#605 - How to Build a Cleaner Sales Pipeline in the Age of AI | Stevie Case
34m 56s
Sloppy, unqualified outbound outreach generates noise in sales pipelines that can falsely inflate coverage and conversion rates, leading to poor decision-making. Stevie Case, a $300M sales leader, emphasizes that true pipeline quality requires deep inspection—not just surface-level metrics. She advocates for a hybrid approach: AI-driven analysis of call transcripts and deal data to identify patterns and weaknesses, combined with human-led forecasting to maintain strategic insight. A key shift is moving from correcting underperforming reps to inspecting their strategies—discovering that successful reps often use non-traditional methods like events or partner networks. These strategies, while hard to measure, deliver real results and can be scaled. Data governance is foundational, with leaders needing to ensure integrity in account and opportunity data before deploying AI. She recommends starting small—building simple ICP score models and intent signal tables using tools like Clay—without needing engineering expertise. Ultimately, sales leaders must overcome self-limiting beliefs that AI is too complex, that the market is saturated with noise, or that deep sales experience is required. Instead, they should embrace multi-dimensional skills, take small, actionable steps, and prioritize inspection over correction to build adaptive, data-driven sales functions.
Slop sometimes does convert and what all of this automation has done is meet it
very easy to spray and pray. You got folks out there just blanketing the world without reach.
But when you're just doing spray and pray slop, you're not really qualifying those deals,
you're setting up meetings that may not be the right fit, may not be ICP. You end up with this
very messy pipeline, conversion rates sometimes go down. So it just creates a tremendous amount
of noise and those noise might look like real opportunities. The key is you have to inspect
more deeply because the noise isn't just the outbound noise. It truly is noise that's ending
up in your pipeline. Good morning everybody and welcome to this leadership episode of 30 Minutes
to Presidents Club. I'm your host Nick Siegelski with my co-host, the wonderful Mark Costa
Glow. And today we have a $300 million sales leader, the phenomenal Stevie case. Mark this was
a mind blowing one. Why should people listen? Stevie case is one of the best go-to-market operators
on the planet. She understands the data side. She understands the operational side. She understands
the human side. She understands the sales side. And I think what you'll find in this episode
is when you combine those three or four things together, you really start to accelerate.
And the fact that she went from 200 to 300 million in nine months says that this stuff works.
All right Stevie, welcome back to the show. You might remember that we start every single
episode with your top three actionable takeaways. So let's get your three. Okay, let's go.
We're going to start with takeaway number one. And that is everyone knows slop is everywhere.
When you're looking at pipeline inspection, you need to be thinking a lot more deeply
about what real coverage looks like. So the key here is don't just measure pipeline conversion.
You need to be looking at the window within which things convert. Now what that looked like in our
business is we've got lots of segments. One of those is our early stage segment. It's our startup
segment. The deals are the shortest in length there. What we found is that 80% of deals converted
within a 14 day window. So what we started doing is if deals fell outside of that 14 day window,
we excluded them from the coverage ratio because we knew that they were probably not good deals
that we're going to close anymore. The downside if you don't do this is you start deluding yourself
into thinking you've got real pipeline and real coverage when you actually don't. And this is
particularly dangerous when it is so easy to inflate this with slop. That's hard to inspect.
What's number two? You need to build a deal inspection system that's half machine and half woman.
And you got to know which is which. I'm a huge I'm very clawed pilled. I love AI. It's great.
It is also dangerous. You can fall into the trap of just trusting the agent. You know, we have trained
our team deeply on a discovery framework. We use med pick here. We're a med pick shop. We built a
system that takes all that great data from transcripts puts it into CRM without AEs getting involved.
So we're trying to discover how they actually discovered every bit of med pick. And what's the
quality of that discovery? We're then rolling that up into deal insights. And then we're rolling
that up into a score. And that score is telling us, have we actually effectively done discovery?
Are there digital engagement signals here? What's the quality of this opportunity in a way that
is not subjective, but is based on real world conversation? Now, I do still have my leaders run
real live forecasts human led. I want their voice. I want the narrative and the point of view.
I take all those recordings in those decks. I roll them up and I run analysis with my own custom
built agent. That agent highlights those weak spots. And then when I have a conversation with my
leaders, I'm asking specifically about areas. The agent has identified that we need to talk about
in more depth. Let's round us out. What's number three, Stevie? Okay. Number three. Now,
this was a painful one. This is all about outbound, which is a critical part of our business. So
number three is your best outbound reps might be violating your activity metrics and your benchmarks.
They might be right. We all know everything has changed. The landscape has changed the way
you outbound effectively has changed. We like many others who have a big SDR team and do a lot of
outbound had these very clear classic activity benchmarks. Number of emails sent, number of prospects
reached, number of connects and calls. And we were measuring every week if every individual hit those
benchmarks. We had a real awakening when we realized that some of our folks on the leaderboard,
in fact, some even in our top three outbound producers were actually in the red on the activity
benchmarks. And what we discovered when we pulled back the covers there was that they had created
a new playbook that was completely unique to the moment. They recognized there's a ton of written
slops. So they started leaning into humanity and the kind of connections that are actually harder
to measure in some cases, events, social connections and really personalized outbound. And they
were having a lot more success. So we ended up learning from them and rebuilding the playbook.
And through those benchmarks out the window and now we're looking at something completely
different when it comes to quality outbound. You said that there's a lot of slop that's leading to
a lot of extra pipeline. When I hear that in my mind, I'm like, we're sending a lot of slop.
I thought slop didn't convert. Are you seeing slop actually convert into and into,
I'm going to put in quotes pipeline? Yes, I am. What I am seeing here is that slop sometimes does
convert. And what all of this automation has done is meet it very easy to spray and pray.
And some of that stuff does
convert. You occasionally get a bite on that stuff. But when you're just doing spray and pray
slop, you're not really qualifying those deals. You're setting up meetings that may not be the right
fit. It may not be ICP. You end up with this very messy pipeline. Conversion rates sometimes go down.
So it just creates a tremendous amount of noise. And those noise might look like real opportunities.
Stevie, I think I saw recently maybe in the
last six months, Vanta just crossed a hundred million bucks. So you guys are at scale. Wait,
300 million. 300 million. Correct. Yes. Yes. Thank you. Let's go. Wait, we did that about nine
months after we crossed 200 million. So we are actually accelerating right now at this scale,
which is wild. So she's getting a ton of deals that are closing fast, which means she's seeing a
ton of information. So when you're moving at that speed, how are you taking a huge pipeline,
three, 400 million dollar pipeline and looking and finding the deals that are slop generated?
Yeah. We have built a tremendous amount of intelligence and inspection into the machine.
And then we're leveraging our humans intelligently on top of that. So we had to really do a fundamental
re-architecture of our stack. You know, Vanta founded in 2018, we started with what looked like a
very classic stack, you know, your Salesforce or outreach. Like we were doing all the things that
were normal at that point. That doesn't work anymore, especially at this volume. The danger you
run into when you can spray and hit the world, and this is true, whether you're a 10 person company
or Vanta where it's 1600 folks, you can burn through your tam really quickly. You can also with those
kind of reply rates and open rates you were talking about a minute ago, really damage your
email deliverability, your credibility in the market. So what we have done is get really thoughtful
about the system we've built to inspect before it even gets to a human. So what that looks like is
we no longer use Salesforce as our database. All of our data today pushes into snowflake. We built
a semantic layer on top of that. All data in the business is agent accessible. We've hooked up
basically every MCP you can possibly reach. And in the micro sense, what this looks like is every
call is recorded. We're taking all of that intelligence pulling it into a system that analyzes
strengths and weaknesses of opportunities. And then instead of asking for the first point of
you from the RAP or their manager, we're asking them to respond to the machines analysis. We do give
humans the right to correct. Machines not always right, but we are bringing a lot of intelligence
before we get there, both at the individual deal level and at the aggregate level across segments
and across the whole business. Today's show is brought to you by Unify. Top SDRs don't work every
account the same. They tear by intent and then they work the hottest signals first. So Unify actually
builds that list for you. If you just describe who you're targeting in plain English or connect
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at UnifyGTM.com. Today's show was brought to you by Outreach, the
Agentech AI platform for driving execution across every stage of your deal.
Great sellers drive velocity at every stage of their sale,
and one thing that's worked well for me
is assigning red line deadlines to my prospects
when we kick off vendor review.
Even artificial micro deadlines
for things like first cut of red lines
and security review helps my prospects
get their internal team moving.
We built a guide on how to help you drive six
and seven figure deals with our friends at outreach,
get it free in the show notes.
The worst discovery advice is just be curious
and ask lots of questions.
Look, our prospects are not showing up to sales calls
to answer our questions.
They're showing up to get their questions answered
and to figure out how we can solve their problem.
And this is what pipe drive does.
If you want to get clear, complete sales visibility,
you can use pipe drives easy to use
and customizable CRM that is designed to be simple
from day one and stay easy to use as your team grows.
We actually put together an entire resource hub
with pipe drive to help you get to president's club
and you can get them for free
in the 30 NPC pipe drive closures hub.
The link is in the show notes.
Can you give me like a couple examples
where you were surprised, like, oh, this looks good,
but the data is telling me it's not good.
What are a couple examples people could look out for
in their own pipelines?
- Yeah, one big one is on closed date.
So this is one, it can be so hard to inspect for.
You know, reps with happy ears.
We have a very high velocity business we close every month.
So we had a couple of months where what we saw was
we had a lot of pipeline come in into the final week
of the month.
We were looking good coverage ratios were great
and then a ton of deals slipped.
And what we ultimately ended up realizing
is that we actually didn't have access
to the economic buyer in a bunch of the deals there.
We, you know, they were telling us
the rep was telling us they did,
but what we did is when we built this mechanism to pull,
we record our calls with gong,
we pull data out of that in a couple of different ways.
We pull it out using dust,
which agent builder platform we love.
We also pull stuff out using clay.
And so we were pulling all of those med pick fields
into CRM using clay.
And what we found was that like the percentage of deals
without an economic buyer identified was lower
than we thought it was.
So we recalibrated and we started inspecting
for that much sooner.
And we dialed up the focus on that specific field
and the completion rate.
And then we also built a mechanism to inspect
how we actually made contact with that economic buyer.
Or do we have an indirect connection?
So like that deeper level of intelligence,
we can do that deal by deal in our enterprise segment.
We do sell into the Fortune 500.
But to your point, we're doing hundreds of deals
a month with startups.
You can't do that deal by deal.
We needed the machine to be able to inspect that
and tell us if we had access.
That has really helped us get much more realistic
about if a deal is going to close in period and not.
I had reps that were creating opportunities
and they were putting in deal values on opportunities.
The day that the opportunity was created
before they even met with somebody.
And so I'll never forget I was looking
at our pipeline coverage and it went up dramatically.
And I was like, what happened?
And I saw one of my reps had booked a meeting with Comcast.
And they put in $500,000 on the deal value.
And I'm like, dude, you haven't even met with this person yet.
And so we put in place, okay, you have to have gotten
to a certain stage before we put the deal value
and start counting that.
But that was a mistake that I made.
I was reporting on data that was really different
than what matched reality.
That's a very micro small example.
I'm curious about other places where you see this happen
either in your business or in other businesses
where leaders can need to put some control
around the data integrity of what they are reporting on
or basing decisions on.
- Yeah, this is a deep, deep problem.
And I will tell you, this is one of the biggest things
that we struggled with on this journey to create a machine
that actually works for us in this way.
We had data integrity problems at every level.
We had all of our account data.
It was slop.
We realized as we got into this and started doing analysis,
that was our first thing that we recognized was,
oh my gosh, like our account data is slop.
Our contact data is slop.
We don't have the right governance in place.
We just, and then it was like, oh my gosh,
humans haven't entered all of this deal data.
We don't trust it.
So mission one before we could make the machine work
was to ensure that we had a system
to protect data integrity.
So I had to make a key hire on my team
and this is to lead my GTM systems team.
You know, that team used to be a team of system administrators.
That no longer works.
You need like an actual engineering
and product organization now.
I've got a systems architect who leads my systems team.
He's the one that took this project on
and really reinforced both data governance
and data integrity.
He rebuilt our entire account data layer.
He's currently rebuilding the contact data layer.
He worked with our data engineering org
and that's when we started pushing everything
to snowflake with real governance.
And then you sort of get to the deal by deal.
And this is where you do always have humans in the mix.
The data's never going to be perfect.
And this is what we use forecast for.
So in our forecast cadence every month, every week,
we're looking for the patterns you talked about.
So did we miss something?
Is there a layer where we didn't have a control
or we didn't catch something?
And when we catch something,
we try to build automation to catch it going forward.
So that will forever be iterative
and the economic buyer thing was one example.
But to your point, sometimes it's like,
oh, people are putting in deal values too early
or deal values that are speculative.
Or in some cases, one thing we realize
is we were very single threaded on deals.
So then we started inspecting for how many contacts
do we actually have associated with this account?
Every week and every month,
we are looking for new ways to institute additional checks
that will either feed into a gate
that will force the rep to do something differently
or it will feed into a score.
So we know something is off and it'll throw a flag
that the manager can then inspect.
And that is a very dynamic system.
We also built an ICP score.
And we use that ICP score to when we built territories
to tear those territories.
So when a rep gets their territory,
they've got tier one through tier three accounts
and then they've got un-tiered accounts.
The whole hypothesis here is focus them on the accounts
that are most likely to convert.
Well, what we learned this year is we saw our reps,
particularly in our mid-market segment,
we're actually getting tier threes to convert
at a pretty high rate.
And so it was clear we had gotten something wrong here.
And in a way, we had artificially limited
or our addressable market in a way
that actually wasn't serving us.
So we ended up rebuilding the ICP score
and we built a different playbook
because what we learned is that our traditional deals
are that are large and like we want to pursue.
They do end up in tier one or two.
The deals in tier three though
are great like beachhead land deals
at great enterprise and mid-market logos.
We want those, but it's a different playbook.
So then we use that ICP score to recategorize accounts
and help reps understand from day one of the deal
am I pursuing a large platform deal
or am I pursuing a quick land beachhead?
And then they can run a different playbook.
And we've actually seen not just like the top line results
improve, but deal cycle shorten in a very positive way
because of those insights.
- So Stevie, I want to ask you,
I want you to pretend that I'm a sales leader listening
to this and I'm like, this sounds fucking awesome.
I haven't instituted any of this.
Could you give me the use cases
that you would recommend that person prioritize rolling out?
And maybe like the one, two, three steps of how they can
at least start taking action on these things.
- Yes, I absolutely well, and what I will tell you
at the encouragement I would give to anybody
who hasn't started is this stuff sounds complicated.
It's actually really not
and it is best to start from day one.
You don't have to be a huge team in company
with a lot of revenue.
It's actually more effective to start
when you're small with this stuff.
And these small things can be super effective.
So what I would start with is one, doing sure
you've got great data governance in place from day one,
you will save yourselves a lot of trouble.
And that is basic controls over how accounts
and opportunities are created, how contacts are created,
like start there and don't treat what you've got in CRM
as static, the world's super dynamic.
So get yourself multiple data sources, you know,
go out there and use something like a crust data
in addition to like a clay, in addition to a zoom info
and build yourself a system for dynamic data.
That's kind of day one.
And you can do a very simple version of that.
Then you want to start to get to how can I really identify
that addressable market quickly?
Now, there are systems out there
that will make this very easy for you.
Clay is a great system for this.
You don't need an engineer to go build a simple clay table
that will give you not just sort of a targeted list
of what accounts to go after based on your ICP.
You can build a basic ICP score with clay.
You can go identify accounts that fit into that ICP score.
And then you can do outreach.
Then you can also build a clay table
to get yourself basic signaling.
So when somebody does something that is relevant
to your business, it will flag for you
and you can reach out.
That can be basic intent signals.
It could be they visited your website.
It could be they hired somebody with a relevant title
that's relevant to your business.
This is something like I spent a day about a month ago
and built a very sophisticated clay table to do.
this in about six hours. This is not something you need to take months to do. You don't need an
engineer like you can sit down as a sales leader and build this today. And that signaling system,
again, don't need to stand up a back end. The way I've done this is I've set up the clay table.
It fires off alerts that go into Slack and get those real time alerts and Slack. I've also built
a canvas and Slack that gives me a little bit more of an ongoing sort of briefing. And that
gives me my focus areas. So you can use the tools you've already got to build these very simple
signaling systems that will make you way more effective, especially when things are changing in
real time. So I would start there. That alone is going to give you a huge head start over lots
of others. Today's show is brought to you by Insightly. The CRM helps you spot deal risk early.
So pipe reviews actually drive your number. One question I ask in nearly every deal review is
what do we need to get in our next interaction with the customer? If my rep can't answer that
question, the deal has a risk and I have something I can coach to. Now we built a guide with our
friends at Insightly CRM on how to run pipe reviews that surface real risk and get deals moving
again. And you can get it for free at the link in the show notes. We actually put together an entire
resource hub with pipe drive to help you get to precedence club and you can get them for free
in the 30 MPC pipe drive closures hub. Today's show was brought to you
by outreach, the agentic AI platform for driving execution across every stage of your deal. Great
sellers drive velocity at every stage of their sale. And one thing that's worked well for me is
assigning red line deadlines to my prospects when we kick off vendor review. We built a guide on how to help you drive six and seven figure deals
with our friends at outreach. Get it free in the show notes. When we're talking about pipeline
that your third takeaway becomes more interesting now because okay I'm going to have my reps hit
their their activity metrics. That's going to create a certain quality of pipeline. Now I have
these other people that aren't hitting their number and I think our normal way of dealing with that
is correction, not inspection. It's like you need to make more phone calls, Bobby. What's going on,
Bobby? Make more phone calls, not Bobby. What did you trip into this causing you to make less phone
calls, right? So talk to me about like that's a pretty big mindset shift for a revenue leader.
Correction is easy. Inspection requires work and changing a bunch of stuff. So what in your
mate? What happened? What's the story? Why would you all of a sudden start doing that?
You know, I think that the key here is really understanding for each individual. And this
is possible now with AI. You can really get deep on individual plans. What is working for that
individual and why are other things not working for them? And I'll give you an example of this. So
we had a guy on our team totally missing his activity benchmarks and was crushing his pipeline
targets. We went super deep into what he was doing. Turned out what he was doing was deeply
leaning into a partnership with one of our partners. And so we inspected how he was doing that.
And it turned out like he had networked with that partner in person. He had gotten a bunch of
their reps on the phone and done count mapping with them. And none of that was showing up in his
activity metrics. But he had almost created like a monopoly on opportunities from that partner for
himself through his own relationships. That playbook was so good that we ended up taking that and
teaching others to run it and to do it at scale. And then we created, you know, better ways to
track the deals and ways to actually track the activity. In time, we've had folks who are missing
the activity benchmarks and missing the pipeline targets. What we'd like to do there is go deep
into there's always something working for these folks. And sometimes they'll have a story for you
about, oh, well, I'm not hitting the activity benchmarks because I'm really busy writing super
personalized emails. That's my style or my style is I go to events. In those cases, you've got to
look really deeply at like when they did produce successful opportunities. Where did they come from?
What worked about it and how can you help them amplify that rather than trying to like a course
correct them onto like suddenly, you're going to put somebody on the phone who's not great on the
phones. Look at what they're good at. If it is events, great. How can you do more of that? How can we
help you go bigger on that because you still have to hit your pipeline targets? And if you're pursuing
something that cannot generate that level of volume, then you got to challenge them and give them,
okay, maybe there's you can do that, but maybe there's second path you need to add that'll help you
create more. So you're you're saying to them, hey, I'm going to look first. And if the results are
down, do you immediately dismiss what they're doing? Or are you taking an extra layer of inspection?
We are absolutely taking an extra layer of inspection. It is like everything has changed in the world.
So we are seeing the things that work to create pipeline change. You know, we email barely works
anymore, even though we track email sent. You know, what we see is events are working. Real world
events are working. We're seeing, you know, personal introductions and working of your network
working. Partner source is working. Social engagement and not just like I'm going to fire off
like a cold, you know, email on LinkedIn. This is like, you know, if you're working to build a
relationship with somebody over social, that's working. It's the humanity. It's the stuff that sets
you apart from the robots. And that stuff can be harder to track. And the reality is like even
of that human stuff, what works is changing month to month right now because the landscape is so
dynamic. So you got to pause first and really deeply inspect, is it a lack of effort? Or is it that
they're pursuing a different strategy that's worth consideration? Are you taking what you're learning
from an individual unless it's really working like this partnership thing? Did you stretch that to
the whole team? Or did you just say, Hey, Bobby, you can keep doing the partner thing because it's
working for you. Like, how do you know when to do a up level of the process or just like let
somebody have individual freedom? Yeah, we stretch to that to the whole team because two reasons.
One is the playbook that works. Two, we know it will scale. We know that, for example,
Anta, this partner ecosystem is a huge part of our future. There are dozens of massive partners
out there that we work well with that have their own sales forces. They've got hundreds of sellers.
And this pocket of proof that this playbook worked was the proof that we wanted to build on.
So what we did is we took that that person and we put him in front of the team with the next
all hands and had them share that case study of theories what I did. I got on the phone with their
reps. I met the sales rep through this person. And then I did account mapping with them. And here's
what it looked like. And here's how I convinced them to spend the time with me. And then we taught
the whole team that playbook. And now we've got dozens of reps doing that. And it's working for all
of them. So that's how we kind of turned it from one person's success into a strategy that scaled.
I'm curious the guidance that you have given to your leaders and your reps around where they
should be allocating their outbound time. It sounds like email is having a diminishing return.
What sort of guidance are you giving folks? What are you seeing work? Yeah, what we're seeing work.
Number one is cold calling. I know everybody hates to hear that answer. But man, is it effective?
I've got a whole floor here in San Francisco of SDRs and they are picking up the phone and
they're smiling and dialing. And it works. It works. And we are spending a lot of time evolving
their skills in this area, getting them stronger at the talk track, getting them better at
objection handling, understanding our value prop. Like that cold calling does work. The math is
unbelievably favorable. And that has not changed at all in the last year. That remains true.
Beyond that, events, events is a huge winner for us right now. Whether that is everything from
like huge conferences and really getting tight on working the booth and the way we tee up people
to meet people. We had one of my SDRs told me the other day, we ran a kind of medium sized event.
And the thing he did was he sent out emails in advance of the event. And he sent a here is a
headshot of my rep who's going to be at the booth. His name is Sean. He's going to be looking for you,
like say hi to him. Here's his story. So we need to feel much more personal and he introduced the
human who was going to be at the event. And we had off the charts conversion for that event. And like
that rep was ecstatic. Like it was a huge success. And then we're seeing it with small dinners too,
but it's that in-person connections. And this is going to pain some people, but you really got to
get outside of New York and San Francisco and London with your events. Like everybody in the coastal
cities, they're sick of it. It's so hard to get people to events. Well, go to Kansas City, man.
Go to Dallas. Go to these places where people are excited to get together. The payoff in these
these large cities that aren't tech hubs is actually greater than it is in tech hubs.
You have the opportunity here to like talk to thousands of sales leaders listening to 30 NPC.
I'm wondering if you can give me the top three things that you think many sales leaders believe
to be true, whether that's about sales or sales leadership, that you think are wrong and are
hurting them more than they help. I'm looking for your top three. So the number
one thing I would put on this list, that's a self-limiting belief, is that building with AI or
building these kind of machines is beyond the organization or requires heavy duty engineering.
Absolutely not true. I expect every leader on my team, regardless of their technical or not
technical, if they've never written code, you can build things with cloud code and with clay.
Sit down and build, if you don't do it, your organization will never change and your team won't buy in.
You have to do it. I would say the second is that there's so much noise in the market that it's
just impossible to break through because, you know, so many new companies, there's all the slop
outbound, absolutely not true, you have to get creative, you have to think differently,
and you just have to like accept that the world is changing what you thought what did work yesterday,
probably it doesn't work today, that doesn't mean nothing is working, so if you can't figure it out
yourself, get out and talk to other people, like that is the way I learn stuff these days,
I go talk to my peers, I talk to other companies and leaders, and you know, ask other people you'll
find things that are working. The third self-limiting belief is that this is not going to be popular,
but that you have to have a ton of sales experience to be a sales leader. I think that is less
true than ever before. I think that this is a moment for people who are unicorns and get product,
get technology, can build with AI, you can lead a killer revenue function with pretty limited
sales experience these days. So this is both good news and bad news, depending on who you are,
but I would challenge everybody who is trying to grow their career at this point,
you have to become multi-dimensional. If you've come up in sales, you need to understand
customer success, you need to understand product and you need to understand AI, this is the moment
to develop new skills, not to try to just stay on sales path. Steve, thank you for joining us,
everybody, stick around for a 60-second recap, coming up soon.
Harding markets time for our 2x2 recap, what do you got for your 2? 2x2, 1. You better start with
that data layer first. If you don't have somebody that can get your data right, there's no chance
that your agents or the AI that you're going to build are going to be worth a darn. Get that data
layer straight, that's the first investment you make when you go into AI. And then second is
don't just correct, take a second to inspect. And I think that, you know, looking at somebody who's
off their numbers and your first reaction is, and go make more calls to get on your number,
your first reaction is, is what a second, maybe they're doing something different, that's cool,
and then inspecting that, and then being unable to understand is that scalable, so you can roll
it out to your team or just encourage them to stay in their own level of personal genius. I just
love that idea. Yeah, my 2x2, where one was a use case, one was a thing that listeners can actually
do. The use case that I thought was really interesting was Steve's team uses medic for forecasting.
And when they get on their pipe review forecast calls, what Steve has done is she set up some sort
of automation to be able to go into the call transcripts and pull all of the medic fields and
fill those out. So it is not based off the reps reporting of the news, it is based on what the
prospect actually says. And then my last one was a thing you can do. And Stevie gave this story
about how she spent six hours building this really sophisticated like clay table that was pulling
all sorts of different signals. And the reality is, it's like nobody sat down and said,
Stevie, here is your tutorial on this. And I think one of the things that she was even telling
us on the pre show is like, this takes work to learn, but you can start. And I think a limiting
belief that she shared a lot of folks have is that this is, this requires you as a sales leader
or seller to be uber uber technical. When a reality, one of my favorite quotes, this actually comes
from Alex Hormozzi, Mark, he says fear. It's a mile wide and an inch deep. And I would say the same
thing applies here, where it is really easy to get overwhelmed. I have felt this. And I still feel
this joy degree by all of the things that you could be doing with AI as a sales leader. That is
the mile wide. You look at this pond of all of the things you have to do. And you're like, holy shit,
that's way too much. But the first step is just an inch deep. The first step is blocking off those
six hours and at least trying. And you will oftentimes find that you get way further. And the biggest
barrier to getting honestly very, very competent at this stuff is just taking the first step into
the inch. That's what we got. Thanks for listening folks. Instead of you helping us out,
help yourself out and go take a step into the inch. We'll see you next week on 30 NPC.
If you are sick of non actionable takes about AI and sales, you might be interested in attending
my live session at Apollo next on September 30th in San Francisco. Come and join if you want to
join actionable sessions with teams like yours. If you want first looks at Apollo's biggest
launches. And if you want to network with operators who are talking action, not academia, I promise
that you were going to walk away with stuff that you can put into action right away. And you can
register for free. If you use our 30 NPC link or code in the show notes, I will see you there.
Look our prospects are not
showing up to sales calls to answer our questions. If you want to get
clear complete sales visibility, you can use pipe drives easy to use and customizable CRM that
is designed to be simple from day one and stay easy to use as your team grows. We actually put
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Podcast Summary
Key Points:
Slop-generated pipeline creates noise and misleads coverage ratios, requiring deeper inspection to distinguish real opportunities from unqualified deals.
A hybrid deal inspection system—using AI for objective data analysis and human leaders for narrative context—is essential to ensure quality and accuracy in pipeline evaluation.
Outbound reps may violate activity benchmarks by adopting human-centered, personalized strategies (e.g., events, social connections), and leaders must shift from correction to inspection to identify and scale effective, scalable playbooks.
Summary:
Sloppy, unqualified outbound outreach generates noise in sales pipelines that can falsely inflate coverage and conversion rates, leading to poor decision-making. Stevie Case, a $300M sales leader, emphasizes that true pipeline quality requires deep inspection—not just surface-level metrics. She advocates for a hybrid approach: AI-driven analysis of call transcripts and deal data to identify patterns and weaknesses, combined with human-led forecasting to maintain strategic insight.
A key shift is moving from correcting underperforming reps to inspecting their strategies—discovering that successful reps often use non-traditional methods like events or partner networks. These strategies, while hard to measure, deliver real results and can be scaled. Data governance is foundational, with leaders needing to ensure integrity in account and opportunity data before deploying AI.
She recommends starting small—building simple ICP score models and intent signal tables using tools like Clay—without needing engineering expertise. Ultimately, sales leaders must overcome self-limiting beliefs that AI is too complex, that the market is saturated with noise, or that deep sales experience is required. Instead, they should embrace multi-dimensional skills, take small, actionable steps, and prioritize inspection over correction to build adaptive, data-driven sales functions.
FAQs
Slop refers to low-quality, unqualified outbound efforts like mass email blasts or random connections that don't align with a company's ideal customer profile (ICP). It creates noisy pipeline data, leads to poor conversion rates, and can mislead sales leaders about real deal quality and coverage.
By implementing deal inspection systems that analyze call recordings and discovery data using AI to assess quality. This helps distinguish genuine opportunities from slop, especially by checking metrics like economic buyer identification and deal value timing.
Activity benchmarks can be misleading, especially when reps use creative, human-driven outreach like events or partner networking that doesn’t show up in email or call counts. A deeper inspection of what’s working is more valuable than surface-level corrections.
AI analyzes call transcripts and discovery data to identify key signals, such as intent, engagement, and quality of discovery, providing objective, non-subjective insights into whether a deal is genuine or generated by slop.
Personalized cold calling, in-person events, and real-world networking have proven more effective. These strategies build trust and establish human connections, which are increasingly more valuable than generic, automated outreach.
By using AI-powered tools to analyze signals like website visits, job title changes, or social activity to build an ICP score. This score helps prioritize accounts and assign deals to the right playbooks—like beachhead or large platform deals—based on likelihood of conversion.
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