Eddie Reynolds

Over +20 years in sales, customer success and marketing, I’ve developed a passion for data-driven strategy and process. I’ve always been fascinated by the edge it can give an IC or an entire company. 

Ten years ago, I joined Salesforce as an AE covering high-growth, VC-backed B2B SaaS companies. I met with hundreds of executives and revenue leaders and they all wanted this edge. They wanted to hear how Salesforce and my other customers operated, not how the software worked. 

Our consulting partners, however, were solely focussed on the software, often missing the broader strategy. This inspired me to start a unique consulting firm, merging system implementation with data-driven GTM strategy. 

Since then, I’ve seen some of the deepest ingrained revenue growth problems plaguing B2B SaaS – and we’ve developed a system to solve them.

𝘾𝙍𝙊𝙨, 𝘾𝙈𝙊𝙨, 𝘼𝙉𝘿 𝙑𝙋𝙨:

Does this sound familiar?

💭“I don’t understand what my data is telling me. I’m not even sure it’s accurate.”
💭“We’re getting tons of leads but they’re not converting enough to outpace churn.”
💭“We’ve spent so much money on tech, marketing, training, etc. – but there’s no ROI.”

Or maybe even…

“I don’t want to end up like those guys ☝️ How do I get in front of this?”

𝙔𝙊𝙐’𝙍𝙀 𝙄𝙉 𝙏𝙃𝙀 𝙍𝙄𝙂𝙃𝙏 𝙋𝙇𝘼𝘾𝙀

I founded Union Square Consulting to help B2B recurring revenue brands increase profitability by targeting the source of their revenue engine issues (not just the symptoms).

𝙒𝙃𝘼𝙏 𝙒𝙀 𝘿𝙊

Fractional RevOps that delivers both systems implementation and go-to-market strategy.

𝙒𝙃𝙊 𝙒𝙀’𝙑𝙀 𝙃𝙀𝙇𝙋𝙀𝘿

High-growth brands from Series A to C and beyond like Techstars, Luminate, and Federato.

𝙂𝙀𝙏 𝘼 𝙁𝙍𝙀𝙀 𝘾𝙊𝘼𝘾𝙃𝙄𝙉𝙂 𝙎𝙀𝙎𝙎𝙄𝙊𝙉

Sit down with one of my Senior Revenue Strategists for a personalized Revenue Operations Coaching Session. Walk away with advice you can use immediately.

👉 CLICK HERE: unionsquareconsulting.com/workshop

𝙎𝙐𝘽𝙎𝘾𝙍𝙄𝘽𝙀 𝙏𝙊 𝙊𝙐𝙍 𝙍𝙀𝙑𝙊𝙋𝙎 𝙉𝙀𝙒𝙎𝙇𝙀𝙏𝙏𝙀𝙍

Get actionable and practical tips on optimizing your marketing, sales, and customer success engines.

👉 CLICK HERE: unionsquareconsulting.com/newsletter

Host

RevOps Corner is a podcast we produce at Union Square Consulting to share best practices for Strategic Revenue Operations. 

We do this by interviewing amazing guests, ranging from Revenue Operations Professionals to Revenue Leaders to Venture Capitalists and many other experts in the B2B SaaS community. 

We also share what we're seeing in the trenches every day in our work as Revenue Operations Consultants here at Union Square Consulting.

Listen to the Podcast on:
Spotify: https://open.spotify.com/show/1CM3rmelcdtm6gJVL0PtMK?si=00aae750ea7a4937
Apple: https://podcasts.apple.com/us/podcast/revops-corner/id1619571311

Just received the perfect cold call intro.

“Hi, I’m calling from Acme. This is a cold call. I’ll take 30 seconds to explain why I’m calling and you can decide if it’s worth discussing. Fair?”

I really can’t think of a better way to do this. 

As the recipient this felt:
 - Respectful
 - Honest
 - Efficient

What do you think?

CEO | GTM Strategy & Ops for B2B SaaS CROs

You're already spending $2 to generate $1 in ARR.
And you're asking about the ROI of GTM Ops?

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐚𝐯𝐞𝐫𝐚𝐠𝐞 𝐩𝐮𝐛𝐥𝐢𝐜 𝐒𝐚𝐚𝐒 𝐜𝐨𝐦𝐩𝐚𝐧𝐲
- Spends 33% of revenue on sales and marketing
- For a $100M company, that's $33M
- To generate $16M in new ARR
- Costs $2.00 to grow ARR by $1.00

𝐍𝐨𝐰 𝐥𝐞𝐭'𝐬 𝐬𝐚𝐲 𝐰𝐞 𝐬𝐩𝐞𝐧𝐝 $𝟐𝟓𝟎𝐊 𝐨𝐧 𝐆𝐓𝐌 𝐎𝐩𝐬
- To match our current ROI
- We'd need ARR to grow by just $125K
- That's it

You're telling me we can't get $125K in additional ARR?
From fixing broken processes across your entire funnel?

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞'𝐫𝐞 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐚𝐬𝐤𝐢𝐧𝐠

Should I invest in fixing:
- Inbound lead routing and follow-up
- Pipeline qualification and management
- Sales handoff and onboarding
- Customer health tracking and renewals

Or keep spending $2 to make $1?

The answer should be obvious.

𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦
- Success isn't about financial investment
- It requires alignment
- Commitment from leadership
- Accountability across the GTM team

Without that, no amount of money helps.

With it, the ROI is obvious.

𝐈𝐧 𝐥𝐚𝐬𝐭 𝐰𝐞𝐞𝐤'𝐬 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙 𝐞𝐩𝐢𝐬𝐨𝐝𝐞:
- Why the ROI question misses the point
- The real cost of broken GTM
- How small improvements compound
- What actually drives success

What has made GTM Ops an obvious investment for you?

🤔

Listen to the episode "The ROI of GTM Ops" on 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙 here: https://bit.ly/4b28Nwn 

Get access to the below sheet in the Newsletter here: https://bit.ly/3M6IbQu     

✌️

Here's what no one wants to admit about GTM and RevOps

We're completely at the mercy of what's upstream.

1. Product Market Fit
 - We think of this as a startup thing
 - An inflection point around $1M in ARR
 - But it's a persistent problem at +$100M ARR
 - Because both products and markets are evolving
 - So GTM is left pushing inferior products to market
 - And expanding into new markets with poorer PMF

2. ICP/BP
 - By extension, ICP/BPs keep expanding
 - GTM sees diminishing returns by definition
 - The next expansion yields lower conversion
 - Making it harder and more expensive to close
 - And both ICP and BPs are evolving continuously

3. Channels
 - "Go where your customers hang out"
 - This is simple and highly effective advice
 - Picking the right channels makes all the difference

4. Messaging
 - It's not just Product Market Fit
 - It's Product Market Messaging Fit
 - It could eve be Product Market Channel Messaging Fit
 - Good messaging for cold email is VERY different from events

So, as CROs, CMOs, VPs of GTM Strategy and Operations.......

We're held accountable to drive results.

We're being told every day.... 
 - Go implement AI!
 - Hire more salespeople!
 - Improve marketing conversions!
 - Generate more pipeline and close more!

Often ignoring we're at the mercy of these upstream variables.

I'm not exactly complaining. 

This is the job. 

This is what we signed up for. 

But worth thinking about this more. 

✌️

I can't think of a bigger waste of time than CROs building AI agents. 

Can we just think about this through another lens. 

Imagine it's 2000. 
 - CRO manages GTM team of +100 people
 - Carves out 10 hrs per week to code
 - To build their own on-prem CRM
 - To build their own email tool
 - To build their own website

Would that make any sense?

Imagine it's 1970
 - CRO carves out 10 hrs per week
 - To learn the basics of IT
 - How to setup a mainframe
 - How to setup computers
 - How to wire telephone systems
 - IDK. Whatever IT did back then

Does this make any sense at all?

What did GTM leaders do back then?

1. Set the vision
2. Build the team
3. Outline the process
4. Drive execution
5. Hit targets

That's it!

In the 70s they had IT

In the 2000s they had Salesforce
 - And Gmail
 - And web designers
 - Etc. 

Why do I feel so strongly about this?

Because I did this!

I learned Salesforce
I learned to build websites
I learned Marketing Automation

It was a HUUUGE effing waste of time.

Because the second I met someone who actually knew what they were doing, they ran circles around me. 

So I hired them instead.

And focussed on my area of genius. 

That's what CROs should do with AI today. 

1. Set the vision for GTM
2. Build the right team
3. Outline the process
3B. Leverage the team to see where AI can help with the process
4. Drive execution (of humans + AI)
5. Hit targets

This requires.....
 - Picking very specific use cases for AI
 - In specific areas of the GTM process
 - Where the most opportunity lies
 - Knowing what good looks like
 - And driving the execution
 - To hit a specific target

That's it. 

There are plenty of crazy smart technologists that can build the thing. 

We've got some of those people on our team. 

And I am not going to try to be one of them!

Just like I'm not the Salesforce expert here!

✌️

Hype around AI GTM is at an all time high. 

I talk to ~5 VC/PE firms about this every single week. 
 - Firms you would know by name
 - People in those firms specialized in GTM
 - Mostly former CROs, CMOs, VPs of RevOps

Here's a secret:
They have no idea either. 

Everyone "has to implement AI"
No one knows how to see results from it. 

Here's what I'm hearing from top PE/VC firms:
 - "We need to implement AI in GTM"
 - But when I ask what's working right now...
 - They don't have many answers to this question

To be sure, there are exceptions. 
 - I'm posting more on them this week

But by and large
 - It's so much hype
 - So much "we have to do something"
 - And so little specifics
 - Like "this is the thing we want to do"

Why is that?

🤔

"What's the ROI of fixing our GTM Operations?"
I hear this question every week.

Right after they tell me everything is broken.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐣𝐮𝐬𝐭 𝐭𝐨𝐥𝐝 𝐦𝐞
- Forecasts are consistently off
- Inbound leads aren't getting followed up
- Pipeline is filled with deals that won't close
- NRR is slipping with no real process
- No one has visibility into anything

Then they ask if fixing it will pay off.
 🤯

𝐑𝐎𝐈 𝐢𝐬 𝐭𝐡𝐞 𝐰𝐫𝐨𝐧𝐠 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧
We can't measure ROI when metrics are broken

We could just look at ARR Growth
 - But that's taking too much credit

What we really WOULD need is conversions
 - How did our Close Rate improve? 
 - How did MQL conversions improve?
 - How did outbound prospecting improve?
 - How did NRR and GRR improve over the year?

𝗢𝐮𝐫 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐢𝐬 𝐚 𝐩𝐫𝐢𝐦𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞
- Some reps create opps for every meeting
- They have a 5% close rate
- Other reps wait until they get the verbal
- They have an 85% close rate
- Our blended rate is 25%

That 25% means absolutely nothing.

𝗪𝗲 𝐜𝐚𝐧'𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐑𝐎𝐈 𝐨𝐟 𝐟𝐢𝐱𝐢𝐧𝐠 𝐢𝐭
- Until we've actually fixed it
- Trained the team on the new process
- Waited months or quarters
- To see which qualified opps actually close

It's a chicken and egg problem.

𝐒𝐨 𝐡𝐞𝐫𝐞'𝐬 𝐚 𝐛𝐞𝐭𝐭𝐞𝐫 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡:

Look at the problem systemically.
- If you cleaned up the pipeline.
- Removed all unqualified opportunities
- Ensured reps only worked real opportunities.
- And also gave managers visibility and data to coach.

Do you believe that would result in more revenue?

Of course it would!

Is there really any doubt here?

𝐀𝐧𝐲 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐩𝐚𝐲𝐬 𝐟𝐨𝐫 𝐢𝐭𝐬𝐞𝐥𝐟.
- Imagine above work increases Close Rates just 1%
- From a Close Rate of 15% to a Close Rate of 16%
- On $100M of pipeline, that's $16M Closed Won
- That's an extra $1M of ARR just this year
- Plus $1M x Retention Rate next year
- We're not spending $1M on ops!

𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐲 𝐭𝐡𝐢𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐡𝐞 𝐟𝐮𝐧𝐧𝐞𝐥
- Fix inbound lead routing and response time
- Improve outbound territory design and focus
- Tighten sales handoff and onboarding
- Track customer health and manage renewals

Each improvement compounds with the others.
 - More qualified pipeline
 - Higher close rate
 - Better retention
 - More expansion

𝗕𝘂𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗮 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁
- It requires alignment
- Commitment from leadership
- Accountability across the GTM team

If everyone's committed to executing better?
These improvements are very achievable.

If not, they're impossible.

What's your take on the ROI of GTM Ops?

🤔

I went into this topic in much more depth in yesterday's 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙Podcast.

Check comments for the link 👇

✌️

One of the biggest mistakes I see in GTM is trying to fix everything at once. 

When everything is a priority, nothing is a priority. 

𝐘𝐨𝐮 𝐜𝐨𝐦𝐞 𝐢𝐧 𝐨𝐧 𝐝𝐚𝐲 𝐨𝐧𝐞 𝐚𝐧𝐝 𝐬𝐞𝐞 𝐢𝐭 𝐚𝐥𝐥:
- Inbound leads aren't getting follow-up
- Pipeline management needs work
- Outbound isn't converting
- CS isn't tracking health
- The list goes on

So you try to show value quickly...

By fixing everything you see

Unfortunately, this is a recipe for failure

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤𝐬 𝐢𝐧 90 𝐝𝐚𝐲𝐬
- Pick 1-2 specific areas in GTM
- Focus on the fundamentals
- Design a repeatable process
- Drive the team to execute consistently

This is the best anyone can hope for
- It's also a monumental improvement to GTM

The first 90 days aren't about how much you build
- They're about proving you can drive real change
- You're not trying to optimize yet
- You're trying to get from broken to good enough

Because here's the reality
- You can build a pipeline report in 5 minutes
- But your goal is to get everyone to trust it
- You want to get there in 90 days

𝐈𝐧 𝐨𝐮𝐫 𝐥𝐚𝐬𝐭 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰, 𝐰𝐞 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤
- Step 1: Pick the one thing
- Step 2: Assess the fundamentals
- Step 3: Build a roadmap and get alignment
- Step 4: Execute with sprint planning

Why do you think it's so easy to fall into this trap?

🤔

More on this in our latest 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰

We broke down exactly how to do this
 - How to pick the one thing
 - How to assess the fundamentals
 - How to build a roadmap and get alignment
 - And how to execute that roadmap and get traction

Link in comments below 👇

✌️

I spoke to a CMO in a +$100M B2B SaaS company the other day and she told me they have leads requesting a demo that never get a single response from sales!

How is this possible? 

One problem I see:
 - Mixing hand-raisers with score-based leads

Here's how this breaks down...

𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐬 𝐡𝐚𝐧𝐝-𝐫𝐚𝐢𝐬𝐞𝐫 𝐥𝐞𝐚𝐝𝐬
- Someone fills out a demo request
- They want to talk to sales right now
- But they get routed with 9,000 other leads
- People who DON'T really want to talk to sales

𝐒𝐨 𝐡𝐚𝐧𝐝-𝐫𝐚𝐢𝐬𝐞𝐫𝐬 𝐝𝐨𝐧'𝐭 𝐠𝐞𝐭 𝐩𝐫𝐨𝐩𝐞𝐫 𝐟𝐨𝐥𝐥𝐨𝐰 𝐮𝐩
- They wait and wait and wait
- By the time an SDR calls them back
- They've already talked to your competitor

𝐘𝐨𝐮𝐫 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐨𝐫 𝐫𝐞𝐬𝐩𝐨𝐧𝐝𝐞𝐝 𝐢𝐧 𝐣𝐮𝐬𝐭 5 𝐦𝐢𝐧𝐮𝐭𝐞𝐬
- They ran discovery and set the next meeting up
- Before you even responded to their first inquiry
- Industry studies show the first to respond wins
 - So you lose the deal before it even began

𝐌𝐞𝐚𝐧𝐰𝐡𝐢𝐥𝐞 𝐲𝐨𝐮'𝐫𝐞 𝐟𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧:
- Analyzing lead scores
- Optimizing campaigns 
- Building attribution models

But nobody's fixing the obvious problem
- Your hand-raisers are getting lost in the noise

𝐓𝐡𝐢𝐬 𝐢𝐬 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐭𝐰𝐨 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐰𝐚𝐲𝐬 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐰𝐚𝐬𝐭𝐞 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐛𝐮𝐝𝐠𝐞𝐭

The other?

𝐒𝐜𝐨𝐫𝐞-𝐛𝐚𝐬𝐞𝐝 𝐥𝐞𝐚𝐝𝐬 𝐭𝐡𝐚𝐭 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 𝐨𝐮𝐭𝐛𝐨𝐮𝐧𝐝
- You're paying to generate those leads
- Then you're paying SDRs to chase them
- But those same SDRs could call cold prospects
- And those same SDRs could close way more deals

Why are we doing this?
- Most teams skip the basics
- Too busy chasing more demand
- Chasing more pipeline
- Chasing more leads
- Never asking if the process actually works

𝐈𝐧 𝐚 𝐫𝐞𝐜𝐞𝐧𝐭 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙𝐞𝐩𝐢𝐬𝐨𝐝𝐞, 𝐰𝐞 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬
- To separate hand-raisers from scored leads
- To define your lead qual and routing process
- To track actual response time and follow-up
- To coach reps with data, creating a feedback loop

𝐓𝐡𝐢𝐬 𝐜𝐡𝐚𝐧𝐠𝐞 𝐡𝐞𝐥𝐩𝐞𝐝 𝐨𝐧𝐞 𝐨𝐟 𝐨𝐮𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐥𝐞𝐚𝐝-𝐭𝐨-𝐜𝐥𝐨𝐬𝐞 𝐛𝐲 𝟐𝟓𝐱

25x increase!
- Not lead-to-pipeline.
- Not close rates on deals.
- Full lead-to-close by 25x.

If your lead conversion rate is terrible?
- The problem isn't just lead quality.
- It's operational.

Or am I missing something? 

🤔

Listen to the episode "The 9-Step Process for Improving Lead Conversion" on 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙

See comment below for the link 👇

✌️

There's a lot of hype around AI in GTM right now.
But we've seen this pattern before.

At one time.....

𝗪𝗲 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲 𝘄𝗮𝘀 𝗮 𝗺𝗮𝗴𝗶𝗰 𝗯𝘂𝗹𝗹𝗲𝘁
 - We left Excel or on-prem systems
 - We created one single source of truth
 - We had real-time visibility into everything
 - We expected to see immediate revenue growth

It didn't happen. 

Here's what did happen
 - GTM Team spend a lot of money
 - They spent even more time on it
 - And it was a massive improvement
 - But it reinforced existing GTM process
 - Some teams improved those processes
 - But many didn't change those processes
 - And many reps refused to use Salesforce
 - So they ended up doing all the same things
 - Not seeing any real improvement in revenue

Then a few years passed.

𝗪𝗲 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝗢𝘂𝘁𝗿𝗲𝗮𝗰𝗵/𝗦𝗮𝗹𝗲𝘀𝗹𝗼𝗳𝘁 𝘄𝗮𝘀 𝗮 𝗺𝗮𝗴𝗶𝗰 𝗯𝘂𝗹𝗹𝗲𝘁
 - We all read Predictable Revenue
 - We built huge teams of hungry SDRs
 - We implemented Outreach/Salesloft
 - We automated our outbound sequences
 - And pressed "Send All" on email templates
 - Cranking activity to 10X what we we're doing
 - And we saw no improvement in actual pipeline
 - It was a race to the bottom of quality vs. quantity
  - At least for most teams. Some were the exception.
 - Some teams improved their process and saw results. 

𝐀𝐈 𝐢𝐬 𝐧𝐨 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭

Scale Venture Partners' recently released their State of GTM AI Report.

Craig led the year-long study
- 270+ survey responses
- 3-5 interviews per week
- AI labs and hackathons

We had him on the🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙Podcast to dig deeper.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐫𝐢𝐯𝐞𝐬 𝐀𝐈 𝐬𝐮𝐜𝐜𝐞𝐬𝐬
- Understand your current state first
- Figure out strategy, people, process
- Then decide where AI fits into process
- Know exactly what you want AI to produce
- Let RevOps lead the implementation of the AI
 
Successful teams don't say:
"Let's use AI."

They say:
"We'd like AI to create a QBR that's 4 pages long with this on page 1, this on page 2, pulling from these 6 sources."

They know what they want.
Then decide how AI helps.

𝐈𝐧 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲'𝐬 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙𝗽𝗼𝗱𝗰𝗮𝘀𝘁
We dug into:

- Build vs buy (both work)
- Human in the loop requirements
- Why it's harder to do GTM than ever
- Phase 1 vs Phase 2 AI (3-5x difference)
- Why RevOps should lead (22% more impact)
- The "pacemaker" concept and taste in GTM
- Campaign analysis and micro-segmentation
- Why 85% say AI works but can't prove ROI

Listen to the episode "The State of GTM AI with Craig Rosenberg" on 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙here: https://bit.ly/4b28Nwn

✌️

Pavilion built a microsegmentation engine.
Here's what worked and what didn't.

𝐓𝐡𝐞𝐲 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚, 𝐧𝐨𝐭 𝐠𝐮𝐞𝐬𝐬𝐞𝐬
- Member Success ran cohort analysis
- Who stayed, who contributed, who got value
- Regression on churners vs renewals
- Identified properties of highest LTV

𝐓𝐡𝐞𝐧 𝐭𝐡𝐞𝐲 𝐛𝐮𝐢𝐥𝐭 𝐭𝐡𝐞 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞
- Connected website visitor tracking
- Automated enrichment workflows
- Scored leads on fit criteria
- Generated personalized outreach
- Referenced similar existing members

This required tight collaboration.

𝐌𝐞𝐦𝐛𝐞𝐫 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐝 𝐜𝐫𝐢𝐭𝐞𝐫𝐢𝐚
- RevOps built infrastructure
- Content created assets
- Product Marketing crafted messaging

Someone has to own the vision.
 - At Pavilion, that was Josh Carter.
 - Without that champion?
 - It stays theoretical.

𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐞𝐫𝐞 𝐢𝐭 𝐠𝐨𝐭 𝐭𝐫𝐢𝐜𝐤𝐲
- Technology can scrape everything
- Job postings, press releases, LinkedIn
- Just because you can, doesn't mean you should

𝐓𝐡𝐞 𝐥𝐢𝐧𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐡𝐞𝐥𝐩𝐟𝐮𝐥 𝐚𝐧𝐝 𝐜𝐫𝐞𝐞𝐩𝐲
- Reference something that changes priorities? 
- Helpful
- Reference something that goes too far? 
- Creepy

Focus on what prospects care about right now.
Everything else is noise.

𝐓𝐡𝐞𝐲 𝐭𝐞𝐬𝐭𝐞𝐝 𝐬𝐦𝐚𝐥𝐥
- Target 10, 50, or 100 people
- Test different motivators
- Learn what works
- Roll out to other populations

The cost of a miss is much lower.

𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭𝐬
- Open rates went up
- Response rates climbed
- Lower churn, higher LTV
- Conversions increased significantly

But the real win?

𝐁𝐫𝐢𝐧𝐠𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐦𝐞𝐦𝐛𝐞𝐫𝐬
- People who stayed longer
- Contributed more
- Made the community stronger

Volume metrics look good in reviews.
But wrong prospects create downstream problems.

𝐈𝐧 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲'𝐬 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰 we covered:
- The operational microsegmentation playbook
- How to build the infrastructure
- How to test and scale
- What this means for CROs, CMOs, RevOps

What segmentation play are you seeing win the most these days?

🤔

More on this in tomorrow’s 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛📰
Check comments for the link 👇

✌️

There's a better way to segment in marketing. 
Not by demographics but by situations. 

Not "CMO at Series B SaaS"
But "CMO preparing for first board meeting"

Kathleen Booth built this at Pavilion.
Conversions increased significantly.

Here's how they did it:

𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐰𝐡𝐨 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐢𝐧𝐬
- Run cohort analysis on best customers
- Who stayed, contributed, got value
- Regression on churners vs renewals
- Only then build your segments

𝐅𝐢𝐧𝐝 𝐭𝐡𝐞 𝐦𝐨𝐦𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫
- Just raised funding
- Got a new job
- Lost a job
- Preparing for board meeting

𝐁𝐮𝐢𝐥𝐝 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐟𝐨𝐫 𝐭𝐡𝐨𝐬𝐞 𝐦𝐨𝐦𝐞𝐧𝐭𝐬
- Board prep guides with sample decks
- First 90 days checklists
- Main content ungated
- Practical tools required email

𝐁𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐞𝐧𝐠𝐢𝐧𝐞 𝐟𝐨𝐫 𝐬𝐜𝐚𝐥𝐞
- De-anonymize website visitors
- Clay enriches automatically
- Scores and customizes outreach
- Copy AI generates messaging
- References similar happy customers

𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐜𝐚𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐧𝐨𝐰
- The pain they're feeling
- The goals they're chasing
- Challenges specific to their moment

𝐓𝐞𝐬𝐭 𝐬𝐦𝐚𝐥𝐥, 𝐥𝐞𝐚𝐫𝐧 𝐟𝐚𝐬𝐭
- Target smaller populations
- Maybe 10, maybe 50, maybe 100
- Test different motivators
- Roll out what works

𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭𝐬
- Open rates went up
- Conversions increased significantly
- Response rates climbed
- Churn decreased
- Lifetime values increased

They brought in members who stayed longer.
Contributed more. Made the community stronger.

How often do you receive outbound this specific?

🤔

More on this in tomorrow’s 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰

Check comments for the link 👇

✌️

You're 5x more likely to improve conversion using AI for campaign analysis and scoring.

According to Scale Venture Partners' "State of AI Report"

We interviewed Craig Rosenberg:
- To dig deeper on the report findings
- To see what winning teams did differently
- And why other teams couldn't measure AI ROI

Tons of gold in that interview
Yesterday's newsletter focused on one bit:

𝐓𝐞𝐚𝐦𝐬 𝐮𝐬𝐞 𝐀𝐈 𝐢𝐧 𝐭𝐰𝐨 𝐝𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐩𝐡𝐚𝐬𝐞𝐬
- Phase one: speed and productivity
- Phase two: revenue impact

The teams seeing 3-5x better results?
They're in phase two

𝐖𝐡𝐚𝐭 𝐩𝐡𝐚𝐬𝐞 𝐭𝐰𝐨 𝐭𝐞𝐚𝐦𝐬 𝐝𝐨 𝘁𝗼 𝘀𝗲𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀:

𝗧𝗵𝗲𝘆 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴
- Which campaigns worked?
- What touchpoints converted?
- What messaging resonated?
- What timing mattered?

𝗧𝗵𝗲𝘆 𝘁𝗮𝗿𝗴𝗲𝘁 𝗯𝗲𝘁𝘁𝗲𝗿
- Use AI to score accounts
- Build tighter segments
- Sometimes 100 vs thousands

Not just using AI to work faster
Using it to drive better outcomes
𝗧𝗼 𝘀𝗼𝗹𝘃𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀

Where are you seeing AI have the most impact on revenue growth?

🤔

More on this in 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛📰
Read it here: https://bit.ly/45v38vv

You can also listen to the episode "The State of GTM AI with Craig Rosenberg" on 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙here: https://bit.ly/4b28Nwn

✌️

People talk about ICP and PMF like it's a one-time task. 
 - We're in our 10th year in business still iterating on it. 

We're still asking "Who's our ICP?"

"Remember Acme, the customer we won 5 years ago, retained for 3 years, made good profit on, the one we have a solid customer story.......?"

"Do we want more customers like that?"

"Does this inhibit our abilities to better serve other types of customers?"

"Will we burn cycles improving our product and service to accomodate their needs that are different from the needs of other types of customers?"

"Will we waste energy in sales and marketing trying to build content, channels, sales motions, etc. to attract these companies at the cost of attracting the customers we think we can help most?"

"When someone comes to us offering us money, that is explicitly NOT in our ICP, that we have literally said right on our website is not our ICP, do we take their money and try our best to serve them?"

This defines everything we do, from how we improve our product and service, to how to attract, retain, and grow customers. 

It get's as granular as.....
 - What content will we produce this week?
 - How will we improve our pitch deck and brochures?
 - When a prospect asks for something odd, do we go fetch or just say no?

These are questions we're still asking, 10 years in, and I'm surprised so many companies think about this once and then just move on. 

Why is that?

🤔

Two thirds of B2B teams are using AI in GTM.
Most can't prove it's working (impacting revenue)

Scale Venture Partners wanted to know why.
- Craig Rosenberg spent a year on it
- Surveyed hundreds of teams
- Ran AI labs and hackathons

Here's what their data revealed:

Teams use AI in two distinct phases.

𝗣𝗵𝗮𝘀𝗲 𝗼𝗻𝗲 𝘁𝗲𝗮𝗺𝘀 𝘂𝘀𝗲 𝗔𝗜 𝗳𝗼𝗿 𝘀𝗽𝗲𝗲𝗱:
- Draft emails faster
- Research accounts quicker
- Save time on existing activities

This is just doing the same things faster.
It doesn't directly impact revenue production.

𝗣𝗵𝗮𝘀𝗲 𝘁𝘄𝗼 𝘁𝗲𝗮𝗺𝘀 𝗱𝗿𝗶𝘃𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀:
- Account scoring
- Campaign analysis
- Micro-segmentation
- Skills coaching for reps

This is doing things better.
This is directly impacting revenue.

𝐏𝐡𝐚𝐬𝐞 𝐭𝐰𝐨 𝐭𝐞𝐚𝐦𝐬 𝘀𝗮𝘄 𝐛𝐞𝐭𝐭𝐞𝐫 𝐫𝐞𝐬𝐮𝐥𝐭𝐬
- 3x more likely to see pipeline growth
- 3x more likely to see faster velocity
- 5x more likely to see conversions

That's from the actual report data.

We had some questions for Craig about this
- So we interviewed him on the🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙Podcast.
- Then distilled the key details in our newsletter

𝐈𝐧 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄'𝐬 📰 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰 ....

𝗪𝗲 𝗯𝗿𝗲𝗮𝗸 𝗱𝗼𝘄𝗻:
- Why phase 1 AI implementation can't prove ROI
- What phase 2 teams actually do operationally
- Who should be leading these AI initiatives
- What this means for revenue leaders

What are you seeing in your work on GTM AI?

🤔

More in tomorrow’s 📰 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰
Subscribe to get it here: https://bit.ly/49RCm0h 

You can also listen to the episode "𝗧𝗵𝗲 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗚𝗧𝗠 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗖𝗿𝗮𝗶𝗴 𝗥𝗼𝘀𝗲𝗻𝗯𝗲𝗿𝗴" on the🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙Podcast here: https://bit.ly/4b28Nwn

✌️

How can we use AI to "personalize at scale" while staying human?

We interviewed Kathleen Booth on the🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙Podcast and she shared some great tips. 

𝐇𝐞𝐫 𝐭𝐞𝐚𝐦 𝐚𝐭 𝐏𝐚𝐯𝐢𝐥𝐢𝐨𝐧:
- Used AI to scrape press releases
- Found companies’ budget plans
- Referenced it in outreach

Not like "Congrats on your recent funding."
- Everyone's doing that

𝐈𝐧𝐬𝐭𝐞𝐚𝐝, 𝐭𝐡𝐞𝐲 𝐰𝐫𝐨𝐭𝐞 𝐭𝐡𝐢𝐬:
- "I see you're using the funding to hire engineering talent."
- "Did you know Company X did the same thing?"

That's personalization.

But here's what most people miss...

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐝𝐨 𝐭𝐡𝐢𝐬 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐧𝐨𝐰
- Tools like Clay scrape websites
- They Identify similar companies
- Reference your happy customers
- Analyze job postings
- Read press releases

𝐓𝐡𝐞𝐧 𝐀𝐈 𝐰𝐫𝐢𝐭𝐞𝐬 𝐭𝐡𝐞 𝐞𝐦𝐚𝐢𝐥
- Tailored to that specific person
- Based on everything it knows
- You just review it
- Add a personal note
- Send it

This used to take hours per email
- Now it takes minutes

𝐁𝐮𝐭 𝐭𝐡𝐞𝐫𝐞'𝐬 𝐚 𝐥𝐢𝐧𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐡𝐞𝐥𝐩𝐟𝐮𝐥 𝐚𝐧𝐝 𝐜𝐫𝐞𝐞𝐩𝐲
- Just because you can
- Doesn't mean you should

The art isn't in the tools

𝐈𝐭'𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐣𝐮𝐝𝐠𝐦𝐞𝐧𝐭
- What do you reference?
- What do you leave alone?
- How do you stay human?
- How do you keep it relevant?

𝐈𝐧 𝐨𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐞𝐩𝐢𝐬𝐨𝐝𝐞, 𝐊𝐚𝐭𝐡𝐥𝐞𝐞𝐧 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐬:
- Where to draw the line
- How to test without being creepy
- Why smaller segments let you fail safely
- How to build prompts that feel human

This is just one part in our broader topic:
𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞

The tools are available to everyone.
The edge is how you use them.

Does your inbox have any "AI personalization" horror stories?

🤔

More on this in the🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙podcast. 

Link in comments below. 👇

✌️

Building perfect metrics can take months. 
But we need visibility today. What do we do?

Accurate pipeline measurement requires us to:
- Define our ICP and buyer personas
- Document qualification criteria
- Build our sales process
- Implement it all into Salesforce
- Build reporting and dashboards
- Train our entire team
- Set up recurring pipeline reviews
- Remind reps constantly to follow it

That's months of work.
Just to answer "how much pipeline did we generate?"

𝐀𝐧𝐝 𝐭𝐡𝐞𝐧 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐭𝐨 𝐰𝐚𝐢𝐭
- For at least one full sales cycle
- To see real Close Rate, Sales Cycle
- To see if our process actually works
- Then iterate when it doesn't
- Then wait another full cycle

For enterprise deals?
That could be years.

𝐌𝐞𝐚𝐧𝐰𝐡𝐢𝐥𝐞, 𝐰𝐞'𝐫𝐞 𝐟𝐥𝐲𝐢𝐧𝐠 𝐛𝐥𝐢𝐧𝐝
- Can't tell the board how we're performing
- Can't make decisions about what to fix
- Can't allocate resources effectively
- Can't see what's working and what's not

So what do we do?
𝐀𝐬𝐤 𝐚 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 

Not "what metrics should we have?"

"𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐰𝐞 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐨𝐝𝐚𝐲?"
- Demo requests from our website
- Demos actually delivered
- Form fills on website
 - Sales emails sent
- Meetings booked
- Closed won deals

Not perfect.
But available right now.

𝐓𝐡𝐢𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐪𝐮𝐢𝐜𝐤 𝐚𝐧𝐝 𝐝𝐢𝐫𝐭𝐲
- Get visibility today with imperfect metrics
- Make decisions based on real data
- Build the perfect foundation tomorrow
- Stop flying blind while we wait

We did this with a +$100M B2B SaaS client.

𝐓𝐡𝐞𝐲 𝐜𝐨𝐮𝐥𝐝𝐧'𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐚𝐧𝐲𝐭𝐡𝐢𝐧𝐠
- No visibility into leads or opps
- Their PE firm needed answers fast
- We couldn't wait six months for perfect
- So we measured what was available today
- While building the right foundation in tandem

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘂𝘀𝗲𝗳𝘂𝗹 "𝗾𝘂𝗶𝗰𝗸 𝗮𝗻𝗱 𝗱𝗶𝗿𝘁𝘆" 𝗺𝗲𝘁𝗿𝗶𝗰 𝘆𝗼𝘂'𝘃𝗲 𝘂𝘀𝗲𝗱?

🤔

We dove much deeper into how to do this on the 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙 Podcast. 

Link to the Podcast in my comment below 👇

✌️

What do we do if we have bigger revenue targets in marketing this year and less resources?

𝐀𝐬𝐬𝐮𝐦𝐞 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐭𝐡𝐞 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 𝐢𝐧 𝐩𝐥𝐚𝐜𝐞
- Sales team is following up consistently
- Conversion rates are decent
- Leads are flowing in

𝐖𝐡𝐞𝐫𝐞 𝐝𝐨 𝐰𝐞 𝐠𝐨 𝐟𝐫𝐨𝐦 𝐡𝐞𝐫𝐞?

𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐛𝐮𝐝𝐠𝐞𝐭?
- More ad spend?
- More headcount?
- Increase production?
- Launch more channels?

What if we don’t have the budget?
Or what if that’s just not enough?

𝗪𝗲 𝗰𝗮𝗻 𝗮𝗹𝘀𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆
- Some lead segments convert at 3x others
- We're giving them all the same resources
- We've never calculated lead value by segment
- Or which channels have best CAC payback

How do good GTM engines become exceptional?

𝐁𝐲 𝐚𝐬𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐝𝐞𝐞𝐩𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬:
- Which segments actually drive revenue?
- Where are we wasting sales effort?
- Are enterprise leads getting SMB playbooks?
- Should we reallocate spend between channels?

𝐈𝐧𝐬𝐭𝐞𝐚𝐝, 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐤𝐞𝐞𝐩 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐰𝐫𝐨𝐧𝐠:

Enterprise leads get SMB follow-up
- Six-figure opps get 3 automated emails
- When they need executive engagement
- And multi-threading across buyers
- That's not a lead quality problem
- That's a process problem

Some segments are worth 10x others
- But we're spending the same to acquire them
- Following up with the same intensity
- Giving them the same resources
- When CAC payback varies wildly

How do we figure this all out?

𝐌𝐚𝐬𝐭𝐞𝐫 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐛𝐞𝐟𝐨𝐫𝐞 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧
- Perfect attribution can wait
- Perfect lead follow up is better
- It tells us which leads are truly valuable
- Then we can track where leads come from
- A simple "how did you hear about us" works
- To see what's really driving revenue and not

But the biggest challenge isn't the analysis.

It's getting the team aligned on priorities

𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐂𝐑𝐎 𝐛𝐮𝐲-𝐢𝐧, 𝐧𝐨𝐭𝐡𝐢𝐧𝐠 𝐡𝐚𝐩𝐩𝐞𝐧𝐬
- RevOps analyzes data
- Makes recommendations
- Then insights sit in a deck somewhere
- Instead of being actioned by the GTM team


𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗻𝗰𝗶𝗹 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽
- RevOps presents analysis with recommendations
- Leadership decides where to cut, improve, invest
- Turns insights into concrete initiatives
- With owners, timelines, and success metrics


𝐈𝐧 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰'𝐬 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰 𝐰𝐞 𝐭𝐚𝐥𝐤 𝐚𝐛𝐨𝐮𝐭 𝐡𝐨𝐰 𝐭𝐨:
- Compare channel performance objectively
- Calculate lead value by segment
- Fix process vs lead quality issues
- Run Pipeline Councils that drive action
- Turn analysis into revenue impact

𝐄𝐯𝐞𝐫𝐲 𝐦𝐨𝐧𝐭𝐡 𝐰𝐞 𝐝𝐞𝐥𝐚𝐲 𝐢𝐬 𝐚 𝐦𝐨𝐧𝐭𝐡 𝐥𝐨𝐬𝐭

The foundation gets us in the game.

Optimization is how we win it.

✌️

More on this in tomorrow’s 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛📰
Subscribe to get it here: https://bit.ly/49RCm0h

✌️

A while back we took on a +$100M B2B SaaS co that couldn't even tell you how many leads or opportunities they had. 

Their systems and processes were so broken they had zero visibility.

Reports were junk in/junk out. 

ELT, Board, Investors were flying blind!

So, first, this blew my mind!

They blew past $100M in ARR despite not having any GTM Ops in place. 
 - Amazing product
 - Even better Product Market Fit
 - Sheer hustle and drive to win their market
 - But this was obviously unsustainable.

So.........

𝐓𝐡𝐞 𝐏𝐄 𝐟𝐢𝐫𝐦 𝐜𝐚𝐥𝐥𝐞𝐝 𝐮𝐬
- Couldn't wrap their head around the numbers
- Couldn't get visibility into what's going on
- Couldn't wait six months for a diagnostic
- Wanted to see an immediate solution
- In tandem w/ a long-term foundation

So here's what we did.

𝐖𝐞 𝐚𝐬𝐤𝐞𝐝: 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐰𝐞 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐨𝐝𝐚𝐲?
- Number of demo requests on the website
- Number of demos actually delivered
- Form fills, emails sent and received
- Meetings booked and meetings held
- Deals closed won

Not perfect metrics.
But better than waiting six months.

𝐖𝐞 𝐜𝐚𝐥𝐥𝐞𝐝 𝐝𝐞𝐦𝐨 𝐫𝐞𝐪𝐮𝐞𝐬𝐭𝐬 "𝐌𝐐𝐋𝐬"
- And demos delivered "SQOs"
- A quick and dirty solution
- While we built the real foundation

Because here's the reality:

𝐓𝐨 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞𝐥𝐲, 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝:
- Clear ICP and buyer personas
- Defined qualification criteria
- Strong sales process documented
- Process implemented into Salesforce
- Reporting and dashboards built
- Team trained on the process
- Recurring pipeline review process
- Management reinforcing it constantly

That's a tremendous amount of work.

Just to answer "how much pipeline are we generating?"

𝐀𝐧𝐝 𝐢𝐭 𝐜𝐚𝐧 𝐭𝐚𝐤𝐞 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐫 𝐲𝐞𝐚𝐫𝐬
- Depending on your sales cycle
- Especially for enterprise deals
- You have to wait to see if it works
- Then iterate if it doesn't

This is why I've learned to ask:

𝐖𝐡𝐚𝐭'𝐬 𝐭𝐡𝐞 𝐪𝐮𝐢𝐜𝐤 𝐚𝐧𝐝 𝐝𝐢𝐫𝐭𝐲?
- What can we measure today?
- While building the foundation for tomorrow?
- Get visibility now with imperfect metrics
- Build sustainable measurement for the future

𝐈𝐧 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲'𝐬 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙 𝐞𝐩𝐢𝐬𝐨𝐝𝐞, 𝐰𝐞 𝐭𝐚𝐥𝐤𝐞𝐝 𝐚𝐛𝐨𝐮𝐭:
- How to get visibility into metrics fast
- What you can measure today vs tomorrow
- How to use the GTM Decision Tree to prioritize
- Why this is a never-ending journey

How would you have approached this?

🤔

More on this in the🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙podcast.

Check comments for the link  👇

✌️

Could you eliminate 2/3rds of your MQLs and see more revenue?

Scott Sutton did exactly this at ZoomInfo
 - He shared the story on our Podcast

We wanted to take this a step further
 - So we broke down the steps in our Newsletter
 - What we've seen working with out clients

Here's how you optimize marketing revenue:

𝐒𝐭𝐞𝐩 𝟏: 𝐒𝐞𝐭 𝐭𝐡𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐛𝐚𝐫
- Track activity-based returns
- Calculate CAC payback by channel
- Compare inbound vs outbound conversion
- If outbound consistently beats inbound?
- We have a problem

Why would any rep or company call inbound leads if they could just run outbound and generate more revenue?

𝐒𝐭𝐞𝐩 𝟐: 𝐒𝐞𝐠𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞 𝐥𝐞𝐚𝐝 𝐯𝐚𝐥𝐮𝐞
- Break down by company size, product, geo. etc
- Calculate actual value per lead by segment
- Enterprise leads might be worth $4K each
- SMB leads might be worth $2k
- That changes everything about follow-up

𝐒𝐭𝐞𝐩 𝟑: 𝐑𝐞𝐯𝐢𝐬𝐢𝐭 𝐥𝐞𝐚𝐝 𝐬𝐜𝐨𝐫𝐢𝐧𝐠
- SMB leads with score of 100 might convert well
- But enterprise leads with same score don't
- Maybe we're scoring white paper downloads
- In enterprise, that's junior people
- Who rarely have authority to buy

𝐒𝐭𝐞𝐩 𝟒: 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐢𝐬𝐬𝐮𝐞𝐬
- Are enterprise leads getting SMB playbooks?
- Do we need different messaging by segment?
- Should we multi-thread for certain deals?
- Is our follow-up adequate for segment value?

𝐒𝐭𝐞𝐩 𝟓: 𝐑𝐮𝐧 𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐂𝐨𝐮𝐧𝐜𝐢𝐥
- RevOps presents analysis with recommendations
- Leadership decides where to cut, improve, invest
- Turns insights into concrete initiatives
- With owners, timelines, success metrics

𝐒𝐭𝐞𝐩 𝟔: 𝐓𝐮𝐫𝐧 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐧𝐭𝐨 𝐚𝐜𝐭𝐢𝐨𝐧
- Modified follow-up for high-value segments
- Budget reallocation from low to high performers
- Messaging changes for each segment
- Increased spend in top-performing channels
- Cut segments that are dead weight

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐠𝐞𝐭 𝐰𝐫𝐨𝐧𝐠:
- RevOps does all the analysis
- Makes the recommendations
- Presents to leadership
- Then nothing happens.

𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐂𝐑𝐎 𝐛𝐮𝐲-𝐢𝐧 𝐚𝐧𝐝 𝐟𝐨𝐥𝐥𝐨𝐰-𝐭𝐡𝐫𝐨𝐮𝐠𝐡:
- Sales managers don't change processes
- Marketing doesn't reallocate budget
- Insights sit in a deck
- The inefficient engine keeps running

In yesterday's 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰 we broke down:
- Each step in detail
- How to calculate lead value
- When attribution actually matters
- How to run Pipeline Councils that execute
- How to turn insights into revenue

Which step do you think most companies skip?

🤔

More on this in yesterday’s 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰
Get it here:  https://bit.ly/4sn0FwQ

✌️

It's harder to do GTM today than ever.
Even veterans with proven playbooks struggle.

Craig Rosenberg built Scale Venture Partners' State of GTM AI Report to see AI's impact.

We had him on 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙to discuss it.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐢𝐭'𝐬 𝐡𝐚𝐫𝐝𝐞𝐫 𝐧𝐨𝐰
- Emails don't get read
- SEO is dying
- More crowded
- More competitive

Old tactics that worked for years:
- Outbound SDR emails
- High-pressure sales cycles
Marketing digital/SEO bringing 40-60% of pipeline

They still work. But not like they used to.

𝐀𝐈 𝐢𝐬𝐧'𝐭 𝐚 𝐦𝐚𝐠𝐢𝐜 𝐟𝐢𝐱
- But it does let you rethink everything
- Try different approaches
- Solve problems you couldn't solve before

𝐒𝐨𝐦𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐚𝐫𝐞 𝐮𝐬𝐢𝐧𝐠 𝐢𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲
- Analyzing campaigns before scaling
- Scoring accounts with multiple signals
- Building micro-segments of 100 vs thousands
- Creating automated pipeline reviews

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀.
Not just to work faster.

What did the report say about these companies?

They're 3-5x more likely to see ROI improvement.

𝗖𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝐧 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸'𝐬 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙𝗲𝗽𝗶𝘀𝗼𝗱𝗲:
- Phase 1 vs phase 2 AI implementation
- Data quality requirements for AI
- Tools that are driving better targeting
- Why RevOps should lead (22% more impact)
- Why you can't expect exponential win rate gains

AI won't fix a bad GTM strategy.
But it gives you new options.

What creative uses of AI have you seen?

🤔

Listen to the episode "The State of GTM AI with Craig Rosenberg" on 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙here: https://bit.ly/4b28Nwn

✌️

TLDR: I ensure humans, data, and AI work together to achieve a unified GTM strategy.

Here are a few ways I've done it:

- Leading the AI transformation by embedding AI into GTM strategy, inclusive of signal based outreach, ai powered chatbot and lead handling, and leveraging transcription data to accelerate pipeline.

- Doubled the demo-to-opp conversion through stronger lead routing and prioritization, improved visibility into high intent leads, and chatbot optimization.

- Unlocked compounding ARR growth by overhauling pricing strategy and enforcing unified price configuration via deal desk.

- Reduced system errors by 50% and boosted team confidence with proactive issue monitoring and root cause diagnosis.

- Implemented win-win commission plan design enabling 2x more reps to hit 70+% quota, while reducing unmanaged commission rates for activity that don't contribute to directly to company growth. 

- Removed 7 redundant GTM vendors and unified GTM under a unified tech stack, enabling 100% activity logging and housing data under a single source of truth.

- Introduced a project and change management framework allowing for X-functional OKR alignment, reducing project bloat by 70%.

- Simplified approval matrix, cutting unnecessary manual approvals in half. 

- Established a culture of data-literacy, increasing core dashboard usage by 5x.

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