Episode 9

full
Published on:

27th Nov 2023

GTM Planning and Forecasting, Without the Spreadsheets - Toni Hohlbein

In a world where we have so much tech, it's amazing that revenue planning and forecasting remain relatively primitive in most companies.

You could have literally a million dollar tech stack and yet still be creating your business plan with a spreadsheet and forecasting results with a best guess from sales.

Today we look at how to go beyond the spreadsheet paradigm with the CEO of Growblocks, a revenue planning and analytics platform. We'll explore whether it's possible to have a truly predictable forecast and how operators can spot and fix issues before they become million dollar problems.

Thanks to Our Sponsor

Many thanks to the sponsor of this episode - Knak.

If you don't know them (you should), Knak is an amazing email and landing page builder that integrates directly with your marketing automation platform.

You set the brand guidelines and then give your users a building experience that’s slick, modern and beautiful. When they’re done, everything goes to your MAP at the push of a button.

What's more, it supports global teams, approval workflows, and it’s got your integrations. Click the link below to get a special offer just for my listeners.

Try Knak

About Today's Guest

Toni is the CEO of Growblocks. He spent years as a B2B SaaS CRO and revenue operator, achieving multiple exits. Through this experience, he created a revenue operating operating model that helped his company hit targets 12 quarters in a row. This model was later on used as the basis for Growblocks.

https://www.linkedin.com/in/tonihohlbein/

Key Topics

  • [00:00] - Introduction
  • [01:33] - Toni's background. Founding Growblocks. Sharing a focus on data-driven, system-thinking approach to revenue with his co-founders.
  • [04:27] - Most forecasting tools focus on only on the opportunity forecast, ignoring the rest of the funnel. Why there aren't more companies building software for full-funnel planning. Forecasts are split into silos. Once you forecast the full funnel, you can identify how a gap in one stage translates to revenue impact further down.
  • [07:40] - How Toni explains this vision to the market and what benefits people are connecting with. When people finally see their actual revenue engine end-to-end for the first time, it's a huge impact.
  • [10:09] - Flaws with the planning process today. How the planning process should take place: top-down and bottom up meeting in the middle.
  • [14:09] - The impact of the tech bubble on the planning process and how it has distorted expectations and behaviour. You can only be efficient once you are predictable.
  • [18:00] - The factors that lead to predictability. The first comes from understanding your sales engine as a whole through the entire funnel. Not relying on sales people that can pull rabbits from hats. The second factor is proactively spotting issues and jumping on them quickly.
  • [21:39] - Issues with marketing and sales alignment. Why marketing will hit their number but sales misses theirs. Toni doesn't have an issue with MQLs, so long as the company splits handraisers from non-handraisers. Then marketing can't just hit their number with low-intent MQLs.
  • [25:24] - Outbound is still alive and kicking. Think of it as the delivery mechanism for a message. Importance of choosing channels that work for your audience. Someone may not be on LinkedIn but they are listening to the radio all day.
  • [28:32] - Starting to dive into the Growblocks platform. Growblocks works with any kind of funnel design and is also configurable across different dimensions. Solving for garbage-in-garbage-out problems. If data points are missing, it's best to go up a level and exclude that step, then circle back to it when better data is available.
  • [32:58] - Identifying factors that may influence funnel performance but that are not themselves funnel stages. Toni calls them "monitoring notes" - akin to gauges on a machine that show you certain indicators about its performance.
  • [36:00] - Growblocks can connect to different data sources and mash them together. It applies sophisticated math and statistics to forecast results. They are incorporating machine learning, but Toni is trying to keep it as transparent as possible so customers can have trust in the results.
  • [39:30] - Main value props of Growblocks. The first is more predictability, less choppy results. The second is showing things that were invisible to enable to teams to make changes and improve.
  • [42:21] - Toni's vision for the future of the product.

Resource Links

Learn More

Visit the RevOps FM Substack for our weekly newsletter:

Newsletter

Transcript
Justin Norris:

In a world where we have so much marketing in sales tech,

2

:

it's kind of amazing to me that revenue

planning and forecasting remain pretty

3

:

primitive in most companies, could

have literally a million dollar tech

4

:

stack, and yet still creating your

business plan using a spreadsheet, and

5

:

you're forecasting results with pretty

much a best guess from salespeople.

6

:

Maybe this is okay.

7

:

How's it working out for us?

8

:

Is there a better way to do it?

9

:

here to discuss this and many other

things is Tony Holbein, c e O of

10

:

Growblocks, a revenue planning and

analytics He is also been a longtime

11

:

C R o Ops Professional and is the

host of the Revenue Formula podcast.

12

:

welcome to Rev Ops fm.

13

:

Toni Hohlbein: Thank you

so much for having me here.

14

:

It's a real pleasure.

15

:

Justin Norris: I am excited to

have you here and want to hear the

16

:

Growblocks story, and your story.

17

:

I wanna just give listeners

a little context about how

18

:

I even became aware of you.

19

:

'cause this was a scenario where

I was just thinking to myself, day

20

:

There has to be a better way to do

this than the sort of spreadsheet

21

:

type process that, I just described.

22

:

And I went about looking

over the internet,

23

:

Toni Hohlbein: We.

24

:

Justin Norris: actually find

a product that fit my mental

25

:

model of what this could be.

26

:

I even thinking maybe I should

build one if it doesn't exist.

27

:

And then I, I came Growblocks I became

intrigued and started speaking to your

28

:

team, and that's how we connected.

29

:

But I'm curious your side of the

story, of your background how

30

:

you ended up starting Growblocks.

31

:

Toni Hohlbein: Yeah.

32

:

Wonderful.

33

:

So I think the story that's relevant

here, is really, I started my career

34

:

as a revenue operations professional.

35

:

I didn't actually know it at that point

in time that this was revenue operations.

36

:

What, what I was doing.

37

:

I think it was very much, you know,

sales ops in the beginning and

38

:

then it grew into something else.

39

:

And then I've been doing this for,

for a couple of years, and over time

40

:

accumulated a couple of more folks

around me that were reporting to me,

41

:

got more responsibility and so forth.

42

:

And then, then I, uh, somehow

made the jump to chief revenue

43

:

officer of that organization.

44

:

So I basically became C r o, um, 15

million of the company, and really was in

45

:

charge of all marketing, sales, and css.

46

:

Plus revenue operations, obviously.

47

:

we scaled the organization

to around 50 million.

48

:

Did the exit there.

49

:

then, because Exit done jumped to the

next one, also as C R O exited that

50

:

team within one and a half years,

of those cases went like the, the

51

:

unicorn exits, might read about on

TechCrunch, but they were like very,

52

:

very sizable, really nice exits actually.

53

:

after those two experiences, really

looking back thinking, Hey, What

54

:

is special about me, you know, what

differentiates me and, you know,

55

:

what, what could I take in order

to bring it forward and maybe, you

56

:

know, build a company around this?

57

:

and my co-founders and I, we pretty much,

you know, realized that it's really this,

58

:

this is shared between the three of us.

59

:

It's really this.

60

:

Data-driven system thinking

approach to producing revenue.

61

:

That was the initial point.

62

:

And, and what the three of

us shared was really the.

63

:

Okay, we were charge of building revenue

in those organizations and the first

64

:

thing was like, okay, uh, you know,

what's the budget that I'm getting

65

:

and what do you want to have back?

66

:

Right?

67

:

So this is number one, really

difficult to actually figure this out

68

:

and Excel spreadsheets and so forth.

69

:

But, you know, once you kind of get

over that hump, which I think you

70

:

can do, then it's really in the, the

day-to-day forecasting, understanding

71

:

where something is going wrong.

72

:

And I was, I'm a paranoid person.

73

:

Well, not, not clinically so, but,

you know, generally speaking, I'm

74

:

pretty paranoid and I wanted to know

immediately when there was something

75

:

going off somewhere in my organization,

jumping on this and fixing this, right?

76

:

Because as all of those little, you

know, uh, kind of pile up, you'll

77

:

just end up missing your revenue

target and you end up looking like a,

78

:

like a douche in front of the board

to your, to your c e O and so forth.

79

:

And, and the funny thing is when you think

about what are the tools available today,

80

:

it's sales forecasting that's kind of

the tool that's available today, right?

81

:

That's the only thing that you have

to look into the future, you know, in

82

:

your go-to market operation basically.

83

:

and my, my experience was simply like,

well, whenever there's a problem showing

84

:

up in my forecast, It's kind of too late.

85

:

It's not like I can do

anything about this.

86

:

Right.

87

:

So really what we then, you know,

then back then did, and basically

88

:

now doing here in Growblocks is,

well, you need a pipeline forecast.

89

:

You need a hiring forecast.

90

:

Yes, you need a sales forecast, but

you also need a retention forecast and

91

:

an upsell forecast and what have you.

92

:

Um, I think this was kind of the,

starting idea around Growblocks

93

:

and we've been doing this now for

two years, something like that.

94

:

Justin Norris: and you mentioned And it's

just a sales forecast and it's too late.

95

:

And I was, struck by that.

96

:

I was looking around at other

forecasting tools are out there.

97

:

There's some I'm familiar with.

98

:

They all seem very focused

just on opportunity forecasting

99

:

Toni Hohlbein: it.

100

:

Justin Norris: they're just like

that one piece of the funnel.

101

:

I've lived what you just

described many times.

102

:

Oh, we ahead?

103

:

Are we behind?

104

:

Are we missing

105

:

Toni Hohlbein: Mm-hmm.

106

:

Justin Norris: And if we are gonna miss

our number, let's say in marketing, why?

107

:

And then every time you're breaking

out, you know, the back of the envelope

108

:

and digging into all these different

channels, it's very time consuming.

109

:

So people don't do it as often.

110

:

like that you, uh, upon, I

think a, fairly elegant for that.

111

:

And we'll talk about it a little bit more.

112

:

just want to know, though,

to the point that I kind of

113

:

alluded to in my little intro.

114

:

we have thousands and thousands of

pieces of technology for so many

115

:

things, but no one, maybe one or two

other companies, but few companies are

116

:

really doing this for the entire funnel.

117

:

And I'm just wondering why it seems

a kind of logical thing to do.

118

:

'cause every company has this

spreadsheet, you know, for their

119

:

Toni Hohlbein: Yeah.

120

:

What, what everyone is doing, by the way.

121

:

So, everyone is having five or six

or seven different spreadsheets and

122

:

trying to do the same thing, right?

123

:

just today talk to a C R O of

like an 80 million a company.

124

:

know, I was kind of talking

about go to market forecasting,

125

:

kind of how we see the world.

126

:

and he was like, no, no, I don't do go to

market forecasting, but, you know, I have

127

:

a forecast for my sales team, obviously.

128

:

Then I have like an opportunity

production forecast.

129

:

I have my talent attraction forecast.

130

:

I have a retention and upsell forecast,

and the C F O I need to do, um,

131

:

recognize revenue forecasting, right?

132

:

Total, total ar basically.

133

:

I'm like, yeah, and, and how many

spreadsheets do you have to manage that?

134

:

Well, you know, 20 and would, you know,

all of these things hang together.

135

:

Right?

136

:

It's like, yeah, well that's actually what

you want to have at the end of the day.

137

:

Right?

138

:

When you, when your pipeline forecast

or your, your opportunity production

139

:

forecast is slowing down in one region.

140

:

It's not like it's, it's overall, you

know, overall everything might be green,

141

:

but it's slowing down in one region.

142

:

Well, that will have a knock on

effect on your sales forecast, and

143

:

that will eventually have a knock

on effect on your customer for

144

:

et cetera, et cetera, et cetera.

145

:

And he totally, I mean, and

that, I think this is the moment

146

:

where kind of then he got it.

147

:

What's pretty cool, like once you model

this out, once you kind of really do

148

:

this, go-to market forecasting in a, in

a, in a productized manner, how we are

149

:

doing this, you really kind of get to

a couple of cool magic features, right?

150

:

instead of you being 10% behind an MQL

somewhere, um, say you're 10% behind

151

:

an mql, we're saying you're $1.2 million

behind, the end of Q three on, you know,

152

:

because of your missing, uh, mql, right?

153

:

So we basically can kind of.

154

:

Justin Norris: that in the future.

155

:

Toni Hohlbein: Exactly.

156

:

We can basically kind of price it all

in and when, especially, you know, when

157

:

we're selling them to CROs and you know,

not necessarily only revenue operations.

158

:

They, they kind of don't

give a shit about, oh, this

159

:

conversion rate dropped by 5%.

160

:

I mean, who cares?

161

:

Well, that conversion rate dropped

by 5% and in three months, that's

162

:

a $2 million problem for you guys.

163

:

And, and suddenly everyone's,

oh, you know what, actually I

164

:

do care about that right now.

165

:

Right.

166

:

So really the pricing in of those

different, um, steps is extremely

167

:

important And giving this . Transparency

across and giving transparency

168

:

is usually, kind of euphemism for

almost also accountability, right?

169

:

Kind of really making sure you know

where, what is going wrong and being able

170

:

to, jump there and, and try and fix it.

171

:

Right?

172

:

are the kind of things that if

you have five or six different

173

:

spreadsheets you're trying to

connect, you won't be able to do.

174

:

Justin Norris: So I'm actually curious as

you're alluding to these conversations.

175

:

For me, I have a sort of innate sense

of order in, in how I like to do things.

176

:

And so having all these spreadsheets

and they change in one place

177

:

and they don't change in another

place, it bothers me so much.

178

:

Not everybody

179

:

Toni Hohlbein: Yeah,

180

:

Justin Norris: aversion.

181

:

So when you're having these

conversations with people, are you

182

:

finding that people are connecting

with this vision kind of immediately?

183

:

Are you needing to really try to bring

them around to a different point of view?

184

:

What do those typically go?

185

:

Toni Hohlbein: I'm always

careful, the category word.

186

:

I think that's, usually a bit overblown

when you like seed series a stage.

187

:

at least you are bringing forward a.

188

:

New set of capabilities, right?

189

:

Or a new group of capabilities.

190

:

And some people might refer to

it as a new category, right?

191

:

So I think number one, you do need

to win the, the category argument.

192

:

You know, why can't we do this in Excel?

193

:

Why can't we do this?

194

:

And I dunno, why, why do we need this?

195

:

We didn't need this for the last 20 years.

196

:

Why do we need it now?

197

:

this is, I think, one thing, I think the

. The other thing is that, usually you see

198

:

that they're doing all of these things,

you know, across the organization, but

199

:

basically don't have the visibility Right.

200

:

stitch it all together.

201

:

It's funny when you show people,

when after we onboard them, show

202

:

them their, actual revenue engine

end to end for the first time.

203

:

It's a little bit like, You know, on,

on Instagram, sometimes you have those,

204

:

you know, blind people that suddenly

see it's . That's, that's kind of

205

:

this emotion that's going on there.

206

:

It's like, oh my God, finally

I can see the whole thing.

207

:

And it's pretty cool because, you

know, we have like, I dunno, 20,

208

:

30 different customers and you

see the, um, of these engines look

209

:

completely differently, right.

210

:

And, and really having this moment also on

the demo, when you show just the demo kind

211

:

of environment, okay, so this is actually

how the engine looks like, and this is how

212

:

I break it down into my sales forecast,

into my, you know, pipeline forecast

213

:

into my, you know, people forecast.

214

:

that's where it make

clicks, click for people.

215

:

I think then the other piece

is, is really the accessibility.

216

:

So when you think about Excel

spreadsheets, people like you and I,

217

:

super, we happy with Excel spreadsheet.

218

:

You know, the CFO is happy with

Excel spread, fp and a is happy

219

:

with Excel spreadsheets, you know,

who isn't your VP of sales, your C

220

:

M O, your VP of, customer success.

221

:

Those folks just actually are not able to

kind of really dive deep into these Excel

222

:

spreadsheets and feel comfortable with it.

223

:

And you probably wouldn't even

wanna share that in the first place.

224

:

Right?

225

:

And making that stuff available, making

it, you know, available for them to use

226

:

it, play around with this, understand

the impact, and actually learn how

227

:

some of the dynamics of the revenue

engine of their own and revenue engine,

228

:

how that actually works together.

229

:

it's actually pretty powerful.

230

:

Justin Norris: So thinking about

planning as a, process, there's kind of

231

:

the technology that we use to, it and

track it and all of those things, and

232

:

Toni Hohlbein: Mm-hmm.

233

:

Justin Norris: But just as a process

in general, what I typically experience

234

:

is you have You know, finance, C

F O or c e O, some combination of

235

:

executives sketching up this plan

in a spreadsheet at a high level.

236

:

It gets improved by the Board, and then

it's kind of handed down like sales,

237

:

here's your number, marketing, here's your

number, and then you have to go and figure

238

:

out how am I going to get this number?

239

:

With, with the budget that

we have, as you described.

240

:

So it's in many ways upstream

of or of certain types

241

:

Toni Hohlbein: Yep.

242

:

Justin Norris: strategy.

243

:

Is this the right way to do

things or how do you advocate

244

:

that planning cycle taking place?

245

:

Toni Hohlbein: So there's the dreamy

reality that we would like to see.

246

:

then there's the reality that's

just simply out there, right?

247

:

I think what is correct is you

need to have this strategic

248

:

conversation on a very high level.

249

:

Um, think you need to craft this

into top-down driven plans and,

250

:

top-down driven plan usually trying

to achieve a, financial profile of the

251

:

organization that is attractive for.

252

:

capital markets, whether or not those

are private capital markets with VCs

253

:

and private equity vendors, or if

it's the actual public markets, right?

254

:

That's what this is about.

255

:

Kind of roughly sketching this out.

256

:

This is how this could look like.

257

:

then should happen though is that

once you get to a certain level of,

258

:

Hey, this is what we are gunning for.

259

:

I believe what then should happen

is that, very quickly there's

260

:

a bottom up process started.

261

:

In many organizations, so small

organizations below 250 people, it's

262

:

someone in revenue operations, you

know, pulling together some numbers.

263

:

Sometimes it's within a week's

time, trying to, of get there.

264

:

And what usually happens is they're

trying to reverse engineer, right?

265

:

It's really a reverse engineering kind

of exercise, which is, you could say by

266

:

default already flawed, but I think this

is what's happening in, in many, many

267

:

cases, what I believe is, actually the.

268

:

still the better way.

269

:

Yes, you will need to work

towards those top down numbers.

270

:

I don't think you have a choice.

271

:

I think that's there.

272

:

But number one, you should be including,

actual subject matter experts in this

273

:

field, which is . Your VP of sales, your

VP marketing, kind of the commercial

274

:

leaders, they are the ones after you

have, you know, put in your headcount.

275

:

They're the ones that figure out, well,

where can we tweak the engine to improve?

276

:

Right?

277

:

What, what could we do?

278

:

And revenue operations sometimes has

a good pad in that conversation, but

279

:

you need to actually enable those

folks to kind of, you know, carry

280

:

this as well, which I've seen many,

many times in Excel spreadsheets.

281

:

Extremely difficult.

282

:

and then ideally kind of meet

finance, bottom up to top down.

283

:

What usually then happens is there's

a gap, obviously there's always a gap.

284

:

and what I found is extremely difficult

is, especially if it's a VP sales leading

285

:

that kind of negotiation, it always has

this feeling of like, oh, it's the VP

286

:

sales trying to pull down his or her

target to get more commissions, right?

287

:

It's almost like a, egoistical

kind of, you know, approach to it.

288

:

and if you use something that's more

data driven, it's actually . Easier

289

:

for the C F O to be like, okay,

actually I fucking get that.

290

:

Yeah, that's right.

291

:

Yes.

292

:

You know, if we need to do something

here or, and this is what I've seen

293

:

talking to like Gong and FreshWorks

and a couple of those companies, it's

294

:

like, yes, we know, we knew already

there's a gap and now we need to all

295

:

already now work on the gap plan.

296

:

Right?

297

:

And that's how you enter the year end.

298

:

You know, you, you basically then

track the gap plan, you know, what are

299

:

the things that you wanna do, who's

responsible, how they're tracking and

300

:

so forth and . I mean, you want to

have that enabled by a tool, right?

301

:

So I've seen it so many times.

302

:

If it's , It doesn't matter if it's a

gap plan in the beginning of the year,

303

:

your gap plan, in the middle of the

year everyone holds up their hands and

304

:

says, I'm gonna do X, Y, and Z, and

then who checks in three months later?

305

:

Right?

306

:

Kind of no one.

307

:

and you wanna have some of those

things a bit more formalized and, you

308

:

know, tracking the metric and seeing

the revenue impact of that and seeing

309

:

how it actually behaved, order to

really, you know, make sure you can

310

:

actually check in on this and, and have

some accountability going on there.

311

:

Justin Norris: the process that

you described makes a lot of

312

:

sense, and I think, I think in

some ways that does happen, the,

313

:

the meeting in the middle part.

314

:

Doesn't happen as much

as it, as it should.

315

:

Toni Hohlbein: Mm-hmm.

316

:

Justin Norris: a lot of top down

and, and kind of deal with it.

317

:

Or gap plan involves a lot

of unrealistic assumptions on

318

:

improvements on certain things,

conversion rates, channel performance.

319

:

curious, you know, you

mentioned capital markets.

320

:

It's hard to think about this

without tying in the decade

321

:

long bubble we've had in in tech

322

:

Toni Hohlbein: Yep.

323

:

Justin Norris: with insane valuations,

insane raises from venture capital

324

:

the unrealistic growth expectations

that have come along with that

325

:

and, and those chickens very much

coming home to roost right now.

326

:

how distorted is our view of,

whole process based on the

327

:

last 10 years, or, or is it.

328

:

Toni Hohlbein: mean it certainly is

distorted and, you know, one of the

329

:

simple ways of how planning top high

level planning works in the boardroom

330

:

is, Hey, you're on this track.

331

:

so insanely top down.

332

:

It's even further top down than you

think it is because really it's okay.

333

:

We raised for x million

dollars, valuation.

334

:

next time we wanna raise ak when we

run out of money, we need to be worth

335

:

three times as much because otherwise

the rest of the board won't be happy.

336

:

Okay, so in order.

337

:

To raise at that valuation?

338

:

How does the organization

actually need to look like?

339

:

Well, we need to have that growth.

340

:

We need to be at that,

you know, a r R level.

341

:

And then we might get the multiple

to kind of reach that new valuation.

342

:

And then it's like, okay, now, now that we

know this is the a r level, by the end of

343

:

that timeframe, Now let's work backwards

and figure out how many a we need to hire.

344

:

Right?

345

:

Kind of that's, how it works.

346

:

I think what has happened now is, some of

those expectations are a bit more muted.

347

:

I think founders have started

to push back against the board.

348

:

It's like, Hey, that's not gonna happen.

349

:

I think many boards actually also

starting to be like, well, you know,

350

:

are you sure about this number?

351

:

I think this is, is a lot of.

352

:

pushback is now just coming,

which previously was just,

353

:

Hey, let's just spend more.

354

:

It doesn't matter.

355

:

Let's go.

356

:

I think this is where, where

some of this is coming from.

357

:

I think the other piece, and this

has been systemic over the last

358

:

1,000 years this is kind of a

bit of a new, forming in my head.

359

:

You can only be efficient

after you became predictable.

360

:

Right.

361

:

So in order to be efficient, you

have to be predictable first.

362

:

and what this means is, the following.

363

:

So, we've seen now for the last two

years is people basically kind of cut

364

:

indiscriminately, spend on the marketing

side, people on the sales side, on

365

:

the marketing side, and so forth.

366

:

Basically kind of they say,

okay, we need to shrink 30%.

367

:

Just cut 30% from everyone, right?

368

:

And how this works is like VP of sales,

I need 30% from you, VP of marketing.

369

:

I need 30% from you.

370

:

VP C is, I need, I dunno, 20% from you.

371

:

It's usually a bit different there.

372

:

that's how they go in

and cut stuff, right?

373

:

But that is actually not

making anyone more efficient.

374

:

You just have now a lower cac

that's great, but you will, you

375

:

know, equally also bring in less.

376

:

Revenue.

377

:

So you didn't become more efficient

that moment actually, you just,

378

:

on a lower level, on a lower burn.

379

:

really what you need to actually

do is you need to have an

380

:

understanding of the revenue engine.

381

:

You need see where you are

predictable and where you're not.

382

:

And based on that understanding,

you can now go in.

383

:

Instead of indiscriminately just

cutting across the board, you

384

:

basically take things away that

you know are not working or just

385

:

have a lower efficiency of working.

386

:

Right?

387

:

So things that might have a massively

higher attack payback compared to

388

:

some other streams, you cut them down.

389

:

By doing that, you basically kind

of improve your efficiency, right?

390

:

So the cost will go down faster than

the revenue associated with that, right?

391

:

And that is actually kind of the

way that, I've seen, uh, work myself

392

:

kind of when I was a c o of Falcon,

which is now a brand watch, kind of,

393

:

that was, you know, one of the ways.

394

:

So we got through like a really terrible,

you know, war story, if you will.

395

:

and I think this is, you know,

the, the teams that are actually

396

:

being efficient, that are actually

getting to this 12, 13, 14 month kick

397

:

payback, that's how they're operating.

398

:

It's like, okay, I know

how this thing works.

399

:

This doesn't work.

400

:

Let's just cut it away.

401

:

Then they have two options, either

that put that money in the bank

402

:

account, which, you know, in many

cases is what's happening now.

403

:

Or they can deploy that cash into

the higher performing, so the, the

404

:

more efficient tech payback channels.

405

:

then buy that, you know, increase

revenue, but cost lesser.

406

:

So,

407

:

Justin Norris: mean, predictability

is magic word, right?

408

:

That's what everybody, Wants,

that's what every company needs.

409

:

Toni Hohlbein: Yeah.

410

:

Justin Norris: what are

the factors you've seen?

411

:

I mean, this is a, kind of broad question,

and the answer will be different for

412

:

different teams, but what are the things

that predictable teams do or have?

413

:

is it process, is it

consistency in a sales approach?

414

:

So we've all seen, you know, sales teams.

415

:

You've had one superstar

that can pull rabbits out of

416

:

hats again and again, others

417

:

Toni Hohlbein: Yep.

418

:

Justin Norris: Where, where does this

predictably come from in your experience?

419

:

Toni Hohlbein: Yeah, I think

it comes from, two things.

420

:

One really comes from

understanding your engine.

421

:

I.

422

:

And in many cases, it's not this sales

person that pulls rabbits out of the hat.

423

:

It's that person probably

is getting dealt better.

424

:

Inbounds maybe is doing more work

on finding the right accounts.

425

:

Maybe it's simply just

a better salesperson.

426

:

So I think it's less so luck involved.

427

:

It's just, you know, there,

there's some system to this.

428

:

and that sometimes leads people to

think that all the predictability is

429

:

encapsulated in the sales team, right?

430

:

Which is going back to

the sales forecasting bit.

431

:

It's like, oh, you know,

sales forecasting, that's

432

:

really what's important.

433

:

We need to have a great sales forecast

and, you know, run a thorough process

434

:

and then we're gonna be predictable.

435

:

I call beers on that.

436

:

I think I ran one of the most thorough

forecasting, you know, habits, probably

437

:

in the Nordics, I don't know where else.

438

:

and you know what, the only thing

we got better at the, the reps,

439

:

you know, over month and quarters

and quarters got better, I think

440

:

is a great sales enablement tool.

441

:

But number two.

442

:

We really just were getting better

at calling, you know, whether or

443

:

not we were missing or, or hitting.

444

:

at that point in time you couldn't

do anything about this anymore, and

445

:

I wouldn't call this predictability.

446

:

Predictability means, really

kind of having consistency in

447

:

your performance and a forecast

usually doesn't help you with that.

448

:

So where predictively comes from

is understanding that the sales

449

:

rep is not . Who's creating

revenue, it's the top funnel that's

450

:

actually, you generating revenue.

451

:

That's then just being, you know,

converted all the way through.

452

:

And symptoms of companies that

I see that are less predictable.

453

:

always talk about the sales forecast.

454

:

They always talk about, you know, good

or lazy account executives, they always

455

:

talk about enablement for the sales team.

456

:

They always talk about, Hey, the

forecast needs to get better.

457

:

That's only 10% of the reason

why you're hitting target or not.

458

:

When you kind of imagine your full bo

volatile funnel, there are 20 other

459

:

places where it could be improving, right?

460

:

And kind of that's, that's

not what happens, uh, you

461

:

look at sales forecasting.

462

:

then I think the other thing is, Do

you just need to really, you know, check

463

:

and see what's going on and everything

that's trailing off just a little bit.

464

:

You wanna see this, jump on it, fix it.

465

:

predictability is not only a engine,

but it's also monitoring the engine

466

:

in the right way and steering it

even kind of in smaller increments,

467

:

jumping in and fixing stuff.

468

:

And what I've seen myself, is.

469

:

People just don't see that thing

when it kind of goes off the rails.

470

:

maybe have a Q B R, maybe they're catching

it, maybe they're catching it 90 days

471

:

later, maybe they're never catching it.

472

:

Just, you know, earlier this week

had a, customer call where we

473

:

found massive leak, top funnel.

474

:

that, hey, there's something not,

going in the right direction.

475

:

And it was extremely simple with the

software to be like, Hey, it's over here

476

:

and it's been trending down and you know,

just checked in with them earlier today.

477

:

And it's like, ah, you know, it's

actually, um, . processing step

478

:

was handled by a, by a person,

and that person was overloaded.

479

:

There was some, you

know, changes going on.

480

:

and or she had a very inconsistent

way of actually dealing

481

:

with that conversion step.

482

:

And now they're fixing it.

483

:

And, now it can see like conversion,

you know, jumping back up to where

484

:

it usually used to be, right?

485

:

And these things, you don't

see it, you can't catch it.

486

:

you can't fix it.

487

:

Then suddenly what you thought,

you understood what you thought

488

:

was predictable, is going in a

different direction right now.

489

:

And guess what?

490

:

It's suddenly you don't

understand it anymore.

491

:

Suddenly it's not that

predictable anymore, and it's

492

:

simply a visibility issue.

493

:

It's not a, oh, I don't

understand my engine anymore.

494

:

It's just you didn't, you didn't see it.

495

:

Justin Norris: You talked about marketing

component and the sales component,

496

:

and often see out in the market,

I think is marketing can hit their

497

:

number, but sales will not hit theirs.

498

:

And a criticism that I think is, becoming

more common in the market right now

499

:

is that this is due to marketing being

incentivized on the wrong things, like

500

:

mql, something that's kind of basically

501

:

Toni Hohlbein: Yep.

502

:

Justin Norris: BSS metric or k p i.

503

:

What are your thoughts about

504

:

Toni Hohlbein: Mm-hmm.

505

:

Justin Norris: be aiming at so

that and marketing hit their number

506

:

together rather than having this gap?

507

:

Toni Hohlbein: So I don't

actually have any beef with MQs.

508

:

I think there is a

problem in target setting.

509

:

I think there's a problem in

aligned target setting and so forth.

510

:

simply, you know, the funnel.

511

:

Don't treat every M Q L the same,

but say, here's a hand raiser, M

512

:

Q L, there's a demo or a trial,

and here's a non handraiser M Q L,

513

:

which is a webinar download webinar

or a download of a news, you know,

514

:

white paper, whatever it might be.

515

:

Just have those two things,

d you know, separate.

516

:

And what you're gonna see is,

the volume of the hand raiser, M

517

:

Q L is gonna stay fairly steady.

518

:

The conversion rate is

gonna stay fairly steady.

519

:

and, you know, the C M O of

VP marketing won't be able

520

:

to, you know, pull any stunts.

521

:

To pull this up in the last day of

the quarter to hit his or her target.

522

:

part you actually can't change.

523

:

What they usually do is, hey, let's,

you know, flush some more money, into

524

:

ads and try and get some of those.

525

:

Lower value m qls and

overall hit the M Q L number.

526

:

And if you split those two things apart,

if you measure them differently, then I

527

:

think this M Q L thing is not a problem.

528

:

Honestly, I don't, I

don't think it's an issue.

529

:

and you know what's gonna come out of this

is that, gonna miss the demo and the trial

530

:

number that they should have been hitting.

531

:

and they're gonna, you know, fail there.

532

:

Sales is gonna fail there because of it.

533

:

And it doesn't matter how much

they're over hit on the whitepaper

534

:

downloads, is not going to drive

the same amount of revenue.

535

:

Right.

536

:

So, you know, having the ability to easily

split the funnel, that ability and setting

537

:

targets that then align with sales.

538

:

I think if you do that, VP sales and VP

marketing won't hate each other as much.

539

:

Every VP marketing C M O

knows this, by the way.

540

:

This is not a, oh, oops, we didn't

know all of them know that stuff, but

541

:

they're doing it because of, know,

C F O gives a top-down target five

542

:

K mql, that's what you need to do.

543

:

And then they're basically kind

of working around on the MQL

544

:

definition and then they get there.

545

:

I think that has something to do with it.

546

:

And for the sake of the organization,

split the two hand raiser non

547

:

handraiser pieces and then it

gets, usually gets like 95% better.

548

:

Justin Norris: What I have seen is

probably 80 to 90% of opportunities tend

549

:

to come from those hand raiser leads.

550

:

Is that your observation with

your client base as well?

551

:

Toni Hohlbein: Yeah.

552

:

it's hard to kind of put a specific

number on this, but it's, if you were

553

:

to say revenue, then Yeah, for sure.

554

:

because you can put your whole s d

r team, on scouring through all of

555

:

those, you know, non hand raisers

and you'll get some opportunities.

556

:

But just thinking about the sheer

amount of costly incurring on,

557

:

on that pile, it's insane, right?

558

:

And what I've seen in some

organizations and, we've been able

559

:

to, intervene there a little bit.

560

:

Was, massive, ebook,

download, drop, and then SDRs.

561

:

So there was inbound

SDRs flooded with leads.

562

:

didn't differentiate.

563

:

And basically what happened

is their conversion rate

564

:

on requests to opportunity.

565

:

It tanked.

566

:

What did it tank?

567

:

Well, they didn't get to call them

fast enough because we all know

568

:

that's of that journey, right?

569

:

'cause they're flooded with, uh,

downloads that didn't convert at all.

570

:

Right?

571

:

So it's like, I think you can get

opportunities from that staff.

572

:

I think it's just more

costly and more difficult.

573

:

I think in today's environment you

probably can't pay for that, right?

574

:

So probably it's not gonna work out.

575

:

So I would say that, I wouldn't discourage

anyone you those eBooks by the way, or

576

:

those webinars is a great way to build out

the top end of the funnel and so forth.

577

:

I think you just need to

treat them differently.

578

:

and maybe . an automatic fashion,

maybe in a retargeting fashion, maybe

579

:

you do something else instead of

putting your SDRs or inbound S Stss

580

:

or whatever kind of against that.

581

:

Justin Norris: what about

outbound thoughts there?

582

:

I know you know there's

the perennial debate.

583

:

Outbound is, dead, outbound is alive.

584

:

do, what do you think?

585

:

Toni Hohlbein: Yeah, I think outbound

is alive in kicking, and I think,

586

:

is a bit of an equal chamber in

terms of how outbound is that?

587

:

I think a lot of organizations

are extremely successful with

588

:

this, but yes, goalpost has been

changing in terms of outbound,

589

:

reason why people do outbound is

590

:

They basically kind of, okay,

you know, we've been pretty

591

:

successfully growing on Google.

592

:

there's nice demand.

593

:

We can capture that.

594

:

Great.

595

:

And now we're going, you know, depends

on LinkedIn or Meta or wherever you go.

596

:

And suddenly it's like, oh shit, you

know, this is not really working out.

597

:

What else can we do?

598

:

And then boom, you know, outbound pops

up, outbound and many of it can be

599

:

extremely, extremely successful, right?

600

:

And, and there are many

different places you can execute.

601

:

You can go completely cold into

the market, it's gonna get a

602

:

little bit more difficult with

AI and the content and so forth.

603

:

And I haven't figured this out either, so

don't get me wrong, but it still works.

604

:

I think what some other companies

are doing is . basically only,

605

:

this towards their, customer base.

606

:

So kind of one, one I can't name the,

brand, but they have a super P l g

607

:

super achieve entry product for like 10

bucks a month or something like this.

608

:

And they have thousands of those,

customers their sales team is only

609

:

working on those leads, quote unquote

it's actually customers, but only

610

:

on those leads in order then to

upsell them into their bigger tiers.

611

:

Right?

612

:

And there there are so many different

things where . Simply a phone call

613

:

or a, direct email or a LinkedIn or

something like that, where this is

614

:

just a channel that activates someone

differently than an ad, or than, you

615

:

know, a webinar invite and so forth.

616

:

So, of always saying Outbound

doesn't work or does work, just

617

:

think about does that channel of a

phone call, does that work for you?

618

:

And if the answer is yes, then

yeah, maybe you can probably

619

:

make outbound work for you.

620

:

Justin Norris: So thinking of it as

a delivery mechanism for a message.

621

:

Just like an advertising platform and

622

:

Toni Hohlbein: No, absolutely.

623

:

So

624

:

Justin Norris: to that?

625

:

Can you do it efficiently?

626

:

I think that

627

:

Toni Hohlbein: when, when I was

kind of plan day, we basically were

628

:

selling, tool to an SS m B audience,

a very non-digital SS m B audience.

629

:

unfortunately didn't get to execute

this, but we had like plans for radio

630

:

and we had plans for TV and, you

know, all of those things where, you

631

:

nowadays, B two B SAR is like, Ooh,

you, you know, shouldn't be doing this.

632

:

You can't do this.

633

:

but we had like customers sometimes

were 50, 60 year old, running a cafe or

634

:

running a restaurant, you know, or running

a kind of a small bat and breakfast.

635

:

they just don't wake up one morning and

be like, you know what, what I did for 40

636

:

years, On this, uh, blackboard with chalk.

637

:

I can do this tomorrow on my phone.

638

:

Right?

639

:

No one wakes up and is like, you

know, let me Google this thing.

640

:

it's also not like they're scrolling

LinkedIn, uh, or, or Instagram.

641

:

so how do you reach them?

642

:

Well, guess what?

643

:

They're listening to the radio all

day, so you know that might be the

644

:

channel to actually reach them.

645

:

Right.

646

:

And, and I think this is how people, you

know, much rather need to, you know, think

647

:

about all of these different channels and,

and the phone is just another channel.

648

:

that's, that's basically kind

of how I need to think about it.

649

:

Justin Norris: we did radio for S M B at

a past job and it was very successful.

650

:

So have to meet people where you are

the people that are like, you know,

651

:

marketers, selling to marketers that

spend all day in the same LinkedIn groups.

652

:

I.

653

:

Are lucky,

654

:

Toni Hohlbein: Yeah.

655

:

Justin Norris: advantage.

656

:

Not everybody has easy option.

657

:

I wanna get a little nerdy if we, can,

with platform and, how it actually works.

658

:

it starts with data model.

659

:

You know, like Lead M Q L opportunity.

660

:

What, however you think about that

Growblocks of prescriptive on what sort of

661

:

funnel you have or can you define any sort

of arbitrary ad hoc stages you want terms

662

:

of what your revenue engine looks like?

663

:

Toni Hohlbein: we actually started out

being super prescriptive because we need

664

:

to build out better use case basically.

665

:

now we're like, don't care.

666

:

We honestly don't care.

667

:

I mean, it's like, how many and

how few steps you want to have.

668

:

you call them SS, Q L M, Q L S A L

or SS Q and whatever, we don't care.

669

:

so this is completely configurable

from, the customer side.

670

:

then also, uh, dimensions that you want

to split this whole thing by kind of,

671

:

it might be different regions or markets

or segments or channels or . products

672

:

or initiatives, whatever it might be.

673

:

We actually don't care

about any of these things.

674

:

only thing that it requires is, you

know, if you can do a report on this,

675

:

we can pull it into, Growblocks, right?

676

:

, this conversation usually doesn't

happen like this with rev ops.

677

:

It's more like with the CROs.

678

:

It's like you guys don't have that data.

679

:

So no, we won't be able to split

it by that . But when you talk

680

:

to revenue operations, they know

what they have and then they also

681

:

know, we what we can pull in.

682

:

But as long as you already have a report

on this, we can split and add it as a, as

683

:

stage or split it into a dimension, right?

684

:

So this, this doesn't matter to us.

685

:

Justin Norris: This is

what is, hardest part.

686

:

I find, I mean, whether you're doing

this with and Looker, or in your

687

:

platform or anywhere, it's, you know,

the garbage in, garbage out problem

688

:

for example, one that you wouldn't

think would be so hard, but, uh,

689

:

qualification calls or discovery meetings.

690

:

You want to track that, but,

Sometimes a rep will book it through

691

:

Chili Piper, so you have an event

692

:

Toni Hohlbein: Mm-hmm.

693

:

Justin Norris: Salesforce

at the data level.

694

:

Sometimes they log it as a

call sales law for wherever.

695

:

So it looks a bit different there.

696

:

Sometimes they don't do it at all.

697

:

So you actually have a lot of, you

know, marketing's kinda lucky 'cause we,

698

:

digitize and automate a lot of things,

but when you're dealing with these people

699

:

who are not process it's a big struggle

I've found to get that consistency.

700

:

do you tackle that

701

:

Toni Hohlbein: so what we found is

it depends on the granularity that

702

:

you should be starting with, right?

703

:

So basically in this case where someone

struggles with that data point, we would

704

:

just say, Hey, let's, go one level up.

705

:

Let's exclude this step for now, or

let's exclude that data point, you

706

:

know, build out the overall model.

707

:

Maybe you only have six or seven steps,

maybe only of two or three dimensions and.

708

:

Let's stay here.

709

:

And then as you use this, and as you see,

you know, not only you on a theoretical

710

:

level, but also your leaders on a more

practical level, see the need increase

711

:

for that specific data point to actually

be more consistent going forward.

712

:

Then let's kind of pull it

into a platform and, you know,

713

:

this to the model and so forth.

714

:

Everyone thinks the data is shit.

715

:

Everyone like . All the time.

716

:

what we are seeing those that,

you know, that's true from

717

:

someone 2 million to 250 million.

718

:

So there's no difference there.

719

:

Everyone thinks the same thing.

720

:

in most cases, large amounts of . Staff is

actually fairly accurate, fairly correct.

721

:

especially when you aggregate,

especially if you kind of put it on

722

:

a cohort level instead of on a, you

know, individual level, uh, which is

723

:

actually enough in order to do the

modeling, the forecasting and so forth.

724

:

then over time as data maturity of the

organization, you know, increases maybe

725

:

because of Growblocks, maybe because

of 20,000 other reasons, by the way,

726

:

then just keep adding to the model

as you go and kind of build it out.

727

:

And the reason why you would wanna do

this is the more detail you add, the

728

:

more granularity you add, the more

information you will have on, okay,

729

:

here's something going wrong, right?

730

:

What we are seeing so, so many

times is you look at the revenue,

731

:

everything's kind of green.

732

:

You're kind of 10% behind on,

hitting this quarter, hitting next

733

:

quarter, whatever you want to at.

734

:

But if you drill in and double click

on this, suddenly you see two or three

735

:

areas that are like nicely green,

nicely shooting in the right direction.

736

:

But that one thing that's at 50%.

737

:

and you know, this is just one example.

738

:

Usually you kind of see it further

down and, and if, if you don't

739

:

have that granularity, if you

don't have those insights, you

740

:

would just never simply see it.

741

:

Right?

742

:

And in our cases, one of those

magic features, we can just

743

:

like it up for you, right?

744

:

Kind of.

745

:

You don't need to go and click around

and find this one thing, we can bring

746

:

it to your attention and be like,

Hey, here's something going wrong.

747

:

It's leading to X revenue impact,

and give you then the tools to say

748

:

like, okay, is this important for

me to prioritize this right now

749

:

with all the other shit going on?

750

:

or should I not?

751

:

Right?

752

:

And as you add this additional level of

detail, basically go deeper and deeper and

753

:

deeper and help you to find these things.

754

:

Justin Norris: I'll give a

personal example to, I guess

755

:

highlight what you're saying.

756

:

was maybe about 10 years ago.

757

:

I was working for, uh, company that

was selling SaaS to SS M B, but it

758

:

was very much like a product led

motion before that buzzword existed.

759

:

But it was, you know, selling

$300 e-comm purchases to SMBs, and

760

:

there was a drop in conversion.

761

:

nobody could explain.

762

:

folks looked into it,

you very tenaciously, and

763

:

eventually it emerged that.

764

:

SendGrid, which was sending our welcome

emails when somebody signed up for

765

:

the product, had stopped working.

766

:

And that apparently had a big impact

767

:

Toni Hohlbein: Yeah.

768

:

Justin Norris: So when you modeled

those two things against each other, you

769

:

could totally see how they correlated,

770

:

Toni Hohlbein: Yeah.

771

:

Justin Norris: you pulled that

out, so that was super interesting.

772

:

But it took, you know, many, wasn't

necessarily myself, but many hours of

773

:

people digging into that to find it.

774

:

can Growblocks help you model

those sort of peripheral things?

775

:

Like the, the email isn't necessarily a

funnel stage, but it's certainly something

776

:

that influences the funnel stage.

777

:

Does it capture that sort of thing

or is that kind of a bridge too

778

:

justin-norris_1_10-13-2023_090341: far?

779

:

Toni Hohlbein: What we are working on,

not towards the end of the year, but

780

:

kind of soon thereafter, is basically

something we call it a monitoring note.

781

:

think of as like you have a big, heavy

piece of machinery in real world.

782

:

And you wanna have a valve that tells

you, oh, the, the pressure is too high

783

:

or too low, or something like that.

784

:

You can just attach, it doesn't do

anything with the, you know, it still

785

:

produces the same shoes or whatever it's

producing, but you have this valve there.

786

:

and uh, what we are calling,

uh, monitoring notes.

787

:

And you can basically kind of pull

in all kinds of other things that

788

:

you feel are connected to that stage.

789

:

It will not influence the engine itself.

790

:

It will not be part of the model.

791

:

You basically can say like, well, if

this year going down, then there are

792

:

those other two, monitoring nodes that

might give you a clue where to look next.

793

:

Right.

794

:

And the real idea around

Growblocks and the modeling piece,

795

:

It's not in all cases to give you the

true end of the line root cause of, hey,

796

:

it's because the sales rep is currently

getting divorced is drunk all the time

797

:

that your conversion rate is dropping.

798

:

We won't be able to tell you that,

but we will be able to tell you

799

:

where to look specifically, right?

800

:

Kind of very quickly.

801

:

We can tell you pinpoint

here, you need to look.

802

:

. there's a piece of information that sits

behind the data that you need to dig

803

:

into, and sometimes it's like humans.

804

:

sometimes it's, it's other peripheral,

you know, tools that aren't working out.

805

:

can be sitting in your product.

806

:

You know, something isn't pinging

anymore, maybe something is broken, maybe

807

:

I mean, I've, I've did so many times.

808

:

The demo demo form is down

or the trial form is down.

809

:

I mean, all of those

different things, right?

810

:

that, might not be part of the overall

model, but you can, you know, later on

811

:

kind of adjust them as a monitoring note

and monitor specifically there, right?

812

:

And kind of then use this in

order to speed up your, root cause

813

:

analysis even further to jump on

it, fix it, and you're flying.

814

:

Justin Norris: We have observed that

something like speed to lead, for

815

:

example, how quickly we reach out

to a hand raiser has a big impact.

816

:

Is

817

:

Toni Hohlbein: Yeah.

818

:

Justin Norris: of something

you could track with a

819

:

Toni Hohlbein: Yeah.

820

:

Justin Norris: note in this way?

821

:

Toni Hohlbein: absolutely.

822

:

that would be super easy because it's

in the same, I mean, depends on the

823

:

setup, but it's in the same tool, right?

824

:

You, need to timestamp when the first call

happened or when the first touch actually

825

:

happened versus when the lead was created.

826

:

we are basically kind tracking

kind difference between those two.

827

:

Justin Norris: So we're talking about the

data piece so it sounds like you have the

828

:

ability to connect to different source

systems to pull all this data in together

829

:

Toni Hohlbein: right.

830

:

Yeah.

831

:

Justin Norris: and do some kind of, know,

big mashup, all that data to produce.

832

:

The, the funnel,

833

:

Toni Hohlbein: Yep.

834

:

Justin Norris: the modeling aspect, which

is what do you do with those inputs to

835

:

Toni Hohlbein: Mm-hmm.

836

:

Justin Norris: what's gonna happen

in the future, is the part that's

837

:

really hard, I think, to do for

a human or, or in a spreadsheet.

838

:

how does that work?

839

:

Is it, I hesitate to use the, the

AI word, but I'll, I'll use it.

840

:

Is it ai?

841

:

Is it machine learning?

842

:

Like what's going on in there?

843

:

Toni Hohlbein: it's pretty

sophisticated math and statistics.

844

:

I think we are starting to add a couple

of machine learning pieces to it, which

845

:

are, you know, yes you can label this

ai, but still very much explainable.

846

:

know, let's just say it like that.

847

:

and we want to keep it like

this as much as possible because

848

:

while it's sometimes it's

easy to, how does this work?

849

:

And you say AI and everyone's okay.

850

:

Okay.

851

:

I understand.

852

:

which means I don't understand anything,

but I'll just accept the answer.

853

:

our case, can actually explain why things

are happening, which is especially when

854

:

you need to trust something like this,

because this is basically the backbone

855

:

for your, you want to be able that

it's not just some random AI thing.

856

:

It's like, oh, you know

that . never behaved like that.

857

:

Right?

858

:

I don't want to be sitting on a

customer call and be like, saying that.

859

:

It's like, oh, you know,

I think the AI went crazy.

860

:

so kind of keeping it, as

explainable as possible.

861

:

But, what we are adding is, um, series

machine learning, and a of those

862

:

things that are, on the, call it the

leaf node, on the leaf node level.

863

:

It's basically kind of adding some

additional intelligence on top right.

864

:

But Jenny speaking, how modeling

works is, in layman terms, we

865

:

would call it the revenue formula.

866

:

and the revenue formula in

simple words is, you know, in

867

:

this case opportunity creation.

868

:

Then there's a conversion rate to it.

869

:

There's a time delay to it,

which is usually a distribution.

870

:

It's not like everything

closes in 45 days, but 10%

871

:

closes in 10 days and so forth.

872

:

and then there's an a C V.

873

:

the revenue piece.

874

:

And then you have, you know, one

revenue, one opportunities, right?

875

:

And that idea of conversions

and time delay, that is true

876

:

throughout your full funnel.

877

:

usually a mix of both.

878

:

and then the a CVPs, especially on

customer side and, you know, As you use

879

:

these things kind of going back and forth,

that is basically the glue that, you

880

:

know, glues together the different, right?

881

:

And that's where all of those dimensions

are so important, because again, if

882

:

your dimension is hand raiser versus non

hand raiser, the conversion rate from

883

:

that to the next stage is much higher.

884

:

The speed from that to the

next stage is much higher.

885

:

And then, you know those opportunities

coming from hand raisers.

886

:

they convert much faster to a lot more

revenue than the other stuff, right?

887

:

So you wanna start slicing and dicing

all of that stuff down to accurately,

888

:

you know, use all of the data in

those, in those customer journeys in

889

:

order to do as accurate as possible.

890

:

You know, forecasting

coming out of this, right?

891

:

And this is really where, just gets.

892

:

Theoretically you can do any, I mean,

you can build Salesforce in Excel, right?

893

:

I mean this is where, where

it becomes nifty in Excel.

894

:

But when you then add on top, you know,

this revenue impact and you know where

895

:

isolated and shared and you know, all of

that stuff, then it, it becomes something

896

:

where you probably want to have a tool

instead of using this in, in spreadsheets.

897

:

Justin Norris: So that's of where I

was gonna go because when you think

898

:

about As I said, I, I really don't

like doing this in spreadsheets.

899

:

So for me, the just efficiency benefit

of having a tool to wrap around the

900

:

process, is an inherent attraction for me.

901

:

There's probably many people,

like you said, that are like,

902

:

I'm doing it in spreadsheets.

903

:

It's okay.

904

:

I don't need to buy a tool

to solve that problem you.

905

:

do you lean more on the benefit of yes.

906

:

But now we'll give you insights

that allow you to intervene and

907

:

make changes that then produce

a significant revenue outcome?

908

:

Like, is that how you about

909

:

Toni Hohlbein: So we.

910

:

So we, is kind of the value

prop, if you will, right?

911

:

I think the value prop is two things.

912

:

One is . a less choppy quarter results.

913

:

So if you're like very much sales

forecasting driven, if this is

914

:

the way you see the future, you're

overly focusing on new business.

915

:

You're overly focusing on

the end of the quarter.

916

:

You're overly focusing on, you know, the

magic of the rep, of the whole machinery.

917

:

Right?

918

:

So it's kind widen your view

from this to the full bow tie.

919

:

You get more predictability, period.

920

:

So I know this, we've proven this a couple

of times and what that predictably then

921

:

kind of enables you to do is you are

more confident in your decision making.

922

:

You can be more efficient

in, you know, moving resource

923

:

allocation around and so forth.

924

:

Right?

925

:

And then the other piece is really

the . We can show you stuff that he

926

:

otherwise wouldn't have seen, period.

927

:

It's not about, oh, you know, you could

have done this Q B R, but you were too

928

:

lazy and therefore you didn't see it.

929

:

Or you could have seen this

in this Excel spreadsheet.

930

:

No, there's literally so much

stuff that in Excel spreadsheet

931

:

world, you usually limited it.

932

:

To like two dimensions,

maybe three dimensions down.

933

:

And then, you know, we, we tried, by the

way, we, the first year, this is how we

934

:

deliver to customers and spreadsheets.

935

:

And it's like, it didn't work.

936

:

in a tool, we don't care how many,

uh, dimensions he had, how many

937

:

cuts he add to this whole thing.

938

:

And, you know, because of

that depth, we can see very

939

:

granularly what's going wrong.

940

:

In some corner, we can bubble it

up all the way to your attention.

941

:

We can say, Hey, this is gonna, if

you don't fix this, this is a $1

942

:

million problem by the end of the year.

943

:

And no Excel spreadsheet.

944

:

Can't do that for you, by the

way, writing of really having the

945

:

predictability leading to efficiency.

946

:

And then the, we can show you stuff

that P receive was invisible for you.

947

:

And those are the two main value props

where we're kind of angling our return

948

:

of investment basically around, right?

949

:

Whether.

950

:

You can have a whole other

conversation around r o I.

951

:

those are the two value props

the C F O goes like, okay, I

952

:

can, I can fucking see this.

953

:

Same with the C R O, right?

954

:

Usually, by the way, I'm not

sure who's listening most of

955

:

the time, revenue operations.

956

:

Still surprising for them.

957

:

These, these words are too fluffy.

958

:

They don't really kind of

fully get it sometimes.

959

:

What revenue operations always

kind of, you know, huddles around.

960

:

It's like, oh, visibility and

it's gonna make my life easier.

961

:

Guess what?

962

:

You know, your C F O doesn't care about

your life being easier, you know, , it's,

963

:

Justin Norris: They do not

964

:

Toni Hohlbein: they, they

don't care about this.

965

:

Right.

966

:

And, um, uh, and you know, that's also

sometimes what we are seeing when we, when

967

:

we don't get to talk to the CFO or the

c o, rev ops stumbles in trying to pitch

968

:

this upward because they kind of say like,

well, I can see the whole revenue engine

969

:

and, and it's gonna save me so much time.

970

:

And everyone goes like,

okay, you know, next.

971

:

Right.

972

:

And, and that's why it's

kind of really important.

973

:

It's, really around predictability,

producing efficiency, fluffy words.

974

:

I get it.

975

:

But, you know, I think I explain it now

a little bit and then seeing stuff that

976

:

you otherwise simply couldn't be seeing.

977

:

Justin Norris: that makes perfect sense.

978

:

I think the.

979

:

The efficiency play is, wonderful,

but it's not what's going to move

980

:

the needle at the executive level.

981

:

maybe last question that we have

time for, just in terms of vision

982

:

kind this product will develop.

983

:

Like what do you, what you want

to achieve, whatever you're

984

:

comfortable sharing, where

where do you see this going?

985

:

Toni Hohlbein: So I think, what we

are building here is, and I'm, you

986

:

know, usually not allowed to use that

word, but we are building a digital

987

:

twin of your commercial organization.

988

:

That's what we're basically doing.

989

:

and currently we're focusing on

the revenue part, modeling this

990

:

out, on the team and initiative

and regional level, kind of, you

991

:

know, doing the, the overall stuff.

992

:

No one has done this.

993

:

There's no one else.

994

:

this is what we're focusing on.

995

:

That's kind of what the starting point is.

996

:

then we really have two

additional directions.

997

:

One is, further into the people side.

998

:

It's really kind of doing the

people understanding of what are

999

:

they doing, how does it work?

:

00:43:01,804 --> 00:43:03,904

Who is behind versus

who's not, and so forth.

:

00:43:03,904 --> 00:43:04,184

Right.

:

00:43:04,184 --> 00:43:08,224

There's a, there's a whole other chapter

to unpack, which you have some competitors

:

00:43:08,224 --> 00:43:09,904

obviously doing, being very strong there.

:

00:43:10,244 --> 00:43:13,584

And then the other piece is going

much deeper into the cost side, which

:

00:43:13,904 --> 00:43:17,897

probably is going to be, uh, of our next

steps, order to really drive question

:

00:43:17,897 --> 00:43:19,737

on what is my most efficient channel.

:

00:43:20,032 --> 00:43:21,552

Where am I getting my most money?

:

00:43:21,652 --> 00:43:23,899

You know, if I hired

this, what would happen?

:

00:43:23,919 --> 00:43:25,539

You know, how much money do I get back?

:

00:43:25,559 --> 00:43:26,179

And so forth.

:

00:43:26,336 --> 00:43:29,845

so it's really, defining and

explaining and replicating.

:

00:43:30,410 --> 00:43:33,850

commercial or the revenue, organization

that you're having, and, you know,

:

00:43:33,850 --> 00:43:37,010

in multiple different dimensions

then instead of only on the,

:

00:43:37,617 --> 00:43:38,657

modeling that we're currently doing.

:

00:43:38,657 --> 00:43:38,897

Right.

:

00:43:38,997 --> 00:43:42,711

So, once you go to that level,

It's a pretty insane fucking

:

00:43:42,781 --> 00:43:43,951

tool at that point, right?

:

00:43:43,951 --> 00:43:46,831

Because you sure you can use it for

revenue planning and, you know, some

:

00:43:46,831 --> 00:43:47,991

of the cost planning around there.

:

00:43:48,031 --> 00:43:50,431

I don't think we will ever be

the planning tool, by the way.

:

00:43:50,471 --> 00:43:51,151

I don't believe that.

:

00:43:51,448 --> 00:43:54,758

but, know, just the power of insights

and how you could run through

:

00:43:54,998 --> 00:43:58,051

scenarios and check things out

and, and, and expectations of, you

:

00:43:58,051 --> 00:44:00,731

know, how things should be going

versus what's happening in reality.

:

00:44:01,451 --> 00:44:04,771

I think this is a problem from

like a $1 million organization up

:

00:44:04,771 --> 00:44:06,660

to . $20 billion organizations.

:

00:44:06,740 --> 00:44:08,982

I mean, I talked to Salesforce

and you know, they're running

:

00:44:08,982 --> 00:44:10,022

everything in spreadsheets.

:

00:44:10,042 --> 00:44:11,126

And, talk to HubSpot.

:

00:44:11,300 --> 00:44:15,060

have this, Hey, we have a forecast

for customers, for pipeline, for

:

00:44:15,060 --> 00:44:18,660

sales, and, and they have that per

region, per product, per segment.

:

00:44:18,660 --> 00:44:21,580

They're literally running like a thousand

spreadsheets or something like this.

:

00:44:22,074 --> 00:44:24,004

It's like, wouldn't you like

to connect all of that stuff?

:

00:44:24,004 --> 00:44:25,604

It's like, yes, I would

really like that actually.

:

00:44:25,604 --> 00:44:28,444

But it's, you know, I'll probably

not sell to HubSpot tomorrow, but,

:

00:44:28,484 --> 00:44:31,604

mean, this is a problem that kind

of is, is there across, right.

:

00:44:32,053 --> 00:44:34,953

and especially if you then get on top

some of the costs and then the people

:

00:44:34,953 --> 00:44:36,313

piece is going deeper into those.

:

00:44:36,433 --> 00:44:39,230

I mean, think it's gonna be pretty nuts,

but you know, we need to go there first.

:

00:44:39,478 --> 00:44:40,448

Justin Norris: Hey, super exciting.

:

00:44:40,855 --> 00:44:43,028

a fan of what you're doing

gonna watch it closely.

:

00:44:43,348 --> 00:44:45,388

I really appreciate you taking

the time to chat with me.

:

00:44:45,388 --> 00:44:46,761

Tony, for joining us.

:

00:44:47,123 --> 00:44:49,523

Toni Hohlbein: for having me, uh,

once again and, uh, hope it was

:

00:44:49,943 --> 00:44:51,079

useful for everyone listening.

Next Episode All Episodes Previous Episode

Listen for free

Show artwork for RevOps FM

About the Podcast

RevOps FM
Thinking out loud about RevOps and go-to-market strategy.
This podcast is your weekly masterclass on becoming a better revenue operator. We challenge conventional wisdom and dig into what actually works for building predictable revenue at scale.

For show notes and extra resources, visit https://revops.fm/show

Key topics include: marketing technology, sales technology, marketing operations, sales operations, process optimization, team structure, planning, reporting, forecasting, workflow automation, and GTM strategy.

About your host

Profile picture for Justin Norris

Justin Norris

Justin has over 15 years as a marketing, operations, and GTM professional.

He's worked almost exclusively at startups, including a successful exit. As an operations consultant, he's been a trusted partner to numerous SaaS "unicorns" and Fortune 500s.