At Tentt, we build custom growth systems that combine cold outbound strategies, AI, and automation to accelerate your business.

Two Campaign Types Every B2B Company Should Be Running

Most founders think cold email is one thing.

It’s not.

There are 2 completely different campaign types…

And if you’re only running one?

You’re leaving pipeline on the table.

1️⃣ Volume-Based Cold Email (TAM Scan)

This is your market sweep.

You identify your Total Addressable Market.

You build a clean, enriched list using tools like Clay.

And you systematically test messaging across segments.

The goal?

→ Validate positioning
→ Find message-market fit
→ Generate consistent pipeline
→ Spot hidden pockets of demand

This is not “spray and pray.”

It’s structured testing at scale.

You:

- Segment by industry, size, tech stack, hiring signals
- Run multiple angles
- Track reply sentiment (positive, neutral, negative)
- Double down on what hits

Volume campaigns answer one key question:

“Who cares about this right now?”

2️⃣ Signal-Based Campaigns (High Intent)

This is where the real money is.

Signal-based outreach targets accounts that are already raising their hand.

They’re:

- Hiring for roles you replace
- Just raised funding
- Using a competitor
- Engaging with relevant content

Instead of emailing 5,000 accounts…

You email/call 150–300.

But every message is:

- Context-rich
- Pain-specific
- Often manually reviewed
- Deeply personalized

The goal here isn’t testing.

It’s precision.

This is where you pre-research.

Where you reference exact triggers.

Where reply rates spike because timing is perfect.


The Mistake Most Teams Make

They choose one.

Either:

“We’ll just scale volume.”

Or:

“We’ll only do hyper-personalised manual outreach.”

The best outbound teams run both.

Volume finds opportunity.

Signals capture intent.

One builds coverage.

The other drives conversion 

If you’re serious about building a predictable pipeline, you need:

• A system to scan your TAM
• A system to capture buying signals
• A process to test and iterate messaging
• Infrastructure to scale what works

Outbound in 2026 isn’t about writing better emails.

It’s about building better systems.

If you had to pick one to fix first in your business…

Would it be volume or signals? 👇

David Walker-Dobson 

Everyone is obsessed with deploying AI agents to run their sales and marketing end to end.

But outsourcing the lifeblood of your business(growth) to fully autonomous systems is one of the most irresponsible things you can do.

AI should enhance judgement, not replace it.

There has to be a human in the loop.

Sales is nuance.
Marketing is positioning.

Both require context, timing, and emotional intelligence.

This is why most “AI SDR” programmes fail.

They automate activity, which is a very important aspect but by no means everything

It remove accountability, ownership, and strategic thinking.

AI is a multiplier.
Not a substitute.

What am I missing?

CROs and Revenue leaders - Your SDRs are wasting 60% of their time.  
Not on calls. Not on follow-ups. Not on closing. But on manual research.

Opening 10 tabs just to understand a single lead

Copy-pasting data from LinkedIn and company websites

Jumping between tools, trying to build a profile

Losing momentum between finding info and taking action

All while your competition is using AI + scraping to get that same context in seconds and delivering insights straight to your SDRs lap

Here’s the truth:

SDRs shouldn’t be spending hours gathering context.
They should be using it to convert.

That’s where ZenRows comes in.

→ Scrape any web data 
 → Pipe it directly into your workflows
 → Deliver leads to reps with the right timing and context

What does this mean for your team? 

✅ Happier SDRs
✅ Faster pipeline velocity
✅ More conversations that actually convert

If your outbound team is still doing research manually…
You're burning time, budget, and pipeline potential.

Your sales team arent lazy.
They’re drowning in 1000's data points.

100s of leads dumped in a CRM

The data isn't even reliable

A chaotic mess of "signals" with no direction

And leadership wonders why they’re not hitting quota…

Your sales team don’t need more data 
They need better signals and scoring.

Without a scoring system, they’re guessing:

- Which leads are ready now
- Which signals actually matter
- Where to spend their time today

That leads to…

-  Wasted hours
- Missed opportunities
-  Burnt-out reps

So what’s the fix?

Every team must build a signal scoring system that changed everything:

✅ Assigns points based on signal type (e.g. hiring vs. website visits)
✅ Weighs recency + intensity of the action
✅ Scores leads automatically using Clay and pushes them to their CRM
✅ Syncs high-score leads to reps daily with clear next steps

This means:

→ SDRs prioritise their top 20 leads every day
→ Pipeline velocity went up 32%
→ No more guessing. Just action.

If your team is swimming in signals with no direction…

Stop looking for more leads.

Start scoring the ones you already have.

Most companies don’t have a tech stack.

They have a Frankenstein.

One tool for enrichment.
Another for outreach.
A CRM duct-taped together.

Someone built a Zap 6 months ago...no one knows what it does.
Half the stack breaks when one field changes.

Sound familiar?

Here’s the real problem:

You’re stacking tools.
But you’re not designing a system.

So instead of momentum, you get mess.

Leads fall through the cracks

SDRs waste hours doing manual cleanup

Marketing blames Sales, Sales blames Ops

No one knows which metrics are accurate

It’s not a tech issue.
It’s a systems issue.

A stack should feel like a flywheel.

Every tool should feed into the next.
Every action should create downstream motion.
Every rep should trust the data and know what to do next.

That only happens when you:

Design before you build

Map your workflow end-to-end

Align tooling to roles, not just features

Close the loop between inputs and outcomes

Stop buying tools to solve symptoms.
Start building systems that compound over time.

Frankensteins break.
Flywheels scale.

Which one are you building?

AI adoption is still shockingly low.
Most people think we’re already in an “AI-first” world.
We’re not.

We’re in an AI-bubble where builders + marketers + tech Twitter + LinkedIn assume everyone else is using it daily…

…but the real economy hasn’t caught up.

I was with a friend, and in front of me, they used the free version of ChatGPT. 

I couldn't believe my eyes. 

I assumed everyone was paying for it like I have for the past few years

In boomer-heavy industries like finance

They are likely lagging behind even more.

And that gap is about to create winners and losers.

The misconception:

“Everyone’s already using AI. We’re late.”

The reality:

Most companies are still stuck at:
“We’re exploring it”
“We have a pilot”
“Compliance says no”
“We’ll revisit next quarter”
"Let's just hire a generic AI specialist"(big mistake)

Meanwhile… the firms quietly rolling out AI in the unsexy places are compounding:

lead research
account scoring
personalisation
follow-ups
Market Intelligence 
CRM hygiene

Not “replace humans” but instead replacing the busywork.

If you’re in a traditional industry, start here:

Pick 1 workflow that drains time weekly (reporting, outreach, research).

Automate the input (data enrichment + scraping + triggers).

Keep a human in the loop for approvals (at first).

Measure time saved + output (not “AI usage”).

Scale to the next workflow only when the first one is stable.

We are still at the very beginning of AI adoption.

9 times out of 10, the best outbound campaigns I’ve run didn’t start with legacy data platforms

They started with public databases/directories sourced using AI.

Instead of scraping LinkedIn or paying for stale lists, I ask tools like GPT/Claude in deep research mode the following:

> “Find niche, non-mainstream directories where my ICP hangs out when they’re hiring, expanding, raising, or buying.”

Think:

* Startup directories
* Accelerator cohorts
* Grant recipient lists
* Industry member registries
* Event attendee databases

Act as a market research expert.

Find niche directories, public registries, or specialised databases where {INDUSTRY} companies matching this ICP: {ICP_DESCRIPTION}, doing this: {INTENT_SIGNALS}, are likely to show up.

Exclude LinkedIn, Apollo, Crunchbase.

Return: name, link, type of source, key data available, cost, and why it’s relevant.

I feed that into GPT, Claude, or Gemini and get 5–15 rich, focused databases to build my lists from.

Then I plug the URLs into Clay, enrich the companies, and launch outbound.

This beats static contact lists every single time.

taplio

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