Sarah McConnell & Kristin Swindle

Orchestrate Your Entire Sales Process with Salesloft AI


Kristin Swindle, Manager, Enterprise Sales Engineering, shows us how Salesloft AI, including their new product Rhythm, can be the AI co-pilot sellers need to be more effective.



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[MUSIC]

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>> Hello, everyone, and welcome to Go to Market AI,

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the future of your Go to Market tech stack.

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I'm your host, Thera 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 firsthand how

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businesses are actually applying AI to solve your business challenges.

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We're going to go deep into the use cases and showing you live demos of

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the latest and greatest in AI technology.

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Today, I'm joined by Kristin Swindle, manager of Enterprise Sales Engineering

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at Sales

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Loved. Kristin, welcome to the show.

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Thank you so much for joining us.

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>> Thank you so much for having me.

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I'm really excited about today.

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>> Awesome. So first thing,

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can you tell us a little bit about who is Sales Loved?

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What do you guys do and then who do you guys help?

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>> Yeah. So Sales Loved is an AI-powered sales engagement platform where we're

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actually serving every member of the revenue team.

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So you can build pipeline,

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close more deals, coach reps to success,

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but ultimately it's to better serve your customers.

1:01

What I think is really interesting is historically,

1:04

I feel like Sales Engagement has gotten this reputation of just being focused

1:08

on

1:08

prospecting or the SCR motion.

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While that is definitely very important still,

1:14

Sales Loved really wanted to focus on getting out of the mold,

1:17

breaking the mold there,

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which is why we introduced Rhythm in July,

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which we'll talk about today.

1:22

That's the, you'll hear me say a lot, the nucleus of our AI.

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So by leveraging that AI and Rhythm,

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we've seen a significant increase in the impact for our AEs and our other

1:32

revenue

1:32

teams, including our STRs,

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by having that buyer behavior turned into seller action from all the signals

1:39

with the tech stack. So it's really helped them focus on the right prospects,

1:43

the right accounts at the right time to get to those outcomes.

1:46

We're already hearing such an impact of,

1:50

you know, seller saying they're getting more meetings with less or the same

1:53

amount of

1:54

activity. So really pumped about that.

1:56

Yeah, that's awesome.

1:57

I know I'm really excited to see your demo for them because I've heard a ton

2:00

about it.

2:00

I feel like when you guys launched it back in June,

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it, did you say it was June? I remember July, July.

2:06

Okay. I remember just seeing a ton of news about it in my LinkedIn feed.

2:09

So I'm really excited to see the demo.

2:11

So because you touched on a very good point, which is the impetus of the show

2:15

was we're hearing a lot about AI and how you kind of mentioned it.

2:18

It's making sellers and go to market teams better at their jobs with the less

2:21

of an

2:21

effort. And I think seeing that in action during the demo just makes it come to

2:26

life.

2:26

It really starts to connect the dots of like, okay, this is going to make me

2:28

better.

2:29

I job with less time, but how and what does that look like?

2:32

So that being said, I would love to transition into a demo.

2:35

So you can show us sales off the AI functionality, especially sales off rhythm.

2:39

Yeah. So what you'll see in the demonstration today is that we have weaved

2:44

a AI throughout the entire sales off platform from the administrative side to

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the workflow side

2:49

to managing opportunities and forecasts and to the data and analytics.

2:53

So I do want to start at the beginning as we think about where our customers

2:56

start their journey,

2:56

which is usually the configuration building out the processes and the content.

3:00

So one big piece of AI we introduced was our generative AI with our email

3:04

templates

3:05

that really focus on helping to draft email templates for more of those high

3:08

volume outbound

3:09

motions. You'll see here, I put in some inputs as far as the role type I'm

3:15

going after,

3:16

my company name, and any sort of inputs, I just did a write up on sales off,

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but this could be

3:20

value propositions. And then finally, what do I want that call to action to be?

3:24

So as I'm generating this, this is actually going to give me three different

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options that I can

3:29

choose from to either utilize as is, or if I want to make any changes to this,

3:36

I can obviously

3:37

make any adjustments to fit my business or change up the language. But really,

3:41

the goal with that

3:41

was to give a foundation to help ramp our teams quicker and make sure they're

3:45

starting with some

3:46

powerful messaging straight out of the gate. That's really, I really like this.

3:51

I think something

3:52

we've seen a lot with generative AI. And what I love that you've built into

3:55

sales lot is the like

3:56

prompt assist is that there's already pre-built text options that you just have

4:01

to plug in. So

4:02

it helps one, I think, rests with prompting. And some I've seen someone, it sp

4:07

its out three different

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options, which I love. So it's not just one thing that you have to edit. You

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can go through three and

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like kind of mix and match or see what really fits your tone. So giving three

4:15

options, I think,

4:16

is really, really interesting. Yeah, we wanted it to be guided, but to your

4:20

point with having

4:21

some flexibility there with some options to choose from was definitely our goal

4:24

to make sure

4:25

we're matching as closely to give you more of that foundation than you're after

4:28

Very cool. Very cool. So this is really where a lot of it begins, right? It's

4:32

like we're

4:33

we're focusing on the processes, we're pulling in the content. And then once we

4:36

have that,

4:37

then that's where we start having the reps pull people in, start engaging with

4:41

them through the

4:42

workflow. So I want to transition over to the workflow side of things. And

4:45

before I get into

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rhythm, which you've heard a lot about and really again, our center of the AI

4:51

here,

4:51

one thing that we introduced was having what we call focus songs because, you

4:56

know,

4:56

kind of talking about the content, we want to make sure we have that

4:58

flexibility with how people

4:59

like to approach their day. So we still have more of what we're calling our

5:03

structured workflows.

5:05

So maybe focusing in on my cadences for those kind of predefined processes to

5:10

reach some sort of

5:11

end goal that I'm after prospecting, renewal, whatever it may be. And then my

5:15

close, which is

5:16

really it's in the quarter. I want to be hyper focused on just the

5:19

opportunities themselves.

5:21

And then finally, that's where we have rhythm coming to play, which is more of

5:25

that unstructured

5:26

workflow where as we talked about, it's really bringing in all of the signals

5:30

of the ecosystem

5:31

and pulling it into a prioritized workflow here. Very cool. Yeah, so I want to

5:37

talk a little bit

5:38

more around what is making up rhythm, what is feeding it, how is it priorit

5:42

izing? So what we've

5:44

done is we wanted to make sure we were looking at as many different signals as

5:47

possible to prevent

5:48

all that different swivel chairing. So we pulled one of the main things that I

5:51

think a lot of our

5:52

teams have been excited about is third party integrations, taking advantage of

5:55

the tech stack

5:56

you already have. So you'll see DocuSign in here, G2, vidyard, seismic, high

6:01

spec, there's tons of

6:02

others that we have now and that we're rolling out through the end of this year

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and beyond.

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Our goal is really to kind of open that up down the road to have more signals

6:10

ingested.

6:11

And then on top of that, we're also looking at your CRM information. So looking

6:16

at some of the

6:17

opportunity information you have, we're also going to be looking at your buyer

6:20

engagement,

6:21

which is taking action on your emails, what kind of engagement are you having?

6:25

And also the seller

6:26

activity, right? I have a meeting coming up or I just wrapped a meeting and I

6:29

need to be prompted

6:30

for a really thorough follow up. And what it's doing is AI is looking at the op

6:36

information like

6:37

you see here and the buyer engagement and it's ranking those due actions based

6:41

off of the immediacy

6:42

and the impact and prioritizing them on the likelihood for me to get a booked

6:46

meeting,

6:46

an opened opportunity, as well as that deal importance. And I think what's

6:51

really critical to

6:52

call out here that sales off focus is in on is making sure we have that explain

6:56

ability in the

6:57

workflow. So as we're hovering over maybe this opportunity, this is letting me

7:02

know the key factors

7:03

that our conductor AI used to prioritize this at the top from our deal

7:08

engagement score, which

7:09

will talk more about the ARR, the closed day. And then similar with that, with

7:14

the buyer side of

7:15

things, why is Dylan at the top of our priority list from a buyer engagement

7:20

perspective? So I'll

7:21

have all of that insight pulling in here. And this really ultimately helps the

7:26

reps prioritize their

7:27

due activities to help them book more meetings and close more of those deals

7:30

faster.

7:30

That's very cool. I think two things here I really, really love seeing is one.

7:36

I think the use

7:37

case I immediately think of with sales loft and rhythm in particular is you

7:40

mentioned earlier reps

7:41

is this helps SDRs. But I have to imagine there's such a good use case here for

7:45

ops, like a sales

7:46

off team revenue ops where I'm like, oh, you need to build this in dashboards

7:50

anymore. You don't

7:51

have to go into multiple systems to have these dashboards of prioritization. It

7:55

just exists in

7:56

the system that you're already spending a lot of your time outbounding in. So I

7:58

feel like this has

7:59

to stable up not only for rest, but for the ops teams that are helping build

8:03

normally the dashboards

8:04

that they would need from a prioritization standpoint. Yeah, absolutely. And

8:09

then I really do, I've not

8:12

seen the ability to hover and see why something is prioritized. Like, I think

8:16

AI is very cool.

8:17

And it's like, okay, I know it's helping me be better and faster. But to be

8:20

able to see why it's

8:21

sort of unsealed, like, okay, yes, AI is telling me that I should fall with

8:25

Dylan now. But if I'm

8:26

like, I don't know why, like, I'm just, I can jump in and see like, okay, this

8:31

is, it does make sense.

8:32

So unveiling a little bit more behind the why AI is telling you to do that is

8:36

very interesting.

8:37

Yeah, I think another thing too is when you were talking about the rev-offs,

8:42

one thing that I

8:42

particularly seen as kind of feedback is that we think about, you know, the

8:46

buzz is like tech

8:47

consolidation, right? And I think a lot of times we think about tech

8:50

consolidation, sometimes it's

8:51

not necessarily removing something from the tech stack, which does play into

8:54

that. But to your point,

8:56

I think it's like, let's go ahead and take advantage of the tech stack you do

8:59

have. So you do have that

9:00

adoption. I think that's another piece that ops really likes is we bought these

9:04

tools. I want to

9:05

make sure my team's adopting it. And this prevents me from needing to jump to

9:08

those different tabs

9:09

to take advantage of all those different signals. Yeah, and I have to imagine

9:12

even the, the hover

9:14

and telling you why has to help with adoption. Like, I know as a human, if I am

9:17

understanding the

9:18

why behind it, why you're telling me to prioritize one deal over the other or

9:22

one contact over the

9:23

other, that's going to help me from an adoption standpoint, because I trust it.

9:27

I trust a little

9:28

bit more understand why it's asking me to do something or why I am doing

9:31

something. So I have,

9:33

your team did a really good job here, I think, from building in features from

9:37

an AI perspective

9:38

that are going to help with adoption, which is great. I love to hear that. And

9:41

so we'll our product

9:42

team. And the other thing too, that I think is nice about this, as you're

9:45

looking at the why and

9:46

that prioritization is doing it real time. So as things are happening, it's

9:49

constantly adjusting

9:51

that prioritization. Yeah. So I want to show you what it looks like to kind of

9:56

action upon

9:57

some of these different things, specifically in the the rhythm flow here. So

10:01

one that I know has

10:02

been a huge kind of value add for our teams, we've heard a lot about this will

10:06

start introducing

10:07

some of our conversation. AI that we have is the meeting follow up or the

10:12

meeting reminder.

10:14

So let's just say I've already had this meeting with this particular off. And

10:18

based off of the

10:18

prioritization, this is letting me know I should go ahead and send that follow

10:21

up to them right away.

10:22

So when I go to click to action this, those that are maybe already familiar

10:26

with sales

10:26

often confusing just the cadencing functionality previously, we still wanted to

10:30

keep that same

10:31

sidebar experience. But again, this is now looking at everything across the

10:35

ecosystem and prioritizing

10:36

that. So you see it's pulled up my template here, I have my opportunity to

10:41

reference in case I want

10:42

to personalize further. But I specifically want to talk about where we're

10:45

pulling this from. So

10:46

this is where we'll expand a bit into our conversations here and get an

10:50

understanding of what AI capabilities

10:52

we have there. So right now that is located in our I'll tab this down, our

10:57

recap section. So we have

10:59

two main areas here, we have our summary, and we have those action items. So

11:04

the AI is providing

11:05

all of this information. And then we have automation to automatically pull

11:09

those action items into

11:11

this template. So one, making those follow ups really seamless and making the

11:15

rep more efficient.

11:17

But two, where I really think a lot of this adds value is thinking about

11:20

collaboration with either

11:22

handing off to other teams, or maybe as a manager, you know, you don't have the

11:26

time to be everywhere

11:27

at once. So this kind of allows you like a real quick glimpse into how did this

11:31

go? And what is

11:31

the expectation of what my team is supposed to live on as a follow up. So it

11:35

could be really

11:36

impactful from from that point of view. Yeah, I do. I love the use case of AI

11:41

for like summary,

11:42

because I do think there is a great use case for any managers who have larger

11:46

teams.

11:47

Where it's hard to scroll through things, it's hard to digest through all this

11:49

information.

11:50

You're already so busy as it is, I think using like generative AI to have that

11:56

recap is so helpful.

11:58

Yeah, absolutely. I think it's just as we talk about productivity and

12:02

efficiency,

12:03

the more things that you have that can help just say I can focus on the selling

12:07

part,

12:07

right? And more of that human aspect, the better. Yeah, that's awesome.

12:13

Well, let's show it from, you know, we looked more from the opportunity

12:15

perspective. So I'd

12:16

love to show you what it looks like for maybe that Dylan person we saw earlier,

12:21

and we have this

12:21

prompt for the call. So you'll see it's kind of changing my environment based

12:25

off of what that

12:26

action is. So I have my call now prompting. Now it's been replaced with a

12:32

profile.

12:32

And there's a lot of information I won't dig into everything that we have here,

12:36

but there's a lot

12:36

of ways that we can manage our contacts in less time and using the AI to make

12:41

those tasks

12:42

at this list. So as I'm thinking about preparing for this call, some things

12:45

that we'll have in here

12:46

is we'll have out of office detection. So if they're prompted to be on my cad

12:50

ence,

12:50

it'll actually automatically move the due date based off of when they're back

12:53

in the office.

12:54

Oh, that's cool. Yeah. We also have some jobs in your day, which kind of does a

13:00

macro job title,

13:01

says we're thinking about that messaging we saw earlier, we can make sure we're

13:04

aligning it at

13:05

more of that macro parent level to have that consistency. And then we also have

13:10

some other

13:11

things like data enrichment, which is pulling from the person's signature and

13:16

enhancing the data that

13:18

we have. So we always have those up to date information. And then finally, as

13:21

we're thinking

13:22

about, I'm ready to engage with Dylan now that I have this other insight, we're

13:26

also going to be

13:26

able to showcase email sentiment not only in here, but also in our analytics.

13:30

So you kind of know

13:31

how should I be approaching this conversation? Has it been positive as of late?

13:35

Am I getting

13:35

some objections? So it truly helps me restrict strategic point of view. Oh,

13:39

that's super interesting.

13:40

I do really like the sentiment aspect of it. I think especially if time has

13:44

passed after a call,

13:46

you might even forget what the sentiment was. So having that sort of automated

13:50

and AI helping

13:51

me understand like, do I need to approach this deal with a little bit more

13:54

finesse? Is it something

13:54

where they're like really gone, ho, and we know we can push this forward

13:57

quicker? That would be,

13:59

I can see where that's really useful for a seller. Yeah. And the other piece

14:04

too, is as I mentioned,

14:05

it pulls into the analytics. So maybe for that particular seller and that

14:08

particular relationship,

14:09

but I can see how that's trending across my team to see, are there themes we're

14:13

getting a lot of

14:13

objections? Or maybe this individual as a whole is getting a lot of positive

14:17

interactions or

14:18

objections. So it really kind of helps to hone in from a coaching perspective

14:20

too. Yeah.

14:21

Yeah. See, from a manager's perspective, being able to look at rep level and

14:25

say, like,

14:26

hey, are there really common trends with this rep where like there's negative

14:28

sentiment or we're

14:29

not seeing like next actions and all of their calls and being able to use that.

14:33

And then even to

14:34

what you just showed right before this, going into the calls themselves and

14:37

having a recap,

14:38

would help someone like a coaching perspective, a manager perspective, you can

14:41

just get that data

14:42

so much faster and hopefully catch on to any like negative trends that are

14:47

happening and address

14:48

them sooner before it makes a larger impact. So that's, that's great. That is

14:52

actually a amazing

14:54

segue to the next piece of the AI, because just like you're mentioning.

14:58

Yeah. Yeah. There are no, this is, the whole goal here is that this is where we

15:04

're

15:05

making, sending emails, making dials, things along those lines that we're doing

15:09

all of those to drive

15:10

to the outcomes, but particularly to your point, that's really where we can

15:14

start kind of providing

15:15

some insight to leaders. Because as we think about the outcomes that they're

15:19

driving towards,

15:20

you know, you have your goal, you have an idea if you're working towards that

15:24

goal.

15:24

But what I think is really critical is being able to have more of that

15:27

objective insight into like,

15:30

how is this truly trending? So as we think about all that information we just

15:34

talked about earlier,

15:35

helping managers, this is kind of a pulse point, right? Of how am I trending

15:39

towards those overarching

15:40

things I'm trying to accomplish? So for example, I'm in our outcomes dashboard,

15:44

but you'll also see

15:45

this in our, in our deals as well. But you'll see I have meetings booked,

15:49

opportunities created

15:50

and closed one. And each of these have not only a goal, they have what I've

15:54

achieved, but you'll see

15:56

we now have this projected piece in there. So that is actually looking at the

16:00

expected result of this

16:02

particular outcome, if I continue that same daily effort through the rest of

16:06

the current period I'm

16:07

looking at. So this really helps to your point to kind of catch early on any

16:11

trends of like,

16:12

am I am I trending positively towards my goal? And if I'm not, I can see that

16:16

earlier on and start

16:17

to focus in that area of like, let me dig into my opportunities in this example

16:22

and kind of take

16:23

that a step further to see if I can get this back on track and work towards the

16:26

goal we're trying

16:27

to work towards. Yeah, this is really cool in that I know we talked about like

16:31

the manager use case

16:32

here, but I'm circling all the way back to, I feel like ops, like I think about

16:36

our sales ops

16:36

and then the team trying to forecast concrete of like, where are we standing

16:40

and what like,

16:40

what is the outcome going to be? And it's like constant work for them to have

16:44

to do this in excel

16:46

to be able to have in the back end of a system that you're already using,

16:50

helping using AI to

16:52

forecast these things and tell you, hey, you might be coming up shorter, you're

16:55

doing really well

16:55

and not have to have that constant burden on ops, just freeze them up to do

17:00

other more important

17:02

things. Absolutely. And that's really what we're aiming to do is as we think

17:07

about, you know,

17:08

all the various teams, like it's not just the efficiency in the workflow, it's

17:11

making sure

17:12

we're having efficiency from the data from the focus on the coaching standpoint

17:16

, from a focus on

17:17

what needs you, you need to dive into strategically without needing to kind of

17:21

to your point use all these spreadsheets to determine where that focus should

17:24

be.

17:24

That's awesome. So let's take it up of this one where, you know, I'm looking at

17:29

, again,

17:30

these opportunities that are close one. So this is a nice transition into our

17:35

opportunity

17:35

management and our forecasting piece. So I'm in our deals component here, and

17:40

this is our pipeline

17:41

view that's pulling in all the opportunities that a rep is working tons of

17:45

different filters and

17:46

things I can do in here. But staying with our AI vein that I want to hone in on

17:50

is we do have

17:51

something called a deal engagement score. So this is the AI is providing

17:56

insight and the likelihood

17:57

for this deal to in as a closed one based off of recency, frequency,

18:01

progression and engagement

18:03

of the buying committee that I'm working with. So this is a great way to remove

18:07

that subjectivity

18:08

and truly kind of pressure test if this is really going to close. And it's also

18:13

a really great way for

18:14

not only sellers, but managers to prioritize where their focus should be as

18:20

they're trying to get

18:20

things to hit that goal that we showed earlier and our outcomes here.

18:24

I really like this scoring. It's so relevant because I was just talking to

18:29

someone on our

18:29

sales team and they were talking about like when something's in commit versus

18:32

best case and

18:32

I might be like, well, what's the threshold? Like when does something move and

18:36

they're like

18:36

gut feeling? Sometimes it's like I open a prayer and they're just like moving

18:41

from commit to best

18:42

case to have a score that's telling you it just takes that pressure off the rep

18:46

of having to,

18:48

like I'm going to say this is in commit or in best case, but then you have this

18:51

score to like gut

18:52

check in and say like, hey, based on the engagement of this op, we think this

18:56

should

18:56

in commit or alternatively like you're committing this, but like based on this

18:59

engagement score,

19:00

does it feel like this deal is progressed far enough and it's at risk? Just

19:04

like a nice other

19:07

data point that you're not relying on kind of human subjectivity on where it's

19:11

at.

19:11

Absolutely. And you know, that's a good point to bring up. I think that's still

19:15

important, right?

19:15

It's like you've been in the conversations, you have the relationship, but it

19:19

does help to kind

19:20

of balance that like you still want that rep in sight. But to your point, you

19:23

said it perfectly.

19:24

It's a gut check, right? Like, I think it's going well, but this is not lining

19:28

up. So maybe let's dig

19:30

into why it's not lining up and again, start working to come strategize to get

19:34

ahead of potential

19:35

things that are indicating that it might not land where we want it to. And as

19:40

we talk about

19:41

explainability, that's also something we offer with the deal engagement score

19:45

is when I dig deeper

19:46

into this opportunity, I have the deal site that show me why it's been scored

19:50

the way that it has.

19:52

So to your point, you know, I can kind of have, oh, well, I have had

19:55

conversations or I have been

19:56

doing this. I have not been doing this, which we know that does impact that

20:00

likelihood for it

20:01

to close. So it just really helps to be a bit more prescriptive of where that

20:04

focus should be.

20:05

And whether that be from the manager coaching their rep or the rep seeing this

20:09

themselves,

20:10

it kind of gives them some guidance of saying, okay, well, maybe I should reach

20:13

out to my current

20:14

stakeholders about some of these things going on. And then something else that

20:19

sales up will do is

20:20

it will actually recommend suggested stakeholders based off of people you've

20:23

already been engaging

20:24

with and sales off. So you can make sure that you're having like a

20:27

comprehensive look into the

20:29

opportunity and the entire buying committee and really where everybody fits in

20:33

that, in that

20:34

buyer cycle. So nothing's fallen through the cracks here. This part is really

20:38

cool. I feel like using

20:39

sort of like a recommendation AI, especially as buying committees have gotten

20:43

so much either bigger

20:45

or they're changed. Like I know we're hearing from sales all the time, buying

20:48

committee is

20:48

at what it used to be. There's more people. They have different use cases.

20:53

Having a system in the

20:53

back end that's learning this as you have more opportunities and digesting that

20:57

information and

20:57

then being able to say like, Hey, you're missing this person in your deal and

21:00

you haven't engaged

21:01

with this person. I think it's so useful as times are changing so quickly, like

21:06

things have just

21:06

changed so fast, having AI on the back end to help keep up with that. This is

21:10

so useful.

21:12

Yeah, I think even keeping up with them like pulling them all in, but I think

21:17

it's also you can

21:17

kind of see if interactions are trending one way or another with the

21:21

individuals to like, Hey,

21:23

this is supposed to be my champion, but they dropped off or this is my decision

21:27

maker.

21:27

And you can kind of see as a hover over it, it disappears, but right above you

21:30

can kind of see

21:31

meetings or calls I've had or whatever it is, what it allows you to kind of get

21:36

that really

21:36

granular focus on the stakeholders as well. Very cool. So really the I wanted

21:43

to start with

21:44

the pipeline because the importance of having this up to date and really make

21:48

sure that you're

21:49

aligned with all the notes and what's going on is critical because that is

21:52

ultimately what is

21:54

feeding our forecast here. We're ingesting that opportunity information to

21:59

really kind of align

22:00

those numbers that we're using to make our call. So there's a lot of insight in

22:04

here from a

22:05

forecasting perspective that you can utilize, but the one that in particular I

22:09

want to focus in on

22:10

is, hence the name, our AI forecast. And this is actually using the AI to score

22:15

the probability

22:16

of a deal being a close one during this current quarter based off of how

22:20

similar ones have

22:21

behaved in the past that you've had. So when you think about forecasting is one

22:26

of the most

22:27

difficult things to do in getting it right. So by having this come into play

22:30

and like that gut

22:31

check we talked about earlier and removing the finger in the air, I do have

22:35

some triangulation

22:36

metrics. So this is a really nice insert here just to make sure that again, I

22:40

have a more subjective

22:41

metric in the triangulation. So I'm feeling really confident in whatever call

22:45

that I'm making on my

22:46

forecast. Yeah. So I'm hoping that you saw that we really focused on making

22:54

sure that AI is again,

22:56

we've threw all out the platform, but it's small ways as well as in Lord ways,

23:00

because ultimately,

23:01

we want to make sure that this is all helping to get to whenever outcomes you

23:04

're trying to achieve,

23:06

but making sure it's in the most impactful and efficient manner that you

23:09

possibly can.

23:10

Yeah, this was great. I really appreciated this demo, Kristen a lot because you

23:15

mentioned the

23:15

very beginning of the call. I think sometimes there's a perception that sales

23:19

loft is like for SDR

23:21

teams to prospect, which obviously it is and you guys have AI functionality

23:24

like that generative

23:25

AI email builder that's going to help them. But being able to see all the ways

23:28

through and see

23:29

how it's managing entire deal cycles. I knew based on what I'd heard that you

23:33

guys had some really

23:34

cool AI stuff for like the entire sales process. So it was great to see it in

23:37

action and I appreciate

23:38

you showing it to us. Yeah, of course. Thanks for letting me spend the time to

23:41

show y'all.

23:42

Yes. So with that being said, demo is wrapped. I want to move into our

23:46

lightning round Q&A.

23:47

So Kristen, I have a couple questions for you if you're ready. Yeah, let's do

23:51

it.

23:52

So the first is how long have you guys been building AI into sales loft?

23:56

Yeah, so we've actually been building AI in the platform for years. 2018 is

24:04

when we introduce

24:05

hot leads, which I should have showed you the demo, but it's basically using

24:09

scoring through

24:09

the email interactions and the live website tracking to provide notifications

24:13

that, hey,

24:14

you have a hand raiser, you're more likely to get a meeting with them. Go ahead

24:17

and give them a call.

24:18

But as we think about just any AI that we're introducing into the platform, we

24:23

've always thought

24:23

about jobs to be done. So how does AI help a user do what they need to do to be

24:29

successful in their

24:30

job? How do we see better outcomes? But I think the most important thing as we

24:34

think about

24:36

all the AI that we built and we're building is that we always want it rooted

24:39

around value,

24:41

explainability in the workflow, like you saw earlier. But most importantly,

24:44

that we're grounding

24:45

it in the AI ethics and responsibility. That's critical to us. That's awesome.

24:50

You guys have

24:50

been thinking about it for a while. And I think with the explosion in

24:53

popularity as of late,

24:54

but you guys have been thinking about it for a long time and that you've

24:56

already,

24:57

it sounds like you're a step ahead of the game and you're thinking about this

24:59

ethically from a

25:00

use case perspective. So that's amazing to hear. Yeah. And then Kristin, what

25:06

you showed today in

25:07

the demo with Sales Off Rhythm is this generally available now for customers?

25:11

Yes. Everything that

25:12

we have shown is GA right now to all of our customers. Awesome. And speaking of

25:17

customers,

25:18

who are a few of your customers that are benefiting from Sales Off's AI

25:21

functionality?

25:22

Yeah. We have a ton of different customers. As I said, we've had AI for a while

25:28

in all these

25:28

kind of little or large places. But I would say, when it comes to rhythm, which

25:33

is that main release,

25:35

again, we had in July, all of our Sales Off customers are now on the rhythm

25:40

experience using

25:40

that conductor AI. And the crazy thing is, is I kind of talked about getting

25:45

the meetings with

25:46

more or I'm sorry, with less or the same activities. But we've already been

25:50

hearing improvements from

25:51

sellers across saying that they're saying a 20% decrease in their sales cycle.

25:55

They're seeing a

25:56

25% increase in their closed one ops. And we've had a bunch of different quotes

26:02

, but one of them in

26:02

particular, a Moody's Analytics rep said, you know, cadence equals prospecting

26:08

rhythm equals

26:08

making money. And I thought that was a really nice way to think about why we

26:12

focus on rhythm is,

26:14

again, it being outcome driven and prioritizing those things to do so. Totally.

26:18

So if you're a

26:19

sales off customer, you're benefiting from AI. It's been in the product for a

26:22

long time. You

26:22

have access to it. You're going to get the benefits. Yes. Exactly. And then

26:28

Kristen, what is next in

26:29

your AI roadmap at Sales Off? Yeah. So we just announced our vision for the

26:35

future at our sales

26:37

love on tour in London. And really it's connecting the data insights to actions

26:41

in the platform. So

26:42

again, thinking about rhythm, that created an action engine, which was that

26:46

better AI powered

26:47

workflow more for those individual contributors, like our SDRs, our AEs, our

26:51

customer facing teams.

26:52

But wherever we really start pivoting in owner focus, as we look ahead, is an

26:56

insights engine that

26:58

empowers the middle lines, look, our sales managers, so they can better use the

27:02

data to impact their

27:03

team. So to give you an example, let's say that the data shows that when there

27:08

's less than four

27:09

stakeholders on a deal, there's a 30% lower win rate. So the AI could then

27:14

suggest, hey,

27:15

with these calls you have coming up, they have less than four stakeholders. We

27:19

recommend you add

27:19

these particular stakeholders to increase that likelihood. Or maybe the rep is

27:24

bringing up

27:26

budget in a discount in a budget conversation. And maybe the data showing that

27:31

his pricing is

27:32

usually 12% lower. So the AI could then recommend for him to get a refresher

27:37

enablement training

27:38

on pricing and discounting in general. So really, the next evolution is taking

27:43

that data set of

27:43

that buyer seller interaction we were talking about across the ecosystem. The

27:48

resulting outcomes

27:48

that we're getting and really understanding what's driving those outcomes. But

27:53

I think the critical

27:54

part here is we think about that evolution is that this really only works if

27:58

our sellers are

27:59

behaving in the right way to get that meaningful insight, because if they're

28:02

doing the wrong things,

28:03

that insight's not going to be very meaningful. So that's where rhythm is

28:07

really coming to play,

28:08

kind of being the catalyst is because it's guiding the sellers to have the

28:12

consistent good

28:13

behavior that we want. That's enabling that insight to determine what works,

28:18

which is then being fed

28:19

back into RITA. That's amazing. And then last question, Kristin, are there any

28:24

other AI-powered

28:26

products that your go-to-market team is using that you want to give a shout out

28:29

to? Yeah, so I

28:31

think what a lot of people are using, we definitely use chat GPT a lot as a

28:35

brainstorming partner.

28:37

I've seen our teams use it as maybe revamping some discovery questions or maybe

28:42

even holding

28:43

it further based off of persona and maybe how you can reframe things there, or

28:47

maybe even getting

28:48

further inside it, to specific personas in specific industries. So that's

28:52

really helped to write some

28:53

granularity there as we're thinking about the discovery side and continuing

28:57

that. I know a few

28:59

have also used perplexity to help summarize like we use 10Ks and really trying

29:04

to grab those key

29:05

items. Again, as we're coming with a strong point of view as to how we're going

29:08

to benefit them,

29:09

that's been a really big one to help with the efficiency. And then some tools

29:14

to help beef

29:14

up the emails. We show our agenda of AI, but I think there are some really

29:18

great partners like

29:20

Lavender that uses AI to recommend how you can tweak and clean it up further to

29:25

make sure,

29:26

again, you're having most impactful emails. So you have the data and now we use

29:30

that to kind of

29:30

shave it down, tweak it, change up some of the verbiage of what usually lands

29:34

from an AI perspective.

29:36

That's awesome. I think perplexity. I've heard our sellers say that that's been

29:39

a huge benefit to

29:40

them as well as they're thinking about what to send and summarization. So it's

29:43

great to hear

29:44

your teeth is benefiting from that as well. Well, Kristin, thank you so much

29:49

for joining us on

29:50

GoToMarket.ai today. I did really enjoy this demo. I learned a ton. So I

29:53

appreciate you being here,

29:55

and thank you so much. Of course, thank you for all the time.