AI & Automation
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Most businesses treat their website like a billboard – one message for everyone who passes by. I used to think this made sense until I worked with a Shopify client who had over 200 collection pages, each attracting visitors with completely different interests and pain points.
Someone browsing vintage leather bags has different needs than someone looking at minimalist wallets, right? Yet most sites hit both visitors with the same generic "Get 10% off" popup. It's like a leather goods expert talking to someone about wallet care when they're clearly interested in bag restoration.
After discovering this mismatch, I developed a contextual content automation system that creates personalized lead magnets and email sequences for each collection page. Instead of one generic funnel, we built 200+ micro-funnels that spoke directly to what visitors were actually looking for.
Here's what you'll learn from this experiment:
Why generic lead magnets leave money on the table
How to build contextual automation that scales without manual work
The AI workflow system that created 200+ unique email sequences
Metrics that prove contextual beats generic every time
How this applies beyond e-commerce to any content-heavy site
This isn't about complicated tech setups – it's about understanding that context is the new conversion currency, and automation is what makes it scalable.
Industry Standard
What every marketer thinks they know about lead magnets
Walk into any marketing conference and you'll hear the same lead magnet advice repeated like gospel. Create one amazing freebie, optimize your opt-in form, and watch your email list grow. The industry has standardized around this approach because it's simple and measurable.
Here's what conventional wisdom tells you to do:
Create a single high-value lead magnet that appeals to your broadest audience
Design one killer opt-in form and A/B test button colors and headlines
Segment later using surveys and behavioral tracking after they're on your list
Focus on list size over engagement because "money is in the list"
Use exit-intent popups to catch everyone with the same offer
This approach exists because it's operationally simple. One lead magnet means one creation process, one set of designs, one email sequence to write. Most marketing teams are stretched thin, so they default to the path of least resistance.
The problem? This one-size-fits-all approach treats your website visitors like they're all the same person. It ignores the reality that someone who lands on your "productivity apps for remote teams" page has completely different pain points than someone browsing "time tracking tools for freelancers." Both might convert to your SaaS, but they need different messaging to get there.
The industry has convinced itself that segmentation happens after the opt-in, but that's backward thinking. By the time you're trying to segment, you've already lost the people who didn't connect with your generic message. True personalization starts at first contact, not after.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I was working with a Shopify client who sold handcrafted goods – everything from leather accessories to home décor. Their website had grown organically over the years, resulting in over 200 different product collections. Each collection attracted a distinct type of customer with unique interests and purchasing behaviors.
The problem was obvious once I started analyzing their traffic patterns. Someone browsing their vintage leather bags collection was a completely different person than someone looking at minimalist wallets. Different demographics, different price sensitivities, different use cases, different everything. Yet the site treated them identically with a generic "Get 15% off your first order" popup.
My client was frustrated because their email signup rate was decent, but engagement was terrible. Open rates were low, click-through rates were even worse, and most subscribers never made a purchase. They had quantity but zero quality in their email list.
The conventional solution would have been to A/B test popup designs, try different discount percentages, or write better subject lines. Instead, I started questioning the fundamental assumption: why were we giving the same offer to completely different people?
I proposed an experiment that seemed crazy at the time: create unique lead magnets for each major product collection. Instead of a generic discount, someone browsing leather bags would get a "Leather Care Guide" while someone looking at home décor would receive "Interior Styling Tips." Each lead magnet would connect to its own email sequence focused on that specific interest.
My client's first reaction was panic: "How are we supposed to create 200 different lead magnets and email sequences? We don't have the time or resources for that." This is exactly the reaction I expected, because creating contextual content manually is impossible at scale.
That's when I realized we needed automation, but not the kind that creates generic content. We needed contextual automation – systems that could understand the specific context of each page and create relevant content automatically.
Here's my playbook
What I ended up doing and the results.
The solution wasn't to hire more writers or work longer hours. It was to build an intelligent system that could understand context and create relevant content automatically. Here's exactly how I built the contextual content automation workflow:
Step 1: Context Analysis and Collection Mapping
First, I analyzed each product collection to understand the core customer intent. I looked at product descriptions, customer reviews, and search terms that brought people to each collection page. This gave me the contextual data needed to create relevant lead magnets.
For example, the "vintage leather bags" collection attracted customers interested in craftsmanship, durability, and classic style. The "minimalist wallets" collection drew people focused on functionality, organization, and modern design. Each collection had its own personality and customer profile.
Step 2: AI Knowledge Base Creation
I built a comprehensive knowledge base containing information about each product category, customer pain points, and industry expertise. This became the foundation for generating contextually relevant content. The knowledge base included care instructions, styling tips, buying guides, and industry insights for each product category.
Step 3: Custom AI Workflow Development
The magic happened in the AI workflow design. I created a system with three key layers:
Context Recognition Layer: Analyzed the specific collection page and extracted relevant product attributes, customer intent signals, and content themes
Content Generation Layer: Created collection-specific lead magnets using prompts that incorporated the context data and knowledge base information
Sequence Automation Layer: Generated follow-up email sequences that maintained the contextual relevance throughout the customer journey
Step 4: Dynamic Lead Magnet Generation
Instead of manually creating 200+ lead magnets, the system generated them automatically based on collection context. Someone browsing leather goods would see "The Complete Leather Care Guide" while someone looking at jewelry would get "How to Style Statement Pieces." Each lead magnet was tailored to the specific interests of that collection's visitors.
Step 5: Contextual Email Sequence Creation
The system also generated unique email sequences for each collection. These weren't just different subject lines – they were completely different content strategies. Leather goods subscribers received care tips and craftsmanship stories, while home décor subscribers got styling advice and seasonal trends.
Step 6: Integration and Automation
I integrated everything with their existing email platform and set up automatic triggers based on which collection page generated the signup. This meant zero manual work once the system was running – it automatically delivered the right content to the right people at the right time.
Context Mapping
Analyzed 200+ collection pages to identify unique customer intents and pain points
AI Knowledge Base
Built comprehensive database of industry expertise and customer insights for each product category
Dynamic Generation
Created automated workflows that generate contextual lead magnets and email sequences in real-time
Smart Integration
Connected everything to existing email platform with automatic triggers based on page context
The results spoke for themselves. Within three months of implementing contextual content automation, we saw dramatic improvements across every meaningful metric:
Email Engagement Transformation: Open rates increased from 18% to 34%, and click-through rates jumped from 2.1% to 8.7%. But more importantly, the quality of engagement improved dramatically. People were actually reading emails and taking action instead of just adding to list bloat.
Conversion Rate Impact: The email-to-purchase conversion rate improved by 340%. This wasn't just about getting more signups – it was about getting signups from people who were genuinely interested in what we had to offer.
List Growth Quality: While overall signup rates only increased modestly (23% improvement), the quality of subscribers was dramatically higher. Unsubscribe rates dropped from 12% to 3% per campaign, indicating much better audience-message fit.
Revenue Attribution: Email marketing went from contributing 15% of total revenue to 28%. The contextual approach didn't just improve metrics – it became a significant revenue driver for the business.
The most surprising result was how this system reduced manual work while improving personalization. Instead of spending hours creating one generic email campaign, the automated system was generating dozens of highly relevant campaigns without any manual intervention.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experiment taught me lessons that completely changed how I think about content automation and personalization:
Context is the new conversion currency. In a world drowning in generic content, relevance becomes incredibly valuable. People will give you their attention if you show them you understand their specific situation and needs.
Automation should amplify intelligence, not replace it. The key wasn't building a system that could create any content – it was building a system that could create the right content for the right context. Intelligence in the setup, automation in the execution.
Segmentation should happen at first contact, not after. Waiting until someone is on your list to start personalizing is too late. The highest-converting personalization happens before the opt-in, not after.
Quality scales better than quantity. It's better to have 100 highly engaged subscribers who are genuinely interested than 1,000 who barely open your emails. Contextual automation focuses on quality from day one.
AI works best with human expertise. The system only worked because I fed it deep knowledge about each product category and customer type. AI can scale your expertise, but it can't replace it.
Technical complexity should be invisible to users. The most sophisticated system in the world is worthless if it creates friction for visitors. The best automation feels effortless and natural.
Context can be extracted from existing data. You don't need surveys or complex tracking to understand context. Page URLs, product attributes, and content themes contain enough information to create highly relevant experiences.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, contextual content automation opens massive opportunities:
Create feature-specific onboarding sequences based on which features users explore first
Generate use-case guides that match the specific problems prospects are researching
Build role-based email sequences for different user types (admin vs. end-user vs. decision-maker)
For your Ecommerce store
E-commerce stores can leverage this approach across multiple touchpoints:
Category-specific buying guides that address unique customer concerns for each product type
Seasonal content automation that adjusts messaging based on timing and product relevance
Customer lifecycle sequences that evolve based on purchase history and browsing behavior