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Speaker 1: Global business news twenty four hours a day at Bloomberg

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Speaker 1: dot com, the radio plows, mobile labs, and on your radio.

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Speaker 1: This is a Bloomberg Business Plan for on Bloomberg World Hendquarters.

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Speaker 1: I'm Charlie Pelotondal the SMPNZ SAC. They're all pushing higher

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Speaker 1: off their session highs, but showing green nonetheless, with the

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Speaker 1: SMP up four to forty two, up two tenths of

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Speaker 1: one percent, US stocks halting a three day slide, Volatility

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Speaker 1: is easing. We've got the Dow up twenty three now

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Speaker 1: up one tenth of one percent, as Tank up thirty nine,

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Speaker 1: up six tenths of one percent, the tenure of three

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Speaker 1: thirty second zeal two point one nine percent, Gold up

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Speaker 1: four ninety ounce of four tenths of one percent, and

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Speaker 1: crude oil hired by five tenths of one percent to

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Speaker 1: forty eight dollars eighty four cents. I'm Charlie Peloton. That's

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Speaker 1: a Bloomberg Business Flash, Thank you very much, Charlie Pellett.

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Speaker 1: And it's to forty eight on Wall Street and eleven

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Speaker 1: forty eight in the Bay Area. The following is from

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Speaker 1: Bloomberg View Opinions in commentary from Bloomberg columnists. I'm as Puru,

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Speaker 1: a columnist for Bloomberg View. A new study says that

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Speaker 1: cutting legal immigration in half, as President Trump and two

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Speaker 1: Republican senators want to do, would shrink the economy and

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Speaker 1: reduce the number of jobs. But there is a lot

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Speaker 1: less to this study than meets the eye. The Penhorton

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Speaker 1: budget model says that the bill would cause the economy

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Speaker 1: to be two smaller than otherwise would be. The country

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Speaker 1: would have four point six million fewer jobs. But even

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Speaker 1: if the numbers are right, we still have to ask

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Speaker 1: so what. Of course, the smaller population will mean less

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Speaker 1: economic output and fewer jobs. But if we admit around

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Speaker 1: nine million fewer immigrants between now and and the number

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Speaker 1: of jobs falls only four point six million, it means

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Speaker 1: that reduced immigration doesn't much hurt the employment prospects of

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Speaker 1: those future Americans. The model gives us more useful information

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Speaker 1: when it says that per capita GDP will be point

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Speaker 1: three percent lower in twenty with less immigration. That may

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Speaker 1: be right, but it's not exactly a hair raising number.

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Speaker 1: I'm as Pneu. For more view, please go to Bloomberg

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Speaker 1: View dot com or view go on the Blue terminal.

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Speaker 1: These has been Bloomberg View. Bloomerview commentaries can be heard

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Speaker 1: every weekday this time, but also at eleven time. No,

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Speaker 1: it is a competent's helping generate sales leads using some

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Speaker 1: really uh clever applications of artificial intelligence. The CEO and

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Speaker 1: found with the company fell and Fatomy joins us right

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Speaker 1: now from San Francisco. Foul, I'm sorry not there to

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Speaker 1: see you, but um, congratulations and closing this series a

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Speaker 1: while around and emerging from stealth, he got some big investors,

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Speaker 1: Mark Cuban any A keen in partners. Um, what are

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Speaker 1: they investing? What is what is your problem? Is your

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Speaker 1: company solving? Yes? I mean with the with the information

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Speaker 1: on the way I've being crave in just the last

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Speaker 1: two years. The engaged model with information it needs to

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Speaker 1: change from that of search when we know what we're

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Speaker 1: looking for, to that of proactive and personalized discovery. Um.

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Speaker 1: So we really see ourselves sort of picking up where

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Speaker 1: Google leaves off and building machines to make sense of

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Speaker 1: all the people and companies and products and places on

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Speaker 1: the web, then understand what you care about to then

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Speaker 1: facilitate discovery of those right opportunities at the right time

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Speaker 1: and whatever application you're in. So we're essentially building like

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Speaker 1: that sounds like everything all the time, right when you

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Speaker 1: want it. If we if we focus it just to

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Speaker 1: the world of sales, what does it mean for sales,

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Speaker 1: because because the salesperson knows kind of I think they

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Speaker 1: know what kind of customers they're trying to find, right,

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Speaker 1: you know, you think that, but you know that's part

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Speaker 1: of the reason why we have a funnel today. Were

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Speaker 1: of prospects are the wrong people at the wrong companies

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Speaker 1: at the wrong time, and they're approaching them with the

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Speaker 1: wrong message. So really, what it means, what our solution

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Speaker 1: means fulfilled marketers, uh, is they're going to ultimately get

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Speaker 1: more revenue faster by focusing on those right next set

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Speaker 1: of prospects and markets of opportunities that are going to

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Speaker 1: essentially drive you know, just in a few short months

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Speaker 1: over a hundred million revenue for our customers UM and

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Speaker 1: our o I and the set eight. Uh. Well, we'll

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Speaker 1: break that down. So, so give me a use case studies,

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Speaker 1: something that someone has actually worked. Sure, so one of

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Speaker 1: our customers, Blue Jean's network, they're in industry leading video

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Speaker 1: conferencing solution UM. You know, they could essentially sell to

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Speaker 1: any B two B business. What we helped them actually

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Speaker 1: understand was the world of people and companies essentially their

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Speaker 1: total addressable market. Where are their potential customers located clobally

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Speaker 1: and what's that market opportunity and then how should they

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Speaker 1: actually prioritize their execution on that market in a way

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Speaker 1: that will drive more revenue per unit of time UM.

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Speaker 1: So we for example, then recommended to them that they,

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Speaker 1: for instance, should sell to Blue Apron UM and we

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Speaker 1: explained why in terms of why Blue Apron has a

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Speaker 1: higher deal sized potential and what these signals are. Not

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Speaker 1: only that, not it will actually can even recommend the

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Speaker 1: actual buyers at that company that you should sell of

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Speaker 1: our Canadia. So for instance, we might recommend you sell

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Speaker 1: to the CMO there. So you found so is it

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Speaker 1: like a ven diagram where you found a lot of

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Speaker 1: similarities between Blue Apron and UH consumers and Blue Apron

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Speaker 1: and UH Blue Jeens network potential users. The commonalities besides

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Speaker 1: the word blue um. Yes, commonalities besides the word blue.

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Speaker 1: Looking for signals or attributes UM with Blue Apron as

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Speaker 1: a company UM that would actually make them exhibit a

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Speaker 1: higher deal sized potential or higher propensity to purchase Blue

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Speaker 1: jeans UM at both the company and the people level. UM. So,

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Speaker 1: for example, if we identify that our potential buyer at

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Speaker 1: blue Apron was actually a former customer of Blue Jeens

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Speaker 1: network and just move to a new company, node would

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Speaker 1: identify that and surface that in real time. That's really interesting.

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Speaker 1: But you find some well back up, where do you

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Speaker 1: scrape the information from? We're literally using the webs or

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Speaker 1: database and leveraging AI technologies like natural iuage processing or

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Speaker 1: machine learning to actually UM index all the entities that

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Speaker 1: exist on the web, in these unstructured web pages. So

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Speaker 1: you know, think if you read an article from Bloomberg, UM,

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Speaker 1: this actually a lot of information about people and companies

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Speaker 1: and relationships between them, and we make SUP to that. UM.

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Speaker 1: I'll let you find all kinds of weird connections. Are

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Speaker 1: weird things you wouldn't think that overlap, but actually do. UM. Yeah.

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Speaker 1: One interesting example of something we've learned from our algorithms

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Speaker 1: is that the product category that would be purchased before

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Speaker 1: your your product category would be sold. So for example,

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Speaker 1: for us, you know we're selling a sales and marketing

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Speaker 1: a data intelligence platform. UM what would you buy before

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Speaker 1: that as a company, Well, you probably invest in an

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Speaker 1: AHS solution first. Uh so that's actually the strongest signal

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Speaker 1: for UM for a prioritizing prospect. Well, this is I mean,

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Speaker 1: this is the magic math of Google, right, which is

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Speaker 1: why Google is a better advertising platform than Facebook, because

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Speaker 1: the user shows intent. If a user goes on Google

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Speaker 1: and searches for uh, propeller for Johnson one fifty motor

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Speaker 1: boat engine, they probably want a propeller for a Johnson

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Speaker 1: one fifty motor boat engine, and that's a a good lead,

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Speaker 1: whereas finding someone on Facebook who likes water skiing is

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Speaker 1: a more difficult lead. Yeah, that's correct, UM. And and

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Speaker 1: you know, we're essentially identifying you know, not just it's

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Speaker 1: not so much the intent that you have through search,

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Speaker 1: because at that point you already know what you're looking for.

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Speaker 1: We're actually making sense of the intent and and information

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Speaker 1: about you. You know, who you are, what you care about,

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Speaker 1: who you're connected to, UM, you know, based on the

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Speaker 1: public web, and then marrying that information with with the

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Speaker 1: context that's relevant to you or your business. UM too

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Speaker 1: then suggest you know people are companies you should know

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Speaker 1: that you don't even know toe search for So why

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Speaker 1: wouldn't this be better for branded advertising where you're just

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Speaker 1: kind of creating awareness the sense of the target actually

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Speaker 1: isn't necessarily thinking about buying at that moment instead of

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Speaker 1: sales leads for sales people. Um so actually that's how

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Speaker 1: noted also leverage. It's not just for sales, it's for

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Speaker 1: marketing as well. So you know, today, from an advertising

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Speaker 1: or brain awareness perspective, you might target people based on

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Speaker 1: demographics or you know, keywords. We actually we are sort

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Speaker 1: of shifting that paradigm to actually giving you exactly who

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Speaker 1: the people and companies are that you should advertise against,

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Speaker 1: almost in a one to one fashion, and then you

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Speaker 1: can actually upload that to the Facebook Custom Audiences platform

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Speaker 1: or LinkedIn or Google that now actually allow you to

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Speaker 1: target specific people. It's really interesting. I mean, you know,

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Speaker 1: LinkedIn has a third of their businesses is really the

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Speaker 1: generating sales leads or helping people find sales leads. Um

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Speaker 1: do you imagine you do you look at lessons from

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Speaker 1: LinkedIn in terms of what you're trying to do? I mean,

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Speaker 1: I think LinkedIn is an incredible platform communication platform. Frankly

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Speaker 1: we used it internally as well. Um, I think there's

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Speaker 1: there's definitely a lot of lessons to be learned. We

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Speaker 1: have a number of advisors that formerly used to work

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Speaker 1: at LinkedIn UM. But you know, we're fundamentally building a

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Speaker 1: different graphs than than what LinkedIn has because here LinkedIn

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Speaker 1: data is really it's based on information we provide it.

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Speaker 1: So they're sort of a walled garden in that sense, UM,

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Speaker 1: but we see it as very sort of complementary. It's

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Speaker 1: such an interesting UH company. Fil has been fun watching

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Speaker 1: you put it together, very impressive as well of and

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Speaker 1: Fadom is the CEO of Node. Uh. You know some

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Speaker 1: of the numbers Nodes come up with just just emerging

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Speaker 1: just a few months of being out of Stealth, hundred

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Speaker 1: million in revenue driven three thirty million and increased pipeline

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Speaker 1: for salespeople, the four point seven times higher deal sizes,

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Speaker 1: returning investment just eight weeks for their customers. They boasted,

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Speaker 1: it's an interesting concept with artificial intelligence for finding sales.

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Speaker 1: Lea's thank you so much. Listen to Bloomberg Markets on

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Speaker 1: Bloomberg Radio. This is Bloomberg