Founder | B2B GTM | Scale without headcount by mapping your playbooks into an AI revenue engine

Be honest - how many Linkedin posts have you seen this year saying that XYZ tech/playbook/widget is absolutely essential to your GTM this year?

Probably a lot

We now have thousands of sales and marketing technologies on the market

And all of them are pitching their solution as best in class

With playbooks, agencies, influencers to sell them

And yes, we have preferred tech vendors

Here's the difference

We don't throw you in canned playbook, we actually look at your Go to Market model before making recommendations

Let me tell you a funny story

I talked with an SDR team that was booking 80 meetings a month with parallel dialers and 10k emails per SDR per month

But 60% of their meetings got disqualified

Why?

They didn't qualify target accounts before doing outreach

Tech and playbooks don't save you deploy the wrong one at the wrong time

So what do we do different?

When you work with us, we start with an assessment

We look at your funnel metrics, your best customers, your messaging/copy, and get input from your team on detailed attributes that make up a great customer

That you CAN'T find in an off the shelf tool

After that we use our knowledge of best practices and tooling to identify the biggest opportunities to unlock more revenue per headcount in your team

This could look like

> Customer analysis
> Serviceable Addressable Market Mapping (because TAM is where you lose deals not win them)
> Target Account Lists
> Automated Contact Prospecting & Enrichment
> Pipeline Validation (we build out a script, test it to book meetings for you, help you take the meetings through completed discovery with next steps, then give you a completed call framework, email sequence, and discovery framework)
> Outbound Sales Team Coaching (Phone first)
> Sales Process Optimization
> Managed Cold Emails & Cold Calling
> Inbound Lead Flow Optimization
> AI Business Case Building (which we used to open a $300k+ opp at UPS)
> Implementation of Nooks, Clay, Evergrowth, and other sales tooling

Yes, we do that under one roof

Yes, it was very hard to find a team good enough to do all of this in one place

If you want to learn more, book a time here: https://calendly.com/b2bcatalyst/your-gtm-system-audit-linkedin
> Cold Email Infrastructure

Scott Martinis

Evan Dunn

Lean Growth. Agile GTM. Test-and-Learn. Revenue-centric Marketing & Growth Leader with success leading demand, paid, web, PLG, content, & sales dev, through rapid & robust experimentation. Led AI product strategy & dev.

Avid Experimenter. GTM Scientist. Conversation-First Marketer.

Head of Marketing

TitanX raised $27M today. 

Half a year ago, I joined a company that had grown to serious ARR without running a single conversion campaign.

I’ve spent my career in GTM - from social media back in the zeitgeist of Facebook Ads to product marketing and digital and then the "LinkedIn Ads work like gangbusters era" then the rise of the modern SDR... and the fall of the modern SDR. 

3 unicorns and dozens of startups as FT or consultant.

I’ve seen what works and what’s theater.

TitanX is the rare thing that actually works.

Today’s funding means more product, deeper integrations, and the chance to bring Phone Intent to every outbound org on the planet.

But the mission doesn’t change: precision beats volume. Connects beat dials.

And human conversations remain the atomic unit of growth in B2B.

Head of Marketing @TitanX. Create Conversations. Always Be Experimenting. AI + human expert on ABM, inbound, demand gen, pipeline creation, cold calling, SEO, paid media.

This sequence is going bananas on linkedin right now.

It is not scientific. 

How did we get here that this is touted as great outbound philosophy?

Sequence Science and Other Outbound Myths circa 2026: https://lnkd.in/ewBxNESQ

Years ago, I worked at a unicorn where TWICE I was told to "give some analysis ideas" to PhD data science colleagues.

Overkill.

They were excellent data scientists, but that wasn't the problem.

The problem was the misapplication of the talent. Marketing in b2b tends to work with small sample sizes - pipeline counts, lots of unstructured data, small ad spend, etc. - so while I absolutely DID need the power of their brains...

I never had enough data.

I had a hard time getting all my data normalized and cleaned, sure, but "we're not allowed to help with that."

LLMs have absolutely replaced this for me: on-demand normalization across fuzzy/unstructured data sets (e.g. titles, industries, notes) - and THEN on-demand analysis of the data itself. Hunting for correlations.

There are some tools bringing this way to the front too like Hex or Basedash, even Clay makes it incredibly easy to normalize/standardize/produce structured fields at the row/object level and send anywhere I need with more auditability than Make/n8n.

Time to learn to use Claude Code I'm sure (I see you Joe Rhew, Joel Martinez, GTMCafe.com crew), but just here to say:

The role of the in-house data scientist for low-leverage, large-team scenarios seems to have passed. 

Now when we hire 1 data scientist we will expect them to produce the work of 5 from a few years ago.

Now when we hire RevOps, demand gen, marketing of any kinds, even VP Sales - we expect them to essentially HAVE data science on hand. 

Who's training the skillset to think like one?

Stat sig, law of small numbers, standard deviation, how to hunt through data with agility, when to turn something into a CRM field (and when to not!!!)...

Joey Gilkey has this concept of a super employee and this is absolutely a piece of it. Where there is data, and access to quality/secure AI tooling, one person can move so much faster.

Can they move in the right direction?

Gartner interviewed CFOs in December - a "budget assumptions" survey.

It matters because CFOs are in every deal.

64% (!) expect their spending (SG&A) to grow slower than their revenue growth rate.

Not surprising, but this is the literal definition of "more with less".

So no matter your whole, you're more likely than not facing familiar but ever-increasing headwinds: more targets, less resource.

Sales leaders expect this every year I think. Marketers too. 

Now comes the mad scramble for "random AI ideas to fix this" and an influx of freelancers to promise it, tons of experimentation, and only occasional results.

Best of luck with 2026 y'all.

taplio

The Battle

Scott Martinis

Evan Dunn

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