Turn Buyer Signals Into Action with MadKudu AI
In this episode of GTM AI, see how MadKudu's sales intelligence platform can help turn your company's data into action for your sellers.
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[MUSIC]
Hello everyone and welcome to Go to Market AI.
I'm your host Sarah McConnell.
These days, it seems like every company has AI,
but on this show, we want to go a level deeper so you can see first-hand how
businesses
are actually applying AI to solve your business challenges.
We're going deep into the use cases and getting live demos of
the latest and greatest in AI technology.
Today, I'm joined by Frances Ferrero,
co-founder and chief product officer, Mankudo.
Frances, welcome to the show.
>> Yeah, thanks for having me.
I'm excited to be here.
>> Yep, super excited to have you.
So first of all, can you tell us a little bit who is Matt Kudo?
What do you guys do and who are you helping in the market?
>> Yeah, so we're a sales intelligence platform and
our main goal is to help companies turn data into actions for sellers.
So we typically tend to help the person who owns creating pipeline.
Actually, I'm just going to steal a quote from
one of the prospects that we spoke with yesterday.
I think she summarized pretty well what it is that we do.
The problem that we solve and she was describing the fact that they have a ton
of
tools and a ton of data, they've got Marketo insights, Clearbit,
Sixth Sense, Salesforce, Tableau, and a bunch of other tools.
The problem is that they're lacking a command center where everything comes
into
one place and they're able to get a 360 of the prospect to make all of this
data
actionable by the reps instead of having to log into 17 different tools.
So Matt Kudo really is the solution that brings all of this together into one
place.
And not only just pipes the data in but also makes sense of it by identify what
's
really important and how to make an actionable for a rep.
>> That's amazing.
As someone who thinks about pipeline every single day,
I'm really excited to see this demo.
So with that being said, I would love to jump into the demo and see Matt Kudo
at work.
>> Absolutely.
So we'll focus on three parts of the product where AI is pretty important.
And bear in mind, this is not meant to be a full on demo of everything the
product does,
but rather three little elements where we've implemented AI because I think it
's
relevant to the topic of the show today.
And so essentially for now for this first part, we are viewing Matt Kudo as if
we were a rep.
So we're a rep, we're logging into Salesforce.
Right now we're able to look at all the accounts that are in our instance,
especially we can look at our book of accounts.
And let's say I work for Qualified and one of the things I want to do is I want
to run
a rip and replace campaign.
So I'm going to go after some of our competitors.
Usually running reports in Salesforce is a little bit painful.
So refs might want to run this with natural language.
So a rep could come in and say show me accounts that use, let's say, drift,
because drift is a competitive product, like something that I might want to go
after.
And so essentially what this is doing is in the background is looking at all
the different
fields that are available in Salesforce, in enrichment tools, in any of the
data platforms
that you've connected to Matt Kudo to identify any account that uses the
technology drift.
So from there, the rep is basically able to see these are accounts that are
using one
of our competitors.
And then they could say and have a buyer because it's even better if you have
someone
who can actually make the decision and already exist in Salesforce.
So essentially this is like the kind of like typical generative AI use case
where you're
reducing the complexity of interaction with the system while keeping the
ability to run
pretty complex queries against extensive amounts of data.
So now what we've done is we've allowed reps to interact with substantial
amounts of data
without having to understand which column to use, which field does what.
And now they're essentially able to go in here and find the buyer for that
particular
account.
That's so cool as someone who builds the reports for our sales team, like a lot
of times I'm
helping build those reports, it's been a lot of time in Salesforce helping our
reps prioritize
using the natural language processing to be able to help them ask questions is
something
that just one makes me my job incredibly easier because I'm not going to build
those reports
anymore.
But also it opens up something that was usually very operationally difficult
for the entire
team and now to your point, like I can operationalize this for my entire team.
They can easily pull these reports for themselves.
They could then add in something that I own or something.
But doing it layman's terms in that natural language is just so much easier for
people
to grasp.
So I love this.
And I think it's an interesting point in time where we have system complexity
that has
been increasing pretty dramatically and still is increasing dramatically.
But for the first time with generative AI, we're able to reduce the complexity
of interaction
with the system.
We're able to increase capabilities without increasing complexity of management
So that's going to be a boon for any rev ops team essentially because they're
going to
be able to do more without meeting either more people or more PhDs to figure
out how
to interact with the UIs of the systems.
Absolutely.
That's incredible.
Cool.
And so a second use case I wanted to show is another thing that reps have to
deal with.
So in this case, let's say you have an account that's spiking in engagement.
So awesome.
First off, you get a cute little alert from Mad Critter saying, hey, this
account is showing
an increase in engagement.
But now the question is, who should I be reaching out to?
So you might have a couple people that already exist in Salesforce.
Then the question is, well, who are the people that I'm missing?
So what we've built is a level of AI that sits on top of Zoom info, Apollo, and
and sales app to go and look for the right kind of missing contacts in your
Salesforce
instance.
So essentially what it's able to do, it's able to figure out in the past, when
we look
at your existing customers, these are the types of people that exist on the
account.
Looking at this particular account, these are the people that are missing from
Salesforce.
And therefore, these are the people that you should be going after.
So for us, it turns out that it's a VP of growth.
It's a VP of sales.
It's a VP of customer success, like older people that are in the go-to-market
space and are
looking to build pipeline either from existing or new business.
And so again, it's like simplifying the whole process for the rep so that they
don't have
to log into Apollo or into Zoom info, figure out what query to run and then
remember, okay,
I'm looking for someone, but then having to remember, does this person or the
existence
Salesforce or not?
So it's just like taking a lot of the tedious work that reps have to do and
simplifying
it by just looking at historical data and removing three or four steps from the
life
of a rep.
Yeah, absolutely.
And letting them do it in a system of record that they already spend so much
time in two
point now, I'm not bouncing around to other systems.
I'm not having to go back and reference things.
It's all done in the same view and in a system that they're already so familiar
with and
spend a majority of their day with.
So this is great.
Yeah, and it's really about just simplifying the work of a rep.
Like they just, they don't need to open 17 calves every single time they want
to send
the single email and our goal is to make that simpler.
Totally.
Their jobs are hard enough as it is.
I think if anything we can do from a tech standpoint to make it easier, the
ones out
there driving pipeline, anything we can do to make it easier, I'm all for.
Absolutely.
And so the last part I'll show you is more on the RevOps side of things.
So one of the elements that you might notice here is like we have this concept
of who's
a good fit for the business.
And essentially this is something that's trained on historical data to
determine what
makes for a good customer or not based on who you've closed in the past.
And so one of the things that the system builds out for our RevOps teams is the
ability to
separate good from bad quality leads.
And so what it does, essentially it builds these little decision trees.
What's really great about them is that they're very easy to understand.
So this is saying at this point here, we have a 12.45% conversion rate.
And now we're saying on one side, we're going to take companies that have more
than 100
employees and on this side companies that have less than 100 employees.
So what we see is that the companies that have more than 100 employees convert
at a
slightly higher rate than the folks that are here.
Then it's going to do another split.
Okay, now it's like companies that are more than 25 and then it's going to look
at what
is the clouds spend that they might have so on and so forth.
And so essentially what this is doing is looking at all the different
attributes that
exist inside your Salesforce to figure out what is the right way to separate
into homogeneous
groups, people that are likely to convert from people that are not just simpl
ifying the
work of the ICP determination.
Yeah, this is amazing.
And I feel like even for companies that are looking to understand their
business segments,
like we were just talking about pipeline and forecasting and trying to
understand like
what cohort within your accounts, where do you draw the line?
Like what, where should you make your cohorts to do pipeline modeling?
And that's where I immediately went with this.
I'm like, oh, this will help me understand.
Okay.
Based on conversion percentages, here's where I need to split my different
cohorts of my
accounts and can apply even these conversion percentages to that modeling to
understand
okay, like this particular segment where it's over 100 employees, but up to 150
employees
has a typical conversion rate of this.
And I can see where this has a lot of benefits beyond rev-offs because I'm like
, oh, I could
use this to help with our line modeling and not guessing at what cohorts I
should be
using.
Yeah, exactly.
So that's the whole idea, making it simple and visual so that you can, it
solves the
blank page problem of it comes up with a segmentation and then it's very easy
to go
and tweet things here and there.
At least you're starting off of something that makes sense.
That's amazing.
This is very cool.
And yeah, that's it for the demo of like three kind of key features that
leverage AI
and machine learning with math games.
Amazing.
And for instance, I'm assuming for people watching the show, if you're like,
this was
great.
I want to see a full demo.
They can come to madcuda.com, request a demo and see like the full, the full
capability
abilities.
Absolutely.
Yes.
And we even have, we have a demo video on the website if they want to do that
or they can
request to talk to someone, we'll give them more in depth of view as to what we
can do.
Amazing.
Well, thank you so much for taking us through that demo.
I would love to transition into our Q&A section if you're ready.
Yes.
Perfect.
Okay.
So my first question is how long have you been building the AI that you showed
today into
madcuda?
Thank you.
So it's been evolving over time, but essentially the AI has always been at the
very center
of the company.
Even the name itself was found through an algorithm.
Like we struggled to find a name for the company and we ended up writing.
Yeah.
So I'm a algorithm to pick the name based on the cost of SEO, the cost of the
domain.
We didn't want to end with something that was going to be super competitive and
yeah,
having the domain that would cost a ton of money.
But the core itself, we've been working on this for I would say five years with
the tree
system that we showed, the generative AI side.
It's been about a year as soon as the first kind of readily available GPT
models were
out, we started incorporating this because it was very clear that this was
going to change
the complexity of interaction with systems like ours.
And yeah, we're excited to build more of that into the problem.
I love that you use AI to help pick the name and I will say of all the go to
market companies
out there, I love Mad Kudu's name and I've known it for a very long time and
like heard
it in the industry.
Thank you.
Yeah.
It's really cool.
So is what you show today, is this all available to customers right now?
Yes, it is.
Perfect.
And speaking of customers, who are some of Mad Kudu's customers?
Yeah, we have a lot of different groups of customers.
I would say on the, there's one group which is on the, I call them the Beat of
Dev.
So companies who sell to developers and you could think of like databases,
companies
like a Cockroach, MongoDB, then like security companies like a sneak or even
like core developer
companies like a Unity.
So those are extensive users of the platform because they tend to have a
substantial amount
of people testing the product for small projects here and there, like
developers like to spin
up instances, but they still have a complex enterprise cycle where they have to
figure
out who is the decision maker, where the CTO, the CIO is.
So they're like a typical kind of company is going to have a lot of data from
different
places and try to stitch everything together.
In a similar vein, but maybe on the less technical side like heavy PLG, we have
the collaboration
tools.
If you think of a Lucidchart, Figma, Dropbox, those are all customers that look
to Mad
Kudu to identify where are their big pockets of usage within accounts.
So on the more enterprise plays.
And then we have the traditional, let's say enterprise cells like a Gong and
Autodesk
or an Avallara where you're selling to executives and you potentially don't
have that kind of
PLG motion and it's like a longer cell cycles and you want to understand like,
you know,
which technologies are they using?
They use that in your partners and what's the best way to get into an
opportunity.
Amazing.
And my last question for you is what's next on your guys's AI roadmap?
Where are you taking Mad Kudu's AI in the future?
So it's either two big components to it.
One of them is really on the admin side where we are looking to enhance
capabilities again
to simplify the interaction with the product, both for our internal users, so
our support
team as well as the more RevOps team that tends to configure a lot of this.
And then for the end user on the cell side, we're building more agents to run
through
more of the tedious tasks that reps have to go through, like researching an
account,
drafting emails, they soft of all the information that is available.
Those are some of the big use cases that come to mind.
And maybe if it is relevant to the folks listening, one of the good frameworks
that has been
shared with me that I'm using a lot now is thinking of there's kind of three
almost personalities
to AI that you can think about, especially in features.
It's coach, co-pilot, and agent.
And the idea is like a coach is really using AI to help you do better.
And it's identifying a lot of unknown unknowns, like showing you something that
you might
not have thought about.
Whereas co-pilots are more about helping you do something, you're saying, "I
want to write
an email, please help me write a better email."
And then agents are really more about like, "Do this for me."
Like you give them the task and you expect the full result to be done, like
researching
an account or something like that.
So we're spending a lot of time figuring out where do we need coaches versus
agents.
And what's interesting is that reps don't react as well to agents as rev ops
might.
So there are specific use cases where you're going to need one more than the
other in how
you present the same potential back end technology.
So it's like a very interesting piece that I think is still being figured out
by most
companies out there.
I love that framework.
It's such a good encompassing description of all the different stuff we've seen
on the
show and what I've seen in products.
So that's a really great framework.
Well, Francis, thank you so much for joining us on the show today.
This was a great demo.
I love seeing this in action.
And I also love what you guys are doing at Mad Pudu.
So thank you so much for joining us today.
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