GTM Science - A show for GTM and RevOps leaders

USC's revenue and marketing dashboard used to run on a hand assembled Google Sheet of about 50 metrics, revised by hand roughly twice a month for two years, with comments on cells, notes off to the side, and conflicting numbers the team had to stop and trace in meetings. Gerry Marletta rebuilt it as a dashboard using Claude Code, and the surprising part is that 98% of it is programmatic, with no model involved anywhere in the math.

In this episode, Rachael sits down with Gerry, SVP of GTM Architecture at USC, to discuss how to build an AI powered GTM dashboard the way he did it. You'll hear what had to be settled before a single line of code got written, who decides when two teams disagree on what a number means, which decisions they forced a human to approve mid build, why the chatbot is gated so it can never do the math, and what people are most likely to get wrong on their first attempt.

Resources Mentioned in This Episode:

Build Your Own AI-Powered GTM Dashboard (Guide)

USC Frameworks

Gerry Marletta on LinkedIn

02:17 - What Monday mornings used to look like

04:25 - Why not just buy a BI tool

05:50 - What had to be settled before any code

13:05 - What a good metric definition has in it

16:32 - The no code version they built first

20:29 - Claude Code as a junior dev you hold the hand of

23:05 - The decisions a human had to approve

24:24 - How you end up with vibe coded slop

29:14 - One source per number, no exceptions

32:27 - Why the AI never does the math

38:17 - Pacing targets and half finished months

45:00 - Why the assistant refuses some questions

48:30 - Who gets to see the numbers

1:01:27 - Where to start and what people get wrong

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

Sam Jacobs teaches that product market fit isn't a vibe and it's not a feeling. It's a function of your unit economics, and it isn't permanent either. You can fall out of it when your acquisition costs climb or your retention slips, and that is the signal to pull back on growth spending rather than push harder.

In this episode, Eddie Reynolds sits down with Sam Jacobs, founder and CEO of Pavilion, to discuss what it takes to make revenue predictable and why predictability is what a buyer is actually paying for. Sam walks through the LTV to CAC range he wants a company sitting in and how fast he expects acquisition costs to be paid back, why one private equity backed business sold at roughly half the multiple its owners had expected, and the trade a founder has to accept to build a machine that runs without them. They also get into where AI genuinely helps a sales org and where Sam thinks it shouldn't be, and he closes on why your ownership structure is the most consequential long term decision you will make.

Resources Mentioned in This Episode:

Sundays with Sam #21: Predictable Revenue

Pavilion University (Intro to P&L Fluency, CRO School, AI in Go to Market School)

USC Frameworks

02:28 - The rule of 60 company that wasn't

03:18 - How bankers stage a business like a house

06:25 - Why pre-AI SaaS is reverting to 4 to 6 times revenue

08:37 - Why investors actually pay for predictability

11:49 - AI native reps closing $10M deals

14:30 - Product market fit is not a vibe

18:27 - How you fall out of product market fit

19:48 - The LTV to CAC and payback numbers he wants

25:36 - Treating your business like a code base

27:03 - The close rate a founder has to give up

29:59 - When the person with clout breaks the rules

32:30 - Strategy is deciding what money to turn away

38:10 - Where AI belongs: enrichment and list building

40:30 - Where AI does not belong

49:28 - Your cap table is your corporate strategy

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

Richard Perez leads the sales and go to market practice at Apax Partners, a private equity firm, where he works with their portfolio companies on how they sell. We asked which motions he'd back at the enterprise end, which at the SMB end, and what he wouldn't spend his own money on. Almost every answer came back with conditions attached.

In this episode, Eddie Reynolds sits down with Richard to discuss the GTM motions and channels working right now. You'll hear where he thinks ABM stops working, the three things that decide whether a conference booth pays off, why he's skeptical of most intent platforms, and the number that tells him a sales organization hasn't been engineered properly.

Resources Mentioned in This Episode:

USC Frameworks

01:14 - Motions vs channels, and why the label matters less

06:46 - Why LLMs are now a channel you have to plan for

08:51 - $10K in seven days vs $500K in 19 months

10:28 - What's working at the enterprise end

12:08 - Buyers who know your competitive position better than you

14:19 - The events he likes best

15:25 - Three things that decide whether a booth pays off

17:46 - Why execution beats channel choice

25:25 - What has to be true before you deploy AI

26:41 - What's working at the SMB end

30:22 - Where ABM stops working

32:30 - Why warm and cold leads need different reps

37:57 - Why he's skeptical of intent platforms

40:36 - The cost of a dollar of new logo revenue

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

SaaStr surveyed companies that had implemented AI SDRs and found only 3% had any revenue to show for it. Eddie's read is that this is the same pattern as Outreach and Salesloft a few years ago, and Gmail and Salesforce before that. So we mapped the system that has to be built around an ai-powered outbound motion before it will actually work.

In this episode, Eddie Reynolds and Gerry Marletta, SVP of Revenue Operations Systems at USC, walk through how to build an AI-native outbound engine, agent by agent. You'll hear how they got around the catch-22 of deciding which accounts to enrich before you've enriched them, when to hand a step to an LLM and when to use an orchestration tool instead, what the agent that grades every rep email actually checks for, and the updates Gerry says a machine should never be allowed to make.

Resources Mentioned in This Episode:

Building an AI-Native Outbound Engine (Full Written Article)

The AI-Driven Continuously Self-Improving ICP

Free Outbound Pipeline Inspection

USC Frameworks

04:18 - Only 3% of AI SDR buyers saw any revenue

08:18 - Why reps get 2,000 accounts and work 200

10:44 - The targeting agent and the data source waterfall

16:17 - Triggers and signals, and why one alone isn't enough

20:19 - The tool stack and why Salesforce stays the source of truth

23:33 - Why separate agents beat one big one

26:00 - When to use an LLM and when to use n8n

30:15 - The catch-22 of what to enrich first

39:50 - Scoring: start with a hypothesis and iterate

47:55 - What a good sales email actually looks like

56:57 - Who gets a fully personalized message and who doesn't

01:03:59 - The updates a machine should never make

01:04:36 - The first question for your AI analyst: did we execute

01:12:35 - The GTM council meeting that forces the loop

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

New logos get most of the attention in a revenue org. Robert Clarkson calls that the most obvious of three growth levers, and it's the one he spends the least time on. He has run revenue at PayPal, Stripe, Payoneer and now Fiserv, which moves $4.6 trillion in payment volume a year. At one company he joined that had grown 16% to 19% a year for three straight years, he stopped measuring the sales team on what it sold and started measuring it on what customers actually got out of it. It grew 26% the next year and never had a year under 30% after that.

In this episode, Rachael Bueckert sits down with Robert, Chief Revenue Officer for SMB and Clover at Fiserv, to walk through the model he has carried from company to company. They cover how he writes a customer success measure into the sales compensation plan, how he splits his team into hunters and growers, and why he will accept almost any CAC as long as the long term value holds up.

Resources Mentioned in This Episode:

USC Frameworks

01:15 - The three growth levers every CRO has

04:53 - Inheriting a quilt when you need a mosaic

13:21 - Giving SMBs the analytics only enterprises had

23:27 - The model he runs at every company

27:36 - Putting customer success in the sales comp plan

32:23 - Why net dollar retention has to clear 100%

40:19 - Hunters and growers

41:59 - Six features sold, five in use

46:38 - From 19% to 26% to never under 30%

49:28 - Uber didn't build a better taxi

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

We get asked what's actually working in outbound constantly, and specifically what works for companies that don't have a great brand. So we read the industry reports, more than 100 million emails worth of data, and traced every stat we could back to its original source. The surprising finding was how unreliable the data is.

In this episode, Eddie Reynolds and Rachael Bueckert go tactic by tactic through all six most common outbound strategies: targeted, warm signals, trigger based, third party intent, autonomous AI SDRs and spray and pray. You'll hear which one doubled reply rates, the templated LinkedIn message Eddie used to source millions in business, and the one signal he says he would spend his money on last.

Resources Mentioned in This Episode:

What's Working in Outbound in 2026 (Full Written Report)

Free Outbound Pipeline Inspection

01:36 - 100 million emails and a fake return address

03:45 - Why we went digging for real data

08:53 - Targeted outbound: double the reply rate

10:02 - It's not six tactics, it's three

13:19 - Why Eddie won't use Outreach for this

15:56 - The segmentation step everyone skips

20:56 - Vendors claim 20%, we saw 2 to 5%

27:16 - The bar every hot list has to clear

31:37 - The LinkedIn message that sourced millions

36:49 - Who paid for the intent data studies

43:40 - 3% of AI SDR teams saw any revenue

51:09 - Does bad outbound burn your reputation?

54:17 - Spray and pray works, and that's the problem

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

Adam Coffey priced out the sales tooling he was looking at and the quotes came back at several hundred thousand dollars each. His team built their own instead, and rolled it out globally to all sales reps and managers at Ivanti. It does the meeting prep, flags risks from call transcripts, emails and Salesforce fields, and gives a first pass at the MEDDPICC field updates that the rep can adjust.

In this episode, Rachael Bueckert sits down with Adam, Director of Analytics and Reporting at Ivanti, to discuss what it takes to build your own GTM tools. They get into the real cost behind building your own sales tooling, why he has not seen AI work on a dirty CRM yet, and why he still calls dashboards mission critical with AI apps running on top of them.

Resources Mentioned in This Episode:

Sales Command Center Demo

USC Frameworks

Adam Coffey on LinkedIn

03:04 - What finance and rev ops get wrong in silos

09:52 - The sales command center he built

11:59 - The stack: Copilot, VS Code, Power Apps

15:43 - Build vs buy: the actual numbers

19:57 - Is SaaS in trouble?

24:33 - The security review a build has to pass

35:20 - You can't slap AI on a dirty CRM

39:53 - Data goes to IT to die

43:17 - One data model instead of hundreds

46:14 - Why dashboards still matter

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

Most companies have a dashboard. Some of them even have accurate data. What wayy fewer of them have is someone who can look at that data on a Thursday morning and walk into a meeting and say: here's what I think is wrong, here's why, and here's what we should do about it. The gap between "workable data" and "actual decisions" is the problem we're talking about today.

In this episode, Eddie Reynolds and Rachael Bueckert break down how to close the gap between having a dashboard and actually using it to move revenue. They cover why perfect data is the wrong goal and what to optimize for instead, how to structure a GTM Council meeting so it creates real accountability, and what the dashboard they built with Claude actually does (and how you can build your own).

Resources Mentioned in This Episode:

How to Build Your Own AI-Powered GTM Dashboard (Free PDF Guide)

USC Demo Dashboard (Fictional $100M Company)

GTM Ops Decision Tree

GTM Ops Diagnostic Framework

01:40 - The gap between good data and actual decisions

06:12 - Why "perfect data" is the wrong goal

08:37 - Who should own dashboard insights (CRO vs. Rev Ops)

14:20 - What a GTM Council meeting actually looks like

17:33 - Why USC built their own AI-powered dashboard

22:09 - Ingesting data via MCP instead of building integrations

31:16 - Pipeline metrics: close rate, ASP, and sales cycle by segment

35:28 - Reading rep performance right to left

40:05 - AI email scoring that fixed a new rep's outbound in two weeks

43:52 - Where to start on Monday (the decision tree)

49:51 - $100M company with no lead definitions (and what happened next)

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

Lou Shipley has sat in almost every seat that touches revenue. He's been the rep, the VP of sales, a three-time CEO, a board member, an investor, and now a senior lecturer at Harvard Business School where he teaches the most popular class on campus. When he walks into a company today and something feels off in go-to-market, he knows exactly where to look.

In this episode, Rachael Bueckert sits down with Lou Shipley, author of Unlikely Entrepreneurs and senior lecturer at Harvard Business School, for a conversation on what actually breaks when growth stalls and how to fix it. The conversation covers how Lou rebuilt Black Duck's entire sales motion and took it from $20M to $90M in 4 years, the founder trap that catches most CEOs as complexity increases, and the Plane model that let him develop talent from within instead of chasing the free agent market.

Resources Mentioned in This Episode:

Unlikely Entrepreneurs (Lou Shipley)

GTM Ops Frameworks

00:23 - Intro to Lou Shipley

01:11 - The youth hockey team test for your sales org

01:29 - What Lou looks for first when go-to-market feels off

03:28 - Product market fit vs. sales execution problem

05:07 - The founder trap: when your role changes and you don't

07:19 - What the board sees when numbers are being missed

08:28 - The role of an independent board member

12:32 - The sales math: velocity deals, ASP, cycle time, and forecasting a month ahead

15:00 - The single version of truth

18:34 - SQL definition, zombie pipeline, and why changing it kills your data

24:26 - Access to power and why it determines close rate

28:42 - The Black Duck turnaround: three things that changed

30:23 - The Jamie Dimon signal that triggered the product pivot

36:40 - Rebuilding the team: developing talent vs. chasing free agents

39:19 - The Plane model: gunner, navigator, copilot, pilot

42:30 - From $20M to $90M in four years

45:43 - The thinking machine: distributed leadership at scale

46:06 - Unlikely Entrepreneurs and the problem with the problem

54:31 - Where to start when growth has stalled

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok

Every sales org has one. The rep who ignores the playbook, barely touches the CRM until a deal closes, runs the lowest activity on the team, and still outsells everyone else combined. Most leaders explain it as raw talent. Eddie thinks that explanation is costing them far more than they realize.

In this episode, Eddie Reynolds and Rachael Bueckert break down what that top performer is actually telling you about the business underneath them. The conversation covers why leaning on A players is a growth strategy with a ceiling, how to reverse engineer what your best rep is doing so your B players can replicate it, the territory problem that makes top performers look more elite than they are, and why the goal isn't to clone your all-star.

Resources Mentioned in This Episode:

GTM Efficiency Pyramid Framework

GTM Ops Frameworks

01:10 - What that rep is really telling you

04:53 - The playbook they built that's better than yours

06:41 - What Salesforce's best reps were best at

09:08 - How to reverse engineer it into the org

09:46 - The territory problem hiding performance

10:45 - Account stages, account scores, and the neglected tier one

18:13 - Build for the average rep on an average day

21:29 - ICP, personas, and the process that makes B players succeed

24:51 - Reading metrics from right to left: close rate, ASP, sales cycle

31:19 - How common are shadow systems?

33:52 - C players hiding behind broken data

37:22 - What percentage of quota should come from A players?

40:15 - When star-led growth stops working

42:31 - The culture cost of giving A players a pass

47:58 - Where to start if you're recognizing your org in this

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GTM STRATEGY & AI ENGINEERING FOR B2B TECH

● Website

● LinkedIn

● TikTok