AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, I was reviewing performance metrics from a client project and noticed something that made me stop scrolling. Their AI-powered email sequences weren't just performing well – they were converting at 340% higher rates than their previous "one-size-fits-all" automation. The difference? Context.
Everyone's jumping on AI marketing tools right now, but most businesses are using them like glorified mail merge systems. They're automating the wrong things and ignoring the real opportunity: creating genuinely relevant experiences that feel personal, not programmatic.
After working with multiple SaaS and ecommerce clients to implement what I call "contextual AI marketing," I've learned that the magic isn't in the AI itself – it's in how you feed it the right context to make smart decisions in real-time.
In this playbook, you'll discover:
Why generic AI marketing automation actually hurts conversion rates
The 3-layer context system I use to create truly personalized experiences
How to implement contextual triggers that adapt based on user behavior
Real metrics from 6 months of contextual AI experiments
When contextual marketing backfires (and how to avoid these pitfalls)
Ready to move beyond robot-like automation? Let's dive into building AI marketing that actually understands your customers. Check out our AI playbooks for more advanced strategies.
Industry Reality
The AI Marketing Hype vs Reality Check
If you've been in any marketing community lately, you've probably heard some version of this promise: "AI will personalize everything automatically!" The marketing automation space is flooded with tools claiming they'll revolutionize your customer experience with artificial intelligence.
Here's what most AI marketing platforms are actually doing:
Basic segmentation – Grouping users by demographics or past purchases
Template personalization – Inserting first names and company names into generic messages
Timing optimization – Sending emails when users are most likely to open them
Content recommendations – Showing "people also bought" style suggestions
Automated A/B testing – Testing subject lines and send times
Don't get me wrong – these features aren't useless. But they're treating AI like a fancy mail merge system rather than leveraging its real potential for contextual understanding.
The problem with this approach is that it's still fundamentally broadcasting. You're sending slightly customized versions of the same message to different groups, but you're not adapting the entire experience based on where someone is in their journey, what problem they're trying to solve right now, or how they prefer to consume information.
Most businesses end up with what I call "sophisticated spam" – messages that are technically personalized but feel completely disconnected from what the recipient actually needs in that moment. This is why AI marketing often decreases engagement rates despite all the hype.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came during a project with a B2B SaaS client who'd been using a popular AI marketing platform for six months. They were proud of their "personalized" email sequences and kept talking about how advanced their automation was getting.
When I dug into their analytics, the reality was brutal. Their automated sequences had a 2.1% click-through rate and were generating mostly unqualified leads. Users were engaging with the first email in a sequence but dropping off quickly because the follow-ups felt generic and pushy.
The issue became clear when I shadowed some of their sales calls. Prospects would say things like "I got your emails about X feature, but I'm actually trying to solve Y problem." The AI was recommending content based on one website visit or download, then doubling down on that assumption instead of adapting to new signals.
Here's what their "intelligent" automation was actually doing:
Someone downloaded a case study about integration capabilities → AI tagged them as "integration interested" → They received 5 emails about API features → Meanwhile, they were browsing pricing pages and reading about security features → The disconnect created frustration instead of engagement.
The client had fallen into the classic trap of optimizing for automation efficiency rather than customer relevance. Their AI was making decisions based on limited data points and never course-correcting when user behavior indicated different interests or needs.
That's when I realized the solution wasn't better AI tools – it was better context architecture. We needed to build systems that could understand not just what someone did, but why they might have done it and what they're likely trying to accomplish next.
This project became my testing ground for what I now call contextual AI marketing: using artificial intelligence not just to automate messages, but to continuously adapt the entire experience based on real-time behavioral context.
Here's my playbook
What I ended up doing and the results.
Instead of fighting against generic AI marketing, I developed a three-layer system that gives AI the context it needs to make genuinely smart decisions. Here's exactly how I implemented it:
Layer 1: Behavioral Context Engine
Rather than just tracking what pages someone visits, I set up systems to capture the "why" behind their actions. This means tracking:
Time spent on specific page sections (not just total time on page)
Scroll depth on key content pieces
Click patterns within pages (which features they explore first)
Search queries they use on the site
Download and content consumption sequences
The key insight: someone who spends 3 minutes reading pricing details is in a different mindset than someone who quickly scans and moves to case studies. The AI needed to understand these micro-intentions.
Layer 2: Journey Stage Intelligence
I built custom triggers that identify not just where someone is in the funnel, but what type of journey they're on. For example:
Research Mode: High content consumption, low conversion action engagement
Comparison Mode: Visiting competitor pages, reading "vs" content, checking pricing
Implementation Mode: Reading documentation, visiting integration pages, asking technical questions
Validation Mode: Reading reviews, consuming case studies, seeking social proof
Each mode triggered different AI logic for content recommendations, messaging tone, and next-step suggestions.
Layer 3: Dynamic Adaptation System
This is where the magic happens. Instead of following pre-set sequences, the AI continuously evaluates new signals and adjusts its approach. If someone tagged as "integration interested" starts heavily consuming security content, the system doesn't just add security emails to the integration sequence – it recognizes that security might be their primary concern and pivots accordingly.
The implementation involved:
Setting up behavioral tracking beyond basic page views
Creating context scoring algorithms that weight recent behaviors more heavily
Building decision trees that allow for mid-journey pivots
Implementing feedback loops that improve context understanding over time
The result was AI that felt less like automation and more like having a smart assistant who actually pays attention to what customers are trying to accomplish.
Behavioral Triggers
Context scoring based on micro-interactions rather than just page visits and basic demographics
Journey Mapping
Identifying different journey types (research vs comparison vs implementation) for dynamic routing
Adaptive Logic
Real-time system pivots when new behavioral signals contradict initial assumptions
Feedback Loops
Continuous learning from engagement patterns to improve future context interpretation
The results from implementing contextual AI marketing were significant and measurable:
Engagement Metrics:
Email click-through rates increased from 2.1% to 7.3%
Time spent in email sequences increased by 180%
Unsubscribe rates dropped by 60%
Conversion Impact:
Trial-to-paid conversion rates improved by 45%
Average time to conversion decreased by 23 days
Overall marketing qualified leads increased by 200%
But the most telling metric was qualitative feedback. Sales teams reported that prospects were coming to calls better informed and with more specific questions. The contextual approach had created more qualified conversations, not just more conversations.
Within six months, this system became the foundation for all their marketing automation. The AI was no longer sending messages – it was facilitating genuine customer journeys that adapted to individual needs and preferences.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Building contextual AI marketing taught me several crucial lessons about the gap between AI capability and AI implementation:
Context beats sophistication: Simple AI with good context data outperforms complex AI with limited understanding
Behavioral signals trump demographic data: What someone does matters more than who they are
Adaptability prevents irrelevance: Static sequences become spam, regardless of initial personalization
Journey types vary widely: Not everyone follows your assumed funnel path
Feedback loops are essential: AI systems need continuous learning mechanisms to stay effective
Implementation complexity scales quickly: Start with simple context layers before building complex logic
Quality over quantity: Better context leads to fewer, more effective touchpoints
The biggest mistake I see businesses make is trying to automate their existing marketing approach with AI, rather than rethinking their approach around what AI can uniquely enable. Contextual marketing isn't about sending more personalized spam – it's about creating genuinely adaptive experiences that feel helpful rather than robotic.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing contextual AI marketing:
Start with trial user behavior tracking to identify usage patterns
Focus on feature adoption context rather than just signup source
Build onboarding sequences that adapt based on in-app activity
Use API usage patterns to inform marketing message timing
For your Ecommerce store
For ecommerce stores implementing contextual AI marketing:
Track browsing patterns beyond just product views and purchases
Create dynamic email flows based on shopping behavior and season
Implement real-time product recommendations based on current session context
Use cart abandonment context to personalize recovery message timing