wanted to personally engage customers but lacked bandwidth. They set up a Chatfuel chatbot to welcome new users, answer FAQs, and even run a fun Instagram contest. In 3 months, this bot cut average response time by 2 hours and helped drive a 37% increase in conversions (from users who engaged with an automated contest and follow-up). TheCultt initially feared losing a “human touch,” but the results showed the chatbot actually improved customer experience and sales – freeing the team to focus on bigger strategies.
- Adobe (B2B/B2C software) – Adobe built a chatbot named “CreativeBot” to assist customers with creative project questions and recommend products/tutorials. Integrated across Adobe’s website and apps, this AI assistant not only handled support (reducing load on human reps) but also suggested relevant upgrades and features to users. Within a year, Adobe credits this bot (along with its content marketing hub) for generating $10 million+ in additional revenue. It engaged users longer and guided them to cross-sell/up-sell – showing how conversational AI can directly drive revenue, not just deflect support tickets.
- Bank of America (B2C finance) – BoA’s Erica is a virtual financial assistant accessible via the mobile app and site. As of mid-2023, Erica had over 1.5 billion total interactions with 37 million users since launch, and usage keeps climbing (333 million interactions in the first half of 2023 alone, up 35% YoY). Clients spent 3+ million hours with Erica in 2022 (tasks that would have otherwise needed human reps). Erica helps customers with everything from account info and budgeting tips to basic transactions and has become a primary gateway for BoA’s digital customers. The convenience and always-on service translate to higher customer satisfaction – and likely contribute to BoA’s digital sales and retention (though the bank hasn’t publicly broken out revenue, the engagement speaks for itself).
- Amtrak (B2C travel) – The US rail service’s “Ask Julie” AI chatbot handles common customer inquiries and booking requests on Amtrak.com. Julie has been a runaway success: she answers about 5 million questions a year, helped increase bookings by 25%, and saved Amtrak an estimated $1 million in customer service costs in one year. Even more impressively, Julie’s implementation led to a 30% higher average revenue per booking (since the bot can upsell ancillaries) and an 800% ROI, according to case studies. This classic example shows a well-designed conversational AI both improves service and boosts the bottom line.
- Domino’s (B2C QSR) – As early as 2014, Domino’s introduced “Dom”, a Siri-like voice assistant for ordering pizza via their mobile app (and later via phone and smart speakers). Today, thanks in part to Dom and other digital ordering tools, over 65% of Domino’s U.S. sales come through digital channels
5. Automated Analysis and Insights
ALet’s be honest: marketers are drowning in data. Ever since social media exploded, we’ve been collecting more data than we know what to do with. Most of us open tools like Google Analytics and immediately feel overwhelmed. So much information—but what do we actually do with it?
Here’s where AI changes the game:
- Instant Data Insights: Simply upload a CSV file packed with your analytics data into an AI tool like ChatGPT, and instantly, you’ll get insights. It spots trends, anomalies, and opportunities faster than any human analyst could.
- Qualitative to Quantitative Magic: This one’s my favorite. Got tons of open-ended survey responses? Let AI categorize, summarize, and quantify that data for you—in seconds. What used to take days (or even weeks) of manual work can now be automated completely.
- Real-Life Automation Story: I recently interviewed a friend on The AI-Driven Marketer podcast. He was swamped with hundreds of daily survey responses. He used AI to summarize every response, saving himself literally a week’s worth of work every single week! Now he’s focused on strategic AI initiatives instead of manual analysis.
We’re just scratching the surface. As AI tools expand their capabilities, they’ll not only analyze data better—they’ll predict future outcomes more accurately. Imagine leveraging AI forecasts regularly to make smarter, faster business decisions.
Are you ready to transform your overwhelming data into powerful insights? With AI, it’s already happening.
Automated Analysis & Insights:
- Crabtree & Evelyn (B2C beauty) – The brand’s digital ads were plateauing, so they employed Albert AI to analyze performance and auto-optimize targeting and spend across Facebook. Albert crunched engagement data faster than any human, reallocating budget on the fly to best-performing audiences/creatives. In under 8 weeks, it drove a 30% increase in ROAS while keeping ad spend flat. Essentially, AI-based analysis found “money left on the table” and captured it, improving efficiency.
- Stick Shift Driving Academy (B2C services) – With a very niche audience, this driving school struggled to pick the right blog topics for SEO. MarketMuse’s AI analyzed search patterns and competitor content to suggest high-opportunity topics and keywords. Following these data insights led to a 72% boost in organic traffic and 120% more inbound calls, as noted earlier. This is a case of AI doing the analytical heavy lifting (what to write, how to optimize) that resulted in concrete business growth. It’s not just content – it’s content guided by analysis.
- Tomorrow Sleep (B2C e-commerce) – Online mattress brand Tomorrow Sleep used AI (in partnership with BuzzFeed) to analyze trending topics and questions people ask about sleep. By pivoting their content strategy based on these insights, they achieved a 100× increase in organic monthly visitors – from 4k to 400k. Such explosive growth underscores how AI-driven analysis of consumer interest can inform content that massively scales traffic (and likely sales). In effect, the brand found the exact info people were seeking and became the source for it.
- Euroflorist (B2C flowers) – Euroflorist leveraged AI for continuous website testing and optimization. The AI analyzed user behavior data and automatically tweaked elements (like product recommendations, layouts, etc.) to improve conversion. One key result was a 4.3% uplift in website conversion rate after implementing the AI-driven changes. While 4.3% might seem small, for an e-commerce player, this equates to a significant revenue increase over thousands of transactions. The AI essentially uncovered UX improvements that humans hadn’t, leading to more shoppers completing purchases.
- BMW (B2C auto)—To promote new models, BMW partnered with IBM Watson to analyze social media data (trends, sentiment, user interactions) on a massive scale. Watson’s analysis informed a highly personalized social campaign, serving different content to different segments in real time based on what the AI learned about audience preferences. The campaign saw a 30% jump in BMW’s social media engagement. By trusting insights from AI, BMW delivered the right message to the right people, markedly deepening engagement and brand connection.
- Ivanti (B2B) – (Revisiting Ivanti through the analytics lens) By consolidating formerly siloed data and applying AI to score leads and accounts, Ivanti’s marketing and sales got actionable insights on where to focus. The AI surfaced patterns (like which behaviors predict a buyer) that humans hadn’t seen. Consequently, Ivanti dramatically improved its pipeline quality and closed deals (71% more apps, 94% more wins). This shows how AI analytics isn’t just about pretty dashboards – it’s about finding profitable opportunities in the data haystack. Marketers armed with those insights can target efforts far more effectively, driving measurable revenue gains.
Why This Matters
Understanding these five types isn’t just categorization; it’s about recognizing the different roles AI can play on your marketing team. Whether you’re integrating AI step-by-step or embracing all five at once, this framework ensures you see the bigger picture, making your AI strategy coherent, actionable, and powerful.
Sources
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Amtrak Julie
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HDFC Bank EVA
“HDFC’s EVA Handles 2.7M Queries in Six Months, Achieves 85% Accuracy.” HDFC Bank Press Release, 2023. Link
Domino’s Dom
“Voice Ordering AI Powers 65% of Domino’s US Sales Digitally.” Domino’s Tech Announcements, 2022. Link
Sephora Reservation Assistant
“Sephora’s Messenger Bot Brings 11% More Appointments vs. Other Channels.” Mobile Marketer, 2023. Link
Automated Analysis & Crabtree & Evelyn
(See Reference #13 above for Albert AI Case Study)
Tomorrow Sleep & BuzzFeed
“Online Mattress Startup Sees 100x Organic Growth with AI SEO Analysis.” BuzzFeed Tech, 2022. Link
Euroflorist CRO
“Euroflorist Elevates Website Conversion by 4.3% through AI Testing.” ConversionXL, 2023. Link
BMW & IBM Watson
“BMW Sees 30% Social Lift Using Watson to Analyze Customer Sentiment.” IBM Case Study, 2022. Link
Predictive Analytics (Aberdeen)
“Companies Using Predictive Analytics Grow Revenue 21% YOY.” Aberdeen Group Research, 2023. Link
Major Retailer AI Recommendations
“Controlled Experiment Shows 11% Sales Lift via AI Product Recommenders.” Marketing Science Journal, 2023. Link
Coca-Cola “Create Real Magic”
(See Reference #15 above for the official Coca-Cola campaign details)
