BLOG POST
Picture this:
Imagine Business Development Representatives (BDRs) as modern-day detectives in the world of cold emailing.
To make the email more engaging, it’s important to do some research beforehand and personalize it.
Cold emails are designed to determine if the recipient might be curious about what you’re offering. Just like making a new friend, you’re reaching out in the hopes of making a connection.
A properly formatted email would look like this:
“Introduction?” and “Intro?” are typical subject lines that get high open rates. However, you should also be experimenting with other types of subject lines.
Take a look at this BAD example:
Now look at the revised version:
Here’s another BAD example:
And here’s its revised version:
A lot of research has been done on finding the perfect time to cold email prospects. Here’s what research from the past two years says:
Days: Tuesday and Thursday, closely followed by Wednesday
Times: between 8-11 am in the recipient’s time zone
Days: Tuesday, Wednesday, and Thursday, respectively
Times: 6 am, 8-11 am, and 4-6 pm in the recipient’s time zone
Days: Tuesday, Monday, and Wednesday, respectively
Times: 9-12 pm and 12-3 pm in the recipient’s time zone
CoSchedule
Days: Thursday, Tuesday, and Wednesday, respectively
Times: 8-10 am and 1-3 pm in the recipient’s time zone
While it’s best to avoid using jargon, it can sometimes be a good idea to use some to establish credibility or to speak your customer’s language, but use it sparingly.
Subject: ‘But it lies’ (NOT SALESY)
That’s usually the feedback that we’ve gotten from talking to other leaders in Aftermarket and Field Service that have attempted to use ChatGPT to give advice on how to conduct repair visits and improve customer experience. (PROBLEM- Identify a pain point)
Leveraging their own AI virtual advisor using their specific in-house expertise (manual and human knowledge from decades of experience) is giving companies a real competitive edge – without the lies. (AGITATE- Agitate that pain point)
Steve, is using an AI virtual advisor for field service something on your radar, at all? (SOLVE-Offer a solution)
[Your Name]
[Your Job Title]
[Email Address]
[Social Media Links]
Hi David,
Wrestling with biomedical data to get it ML-ready can take up a lot of valuable time and resources. (PROBLEM)
We clean and curate public data and provide it in ML-ready format to solve this: (SOLUTION)
1800+ single-cell datasets, 451 different diseases, and 39,000+ bulk RNAseq datasets across 2660 diseases – all FAIR data. (CREDIBILITY)
Is that a relevant conversation worth exploring further? (CTA)
[Your Name]
[Your Job Title]
Book a meeting with us today to get started.
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