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Is Marketing Mix Modelling the future of B2B analytics? - Mark Stouse
Episode 2419th March 2024 • RevOps FM • Justin Norris
00:00:00 00:47:44

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Every marketing team  wants attribution. But weirdly, it's often not that satisfying when they actually get it.

I led many multi-touch attribution projects as a consultant, and we got really good at implementing tools, creating taxonomies, and making sure that data was clean.

But I found that when you actually showed these reports to a C-level executive, it was usually kind of underwhelming. The data didn't always pass the common sense test.

Today's guest thinks there's a better way — Marketing Mix Modelling. It's basically the application of mathematical techniques to model relationships between different variables.

However, technology now enables it to happen faster and more cost-effectively than ever before.

Thanks to Our Sponsor

Many thanks to the sponsor of this episode - Knak.

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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.

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About Today's Guest

Mark Stouse is CEO of ProofAnalytics.AI. With over 26 years of experience in marketing communications and strategy, he has a passion for transforming GTM performance with data-driven insights and agile decision making. Prior to founding Proof, Mark was CMO at Honeywell Aerospace, CCO at BMC Software, and a marketing leader at Hewlett Packard Enterprise.

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

Key Topics

  • [00:00] - Introduction
  • [01:15] - Clarifying the acronym “MMM”
  • [02:39] - Mark’s background and how he founded Proof Analytics
  • [07:57] - Limitations of multi-touch attribution (“MTA”)
  • [14:16] - How MMM avoids the shortcomings of MTA
  • [16:42] - The Fischer Price definition of MMM
  • [19:56] - Demand vs. brand investments and their impact
  • [24:09] - A/B vs. multivariate regression
  • [25:21] - MMM is aggregate modelling, no reliance on PII
  • [27:12] - Simple explanation of multi-variate regression
  • [30:29] - Incorporating third-party data sources
  • [31:48] - Historical ROI vs. forecasted ROI
  • [32:52] - Is MMM just for enterprise?
  • [34:51] - Marketing as a non-linear multiplier
  • [38:02] - Getting started with MMM
  • [41:18] - Updating models to include new data sources
  • [42:07] - Competition in the marketing analytics space
  • [44:41] - B2C marketing is more advanced in usage of multi-variate regression

Resource Links

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Transcripts

Justin Norris:

Every marketing team wants attribution.

2

:

But the weird thing is that it's often not

that satisfying when they actually get it.

3

:

I did a lot of multi-touch attribution

projects as a consultant, and we got

4

:

really good at implementing these

tools, technically creating taxonomies,

5

:

making sure that data was clean.

6

:

But I found that when you actually show

these reports to a c-level executive,

7

:

it can be kind of underwhelming.

8

:

They don't always pass

the common sense test.

9

:

People want to pick on details, and maybe

that's because the idea of dividing up an

10

:

opportunity like a pizza between different

touch points isn't actually the way.

11

:

Maybe that's not going to

give us the answers we need,

12

:

but where does that leave us?

13

:

Because we still need to know what's

working in marketing, and today's guest

14

:

thinks there's a better way, and he's

founded a company to make marketing mix

15

:

modeling or MMM available to B2B marketers

and he's gonna tell us all about it.

16

:

So I'm super excited to

welcome Mark Stouse, CEO of

17

:

proof analytics to the show.

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:

Thanks a lot for being here, mark

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:

Mark Stouse: Hey, thank you so much.

20

:

Justin Norris: Mark, maybe

one clarifying question.

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:

I've seen MMM, spelled out as media mix

modeling and marketing mix modeling, which

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:

is the right way from your point of view?

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:

Mark Stouse: actually.

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:

It really represents the evolution of

it over the last say, 40 to 45 years.

25

:

Back when Procter and Gamble first brought

out what was then called econometric, I.

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Analysis it was advertising.

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:

that's what it was really all about,

hence the media mix modeling reference.

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As, time moved on channels proliferated,

it became still very much within

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kind of B2C a reference point.

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It became marketing mix modeling.

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:

Today it is really go to market mix

modeling because it includes not

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just marketing data and channels and

investments and all that kinda stuff, but.

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Sales and customer success and product

data outside data externalities, you

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know, the economy, your competitor

actions, really is today much

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broader canvas as it should be.

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:

Right?

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That's the long and the short of why

people say that MMM means media mix

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modeling or marketing mix modeling.

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:

And actually both of 'em are

kind of A little bit passe today

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:

Justin Norris: Maybe let's take a

step back before that and talk about

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:

what was your professional experience

leading up to founding proof analytics

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:

and what brought you to this direction?

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I.

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Mark Stouse: So I was actually pretty

much a classic marketer and communicator.

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I've worked across all of the different

subsets of marketing at one time or

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another, and I've been a large company,

CMO, . About a little less than 20

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:

years ago I was at HP and we were all

kind of in the middle of an existential

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crisis because the then CEO of hp,

mark Hurd was a very Operations focused

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and a very customer focused CEO.

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And he wanted to know why there wasn't

more evidence of what marketing was

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actually delivering to the company.

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It was actually incredibly unpleasant.

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And about the only good thing that I can

say about that whole experience that I had

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and that other peers of mine had was that

it was, was highly motivating to change.

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I kind of got to a point where I

said to myself, look, I either have

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to do something to fix this or I

just need to like go do something

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else, 'cause it's not just about

budget issues and all that kinda stuff.

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It's about credibility,

am I actually doing here?

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So in my particular case for whatever

reason, herd gave me a project.

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To actually see what could

be done to resolve this.

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And I didn't know Jack at

that time about analytics.

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I was not even a math enthusiast,

and and so I, I remember I went

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home that next Friday I went

through all the stuff in my garage.

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. And found a couple of old math

textbooks from university and started

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to read them in ways that I never

read them when I was in school.

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' cause I was actively seeking an answer

and all of a sudden I got to this and it

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was all about multi-variable regression.

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Which is absolutely the cornerstone of

causal analytics ? It is the cornerstone

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of the scientific method of inquiry.

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It has so much credibility that,

it'd be hard to have more credibility

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than multi-variable regression has.

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Not because it is perfect, but it

is absolutely the best that we have.

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And today, is actually

the bedrock of causal ai.

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So I started working on this project

for Herd, and he liked it a lot.

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And I guess my prize was he

set up a mentoring session.

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With the CFO of hp, Bob Leman.

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The whole idea was, I want you guys to

talk you from your end and he from his end

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and see if we can't get to a meeting of

the mines between marketing and finance.

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This whole experience it just changed me.

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And before he passed away, I had

the opportunity to talk to Mark

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again and thank him for it, right.

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Because it was, not fun at all,

but he did me the hugest favor.

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So I started climbing the

analytics ladder, right?

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The stairway to heaven, so to speak.

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Right?

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By the time I was CMO at Honeywell

aerospace, pre any kind of

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automation for analytics, right?

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meant we had to hire a ton of people

in order to get the latency on the

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recalculation down, the scalability up

the cost be damned at this point, right?

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'cause it was so important.

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And get the understandability of

the outputs to the point where the

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business goes, yeah, I not only get

it and believe it, but I can make a

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better decision today than I could

make before as a result of this.

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:

And then you kind of say, there's not very

many companies that are willing to invest

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seven, eight, $9 million a year just in

marketing analytics particularly in B2B.

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That point you start to realize

that, that automation was gonna

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be absolutely indispensable.

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To solving the underlying root problems,

if Data science has has a number

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of major problems in the business

context, but they can kind of be summed

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up by the fact that it's too slow.

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So by the time they get you the

insights, everything has moved on,

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you've already had to make the decision.

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The information is not comprehensible

many times by normal people.

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it's not scalable.

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So you end up with three or four

mega models that get updated

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once, maybe twice a year.

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And so not agile, right?

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But big opportunity to appropriately

automate and bring ai into that

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whole thing and really elevate

all of this to a new level of

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accessibility and meaningfulness.

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And so that's why we built proof.

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Justin Norris: And so I want to

contrast MMM and regression based

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analytics that you're describing with.

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:

What most B2B marketers are

probably familiar with, which is

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:

multi-touch attribution, that's what

pretty much everybody does today.

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:

What is the problem with this form of

attribution from your point of view?

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Or is there a problem?

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Is it still okay?

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Can they be complimentary?

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Mark Stouse: as long as it's accurate

data, there's absolutely nothing wrong

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with having a really good understanding

of the customer journey, patterns in the

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customer journey, things like that, right?

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:

And indeed, a lot of our customers

will include customer journey

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data in regression models.

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It's valuable.

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:

The problem is that it is not fit for

purpose for what it's being sold to do.

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:

We can just start with the idea of bias.

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There's a lot of bias in that data.

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There's a lot of bias in

the weightings of that data.

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So marketing teams set their

own weightings in advance.

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:

It is antithetical to the mathematical

principles, to say that you can

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:

use MTA data to optimize anything.

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Particularly spend

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Justin Norris: I just want to

unpack that to understand why

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Mark Stouse: mTA data is an effect.

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You're measuring an effect.

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not capturing.

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The cause of that.

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And more specifically,

the time lagged cause.

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So how are you going to optimize the

money that you spent the past at this

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:

point, based on what you think you're

seeing from effects in the present when

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you have no idea what the time lag is?

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Right?

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So there is this assumption,

that the time lag is near zero,

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but that's just so not the case.

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:

Mm-hmm.

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I B2B, the time lags can be extensive.

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What do I mean by extensive?

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Two, three quarters.

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we get into brand investments, it's

double that, exactly are you optimizing

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:

and how do you know which part of your

budget back in time you needed to optimize

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:

based on this effect in the present?

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:

Right?

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:

Justin Norris: If we take an MTA

scenario, just to make it like a

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:

little bit more clear, let's say

we have an opportunity and we have

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three people in the buying committee

that are part of that opportunity.

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And between those three people,

there were sacred brown numbers, 30

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different touch points that they had

with different marketing initiatives.

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:

And those could be like, they

downloaded eBooks, they attended

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webinars, they clicked on ads.

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And so the, traditional way is like,

we're gonna take those 30 touchpoints

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and we're gonna take that opportunity.

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Let's say it's a 3 million opportunity,

and we're just gonna divvy it up.

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And the way that we

divvy it up could vary.

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We may pick some touch points, is being

more influential or not influential?

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:

And I totally agree with you there,

that it's kind of like arbitrary that

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:

they give you different models that

you can choose from and it's like, pick

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the one you want, like on what basis?

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:

Mark Stouse: I don't think

that marketers understand how

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:

transparently untrue the premise of

MTA is to everyone else but them.

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Justin Norris: I wanna get to the

reality of the situation because this

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is the, the industry that we're in.

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Mark Stouse: The reason why it

went through adoption is that most.

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Marketers don't have enough knowledge

of math to be able to recognize

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when something doesn't work.

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And number two, it was presented to

them as a way of mining data that they

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:

already were generating in their stacks.

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So it was easy in quotes, right.

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To do this.

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And I've been around it long enough

and talked to enough marketers about

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it, everybody was shocked when they

went into their first meeting with

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MTA data, Thinking that they were

just gonna be greeted as conquerors

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:

and victors and to your earlier

point at the top of the show, right.

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It was anything but that.

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it was very innervating

for a lot of marketers.

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You can actually do it in

Excel if you only have to do

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it on a very limited basis.

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But if B2B would just adopt B2C analytics

and also I would just add market research

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priorities they would see a very rapid

transformation of their credibility and

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their precision and everything else.

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And.

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can always modify it as needed,

but the core principle is ready to

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go and has been a very long time.

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Justin Norris: So if I recap your

perspective as I understand it, touch

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points, the customer journey data,

what the person did and when that can

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be interesting, that can be valuable.

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The notion of then doling out credit

to those touch points for revenue

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outcomes, not mathematically,

statistically defensible.

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Mark Stouse: would just add this, right?

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MTA at the end of the day

is about pattern matching.

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It's about identifying large

scale, repeating patterns

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in the customer journey.

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And then the assumption is that if a

bunch of people are doing the same thing

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over and over again, it must be causal.

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That is a complete fallacy, the

same math is used for example,

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and in fact, pattern matching too

is used to study climate change.

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Some are causal, some are

absolutely not causal.

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They happen all the time,

but they don't mean anything.

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You can't just assume that

because, a thousand people do it.

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Okay.

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One of your channels, one of your feedback

loops, you know, it just keeps happening

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that ipso facto means that it's causal.

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Justin Norris: I've always had this

unease as well where you a great

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example from earlier in my career,

we had a self-serve SaaS app, what

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would be called product led today.

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And everybody that signed up for

that app got a welcome email.

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So from that point of view, if you're

looking at touchpoints, like the

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welcome email is very influential.

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It's very important but,

but everybody got it.

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So just because people got it and

some people opened it doesn't mean you

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could necessarily infer that that was

driving the outcome in a particular way,

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. So everything we've been discussing

is kinda like the current state.

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Let's contrast it now with MMM and

why does it not have these different

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shortcomings that we've described?

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Mark Stouse: Number one, let's start with

the shortcomings that it does have, right?

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It is very dependent, as is

every analytic of any kind.

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Very dependent on data quality, There's

that old saying about gigo, right?

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Garbage in, garbage out.

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Everyone is going to be

victimized by gigo equally.

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Beyond that though.

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It has a lot of major advantages.

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It is a lean data mathematical process.

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So you do not have to have big data

or even a lot of lean data order

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to run these models accurately.

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And that is actually a huge factor.

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One of the things that CDOs chief

digital officers or data officers are

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grappling with right now on private

LLMs is that they big data stocks.

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They don't have enough training

data and they don't have enough

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operational data to run private LLMs.

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Everyone's been so understandably

fascinated with what they can do

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with public LLMs that they haven't

really thought it through in terms

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of the limitations on private stuff.

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Right?

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regression does not have this problem.

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If you say, oh, I don't have enough

data to run regression analytics.

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not even remotely able

to do any kind of ai.

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And I think that a lot of c-suites

now Are starting to say, your

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creation and maintenance of a high

quality data pool that's relevant to

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your function is a core competency.

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And if you tell us that you don't have

the right data, you don't trust your

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data that's on you, The other thing is

you have to have some basic capability

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on the human side of the equation.

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So with proof, for example, we have

simplified the whole thing, dramatically.

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Automated it significantly

in the right places.

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you don't need a full-blown

data scientist to run it.

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You need a competent data

analyst and you really don't

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even need that person full time.

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That's probably a half FTE, right?

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Justin Norris: Could we give a

Fisher Price version definition of

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what MMM is like, just so people

can conceptualize it in their minds.

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Mark Stouse: we live in a multi-variable

world there's tons of potential

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causes everything that you control

and everything that you don't control.

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That's kind of a really

super easy way to bucket it.

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And these all have interactions with

each other across time and space

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that produce particular outcomes.

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Causally speaking this is a

probabilistic calculation.

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So outside of certain physical laws like

gravity, There is no deterministic outcome

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that's possible to determine, right?

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You'd have to know everything

that there is to know about what

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contributes to a particular outcome

to get to a deterministic answer.

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That's just not the way that

operates, particularly when you're

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talking about human behavior.

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So this is the same math that's

being used routinely to study climate

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change, to study epidemiology.

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To study economics, it captures time lag.

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So in the end, the report will tell

you historically the stack rank

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of everything that you control and

don't control, that's in the model.

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It's relative effect on this outcome.

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It will then forecast all that

into the future so that you can

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then make different choices.

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And because of automation,

we've sped that up to the point

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where you can run multi-variable

regression exactly like GPS.

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say that you're recalculating the model

every week, new data comes in and is

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presented to the model automatically,

it automatically recalculates.

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And you see how the present now

is comparing with the forecast

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that you have for the same period.

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if there's a growing delta, right?

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You see, hey, okay, I

need to make some changes.

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Stick with the GPS analogy.

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need to reroute.

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Or man, this is great.

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It's just totally tracking.

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And it will tell you how long, longer

it's going to take for you to reach your

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objective, your goal, your destination.

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So there's a countdown.

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Regression is a huge part of

the actual GPS that's on your

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phone, you use every day.

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And if you stop and think about

it for a second, most business

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questions, and indeed a lot of life

questions are navigation questions.

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Where am I?

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Where do I want to go?

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What's the best way to get there?

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I have enough time and resources

to achieve my destination?

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Am I gonna run outta

gas before I get there?

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I gonna run out of time

before I get there?

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Am if I have to be there at

nine o'clock for a meeting?

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And the GPS says, you're

not getting there until 11.

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gonna have to make some

choices, that's part of it.

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also captures all the headwinds

and tailwinds that may be speeding

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you up or slowing you down.

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So in many ways, it it gives you

the answer to your questions.

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Justin Norris: Take a practical example

along the lines of one of those questions,

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let's say a company spends a hundred

thousand dollars a month on display

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ads as an act of faith because those

can be notoriously difficult to track.

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They're not always resulting

in a click, but they could have

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impressions that could have an impact.

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So they're spending that a hundred

K month over month, and the CFO

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challenges the CMO and says, why.

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Are we spending this 100 K every month?

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What's it doing?

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And that's the question

the CMOs trying to answer.

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How would MMM, like what does it compare

that spend versus a particular outcome?

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And try to show a causal

relationship between them.

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Mark Stouse: yeah.

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Multiple variables, right?

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So it's highly context oriented.

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All of these models are.

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Seek to capture as much of

a known context as possible.

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what a lot of marketing teams figure

out using exactly that scenario,

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is they've been trying to justify

that display ad budget in terms of

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demand gen, in terms of, performance

marketing, And that is just not the

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way that that typically plays at all.

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That is a brand reputation investment.

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know, you kind of think about

marketing expense as being

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essentially two big chunks, right?

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Branded demand brand is easily two to

three times time lagged in its effect.

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Than demand is.

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It also sticks around a lot longer.

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The effects of brand investment

doesn't deteriorate very quickly

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unless there is a major scandal of some

sort, something like that, that all

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of a sudden breaches the trust wall.

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But absent that right, it hangs

around The halflife is quite extended,

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whereas the Halflife on demand

investment is highly perishable.

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Lasts maybe a couple months and

then it's gone, so the snows melt

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a lot faster with demand than brand.

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You have to then say to finance,

never discussed time lag with you

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:

before specifically, but we all know.

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That marketing takes

time to have an effect.

349

:

That simple statement is insufficient

because if we don't know the

350

:

time lag, we will never know.

351

:

The ROI, so we now have the ability

to say that this investment over

352

:

here doesn't really drive demand,

doesn't really pay off on this side.

353

:

There isn't a quick return a demand

perspective, but from a brand perspective,

354

:

this is how it is improving average

deal size and average deal velocity.

355

:

Those are the two big ones that we see

again and again on brand investment.

356

:

It is grease on the wheel of the deal.

357

:

That's what brand

reputation really is, right?

358

:

It makes people buy more than

they otherwise would and buy

359

:

faster than they otherwise would.

360

:

that That is key in B2B because

it's a higher cost, higher

361

:

risk by decision to begin with.

362

:

And then today, if we layer in all the

risk factors that people are worried

363

:

about it's even more important,?

364

:

I mean, If you really want to understand

how trusted you are by your customer

365

:

base, look at your average deal velocity.

366

:

If you really want to understand how

confident people are in your product's

367

:

ability to generate massive amounts of

value for them, look at their average

368

:

deal size, particularly year one, right?

369

:

Certainly year two, your, a

lot of people, just as a matter

370

:

principle today, are buying small

in year one and testing it out.

371

:

If you see a major uptick in

year two and deal size, that is a

372

:

major vote of confidence in you.

373

:

you don't, you need to

really find out why that is.

374

:

'cause I guarantee there's a reason.

375

:

Justin Norris: Looking at our

example, let's say the dataset that

376

:

you had, the company had always

been running those display ads.

377

:

Do you need a period where those display

ads are not there in order to demonstrate

378

:

the impact of having them or not?

379

:

Or is there a way, even though they've

always been there, to somehow demonstrate

380

:

the impact that they're having?

381

:

Mark Stouse: I think that the answer to

that question is very much contextual.

382

:

if we were talking about a correlation

analysis, meaning this ad buy right

383

:

against revenue, Then yeah, you would

definitely need some interruptions

384

:

to be able to essentially create a

385

:

Justin Norris: Oh, control, that's

the word I was looking for too.

386

:

Yep.

387

:

Mark Stouse: If you're talking about

multi-variable though, where a lot of

388

:

things are changing all the time around

it, it's not necessary that's actually

389

:

one of the things that's really, really

cool about MVR multi-variable regression

390

:

is that because you're bringing in so

many different variables and because

391

:

everything changes, particularly the

stuff that you don't control you're gonna

392

:

see exactly what you're talking about.

393

:

You're gonna see implicit AB test.

394

:

Evolving across time.

395

:

Justin Norris: We're

talking about limitations.

396

:

And one of the things we're talking

about was being an aggregate

397

:

versus tracking individual users.

398

:

Mark Stouse: Yeah, so one of the things

that's really important to say about

399

:

multivariable regression slash go to

market, mixed modeling, et cetera, right?

400

:

Is that it is looking at all the

causal relationships in aggregate.

401

:

So it's not possible to say that all

these things cause this one identified

402

:

person to take the steps that they took.

403

:

That's not what go to market

mix modeling is all about.

404

:

It's about looking at a population wide

trend that's causal for a lot of people

405

:

one of the things that companies really

appreciate though about this is that.

406

:

There's no PII in proof

or in, a regression model.

407

:

So you don't have the security

concerns that you would have in

408

:

MTA or any of the other touch based

approaches, which are trying to tie.

409

:

Broad investments, programmatic

investments to impact on a particular

410

:

human being or particular company.

411

:

And to do that, you have to

pierce the veil on identity.

412

:

And that's a problem.

413

:

Justin Norris: So it's

looking at the big picture.

414

:

So cookies, not an issue,

GDPR, not an issue.

415

:

It's really comparing

bulk data sets and the

416

:

Mark Stouse: In fact, the only GDPR

thing that we ever have to encounter is

417

:

the login information for users of proof.

418

:

So we keep that secured, But in

terms of the data that's in the

419

:

computations, it's a non factor.

420

:

It's just non issue.

421

:

Justin Norris: Is there a way to

explain that multi-variate regression

422

:

to someone who maybe they've got high

school math, first year university

423

:

math, 'cause maybe that's a challenge.

424

:

As you said, marketers, some of them

are more quantitatively oriented.

425

:

Not all of them are.

426

:

And they're gonna need to trust,

this data, so they'll need to be able

427

:

to make sense of how it's produced.

428

:

You know, If it's just a kind

of computer and it spits out an

429

:

answer like, yep, do X or do Y.

430

:

Mark Stouse: They're already effectively

thinking in this way because they're

431

:

spreading out their investments

across all kinds of different

432

:

channels, So they're acknowledging the

multi-variable world in which they live.

433

:

What they are not necessarily

acknowledging is the fact that it's not

434

:

a list of one-to-one correlations, My

investment in display ads versus revenue,

435

:

my investment in social versus revenue,

my investment here versus revenue.

436

:

That's not it.

437

:

It's a tapestry that weaves back and forth

with different time lags associated with

438

:

hmm.

439

:

Literally the only way capture that

is with multi-variable regression

440

:

or econometrics or, marketing

mix modeling, go to market mix

441

:

modeling, right there, it's all

442

:

the same thing,

443

:

Justin Norris: the fir, the

first thing that you mentioned

444

:

Is what we're conditioned to look for.

445

:

Like we were conditioned to say like, I

invested a dollar here and it produced

446

:

two, like these definitive statements.

447

:

I think if I'm interpreting you

correctly, you're saying reality is not

448

:

that straightforward most of the time.

449

:

Mark Stouse: The final output

can be that straightforward.

450

:

Okay?

451

:

But the causality elements on

it, are going to vary with the

452

:

wind, so a great example of this

is uh, Johnson Controls, right?

453

:

So even before Covid really became a

story, uh, their analytics started to

454

:

say in terms of forecasting, right?

455

:

for reasons we don't understand.

456

:

All these different channel investments

that we have historically made are

457

:

showing that they're not gonna be as

effective in six months as they are now.

458

:

A lot of these were physical events.

459

:

, this was not due to marketing

data in these models.

460

:

This was all about external data

was already up these early signals,

461

:

they decided, you know what?

462

:

We are gonna fly by instrument.

463

:

We're not going to fly by what we can see.

464

:

And so they started removing investments

early a lot of these things and they were

465

:

able to claw back quite a bit of money.

466

:

And then all of a sudden, real

life proved the analytics accurate.

467

:

And so they were very happy about that.

468

:

And then finance came later and said, Hey,

so sorry, we're gonna have to cut you.

469

:

30 or 40%.

470

:

I can't remember exactly

what it was, but it large.

471

:

And they said, let us show you something.

472

:

Right?

473

:

And they modeled the effects of those

kinds of cuts over the next three

474

:

years, and it was so detrimental

to the business that finance took

475

:

a lot less and went elsewhere to

get whatever they needed, right?

476

:

So this is an example, not only of

how a marketing team used it to avoid

477

:

waste, but also to avoid cuts that would

be ultimately bad for the business,

478

:

Justin Norris: and the sort of external

data you just described, those are things

479

:

that's not specific to any one business.

480

:

It's common to the macro environment

that many businesses are working in.

481

:

Like if I come to you, do I need to

bring that external data on my own?

482

:

Or do you have sort of global

external data that you can match

483

:

up with my business specific

data and include in the model?

484

:

Mark Stouse: So we are not a

data provider, but we can and do

485

:

all the time help people locate

data sets that are usually free.

486

:

That kind of stuff is usually

free, either from the government

487

:

or major financial institutions

or universities, things like that.

488

:

So that's not a problem.

489

:

And it's great.

490

:

Usually just fantastic data.

491

:

I mean, It's been totally scrubbed,

so that's not a big deal at all.

492

:

In fact, we live in the golden

age of data availability.

493

:

Even if it's not free,

someone is measuring it.

494

:

Purely speculatively in the belief

that somebody is gonna wanna buy it.

495

:

And so today with a credit card,

you can subscribe to all kinds of

496

:

data sources, cost effectively.

497

:

Particularly given how important

it is to maximizing the investment.

498

:

The other thing that I would

just point out is ROI is really

499

:

important, but you only know RROI.

500

:

Looking backwards, right?

501

:

ROI is a historical assessment.

502

:

What is really important is

forecasted, ROI, and then the

503

:

comparison between it and actuals.

504

:

So again, this is very much like

public companies issue guidance

505

:

and then they issue regular

updates against that guidance.

506

:

That is exactly what the C-suites of many

companies are, demanding today, right?

507

:

It's like an investment

uh, to start a new company.

508

:

The first question the investor's

gonna ask is, what do you expect

509

:

this to do in year 1, 2, 3, 4, 5?

510

:

And what's the basis for that projection?

511

:

If you're just extrapolating from hope

and best wishes and all that kind of

512

:

stuff, that's not much of an argument.

513

:

But if you're doing regression based

analysis, that starts to mean something.

514

:

Justin Norris: And listening to the

examples that you described, it does feel

515

:

like a very enterprise oriented solution.

516

:

If I work at a company, 400

people, 50 million a RR, is this

517

:

something that can work for us, or

do I need to be of a certain size

518

:

threshold for it to be useful?

519

:

Mark Stouse: No, actually

it totally scales.

520

:

The reasons for investing in it are

going to be different in a small,

521

:

medium, or enterprise type business.

522

:

But it totally scales.

523

:

And the cost is totally approachable,

even for mom and pop, right?

524

:

So the reasons for doing it as

a small, and let's say the lower

525

:

half of the medium sized business.

526

:

Are mainly because if you make a bad

investment in whatever, It has a almost

527

:

immediately negative effect on cashflow.

528

:

Too much risk, so they are

modeling to avoid that.

529

:

We do have some small customers and

that's their main reason for doing it.

530

:

If you are an enterprise and

you're spending, I don't know, $200

531

:

million a year on marketing, right?

532

:

you spend it wrong, what the CFO

is most concerned about is things

533

:

like opportunity cost, right?

534

:

That's the comparison that's going on

in their mind all the time, particularly

535

:

today, is what are all the different ways

that I can spend this dollar and what

536

:

are the most effective, or what's most

likely to give me the biggest impact?

537

:

That's the competition.

538

:

That is the Game of Thrones,

particularly right now in budget season.

539

:

budget season right now is

sort of year round now, right?

540

:

Because everything is so pressed.

541

:

So if you are a marketing leader, you

are in competition to show that money

542

:

spent with you is a better return.

543

:

Than if they spend it in

it or HR or whatever, right

544

:

you also need to really understand

this, you marketing by definition,

545

:

is a non-linear multiplier of areas

of business performance that are

546

:

linear, one of which is sales.

547

:

So in simple language, your leverage,

you are bringing huge amounts of

548

:

leverage to sales performance that

sales cannot create for itself, right?

549

:

not a ding on sales, it's just the nature

of the reality of the situation, what

550

:

do I mean by linear and non-Linear, okay.

551

:

It means that if I were to go

to my CRO and double goal for.

552

:

For 2024, the first conversation

that they're gonna want to have

553

:

with me is about essentially

doubling their Salesforce, right?

554

:

Because the relationship between

revenue coming in and the cost of that

555

:

revenue in the form of sales team is

it's known it's a linear function.

556

:

And that's because it's the

collective performance of a

557

:

lot of individual performances.

558

:

Okay?

559

:

So a bell curve your sales team's

performance is gonna be on a bell curve.

560

:

It's just fundamentally linear.

561

:

The whole reason why modern marketing

was created in:

562

:

to bring non-linear leverage to

that whole equation, even in B2C.

563

:

So what does that mean?

564

:

It means that because of the way

marketing is, were to have the

565

:

same conversation with the CMOA,

we're doubling the revenue goal.

566

:

Might have to increase marketing spend

by 25%, maybe 20%, there's already

567

:

a ton of leverage built into it.

568

:

And if you want an easy understanding

of ROI for marketing, it is the

569

:

extent of that multiplier, so

we'll talk about it this way.

570

:

You are basically saying that

marketing's mission is to help

571

:

sales more product to more people.

572

:

That's revenue faster.

573

:

That's cash flow from

revenue more profitably.

574

:

That's margin impact than

sales could do by itself.

575

:

That's the key phrase.

576

:

The extent to which that is

true is the ROI to the business.

577

:

So does that actually

look like in real life?

578

:

Well, and a lot of really,

really great B2B marketing go-to

579

:

market kinds of operations.

580

:

ratio is somewhere between 10 and 20 x.

581

:

So that means that if you take marketing

away entirely, which I think would be a.

582

:

Something that not even the most draconian

CFO would ever contemplate, but let's

583

:

just say it is the ultimate AB test.

584

:

So we're gonna completely

shut down marketing.

585

:

You're gonna see a massive fall off.

586

:

It may take a year, but you're gonna

see a massive fall off in sales

587

:

productivity that is going to reveal

the extent of the marketing multiplier.

588

:

It's like literally guarantee able because

sales create the leverage for itself.

589

:

It's just not the way it works.

590

:

Justin Norris: If a company is

getting started with this solution

591

:

what would the process be like?

592

:

What data sources would we need to bring?

593

:

How much time does it take to build

the model, that sort of thing.

594

:

Mark Stouse: the very first step is, and

this is part of the onboarding for us,

595

:

is that we sit down with the customer

and we say, look, what are your top

596

:

questions that you most want to know

The answer to this is a mixed audience,

597

:

usually of marketers and business leaders.

598

:

And doesn't matter what your job is.

599

:

Nobody has any problem rattling

off their list of questions.

600

:

Those questions then generate what's

called a model framework, could easily

601

:

analogize that to a recipe card., So this

is gonna be a punch list of data types

602

:

that you're going to need to be able to

supply to the model to compute the answer.

603

:

then it's gonna outline the model

itself, algorithmically speaking.

604

:

When you actually make the dish

from that recipe, that is the model.

605

:

Usually the way this actually

works is it's very fast.

606

:

It's usually a matter of

weeks, like less than a month,

607

:

that kind of timeframe where.

608

:

The analyst and the business user.

609

:

Could be a marketer, could be

finance guy, could be whatever.

610

:

Are collaborating in the tool

on a minimum viable model.

611

:

And at some point business user

says, that answers my question.

612

:

We need to put this model in production.

613

:

You hit the big red button,

it goes into production.

614

:

After that, it's pretty autonomous.

615

:

There's kind of some DevOps type work,

you you know, you have to maintain the

616

:

model and all that kind of stuff, right?

617

:

But for the most part, it is

doing its thing on an automated

618

:

basis and you're getting whatever

cadence is right for your business.

619

:

Daily, weekly, monthly, you're

getting an update on demand.

620

:

Justin Norris: the.

621

:

. Analyst that you just described, is

that someone that you're supplying

622

:

from your team or is this kind

of a bring your own process?

623

:

Mark Stouse: we We have a large partner

ecosystem that, we can recommend from.

624

:

We also occasionally can do it ourselves

on a managed service basis that is

625

:

actually increasingly popular as

teams get thinned out and they know

626

:

that they need this capability, but

they don't have the bandwidth or the

627

:

expertise to manage it internally.

628

:

And so that's actually extremely popular

these days and very cost effective.

629

:

the key thing is that there's lot of.

630

:

You could easily spend 2020 5K

upfront in time, not in licenses.

631

:

Okay.

632

:

But in time to get everything

set up but once you've done that,

633

:

right, again, the models, unless

you need more models, right?

634

:

The models are doing their thing, right?

635

:

You don't have ongoing major, investment,

know, every month gotta redo the

636

:

whole thing from the ground up.

637

:

That's, exactly the kind of thing

that proof was built to eliminate,

638

:

Justin Norris: if you add a

new, you add a new channel, do

639

:

you have to update the model?

640

:

Or the model can be flexible

enough to just say like, oh, you're

641

:

doing LinkedIn advertising now.

642

:

That's fine.

643

:

We can just incorporate that as we go.

644

:

Mark Stouse: you could

do it either way, right?

645

:

I think be the best practice is that

you clone the model that you have and

646

:

then you add the new data streams to it

that you're maintaining the integrity

647

:

of the original model for comparison.

648

:

And yet you are updating

it that way, right?

649

:

And then at some point you're gonna

dispense with the oldest version

650

:

of the model altogether, So it's

not like you just see a massive

651

:

proliferation of models across time.

652

:

But you do need to do this in

an orderly way so that everybody

653

:

doesn't lose reference points.

654

:

Justin Norris: you mentioned

competitors and I've seen competitors

655

:

popping up in the market, even . A

multi-touch attribution vendor

656

:

that's adding MMM to their mix.

657

:

What's your outlook on how easily other

vendors could recreate these capabilities?

658

:

Do you feel that you have a fairly strong

competitive moat around the offering

659

:

you've built, or will other people be

able to jump into this environment and

660

:

offer similar things fairly easily?

661

:

Mark Stouse: So it's really important

to say this, the competitive advantage

662

:

that anybody has is not in the math.

663

:

So if people propose that they have

some kind of Super Cal Flagal algorithm,

664

:

you need to be very careful about that.

665

:

' cause the odds also are that it's

not transparent, it's a black box.

666

:

And also.

667

:

If it is transparent, you probably

don't have the ability to evaluate it.

668

:

In our particular case, the IP that really

matters is in how we automate it, how we

669

:

scale it, how we make it consumable and

approachable and understandable and how

670

:

we do it at a particular price point.

671

:

And I think that this is the other

thing that is highly relevant today,

672

:

not just in this area, but across

SaaS, is that prices are coming down.

673

:

Uh, We're gonna see a fundamental

change in SaaS pricing.

674

:

The days of, you know, every year

having a pricing increase are over.

675

:

It's just done.

676

:

We are very well fixed

competitively speaking.

677

:

Do have a, I think, a really solid moat.

678

:

So there are a lot of competitors today

that have some great products, but these

679

:

are products that were conceived of

and written by and for data scientists.

680

:

and, And a lot of 'em

are gorgeous by the way.

681

:

Like, graphically, they're gorgeous, but

if you expose them to a normal business

682

:

user, a marketer, sales leader, whatever

gonna stare at that screen and go, I have

683

:

not a clue in the world what that means.

684

:

Like how do I make a better

decision based on that?

685

:

You've just now thrown a

lot of friction into it.

686

:

It's taking more time.

687

:

You've gotta spend a lot of time

translating the data science outputs

688

:

into something that is usable.

689

:

And we don't do that, we built it with

the end user in mind violating any.

690

:

Data science principles.

691

:

That's our big advantage.

692

:

Think one of the biggest things I can

say about this, just to sum up, is

693

:

that there's a reason why large B2C

marketing teams, CPG uh, hotel and

694

:

hospitality, retail, whatever, right?

695

:

They control all four pss of marketing and

they have, at worst, a defacto authority

696

:

over and responsibility over the p and l.

697

:

Of their product with their marketing.

698

:

There's a reason for that, they are

using econometrics slash Go-to market

699

:

mix modeling slash MMM, using that to

optimize and to understand causality

700

:

and to optimize based on that causality.

701

:

And they're also investing a ton of

money in market research, which a lot

702

:

of that ends up in the models, right?

703

:

So if B2B marketers want to have the

same attributes their B2C brothers and

704

:

sisters, gonna have to do what B2C does.

705

:

This is one of those situations where it's

like gravity, You can disagree all you

706

:

want to with gravity, and if you throw

yourself off a building, it's not gonna

707

:

end well, the same thing is true here.

708

:

This is a mathematical principle at work.

709

:

It's mathematical law of gravity

with quotation marks around it.

710

:

And so everything that I've

said today, I've, and I really

711

:

try really super hard to do.

712

:

This is not my opinion.

713

:

I'm just representing a level of fact

that people can either accept or reject,

714

:

but it doesn't change the truth of it.

715

:

Justin Norris: We will include a link to

your website so people can uh, check it

716

:

out, learn more, look at your resources.

717

:

And I'm excited to see where this goes.

718

:

Mark, thank you so much

for chatting with me today.

719

:

Mark Stouse: You're welcome.

720

:

Thank you.

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