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