When you’re first starting out as a fractional, it’s very hard to say no.
People often ask about the tools I use for business development.
It’s an understandable question. There’s a lot of software out there promising to automate, optimize, and “10x” your pipeline.
My own approach is pretty simple, and it’s built on a few principles:
Use tools to save time on repetitive work
Use them to enrich your understanding of people you actually want to help
Don’t use them to pester or trick anyone
You can do everything I’m about to describe with a spreadsheet and manual effort. That’s how I started. Over time, I’ve layered in tools to make it more efficient.
Here’s what my current stack looks like.
The job I’m hiring my tools to do
Before I get into product names, it’s worth being clear on the job:
Find the right people (consistent with my ICP)
Connect with them in a low‑pressure way
Pull their basic info into a central place
Enrich that info so I understand their context
Segment intelligently for targeted outreach
Ask good questions of that data so I’m not guessing
No tool does all of that well. So I’ve assembled a chain.
Step 1: LinkedIn + Dripify – finding and connecting
I use LinkedIn primarily as a phonebook, not as a content platform.
My ideal contacts there are:
Presidents
General managers
CEOs
Founders of 5–50M manufacturing and B2B companies
To manage connection requests at scale, I use a tool called Dripify.
What I use it for:
Sending 40–50 connection requests per day
With a very simple, non‑salesy message:
“Hi, I’m another executive in the manufacturing space here in Minnesota. I’m expanding my network and would be glad to connect.”
What I do not use it for:
Automated follow‑up messages
Long, personalized‑sounding pitches
Scraping and spamming
Those capabilities exist in the tool, and they’re exactly where people slide into “creepy.”
My rule is simple:
Automation for the first, polite “hello.”
Human judgment after that.
Once someone accepts the request, I don’t keep messaging them on LinkedIn. My goal is to move the relationship to email if and when that makes sense.
Step 2: Zapier + HubSpot – capturing and organizing
When someone accepts my connection request on LinkedIn (via Dripify), I want to:
Capture their basic info
Store it somewhere structured
Avoid hand‑typing everything
So I use Zapier to:
Watch for new accepted connections in Dripify
Pull the available fields:
First name
Last name
Job title
Company
LinkedIn URL
Email (if visible in their profile)
Create or update a contact in HubSpot, which I use as my CRM
At this point, the record is still pretty bare:
“John Smith, CEO at ABC Manufacturing, joh[email], connected on [date]”
That’s fine. The important thing is that the data is now somewhere I control, not just floating around in LinkedIn.
Step 3: Clay – enriching context and signals
Next, I want to know more about:
The company
Their tech stack
Any obvious signals that they might be a strong fit
For that, I use a tool called Clay.
Clay can:
Take a list of companies and people (from HubSpot)
Pull in additional information from public sources:
Company size
Industry
Technologies in use (e‑commerce platforms, CRMs, etc.)
Recent news
Social activity
Push that enriched data back into HubSpot
For example, I might ask Clay to:
Identify which of my contacts:
Are manufacturers
Sell through distribution
Have an e‑commerce component
Are using certain platforms (say, Shopify or Salesforce)
Those filters let me say, “Out of 500 enriched contacts, which 80 are most directly relevant to this specific topic or problem?”
That’s the segment I used in the McKinsey email example.
Step 4: A large language model – asking better questions of the data
Once I have enriched data, I’ll often run it through a large language model (like OpenAI or Ella) to ask higher‑level questions, such as:
“Based on this info, does this contact likely fit my ideal customer profile?”
“What might their current digital challenges look like?”
“Are there signals here that suggest timing might be good or bad?”
The goal here is not to “predict” perfectly. It’s to:
Avoid obviously bad fits
Prioritize the most promising segments
Tailor my outreach language a bit based on their context
That intelligence gets stored back in HubSpot as custom fields or tags:
“ICP_fit: strong / moderate / weak”
“Has_ecommerce: yes/no”
“Sells_through_distribution: yes/no”
Now, when I look at my CRM, I can see:
Total contacts (say, 6,000)
Enriched and qualified for my ICP (say, 500)
Highly qualified segment for a specific topic (say, 80)
That’s much more useful than a big, undifferentiated list.
Step 5: Email – where real conversations happen
Once I’ve:
Connected with someone on LinkedIn
Captured them in HubSpot
Enriched their record via Clay and a model
Tagged them appropriately…
…I almost always try to move to email for any substantive outreach.
Why?
Most of my ideal buyers are not hanging out on LinkedIn all day
Email is where business conversations actually happen
It’s easier to send attachments, context, and thoughtful messages
It’s easier for them to forward things to their team
I can control cadence and content without worrying about algorithms
So a typical progression might be:
Connect on LinkedIn (simple message)
Capture and enrich their info in my CRM
Weeks or months later, send:
A one‑off personal email, or
A small‑batch “strategic interruption” email to a segment they’re part of
LinkedIn opened the door. Email is where the real conversation happens.
You can do all of this manually at first
If that stack sounds like a lot, it’s important to say: I didn’t start there.
When I began:
I found people manually on LinkedIn
I copy‑pasted their info into a spreadsheet
I did basic research in a browser
I sent 1:1 emails from my inbox
I made notes about who to follow up with and when
That’s enough to validate your ICP, your pitch, and your basic funnel math.
Only once I knew:
Who I was targeting
What I was offering
How many conversations I needed
…did I invest time and a bit of money in tools to scale the process.
You don’t want to automate confusion. Start simple. Then let tools support a process that’s already working.
The “don’t be creepy” rule
A lot of the tools I’ve mentioned, and others like them, can be used in ways that erode trust:
Long automated sequences that pretend to be personal
Aggressive scraping and messaging
Overly familiar outreach based on tiny bits of inferred info
I try to draw a hard line against that. A few personal rules:
No fake personalization.
If I’m going to comment on something specific about their company, I actually read it myself.
No high‑pressure automation.
I don’t set up “if they don’t reply in 3 days, send this,” and so on.
No pretending we have a relationship we don’t.
“We’re connected on LinkedIn” is factual. “We’ve been in touch for a while” is not, if all that’s happened is an automated connect.
Easy exits.
If someone isn’t interested, I respect that immediately. No badgering. No “just circling back.”
The whole point of this stack is to:
Spend more time with the right people
Spend less time on data entry and manual filtering
Have better, more informed conversations when they’re actually ready
Not to trick anyone into a meeting they don’t really want.
Keep the goal in mind
At the end of the day, as a fractional executive, your tech stack is there to serve a very human goal:
Find businesses you can genuinely help
Understand them well enough to be relevant
Engage at a pace and depth that matches their reality
Build a small portfolio of long‑term, high‑trust relationships
You don’t need bleeding‑edge tools or complex automations to do that.
You need:
A clear ICP
A specific problem you solve
A simple, repeatable outreach rhythm
And, if you choose, a few well‑chosen tools to make that easier
If you keep that frame, you’ll be much less tempted by “growth hacks,” and much more likely to build a practice you’re proud of.
