Read time: 5 minutes

Welcome to the Succession newsletter where 2,000+ life science sales reps improve their skills in 5 minutes per week. If you’re getting value from these newsletters, we'd love it if you could forward it along to your sales colleagues. If you’re new here, subscribe below.

Succession Bio works with life science/biotech companies to help drive sales, licensing, and partnership opportunities.

We do this through market research to identify the right companies and people, craft scientifically credible messages, and then perform the outbound sales and marketing tactics on your behalf to facilitate meetings with the right people at the right companies.

Succession

You've probably received a version of this email before:

"Hi Sarah, I came across your profile and was impressed by your work in oncology. We help companies like yours accelerate drug discovery. Would you be open to a quick 15-minute call?"

Most scientists and biotech/pharma leaders see emails like this every week. Sometimes the sender mentions a funding round. Sometimes they reference a LinkedIn post. They may feel extra spicy and even comment on a recent publication. ✨

Technically, the email is personalised… yet it still feels generic.

That’s because much of what we call “personalised outreach” today is really just mail merge with some scraped data dropped into a template. AI didn't create this problem, it just made it easier to do at scale.

Modern outbound tools are very good at gathering surface-level information automatically. They can pull a contact's job title, their company, a recent LinkedIn post, or even the latest funding round their company announced.

Most campaigns plug those details into a template using custom variables like these:

{first_name}
{company_name}
{job_title}
{therapy_area}
{recent_linkedin_post}
{recent_funding_round}

Before it's sent, this is what the template looks like:

Once those custom variables are pulled in from sources like LinkedIn, company pages, and data enrichment tools, the message that lands in the inbox looks something like this:

Initially, you probably think that email appears personalised, right? The sender clearly knows Sarah's company, her role, and what the company is working on. Everything in that message was pulled automatically. No thinking was required… yay??

The issue here is that Sarah can tell.

She knows it because she has seen almost the exact same structure plenty of times before.

When people talk about AI ruining cold outreach, they often assume the technology itself is the issue.

“This sounds like ChatGPT”

“No normal person writes like that”

“I don’t trust the output”

“This sounds like AI trying to fake personality”

“Who even uses m-dashes?!”

In reality, the underlying problem existed long before AI tools became widely available.

Some teams don’t use AI at all and write every message manually. But if those emails still begin with “I hope you’re well” or “we work with companies like yours,” the result isn’t much better. The email may be written by a human, but it’s still generic. And the person receiving the email doesn’t care whether a human or AI wrote it… if it sounds generic it gets deleted either way. 🙃

On the other side, some teams rely heavily on AI tools. They open a chatbot, type something like “write a cold email to a Vice President of Discovery at a biotech company,” and send whatever comes back.

I hate this. This is not AI-assisted outreach. You aren’t leveraging AI, what you’re actually doing is outsourcing your thinking and causing your brain cells to lyse.

AI can certainly help structure an email or improve the wording. What it can’t do is decide why that specific person should care about receiving it i.e translate the finding! It doesn’t know their pipeline, their research focus, or the problems their team is trying to solve.

That part still most definitely requires human judgment.

One of the biggest misconceptions in cold outbound is that personalisation is the same thing as relevance.

Personalisation usually means referencing a basic piece of information about the recipient. Specificity connects a real observation to a meaningful insight.

Personalisation: "I saw your company recently raised a Series B, congratulations."

Specificity: "I noticed your pipeline is heavily CNS-focused. Teams working in that space have been struggling with reproducibility in neuro assays, recently we've been seeing the same pattern across several labs."

Both messages appear personalised. But one required ten seconds to generate, while the other required someone to spend a few minutes thinking about the company's research and where problems might occur. That difference is usually obvious to the person reading the email.

Most campaigns rely on simple variables that are easy to pull automatically but rarely add any real insight:

{first_name}
{company_name}
{job_title}
{industry}

More thoughtful campaigns use variables that capture specific context about the person or their work:

{recent_publication_topic}
{pipeline_program}
{target_or_modality}
{recent_company_update}

The template might look like this before it's sent:

Once the details are filled in, the message becomes something like this:

The difference between this email and the example I gave at the start is that it’s built around variables that reflect something real about the person’s work. Yes, it still pulls in the data (the facts) which provide useful context. But the key difference is that it goes a step further and says something meaningful about it. It adds perspective.

P.S. Bonus points if you noticed this example doesn't include m-dashes 😉

Most cold outreach fails for a surprisingly simple reason: it avoids having an opinion. AI is literally designed to please. And so how can it please if it doesn't know the person? The safest option is to stay neutral. That's why lots of emails are written in a way that’s overly polite and extremely neutral.

Phrases like:

“I would welcome the opportunity to better understand the initiatives your team is working on and see whether there may be a way for us to support those efforts.”

“I would be delighted to learn more about your current priorities and explore whether there might be a potential opportunity for collaboration.”

“I thought it might be worthwhile to reach out and see if there could be an opportunity for us to exchange perspectives and determine whether our work may be relevant to your team.”

Check yourself - you probably didn’t even read these fully. That’s because YOU KNOW these sentences don't actually say anything.

An opinion, even a small one, changes how a message feels:

"I think many teams approach assay reproducibility backwards. We've been seeing a different pattern emerge in the data."

💡This is instantly more engaging and thought-provoking compared to the other examples.

The sender might not be completely right, and that’s ok! A message that expresses an opinion and has a thoughtful perspective is almost always more engaging than one that simply asks for time.

Let me make one thing very clear. As someone who works at a company whose entire model is built around using AI to automate lead generation and cold outbound, I’m not discrediting AI at all.

What I am saying is that you need to use it the right way. If you rely on it as a shortcut instead of a tool, it becomes very obvious to the person receiving your message.

The teams that are actually succeeding with cold outbound today aren’t the ones sending the most messages. They’re the ones using AI while still bringing perspective, opinion, and a human voice into the message.

That’s the real balance: using AI to achieve both quality and scale.

At Succession we don’t spray and pray.

We tailor then target.

Apple Podcast | Spotify | YouTube

Episode 83: Insights from the first month at Succession with Keith Daly