Sales & Conversion
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
When I was working on a complete website revamp for a Shopify e-commerce client, the original brief was straightforward: update the abandoned checkout emails to match the new brand guidelines. New colors, new fonts, done.
But as I opened the old template—with its product grid, discount codes, and "COMPLETE YOUR ORDER NOW" buttons—something felt off. This was exactly what every other e-commerce store was sending.
That's when I stumbled upon something that completely changed how I think about automated customer touchpoints. Instead of just updating colors, I completely reimagined the approach and discovered that the most powerful automation isn't about being more automated—it's about being more human.
Here's what you'll learn from my experience:
Why traditional chatbot onboarding fails (and what to do instead)
How I increased email reply rates by making automation feel personal
The counterintuitive strategy that turns automated messages into conversations
A step-by-step framework for human-centered chatbot design
Real examples of what actually converts vs. what everyone thinks converts
If you're tired of chatbots that feel robotic and onboarding that feels like a checkbox exercise, this playbook will show you how to create automated experiences that customers actually want to engage with. Let's dive into what I discovered when I stopped trying to perfect the automation and started focusing on genuine user experience.
Industry Reality
What everyone thinks chatbots should do
Walk into any SaaS conference or scroll through any growth marketing blog, and you'll hear the same chatbot onboarding advice repeated like gospel:
Automate everything - "Let the bot handle all repetitive questions so your team can focus on high-value activities"
Guide users through features - "Use progressive disclosure to walk users through your product step-by-step"
Collect data aggressively - "Ask qualifying questions upfront to segment users properly"
Push toward activation - "Drive users to your core activation metric as quickly as possible"
Minimize human intervention - "Only escalate to humans when absolutely necessary"
This conventional wisdom exists because it sounds logical. Automation should be efficient, data-driven, and scalable. Most product teams treat chatbots like digital customer service reps—programmed to answer FAQs, guide feature adoption, and funnel users toward specific actions.
The problem? This approach treats symptoms, not the disease. When users abandon your onboarding or ignore your chatbot, the typical response is to make the automation "smarter"—more sophisticated branching logic, better natural language processing, or more personalized messaging.
But here's what I discovered: the issue isn't that your chatbot isn't smart enough. The issue is that it doesn't feel human enough. When you optimize for efficiency over empathy, you create interactions that feel like talking to a vending machine rather than getting help from a real person.
Most teams are so focused on user activation metrics that they forget the most important metric of all: does this feel helpful?
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breakthrough came from an unexpected place. While working on that Shopify client's abandoned cart emails, I had a conversation with the business owner about their biggest customer support challenges.
"People start filling out the checkout form, then something goes wrong with payment validation—especially with double authentication—and they just leave," she told me. "We get maybe two or three support emails a week about it, but we know way more people are having the issue."
That's when it clicked. The problem wasn't that people didn't want to buy. The problem was that when they hit friction, they had nowhere to turn for help.
Most businesses would have implemented a chatbot with pre-written responses: "Having trouble with checkout? Try these 5 troubleshooting steps." But I'd just spent weeks working on making their abandoned cart emails feel more personal and human, not more automated.
So instead of building a traditional chatbot, I suggested something different: what if we created conversational touchpoints that actually invited real conversations?
The client was skeptical. "Won't that create more work for our support team?"
"Maybe," I said. "But what if those conversations actually help people buy, instead of just managing people who are already frustrated?"
This was the start of my experiment with what I now call "conversation-first onboarding"—using chatbots not to replace human interaction, but to create more meaningful opportunities for it.
Here's my playbook
What I ended up doing and the results.
Here's exactly what I implemented, step by step:
Step 1: Flipped the Script
Instead of "How can our chatbot help you today?" we started with "You had started your order... is there anything specific you're stuck on?" The chatbot positioned itself as noticing the user's journey, not offering generic help.
Step 2: Built Friction-Specific Responses
Rather than generic FAQs, we created responses for the actual problems people faced:
"Payment authentication timing out? Try again with your bank app already open"
"Card declined? Double-check your billing ZIP code matches exactly"
"Still having issues? Just reply to this message—I'll help you personally"
Step 3: Made Human Escalation the Goal, Not the Failure
Most chatbots try to avoid human handoff. We designed ours to encourage it. The final option in every conversation was "Reply to this message and someone real will help you sort this out."
Step 4: Personalized the Automation
Instead of "Our support team will get back to you," we wrote "Sarah from our team will take a look at this and get back to you within a few hours." Specific names, specific timeframes, specific people.
Step 5: Created Conversation Continuity
When people did reply, the human responder referenced the exact issue the chatbot had identified: "I see you were having trouble with the payment authentication. Let me walk you through a couple of solutions..."
The key insight: instead of trying to solve problems with automation, we used automation to better understand problems so humans could solve them more effectively. The chatbot became a bridge to human help, not a replacement for it.
This approach completely changed how we thought about conversion optimization and customer journey design.
Problem Detection
We built the chatbot to identify specific friction points, not just collect generic feedback about user experience.
Human Bridge
The automation's job was to make human help more effective, not to replace human help entirely.
Conversation Continuity
Every automated touchpoint was designed to create seamless handoffs to real people who could actually solve problems.
Personal Touch
We used specific names and timeframes instead of generic corporate language to make interactions feel authentic.
The impact went far beyond just recovered carts. Within the first month, something unexpected happened:
Customers started replying to the automated messages asking questions. Not just about checkout issues, but about product fit, shipping timelines, and customization options. Some completed purchases after getting personalized help. Others shared specific issues we could fix site-wide.
The abandoned cart email became a customer service touchpoint, not just a sales tool. Reply rates increased from virtually zero to about 15% of recipients. More importantly, about 60% of people who replied ended up completing their purchase within the next few days.
But the real win was qualitative: we started getting responses like "Thanks for actually caring" and "This is the first time a company has actually helped me instead of just sending me links."
The conversation-first approach created a competitive advantage that was impossible to copy: it made the business feel like it was run by humans who cared about solving problems, not just collecting revenue.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from this experiment:
Automation should amplify human empathy, not replace it - The best automated interactions feel like they're from real people who understand your specific situation
Friction is a conversation starter, not a conversion killer - When people hit problems, they want help, not troubleshooting guides
Generic help feels robotic, specific help feels personal - "Payment issues" vs "Payment authentication timing out" creates completely different experiences
Human escalation should be the feature, not the bug - Make it easy for people to talk to real humans, don't make them fight through chatbot layers
Reply rates matter more than resolution rates - When people start conversations, you learn about problems you didn't know existed
Context continuity creates trust - When humans pick up where automation left off, it feels seamless rather than frustrating
Sometimes the best strategy is being the most human - In a world of automated everything, genuine human connection becomes a competitive advantage
The biggest lesson? Stop trying to perfect your automation and start using automation to perfect your human interactions. The goal isn't to need fewer people—it's to make the people you have more effective at solving real problems.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS implementation:
Use chatbots to identify specific onboarding friction points, not just guide feature adoption
Make human escalation easy and encouraged during trial periods
Position automation as "noticing" user behavior, not pushing predefined paths
For your Ecommerce store
For Ecommerce stores:
Address specific checkout friction points with targeted, helpful responses
Use cart abandonment as conversation starters, not just discount opportunities
Train support team to reference chatbot context for seamless customer experience