Thoughtfulness Through Inference: A New Approach to Cold Email Personalization
How many times have you received (or written) a cold email that includes a “personalized” line like the following?
“Hey! Saw your recent LinkedIn post on copywriting.”
“Hey there – saw you went to Stanford too!”
“Noticed you’re in NYC. Funny coincidence, me too!”
They’re there for a reason: we’re constantly told to “personalize” our cold emails for better results. This advice is true, but the kind of personalization you use has a massive impact on how much better those results will be.
The type of personalization above is only marginally useful.
It's better than nothing, I’ll give you that. But it hardly qualifies as true personalization or relationship building. You're simply commenting on obvious, public details about the person.
What if I told you there was a better, more effective way to personalize your cold emails?
Why obvious personalization falls short
Let's take a step back—why does personalization work in the first place?
Well, it's effective for two main reasons:
It makes the email feel 1:1 (personal) instead of 1:Many (impersonal)
It builds trust by showing you understand the prospect’s unique situation
Commenting “Cool, go Bucs!” checks box #1. But it doesn't demonstrate any real insight into the person or their unique context in life. It’s a shallow attempt to connect.
“Obvious” personalization signals laziness more than thoughtfulness.
It's better than nothing—but just barely.
What “inference” actually means
The most effective personalization comes from inference: taking public information and connecting the dots to make an informed, value-adding observation about someone.
Here’s an example.
Suppose you notice on LinkedIn that a founder used to work as a freelance writer before starting their tech company. An obvious personalization would be “Saw your LinkedIn post on copywriting!”
Now imagine they had a LinkedIn post on copywriting and you noticed they used to be a freelance writer before founding a startup.
An inference would be:
“Hey - not too often you see founders who write. Emailing you because I’m a writer, too.
Going to make a guess that you plan on using LI as a way to pull in leads—and figure you’d like a hand writing.
Think it might be useful to have some help?”
This shows you understand their interests and current challenges. It's thoughtful, not just personalized.
Why thoughtful inference works
Inference demonstrates that you:
Made an intelligent, non-surface level observation
Understand the prospect's unique situation
Can thoughtfully relate your services to their specific needs
It shows care, nuance, and understanding—all prerequisites for connecting with complete strangers. When you infer and connect the dots, you’re prompted to ask clarifying questions first, rather than pretend you have all the answers. This builds trust quickly through transparency.
How to make effective inferences
Making thoughtful inferences takes some sleuthing and skill. Here are three tips to get it right:
1. Identify meaningful breadcrumbs of data
Don’t just look for obvious data points like job titles. Find pieces of information, however small, that offer insight about interests, values, and priorities.
Possible sources include:
Past work/life experiences
Quotes and ideas they share and engage with publicly
Jobs before they found their calling
Initiatives and orgs they care about
Look across these breadcrumbs to spot potential needs.
2. Connect the dots
Look for relationships between the breadcrumbs that reveal opportunities where you can help.
For example: A founder’s early writing experience + their scaling challenges as CEO → your opportunity to provide valuable writing services
3. Ask thoughtful questions to confirm your inference
Avoid pretending your inference is rock-solid intel. Frame them as interesting questions instead to spark productive dialogue:
“I might be off base here, but seems like there’s a clear need for quality content in your org, but maybe you lack the time? Would it be interesting to chat about getting some help?”
How we used inference in a real campaign
Here’s a real example from an Aurora campaign that effectively used inference:
Pod People produces an extensive annual industry report on trends shaping branded storytelling and experiential marketing. To inform this year's report, we helped them connect with over 1,000 senior marketers globally to gather insights and expertise.
We researched target prospects extensively, looking for "breadcrumbs" that revealed relevant experience. For example, someone's blog posts on new frontiers like AR storytelling or passion projects outside work related to marketing tech innovation.
We made thoughtful inferences about which trending topics and emerging ideas aligned well with each person's demonstrated expertise. Then posed them as questions:
"Seeing your focus on digital storytelling campaigns, we think you’d be perfect to discuss topic X or Y in Pod People’s upcoming report. Does this direction align with innovation projects you’re working on currently and insights you could share?"
Our thoughtful personalization helped the campaign drive an incredible 10.75% cold email reply rate and over 50% positive, high-quality responses from previously complete strangers.
Inference helps create value from the very first touch, effortlessly converting strangers into engaged recipients excited to contribute their knowledge.
Write better emails (and book more sales calls)
Obvious personalization won’t cut it anymore, if it ever did. To craft perceived 1-to-1 messages that build real trust and interest, you need inference—the ability to make thoughtful observations that advance the conversation.
Mastering inference takes work, but the effort pays off exponentially in more—and better—connections. The trick is finding the right balance of sleuthing without veering into creepy territory.
If manually inferring value across prospects at scale sounds daunting, Aurora makes thoughtful, customized outreach fast and effective for teams to implement. Reach out if you'd like to learn more!
