AI-Driven Personalization In B2B E-Commerce

AI is about to make every B2B marketer think like Amazon. You know how Amazon knows you're pregnant before you tell your family? That's coming to B2B. Right now, we're stuck in this weird middle ground where we have amazing account-level data (company size, tech stack, funding) but terrible person-level insights. We know XYZ Corp uses Salesforce and just raised $50M. But we have no clue that their VP of Sales just got divorced, moved across the country, and is now prioritizing work-life balance solutions. That's changing fast. AI is getting scary good at connecting dots we couldn't see before: → LinkedIn activity patterns + email engagement timing = stress levels → Job posting language + Glassdoor reviews = cultural priorities → Speaking event topics + content consumption = personal motivations → Travel patterns + expense reports = decision-making authority We're moving toward a world where B2B targeting looks more like B2C targeting. Where you can identify not just accounts that need your solution, but the exact person most likely to champion it internally. Think about this... Netflix doesn't just know you like comedy. They know you watch it when you're stressed, prefer 30-minute series over movies, and are 73% likely to binge on Sunday nights. Soon, your ABM platform will know Sarah the CTO prefers technical deep-dives in the morning, gets anxious about security discussions, and makes purchasing decisions fastest when her team validates the choice first. Account-based becomes person-based. Generic becomes intimate. Marketing becomes psychology. Prove me wrong.

The future of B2B isn't AI-first or AI-only. It's AI-enabled. But here's what that actually means: Start with basics (done manually):  - Research your prospect - Understand their context - Show you paid attention - Present relevant solutions Then layer in AI to scale: • Automated research • Personalized messaging • Multiple offer sequences • Context-aware follow-ups Example sequence: - 1st touch: Offer free audit with relevant personalization - 2nd touch: Share evaluation with relevant personalization - 3rd touch: Custom assessment with relevant personalization The fundamentals haven't changed. AI just helps you execute them at scale.

How is 1:1 personalization like crafting a great cocktail? 🍹 Personalization is not a new idea. Phil Fernandez and were selling Epiphany Interaction Advisor (a reinforcement learning, multi-channel recommendation engine that picked the best decision in real-time from a library of always-on options) more than 20 years ago. And Marketo Engagement Programs, combined with triggered workflows and dynamic content, were capable of delivering intelligent streams of personalized emails (at least for users with enough growth hacking skills). Yet true 1:1 personalization mostly remains a promise, not a reality, for B2B. And after decades of inflated promises, any vendor talking about "personalized journeys" today is viewed with healthy and justified skepticism. WHY PERSONALIZATION HASN'T WORKED IN B2B 1️⃣ B2B is complicated, with non-linear journeys and complex buying committees. This makes the problem inherently hard. 2️⃣ Rules-based personalization schemes quickly devolve into spaghetti diagrams that are impossible to maintain. Theoretically possible but practically unmanageable. 3️⃣ Limited B2B data sets aren’t well suited to traditional machine learning. And many ML techniques “flatten” the most interesting behavioral data, such as which specific campaigns and messages someone would respond to. 4️⃣ Next-best-action is too simple for long, complex B2B journeys; we need to think more than just one move ahead. WHAT’S DIFFERENT NOW The ideas behind personalization were right, but the technology wasn't ready. Now it is. (Note: successful personalization isn't about superficial elements like mentioning someone's LinkedIn post in an automated email. And as Tejas Manohar recently pointed out, it’s not even about creating unique content for each buyer.) Real personalization is figuring out what to send, when to send it, and how to deliver it for every individual customer and account. Like a bartender mixing the perfect cocktail, you don’t need new ingredients for each person — just the right action at the right time. And as Phil points out, when you mix and match from pre-approved content libraries, you can easily create millions of unique combinations.  Humans and rules can’t manage that complexity, but modern AI can. Unlike traditional ML approaches, new techniques like transformers and reasoning LLMs can work with the smaller, more complex B2B data sets to mix up the right sequence of actions for each person. The same technology powering Instagram's eerily accurate recommendations can now be applied to B2B. In this new model, segment marketers focus on creating quality content, while AI handles orchestration. In other words, marketing teams focus on crafting exceptional experiences, while AI handles the complexity of delivering the right cocktail of actions. #MarketingAutomation #AIinMarketing #B2BMarketing #Personalization #MarketingOnTheRocks