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Episode 39: Talk With Me… Not At Me

1 email 2 takes episode 39 - talk with me not at me - featured image

​​Want to write sales emails like a pro?

Kristina and Will look at common email mistakes (using real examples) and rewrite them so that you can learn what to do differently.

Join Kristina Finseth (Sr. Manager, Outbound Growth Marketing at Greenhouse) and Will Allred (Co-Founder & COO at Lavender) to get TWO perspectives and TWO approaches on real sales emails, every week on Sales Hacker.

Watch the re-write, and check out the before and after emails below! 👇

Talk With Me… Not At Me

Hey Will,

Machine learning (ML) creation can be an overwhelming adventure. After throwing resources into complicated algorithms, organizations like Example, Example, Example, and Example turned to Company Name to speed their R&D pipeline and go to market faster.

Company is augmenting developers and Product teams ability to optimize their solutions with real-world data. The process of building, deploying, and scaling ML applications is easier and faster than ever.

We’d love to learn how we can help you, too. Are you considering using ML in the next 12 months?

Continuously learning,

REP

PS Thank you and Kristina for taking your time to help folks.

PPS I give you permission to rip this email apart

 


Kristina’s Rewrite:

Will, are you considering using ML in the next 12 months?

Curious – because we’re already helping Example and Example speed up R&D pipeline and go to market faster.

^ If you’re working through this now, perhaps COMPANY can be a resource. Let me know.

Continuously learning,

REP

 


Will’s Rewrite:

Approach 1 → What They’ve Built

Subject: ML Infrastructure

I was checking out what you’re building at Lavender, Casey.

Talking with other quick growing teams I hear a similar trend. On paper – we should have a great infrastructure for ML, but we don’t have time to maintain it as we build.

Are you seeing similar tradeoffs?

Approach 2 → Their Experience

Subject: Setting Up ML

{NAME} –

Given your time at Google, is it fair to assume establishing ML pipelines was part of why {COMPANY} brought you in?

As I’m sure you know, this can be more work than expected.

Our tech helps teams speed this up. That way you can get to model optimization faster.

Worth a chat to see if we could apply this at {COMPANY}?

 


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