If you send cold emails or LinkedIn messages, you know the drill: people ignore generic outreach. Everyone wants that personal touch, but who has time to write 500 unique intros a week? This guide is for sales, marketing, and founder types who want to save time and actually get replies. I'll walk you through how to use Jason AI to make your outbound messages feel like a real human wrote them—without spending all day glued to your keyboard.
Why Personalization Matters (and Where Most People Blow It)
Let’s be honest: most “personalized” messages aren’t fooling anyone. Swapping in someone’s first name or company isn’t personalization—it’s mail merge in a Halloween mask. Real personalization means showing you actually paid attention.
When you get it right, your reply rates go up. When you fake it, you’re just training people to ignore you (or worse, hit spam).
Here’s what actually works: - Reference something specific and true: A recent blog post, a role change, or a product launch. - Connect the dots: Explain why you’re reaching out to them. - Keep it short: No one wants a wall of text from a stranger.
But doing this at scale? That’s where most people give up or cut corners. That’s where tools like Jason AI can actually help—if you use them the right way.
Step 1: Get Your Data in Order
Jason AI can only personalize messages as well as your data allows. Garbage in, garbage out. Before you start, make sure you have:
- A lead list with more than just names and emails. Think: LinkedIn profile URLs, recent news, job titles, company info.
- Reliable sources. Tools like Apollo, Clay, or LinkedIn Sales Navigator help, but check their accuracy—AI can’t correct bad data.
Pro tip: Spend time on data quality upfront. It’s boring, but it’s the difference between “Hey, {{FirstName}}!” and “Congrats on launching Version 2.0 last week.”
Step 2: Define What “Personalization” Means for You
Not every campaign needs the same level of detail. Decide what actually matters for your audience. A few examples:
- Light touch: Mentioning company news, recent funding, job title changes.
- Medium: Noting a specific project or product they’re involved in.
- Heavy: Commenting on a podcast episode they hosted or a detailed blog post.
You don’t need to go overboard. The key is relevance, not just filling in blanks.
Step 3: Set Up Jason AI for Personalization
Here’s where you put the tools to work. Log in to Jason AI and start a new campaign.
Upload Your Data
- Import your CSV or connect your CRM.
- Map fields carefully: If you have a “recent_news” or “custom_note” column, make sure Jason AI can use it.
- Double-check sample rows—AI can’t guess what you meant.
Craft Your Templates
Don’t just slap {first_name} into a template and call it a day. Use “dynamic fields” to pull in more interesting info.
Example template:
Hi {first_name},
I saw {custom_note} and thought it was impressive. Curious how you managed {related_challenge} at {company_name}.
Would love to connect and swap notes.
- {custom_note} could be “your recent talk at SaaStr” or “the new AI feature in your app.”
- If you don’t have real data in these fields, Jason AI can generate suggestions—but always check for accuracy.
Pro tip: Don’t let AI guess at facts. If the data isn’t reliable, use a more generic fallback.
Step 4: Use Jason AI’s Automated Research—But Don’t Blindly Trust It
One of Jason AI’s headline features is that it can scrape public profiles and news to suggest custom intros. It’s cool, but it’s not magic.
- Works well: For finding recent LinkedIn posts, company announcements, or role changes.
- Falls short: On nuanced details, sarcasm, or anything that needs real context.
What to do: - Set up rules for what type of info the AI should pull (e.g., “only use news from the last 30 days”). - Spot-check the AI’s suggestions before sending, especially for high-value leads. - Don’t let AI invent compliments (“Loved your recent keynote!” if they didn’t speak anywhere—awkward).
Step 5: Review, Test, and Sanity Check
Before you hit “send to 500,” do a dry run.
- Preview messages: Randomly check 10-20 samples. Would you reply to these?
- Look for weirdness: Outdated news, broken sentences, or cringe-y flattery.
- A/B test: Try different levels of personalization and see what gets more replies. Sometimes less is more.
What’s not worth your time: Over-optimizing the subject line. If the body is bad, it won’t matter.
Step 6: Send, Track, and Iterate
After sending, don’t just watch open rates—focus on replies and positive responses.
- Tag responses: Are people engaging, or just saying “not interested”?
- Update your data: If you get a bounce or someone changes roles, fix it for next time.
- Tweak as you go: Personalization is never “set it and forget it.” Keep refining based on what you learn.
What to Ignore (and What to Watch Out For)
- Don’t obsess over AI “sounding human.” Most prospects can tell it’s AI if you overdo it. Keep it real, keep it simple.
- Don’t automate everything: For your top 10% of leads, write by hand. AI’s fine for the rest.
- Don’t get clever with fake familiarity: No “Hey, fellow coffee lover!” unless you know it’s true.
Red flag: If your reply rates drop after adding personalization, you’re probably coming off as fake. Dial it back.
Wrapping Up: Keep It Simple, Keep Improving
Personalizing outbound messages at scale isn’t about tricking people—it’s about respecting their time and showing you did your homework. Tools like Jason AI can help, but only if you pair them with good data and common sense.
Start small, check your work, and don’t be afraid to keep things simple. The best outbound messages feel like one human reaching out to another. AI’s just there to help you do it more often—without burning out.
Now, go send something worth replying to.