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change some colors, add a second
checkout button above the,

2
00:00:04,620 --> 00:00:07,740
product not just down below where
you know, whatever it might be.

3
00:00:08,280 --> 00:00:10,350
And go watch your analytics.

4
00:00:10,710 --> 00:00:14,315
And did you increase things.

5
00:00:15,785 --> 00:00:16,265
Great.

6
00:00:16,265 --> 00:00:17,285
Keep it live.

7
00:00:17,435 --> 00:00:18,965
Did things stay flat?

8
00:00:19,295 --> 00:00:19,835
Great.

9
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Keep it live.

10
00:00:21,330 --> 00:00:23,435
Did you negatively impact things?

11
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Okay.

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Maybe go back and and take that out.

13
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Right.

14
00:00:26,975 --> 00:00:33,455
And see if that thing that you did was
the actual cause of the negative impact.

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If.

16
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If you rebound or things stay low or
whatever, maybe it wasn't right, like

17
00:00:40,580 --> 00:00:46,850
you can go lo-fi on your science and, and
kind of like just, just test and iterate.

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00:00:51,850 --> 00:00:55,990
Welcome to the e-Commerce podcast
with me, your host, Matt Edmundson.

19
00:00:56,320 --> 00:01:01,180
The E-Commerce Podcast is a podcast all
about helping you deliver e-commerce.

20
00:01:01,210 --> 00:01:01,810
Wow.

21
00:01:01,870 --> 00:01:07,090
And to help us do just that, I am
chatting with today's guest, Matt

22
00:01:07,090 --> 00:01:12,640
Ranta, from Nimble Gravity, about
how to navigate the complexities of

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generative content and copyrights.

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A bit of a hot topic right now.

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00:01:17,470 --> 00:01:20,995
But before Matt and I dive into
our conversation, Let me share

26
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with you a podcast pick, a previous
episode that I think you're gonna

27
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enjoy around this whole topic.

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Uh, very recently I chatted
to Max Sinclair about the

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possibilities of generative AI.

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Do check out that episode.

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You can access our podcast pick and our
entire podcast archive on your free,

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00:01:40,265 --> 00:01:45,215
on our, not your free website, my free
website, uh, ecommercepodcast.net.

33
00:01:45,575 --> 00:01:48,455
Plus, if you sign up to our
newsletter, you'll have all this

34
00:01:48,455 --> 00:01:50,585
information winging its way to you.

35
00:01:50,855 --> 00:01:54,785
Uh, the podcast pick, the notes, the
links from today's show, they all get

36
00:01:54,785 --> 00:01:58,355
sent straight to you, to your inbox at
no cost to you, which is pretty amazing.

37
00:01:58,415 --> 00:02:01,835
So if you haven't signed up to the
newsletter yet, what are you waiting for?

38
00:02:01,835 --> 00:02:05,825
Head over to ecommercepodcast.net
and join us now.

39
00:02:06,625 --> 00:02:07,705
Talking of Max Sinclair.

40
00:02:07,705 --> 00:02:12,685
Max Sinclair was recently on
the e-commerce cohort, which

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00:02:12,685 --> 00:02:15,535
you would've heard me talk about
if you're regular to the show.

42
00:02:15,985 --> 00:02:19,645
Basically, the e-commerce cohort
is a, is something that we do.

43
00:02:19,645 --> 00:02:23,635
It's a, it's a monthly mastermind, a
monthly membership, a monthly, it's just

44
00:02:23,635 --> 00:02:27,115
a monthly group where we all get together
and think about e-commerce and uh,

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00:02:27,115 --> 00:02:28,835
yes, it's called the e-commerce cohort.

46
00:02:29,275 --> 00:02:34,885
And to help businesses like yourself
deliver exceptional customer experiences.

47
00:02:35,185 --> 00:02:40,415
Um, We've got a course for you, a free
course, a free resource that you can take.

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Doesn't take long, uh,
called E-Commerce Cycles.

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00:02:43,505 --> 00:02:44,315
It's a mini course.

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00:02:44,315 --> 00:02:47,735
It walks you through my proven
framework for building a

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successful e-commerce business.

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00:02:50,525 --> 00:02:53,935
I'm gonna show you the specific
steps we take as a team in our own

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00:02:53,935 --> 00:02:58,475
e-commerce companies so you can see
exactly how to put these concepts

54
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into practice in your own business.

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And like I say, the good news is
it is completely free, uh, and

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00:03:05,320 --> 00:03:09,550
you can find out more information
about that at ecommercecycles.com.

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00:03:09,910 --> 00:03:13,390
That's ecommercecycles.com, which
is brought to you by our monthly

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00:03:13,390 --> 00:03:17,830
mastermind e-commerce cohort, which
of course is today's show sponsor.

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00:03:18,040 --> 00:03:19,750
Now that's the show sponsor.

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00:03:19,960 --> 00:03:25,700
Let's talk about our guest, Matt
Ranta, uh, who is a digital dynamo,

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which I just, Matt, I dunno.

62
00:03:27,800 --> 00:03:29,530
I just, just sounds like
an awesome title to me.

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He's the head of practice at Nimble
Gravity which revolutionizes digital

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transformation, e-commerce and
strategy for clients ranging from

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startups to billion dollar giants.

66
00:03:40,190 --> 00:03:42,290
Oh, we've got the billion
dollar giants in the Oh, yes.

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Uh, tackling dive diverse industries like
healthcare,  DTC clothing, and endangered

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species protection, which is just awesome.

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Uh, Matt's expertise propels businesses
into the future with data science, a

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great term, which those guys have coined
and explained very well on their website.

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Uh, they do cutting edge e-commerce.

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Uh, his team at Nimble Gravity, they
sort of redefined success by transforming

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the way organizations operate and excel.

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So great bio that Matt.

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Great to have you on the show.

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00:04:14,075 --> 00:04:14,975
Thanks for joining us, man.

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How are you doing?

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00:04:16,315 --> 00:04:17,065
I'm doing well.

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00:04:17,065 --> 00:04:18,535
Thanks for having me on the show, Matt.

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I really appreciate it.

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00:04:19,465 --> 00:04:22,375
Great name by the way, and looking
forward to a fun conversation.

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00:04:24,385 --> 00:04:29,335
It's funny you can't see my diary, but,
um, we d if you've not figured it out yet,

83
00:04:29,335 --> 00:04:31,855
listener, we, we, we, uh, we batch record.

84
00:04:31,855 --> 00:04:35,395
So we record like two episodes at a time
just so we can, uh, stay on top of things.

85
00:04:35,545 --> 00:04:38,575
And our next guest is also called
Matt, but he's just had to reschedule.

86
00:04:38,575 --> 00:04:41,215
And I was like, this is, this is
the easiest day in the world for me.

87
00:04:41,215 --> 00:04:42,445
I don't have to remember anybody's name.

88
00:04:42,445 --> 00:04:44,155
I'm just gonna call everybody
Matt, and it's awesome.

89
00:04:44,755 --> 00:04:45,385
That's perfect.

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It's a beautiful name too.

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Absolutely beautiful name.

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00:04:50,750 --> 00:04:51,230
You know what?

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This is not related to
e-commerce, Matt, in any way, but.

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Last weekend, a friend of mine called
Rich Rising was over from Dallas.

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I was celebrating quite a
milestone birthday and bless

96
00:05:01,705 --> 00:05:02,855
him, he flew in from Dallas.

97
00:05:03,475 --> 00:05:03,565
Yes.

98
00:05:03,685 --> 00:05:07,945
And uh, we had a, a big, he was just, he
was here for just like three or four days.

99
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I mean a hell of a way to fly
just for three or four days.

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And we happened to go watch an
England football game and they were

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playing near where my mum lives.

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And so I didn't see my mum on my birthday.

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So I said to Rich, I said,
let's just go to my mum's

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house and pop in and say hello.

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And he said, oh, that'd be great.

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So we went over to my mum's house and
Rich said to my mum, he asked her the

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question that I've never thought in
all my years of living to ask my mum.

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And that question was this, why Matthew?

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Why did you name him Matthew?

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And I thought, I just assumed it's
cuz it was like the first book of the

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New Testament Do you know what I mean?

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It's like I, I assumed that that
was probably, cause it definitely

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wasn't a family name, but I never
thought to ask my mum that question.

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Anyway, it turns out when it wasn't
that religious at all, it was

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all to do with an actor from the
seventies called Matthew Hudson,

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which my mom took quite a shining to.

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Oh, that's funny.

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00:06:05,240 --> 00:06:07,520
So that's, uh, do you know
why you are called Matt?

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Uh, I, I don't, uh, exactly.

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I do know that, um, My mother and
father made a deal where if I was

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a boy, my mom got to pick the name
and if I was gonna be a girl, my

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dad was gonna get to pick the name.

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And I'm very happy to have born,
been born a guy, uh, not for any

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gender-based reasons or anything like
that really, but just I didn't want

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to have the name that my father was
gonna pick out, which was Ursula.

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Okay.

127
00:06:38,355 --> 00:06:42,435
So, yeah, that, that's, um, Matt
is definitely, is Matt calling, uh,

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every, all the Ursula's listening to
the show gonna go, hang on a minute.

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Wait a second.

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Yeah, yeah.

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I know.

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Some lovely Ursula's actually,
especially in the digital industry.

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So Ursula's, if you're
listening, uh, we do love you.

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Um, but Matt is a very cool name, so
you should, you should ask your mum.

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Go, mum, listen, where did
the name Matthew come from?

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And then just let me know the
answer, cuz now I'm really intrigued

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why Matthews are called Matthew.

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Um, and it maybe, it's,
uh, it's worth finding out.

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00:07:08,575 --> 00:07:15,895
So, Let's talk about generative ai, um,
because it is a topic which is blowing up.

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It has been blowing up for quite a while.

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I say quite a while in digital terms.

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It's, we've been talking about
it for more than a few weeks,

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00:07:22,315 --> 00:07:23,905
so it feels like quite a while.

144
00:07:23,905 --> 00:07:24,175
Right?

145
00:07:24,805 --> 00:07:30,025
Um, so generative ai, things like
chat gpt, um, and so on and so forth.

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00:07:30,025 --> 00:07:32,545
So how, what's your experience here, Matt?

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00:07:33,175 --> 00:07:34,615
How did you get involved with all of this?

148
00:07:35,455 --> 00:07:37,105
Yeah, same exact thing, right?

149
00:07:37,105 --> 00:07:41,035
Like it's been inescapable since
probably December-ish or so, right?

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00:07:41,215 --> 00:07:41,425
Yeah.

151
00:07:41,485 --> 00:07:48,115
Uh, but we as a, as a group have been
involved with, um, you know, open AI's,

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00:07:48,145 --> 00:07:51,535
uh, GPT models since GPT two Okay.

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00:07:51,540 --> 00:07:57,265
And have been actually trying
to help folks create usable.

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Um, generative AI products from a
standpoint of doing things like SEO work.

155
00:08:03,570 --> 00:08:03,600
Okay?

156
00:08:03,600 --> 00:08:09,360
Can you go in and have it create a, a
huge number of SEO friendly content based

157
00:08:09,365 --> 00:08:12,570
products that surround your business?

158
00:08:13,200 --> 00:08:19,680
And at that point in time, it wasn't
really capable of outputting much.

159
00:08:19,710 --> 00:08:22,620
You know, we kind of got some funny
results, to be quite honest with you.

160
00:08:22,770 --> 00:08:22,860
Mm-hmm.

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00:08:23,250 --> 00:08:28,440
Uh, at this point in time, really, you can
go in and say if you had a sister website,

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Right, and you wanted to rewrite content
from your main primary site over to your

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second site where you were just trying to
grab market share with a secondary brand.

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But you're selling
essentially the same things.

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00:08:40,630 --> 00:08:44,410
You could go through and utilizing
the APIs rather than just the,

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you know, graphic user interface.

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Yeah.

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Utilizing the APIs you could.

169
00:08:48,415 --> 00:08:51,715
Put massive amounts of content
into the engine, have it rewritten

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00:08:51,715 --> 00:08:56,905
and output in a safe way, uh, as
long as you're using good prompts.

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Right.

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Right.

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I think that that's a big key, is
the, the prompt kind side of things.

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00:09:01,885 --> 00:09:05,655
And we're seeing this topic
explode not only from a written

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content standpoint, but.

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00:09:08,190 --> 00:09:11,520
There are literally thousands and
thousands of tools at this point,

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00:09:11,520 --> 00:09:16,620
and they do everything from product
photography to background imagery to ad

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00:09:16,625 --> 00:09:19,380
campaigns, to, to you name it, right?

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And so I think businesses, who aren't
starting to capitalize on this are gonna

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quickly see themselves left behind because
it's like nitrous for a car, right?

181
00:09:31,285 --> 00:09:31,345
Yeah.

182
00:09:31,345 --> 00:09:37,015
Like a business can start utilizing
generative AI tools and accelerate

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their growth, accelerate their
productivity, and do so unbelievably fast.

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And with high, high aptitude.

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Yeah.

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00:09:47,875 --> 00:09:53,335
So, You mentioned this, I'm just writing
notes, uh, Matt, hence the, hence the

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00:09:53,335 --> 00:09:54,985
scribbling sounds in the background.

188
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Yeah.

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00:09:55,125 --> 00:09:55,285
Yeah.

190
00:09:55,705 --> 00:10:00,745
Um, so you talk about using,
um, AI to accelerate growth.

191
00:10:01,135 --> 00:10:04,255
How would you, I mean, just from a
pure practical sense, if I'm, I'm an

192
00:10:04,260 --> 00:10:07,765
e-commerce entrepreneur, I'm listening
to it, AI is something that is becoming

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00:10:07,765 --> 00:10:09,715
more bigger and more and more in my face.

194
00:10:09,715 --> 00:10:09,985
Right.

195
00:10:09,985 --> 00:10:11,005
I can't escape it.

196
00:10:11,785 --> 00:10:11,875
Yeah.

197
00:10:11,905 --> 00:10:14,845
So they we're, we're sitting there
listening to you saying, well, you

198
00:10:14,845 --> 00:10:16,555
can use AI to accelerate growth.

199
00:10:17,635 --> 00:10:22,375
How, how could an e-commerce
entrepreneur use AI to accelerate growth?

200
00:10:22,375 --> 00:10:24,655
What are some of the the key
things that they could do,

201
00:10:24,655 --> 00:10:25,905
which is really gonna help them?

202
00:10:26,860 --> 00:10:27,730
Well, sure.

203
00:10:27,730 --> 00:10:30,430
I'll give you a couple of,
of more concrete examples in

204
00:10:30,430 --> 00:10:31,630
that, in that scenario, right?

205
00:10:31,630 --> 00:10:35,320
Say, say you have to write
blogs or articles, um mm-hmm.

206
00:10:35,590 --> 00:10:39,490
For, for your product, and this
is something that from a content

207
00:10:39,490 --> 00:10:40,990
perspective, you should be doing anyway.

208
00:10:41,320 --> 00:10:44,520
That process, probably say you're
a small business owner, right?

209
00:10:44,520 --> 00:10:47,230
That's an overwhelming
process sometimes, right?

210
00:10:47,235 --> 00:10:47,240
Mm-hmm.

211
00:10:47,620 --> 00:10:48,340
I have to give up.

212
00:10:48,415 --> 00:10:53,365
Hours of time to be able to sit down
and write an article or a blog and

213
00:10:53,365 --> 00:10:59,305
rewrite it and or I have to hire somebody
and pay them, you know, a large wage

214
00:10:59,305 --> 00:11:01,105
in order to do that on my behalf.

215
00:11:01,585 --> 00:11:06,956
And now what you instead need to do is
be able to write a short prompt, uh, that

216
00:11:07,305 --> 00:11:09,865
describes how you want something written.

217
00:11:10,220 --> 00:11:16,250
Uh, imagine you are an expert, uh, in
the fashion industry, and you're talking

218
00:11:16,280 --> 00:11:23,510
about men's active wear, uh, and you're
describing, uh, the new trends in, um,

219
00:11:23,600 --> 00:11:25,790
you know, top layers, whatever, right?

220
00:11:25,880 --> 00:11:25,970
Mm-hmm.

221
00:11:26,270 --> 00:11:28,380
And you go on and describe
exactly what you want.

222
00:11:29,115 --> 00:11:31,515
Almost like a creative brief, right?

223
00:11:31,635 --> 00:11:37,455
And then you say, please generate a
1000 word article, uh, to this, and

224
00:11:37,905 --> 00:11:43,455
that will happen in seconds from a
platform like chat Gpt or jarvis.ai

225
00:11:43,455 --> 00:11:46,966
or any of the other written generative
content platforms that are out there.

226
00:11:47,935 --> 00:11:52,495
My recommendation is that you don't
just utilize that immediate output.

227
00:11:52,615 --> 00:11:54,055
You want to go back and edit it.

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00:11:54,055 --> 00:11:58,675
You want to reprompt the engine in
order to tweak and fix things for you.

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00:11:59,035 --> 00:12:01,915
You might say, no, this
tone is too serious.

230
00:12:01,920 --> 00:12:03,655
Can you please make it more lighthearted?

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00:12:03,985 --> 00:12:07,555
You might go through as an individual
and say, I would never use those words

232
00:12:07,555 --> 00:12:09,116
that turn or phrase, whatever it is.

233
00:12:09,121 --> 00:12:09,315
Mm-hmm.

234
00:12:09,400 --> 00:12:12,805
And put it out and you've taken
a process that takes hours

235
00:12:12,805 --> 00:12:14,665
down to taking minutes instead.

236
00:12:14,785 --> 00:12:15,145
Right.

237
00:12:15,490 --> 00:12:15,790
Yeah.

238
00:12:15,820 --> 00:12:16,660
Here's another one.

239
00:12:17,470 --> 00:12:21,190
Another one would be there are product
photography platforms that are out there

240
00:12:21,190 --> 00:12:26,980
right now that allow you to take a very
small number of photographs of a product.

241
00:12:27,310 --> 00:12:30,670
Could be a sporting product, could
be something like a basketball, it

242
00:12:30,670 --> 00:12:33,920
could be a clothing product, like
a jacket, whatever it might be.

243
00:12:33,920 --> 00:12:34,080
Yeah.

244
00:12:35,015 --> 00:12:39,335
Then you can take those images and you
can upload them into the AI platform.

245
00:12:39,455 --> 00:12:43,415
And instead of going out and doing a
photo shoot, uh, with a bunch of models

246
00:12:43,445 --> 00:12:47,375
and a long timeframe, in order to get
everybody set up, take the photos, go

247
00:12:47,375 --> 00:12:50,945
process them, put them back, go through
and pick out which ones you want.

248
00:12:51,275 --> 00:12:55,745
You tell the AI platform, Hey,
put that jacket on a model of

249
00:12:55,745 --> 00:13:00,320
this ethnicity in this setting,
and boom, you've got it right.

250
00:13:00,350 --> 00:13:00,410
Yeah.

251
00:13:00,410 --> 00:13:05,300
All of a sudden your jacket is on a,
you know, an African American female

252
00:13:05,300 --> 00:13:07,250
in New York City hailing a cab.

253
00:13:07,790 --> 00:13:07,970
Yeah.

254
00:13:08,000 --> 00:13:10,310
And you didn't have to
go do that photo shoot.

255
00:13:10,490 --> 00:13:12,710
You didn't have to pay for
the photographer, pay for the

256
00:13:12,710 --> 00:13:14,030
model, anything like that.

257
00:13:14,035 --> 00:13:17,930
You've saved thousands of dollars
and hundreds of hours of time.

258
00:13:20,300 --> 00:13:25,250
It's interesting, isn't it that, um,
uh, I mean, I was talking to, um,

259
00:13:26,155 --> 00:13:29,635
Literally just before we got into this
call actually, um, I was talking to

260
00:13:29,635 --> 00:13:31,255
a friend of mine who's a copywriter.

261
00:13:31,465 --> 00:13:34,585
Um, she's bidding for some work
and she's like, what do you

262
00:13:34,585 --> 00:13:35,725
think I should charge for this?

263
00:13:35,730 --> 00:13:39,415
And we were just sort of going
back and forth and, um, she's

264
00:13:39,415 --> 00:13:41,995
been out of, out of the workforce
for a little bit cuz of kids.

265
00:13:42,000 --> 00:13:44,215
So she's now doing, you know,
she's becoming back part-time.

266
00:13:44,755 --> 00:13:48,115
And we had this really interesting
conversation because when she, when

267
00:13:48,115 --> 00:13:52,885
she, cuz she used to work for me,
Beth, when she left, it was a case of.

268
00:13:53,635 --> 00:13:56,275
Beth wrote everything from scratch, Do
you know, what I mean, and would spend

269
00:13:56,275 --> 00:14:00,955
hours researching and thinking about
blog posts and, and preparing arguments

270
00:14:00,955 --> 00:14:04,585
and thinking about sales funnels and
the, you know, just the layout of

271
00:14:04,585 --> 00:14:08,275
the text and, and, and what's the,
the route we want to take 'em down.

272
00:14:08,845 --> 00:14:12,865
And I was, I was showing her today
how as long as she knows the prompts

273
00:14:12,865 --> 00:14:16,705
and can get that information, you
know, that prompt written and, and

274
00:14:16,705 --> 00:14:20,575
teach chat Gpt Well, the, the thing
is outputted inside of 30 seconds.

275
00:14:21,205 --> 00:14:21,325
Yeah.

276
00:14:21,325 --> 00:14:25,845
And so, You know, her skill now is not
necessarily just in copywriting, it's in

277
00:14:25,845 --> 00:14:31,285
prompt writing, um, and exactly getting
that copy out and then editing it.

278
00:14:31,465 --> 00:14:31,855
Right.

279
00:14:32,125 --> 00:14:35,755
What, when you, um, here's
a question for you, Matt.

280
00:14:35,815 --> 00:14:40,455
Um, we've got this thing now where,
uh, I'm reading more and more where

281
00:14:40,455 --> 00:14:45,325
Google is, you go to say, chat gpt,
you say, write me a blog post on

282
00:14:47,065 --> 00:14:49,555
men's activewear, or whatever it
was you, you, you talked about.

283
00:14:49,555 --> 00:14:51,835
So write me that blog post and.

284
00:14:52,180 --> 00:14:58,330
Um, Google is starting to recognize
now whether this is AI generated

285
00:14:58,690 --> 00:15:04,390
and it feels like, um, it's starting
to penalize AI generated content.

286
00:15:04,780 --> 00:15:07,390
Now, is that just a mere rumor?

287
00:15:07,420 --> 00:15:08,980
Is that actually true?

288
00:15:09,040 --> 00:15:11,510
And if so, how do we
mitigate against that?

289
00:15:12,550 --> 00:15:14,110
Well, yes.

290
00:15:14,150 --> 00:15:18,100
Uh, there are platforms out there,
I'm sure Google's policing it.

291
00:15:18,430 --> 00:15:23,800
There are things like GPT Zero, uh,
that can be utilized in order to,

292
00:15:23,860 --> 00:15:27,340
you know, put any piece of content
in and say, what's the likelihood

293
00:15:27,345 --> 00:15:28,780
that this was written with ai?

294
00:15:28,780 --> 00:15:30,530
And it will come back and
tell you, you can go test it.

295
00:15:30,885 --> 00:15:33,395
Write, write, write something in chat GPT.

296
00:15:33,395 --> 00:15:35,535
Go over to GPT zero and
see what it tells you.

297
00:15:35,535 --> 00:15:35,895
Right?

298
00:15:35,895 --> 00:15:37,275
Then write something by hand.

299
00:15:37,515 --> 00:15:38,895
Go over and see what it tells you.

300
00:15:38,895 --> 00:15:40,605
And it's amazing that it can even do that.

301
00:15:40,965 --> 00:15:43,845
But yes, that is gonna happen and I think.

302
00:15:44,740 --> 00:15:49,540
I think that we as individuals
are gonna have to get used to one.

303
00:15:49,540 --> 00:15:55,690
The fact that some portion of the content
we have out there is probably generated

304
00:15:55,690 --> 00:15:57,820
by AI at some point in time, right?

305
00:15:58,240 --> 00:16:04,000
But then we also are gonna have to get
that mix right of how much should you

306
00:16:04,000 --> 00:16:09,550
come back in and edit that and change it
and make it unique and, and touch it up

307
00:16:10,120 --> 00:16:15,955
because, I think the search engines might
go, they might overindex on a tendency

308
00:16:15,955 --> 00:16:18,475
early on to penalize against that, right?

309
00:16:18,475 --> 00:16:18,565
Mm-hmm.

310
00:16:18,805 --> 00:16:23,275
Whereas businesses are going
to probably overindex the other

311
00:16:23,280 --> 00:16:25,555
way and they need to not right?

312
00:16:25,555 --> 00:16:27,235
Of like, I'm gonna have 'em do everything.

313
00:16:27,265 --> 00:16:28,825
I'm gonna have 'em write all my content.

314
00:16:28,825 --> 00:16:28,826
Right?

315
00:16:28,831 --> 00:16:35,335
Like recently, I think it was maybe even
back in November, um, uh, uh, CNET got

316
00:16:35,425 --> 00:16:40,225
penalized and had a bunch of articles
called out for having in, in factual un.

317
00:16:40,555 --> 00:16:42,625
You know, incorrect information in them.

318
00:16:42,745 --> 00:16:42,985
Right?

319
00:16:42,985 --> 00:16:47,965
And it was like 75 articles that they
had had done as a test, uh, as to

320
00:16:47,965 --> 00:16:50,485
see is, is AI good enough to do this?

321
00:16:50,485 --> 00:16:53,155
And they had to go back and,
and recorrect those, right?

322
00:16:53,395 --> 00:16:56,425
That's a process problem that
isn't necessarily an AI problem.

323
00:16:56,430 --> 00:16:59,995
That's, you never had sat somebody
down, had them actually fact

324
00:17:00,145 --> 00:17:01,855
check what got put out, right?

325
00:17:02,305 --> 00:17:05,875
So there's probably gonna be some
mix as we move forward in the future.

326
00:17:05,875 --> 00:17:07,045
And I think people need to.

327
00:17:07,405 --> 00:17:11,845
Become probably a little bit more
accepting of that because those tools

328
00:17:11,845 --> 00:17:16,870
really do allow us to accelerate
and I think that it also, Would then

329
00:17:16,870 --> 00:17:22,270
get fed back into the AI engines
and their intelligence will increase

330
00:17:22,270 --> 00:17:24,010
and it will become more unique.

331
00:17:24,400 --> 00:17:27,550
And I do think it goes to that prompt
writing you were talking about, right?

332
00:17:27,550 --> 00:17:31,900
That your, your former employee and,
and colleague is now really good at.

333
00:17:32,530 --> 00:17:33,821
That's the challenge, right?

334
00:17:33,890 --> 00:17:37,690
Like are you coming in and
asking a unique prompt?

335
00:17:37,750 --> 00:17:40,390
Are you telling the engine to
write something unique that

336
00:17:40,390 --> 00:17:41,800
it hasn't written before?

337
00:17:42,010 --> 00:17:45,490
Or are you just asking a basic
question like, is the sky blue.

338
00:17:45,505 --> 00:17:49,165
Yeah, give me 10 ideas for a
15 year old's birthday party.

339
00:17:49,225 --> 00:17:49,495
Right?

340
00:17:49,495 --> 00:17:49,555
Yeah.

341
00:17:49,555 --> 00:17:54,295
Like, don't ask the basic question,
augment it with a lot more information,

342
00:17:54,565 --> 00:17:57,865
and, and then you're gonna start
to get much better content.

343
00:17:58,855 --> 00:18:03,625
Still fact check it, still rewrite it to
be a little bit more like yourself, and

344
00:18:03,625 --> 00:18:05,455
then you're gonna have some kind of mix.

345
00:18:05,845 --> 00:18:10,945
It isn't that much different than
you know, a copywriter and an editor

346
00:18:11,035 --> 00:18:13,575
at a magazine or a newspaper, right?

347
00:18:13,925 --> 00:18:19,225
It's a source of intelligence, outputting
a first draft, and it needs to be refined.

348
00:18:19,435 --> 00:18:19,735
Right.

349
00:18:19,795 --> 00:18:20,965
That's the way I would look at it.

350
00:18:21,295 --> 00:18:21,355
Yeah.

351
00:18:21,355 --> 00:18:22,735
From an SEO perspective.

352
00:18:22,765 --> 00:18:23,695
Yeah, same thing.

353
00:18:24,755 --> 00:18:28,625
It's really interesting because I,
I, I was, uh, last week I had a,

354
00:18:28,630 --> 00:18:33,815
a, a a, well deserved, I would say,
Matt week off, uh, week off work.

355
00:18:33,815 --> 00:18:37,925
And I spent the entire week, um,
either in my wood workshop or

356
00:18:37,925 --> 00:18:39,935
playing around with chat GPT prompts.

357
00:18:39,935 --> 00:18:44,765
So the two things I was sort of, and
um, I've got to deliver some talks,

358
00:18:45,125 --> 00:18:50,885
um, from the stage coming up and I'm
like, I wonder how, cuz normally.

359
00:18:51,535 --> 00:18:56,515
I would spend probably 8 to 16 hours
prepping, say a 30 minute talk.

360
00:18:56,695 --> 00:18:56,785
Yeah.

361
00:18:56,845 --> 00:18:59,725
You know, it's this,
that kind of, uh, ratio.

362
00:19:00,325 --> 00:19:02,845
I'm like how can chat GPT?

363
00:19:03,205 --> 00:19:04,915
And there are obviously
AI platforms out there.

364
00:19:04,915 --> 00:19:07,675
I mean, you mentioned Jarvis.ai,
I think it's now called Jasper ai,

365
00:19:07,705 --> 00:19:09,535
cuz I think wasn't Oh, you're right.

366
00:19:09,540 --> 00:19:09,575
Yeah.

367
00:19:10,055 --> 00:19:13,615
Somebody was gonna sue them from Marvel,
I'm sure for calling it uh, Jarvis.

368
00:19:13,615 --> 00:19:13,705
Yep.

369
00:19:14,365 --> 00:19:19,165
Um, and so, Uh, they now call it Jasper,
which we used to use actually before chat,

370
00:19:19,165 --> 00:19:21,295
GPT four, which we predominantly use now.

371
00:19:22,225 --> 00:19:26,665
And I was playing around on these prompts
and it took my prep time down from, for

372
00:19:26,665 --> 00:19:29,815
the latest talk that I've written, what
would normally take me eight to 12 hours.

373
00:19:29,820 --> 00:19:34,755
Took probably 20 minutes, um, yes,
just going backwards and forwards.

374
00:19:34,895 --> 00:19:39,085
And it just outputted a series
of data, which I can then go back

375
00:19:39,085 --> 00:19:42,625
and I can now edit and change,
feed it back into the system and

376
00:19:42,625 --> 00:19:43,915
see what it it comes out with is.

377
00:19:43,915 --> 00:19:45,355
It's incredible really.

378
00:19:46,210 --> 00:19:47,590
What it can do.

379
00:19:47,920 --> 00:19:50,440
So I guess one of the questions,
and let's let's connect it

380
00:19:50,440 --> 00:19:52,030
to the title of the podcast.

381
00:19:52,210 --> 00:19:52,270
Yeah.

382
00:19:52,360 --> 00:19:57,610
Uh, I guess one of the questions in
all of this is like, um, the same with

383
00:19:57,610 --> 00:19:59,140
the text, same with the image, right?

384
00:19:59,140 --> 00:20:03,160
So it's creating blog posts, it's creating
product images, which are Max, max

385
00:20:03,165 --> 00:20:05,650
Sinclair's, um, stuff does at ecomtent.

386
00:20:05,820 --> 00:20:13,030
And so, Am I, am I breaking any
copyright laws by using what

387
00:20:13,030 --> 00:20:14,560
chat GPT has put out there?

388
00:20:14,560 --> 00:20:15,850
What's the what?

389
00:20:15,880 --> 00:20:17,830
What usage rights do I have, I suppose?

390
00:20:17,830 --> 00:20:22,660
Is this, is this something Yeah, if I,
I guess one of the big questions, one

391
00:20:22,665 --> 00:20:27,420
of the big debates is if I say, um, add
this product, my men's active wear to a.

392
00:20:28,000 --> 00:20:33,240
To a guy which looks a little bit like
Brad Pitt Do you know what I mean?

393
00:20:33,430 --> 00:20:36,970
It's like, and it, an AI goes and
generates a sort of, you know, almost

394
00:20:36,970 --> 00:20:39,010
perfect version of, of Brad Pitt.

395
00:20:40,360 --> 00:20:42,010
It's all a bit gray, isn't it?

396
00:20:42,220 --> 00:20:44,480
Or are there some guidelines
in this that we can follow?

397
00:20:45,670 --> 00:20:49,750
There's definitively some guidelines
that exist in the end user license

398
00:20:49,750 --> 00:20:51,100
agreements for the products.

399
00:20:51,100 --> 00:20:51,370
Right.

400
00:20:51,370 --> 00:20:52,510
So that's step one.

401
00:20:52,780 --> 00:20:57,340
I think that this area, from a
legal perspective, and I'm not

402
00:20:57,340 --> 00:21:00,160
a lawyer, nobody should take
anything I say as legal advice.

403
00:21:00,375 --> 00:21:00,520
Right?

404
00:21:00,520 --> 00:21:00,910
Disclaimer.

405
00:21:00,970 --> 00:21:01,260
Yeah.

406
00:21:01,360 --> 00:21:07,665
But yeah, like from a, you know, from
a legal area, this is gonna be muddy

407
00:21:07,665 --> 00:21:10,155
waters at best for a good while, right?

408
00:21:10,185 --> 00:21:10,275
Mm-hmm.

409
00:21:10,515 --> 00:21:15,495
So here's what I can tell you from like
an end user license agreement standpoint,

410
00:21:15,495 --> 00:21:20,680
and it's kind of, you know, Thought
provoking at a minimum may be scary

411
00:21:20,680 --> 00:21:23,080
for companies at, at, at, at a maximum.

412
00:21:23,530 --> 00:21:27,730
So we did some research as a
company back in February, right?

413
00:21:27,730 --> 00:21:31,180
So a lifetime ago in generative
AI really at this point.

414
00:21:31,240 --> 00:21:35,440
But at that point in February,
we, we researched and surveyed,

415
00:21:35,690 --> 00:21:40,720
uh, working professionals in the
United States and, uh, elsewhere as

416
00:21:40,720 --> 00:21:45,370
well, but majority of them were in
the United States and found that.

417
00:21:45,945 --> 00:21:48,945
Of full-time working
professionals in the US.

418
00:21:48,945 --> 00:21:54,435
45% of the respondents who were familiar
with generative ai, right, if they

419
00:21:54,435 --> 00:21:59,055
first had to pass a gating question
of, are you familiar with any of these?

420
00:21:59,145 --> 00:21:59,475
Mm-hmm.

421
00:21:59,775 --> 00:22:02,775
Of of which around 50% of people were.

422
00:22:02,785 --> 00:22:03,075
Yeah.

423
00:22:03,375 --> 00:22:10,095
And then from there, 45% of those
respondents were taking generative AI

424
00:22:10,095 --> 00:22:13,065
outputs and claiming them as their own.

425
00:22:13,910 --> 00:22:14,245
Right.

426
00:22:14,245 --> 00:22:17,605
So they weren't saying, I
did this with generative ai.

427
00:22:17,815 --> 00:22:17,935
Mm-hmm.

428
00:22:18,175 --> 00:22:20,335
They were saying, this is my work product.

429
00:22:20,695 --> 00:22:20,905
Wow.

430
00:22:20,935 --> 00:22:21,655
And okay.

431
00:22:21,715 --> 00:22:24,685
In that is a huge challenge.

432
00:22:24,745 --> 00:22:25,135
Right.

433
00:22:25,140 --> 00:22:31,425
So Jasper, uh, in their end user license
agreement spells out very clearly,

434
00:22:32,215 --> 00:22:37,735
you may not utilize our content and
claim it as being generated by a human.

435
00:22:38,680 --> 00:22:43,720
So any of the folks that had done that
from Jasper are creating a scenario

436
00:22:43,720 --> 00:22:45,820
where they can't copyright that work.

437
00:22:45,820 --> 00:22:47,650
They can't actually claim it as their own.

438
00:22:47,650 --> 00:22:50,950
If they wrote an article and put a
byline on it as well, you know, Matt

439
00:22:50,950 --> 00:22:53,380
Ranta or Matt Edmundson, uh, wrong.

440
00:22:53,440 --> 00:22:55,060
You know, it's, it's not theirs.

441
00:22:55,060 --> 00:22:55,930
It doesn't belong to them.

442
00:22:56,590 --> 00:23:01,045
If you get into open AI end user
license agreement, You'll see

443
00:23:01,045 --> 00:23:03,295
some even more interesting things.

444
00:23:03,355 --> 00:23:09,085
Kind of where they go is if you output
content that is a hundred percent unique.

445
00:23:09,445 --> 00:23:12,595
Uh, then you can, then you
can start to utilize it.

446
00:23:12,895 --> 00:23:12,985
Mm-hmm.

447
00:23:13,225 --> 00:23:20,575
Um, but if you and 50 other people ask it
the same question and get the same output,

448
00:23:20,905 --> 00:23:23,155
that output is actually public domain.

449
00:23:23,215 --> 00:23:27,566
It is not individual, and it cannot
be copywriting claimed as as your own.

450
00:23:28,255 --> 00:23:33,355
So that's where that prompt writing
actually becomes even more specialized.

451
00:23:33,355 --> 00:23:33,445
Right.

452
00:23:33,445 --> 00:23:34,495
And more critical.

453
00:23:34,675 --> 00:23:35,125
Right.

454
00:23:35,515 --> 00:23:43,195
If it's unique, prompt, if it's completely
new and, and of, you know, Brand new

455
00:23:43,195 --> 00:23:48,235
thought process, then that content
can start to look and become yours.

456
00:23:48,535 --> 00:23:52,975
Hmm, I haven't researched the end user
license agreement of, you know, thousands

457
00:23:52,975 --> 00:23:56,545
of these ais, but here's what I, here's
what I would recommend to people.

458
00:23:56,965 --> 00:23:57,955
You've gotta go read it.

459
00:23:58,515 --> 00:24:03,075
You've gotta understand, can I actually
utilize the output of this as my own?

460
00:24:03,315 --> 00:24:05,745
Some of them are very good
about calling it out, right?

461
00:24:05,745 --> 00:24:09,855
Like if you're buying a logo or something
like that, that AI has generated, they're

462
00:24:09,855 --> 00:24:15,280
very good about saying you have, you know,
Unrestricted use and perpetuity for this.

463
00:24:15,280 --> 00:24:18,190
You're buying it as as
your brand mark, right?

464
00:24:18,220 --> 00:24:19,150
Yeah, go ahead and do it.

465
00:24:20,230 --> 00:24:24,400
Others are burying it in
very, very deep places, right?

466
00:24:24,400 --> 00:24:28,510
So I would recommend go read it, go have
somebody on your legal team if you're

467
00:24:28,630 --> 00:24:29,950
large enough to have one of those.

468
00:24:29,955 --> 00:24:32,230
Read it and understand that deeply.

469
00:24:32,560 --> 00:24:38,380
The second thing I would do from just
if I were a company, Running a business

470
00:24:38,380 --> 00:24:42,790
where any generative AI could touch it is
I would have a policy for my employees.

471
00:24:43,000 --> 00:24:43,420
Right.

472
00:24:43,510 --> 00:24:47,260
And I personally wouldn't make this
policy restrictive in the sense of,

473
00:24:47,265 --> 00:24:49,180
Hey, you can't use generative ai.

474
00:24:49,270 --> 00:24:49,360
Mm-hmm.

475
00:24:49,600 --> 00:24:53,260
What I would say is you can use
generative ai, but we have to know.

476
00:24:53,550 --> 00:24:54,150
Right, right.

477
00:24:54,150 --> 00:24:57,090
Like we as a company have to
be aware of what's going on.

478
00:24:57,120 --> 00:25:00,510
You can't claim a work product
that AI built as your own and

479
00:25:00,510 --> 00:25:04,020
then Right, have us going out and
copywriting it, et cetera, et cetera.

480
00:25:04,050 --> 00:25:04,230
Right?

481
00:25:04,260 --> 00:25:07,050
You're creating challenges
for that organization, so you

482
00:25:07,050 --> 00:25:10,440
need to have something that
acknowledges that AI is here.

483
00:25:10,845 --> 00:25:12,165
It's not going away.

484
00:25:12,195 --> 00:25:13,275
People are going to use it.

485
00:25:13,275 --> 00:25:17,565
People want to be more productive,
but you've gotta have some level of

486
00:25:17,685 --> 00:25:22,995
transparency within your organization
that allows you then to make decisions

487
00:25:22,995 --> 00:25:26,985
downstream from where that content
was generated and how it's being used,

488
00:25:27,255 --> 00:25:29,505
that people are fully aware and not.

489
00:25:30,040 --> 00:25:32,920
You know, setting themselves
up for a surprise six or eight

490
00:25:33,070 --> 00:25:34,480
months or a year from now.

491
00:25:34,900 --> 00:25:36,670
Yeah, that's really interesting.

492
00:25:36,760 --> 00:25:37,870
That's really interesting.

493
00:25:38,080 --> 00:25:40,180
So is that what you guys
do at Nimble Gravity?

494
00:25:40,185 --> 00:25:43,630
You've got a sort of a policy
on, on generative AI usage

495
00:25:43,630 --> 00:25:44,620
that your staff follow?

496
00:25:45,610 --> 00:25:49,800
Uh, we don't yet, quite frankly, and
it's something that we're talking about.

497
00:25:50,100 --> 00:25:51,520
Um, and it, it.

498
00:25:51,805 --> 00:25:56,095
It's something that we utilize,
um, fairly regularly too.

499
00:25:56,155 --> 00:25:56,245
Mm-hmm.

500
00:25:56,485 --> 00:25:56,575
Right.

501
00:25:56,575 --> 00:26:01,075
Like that research process that you're
talking about where you're, where you're

502
00:26:01,075 --> 00:26:06,445
going in, uh, and doing, you know, prep
for a speech or prep for understanding

503
00:26:06,445 --> 00:26:09,326
what are the differences between
software platforms or anything like that.

504
00:26:10,105 --> 00:26:11,965
You can do that in chat, GPT, right?

505
00:26:11,995 --> 00:26:12,115
Mm-hmm.

506
00:26:12,445 --> 00:26:14,065
Uh, you need to go fact check that.

507
00:26:14,335 --> 00:26:16,225
Can you help put together a presentation?

508
00:26:16,255 --> 00:26:17,155
You bet you can.

509
00:26:17,215 --> 00:26:18,775
Can you put together a blog post?

510
00:26:18,775 --> 00:26:19,165
Sure.

511
00:26:19,170 --> 00:26:19,615
You can.

512
00:26:19,615 --> 00:26:24,085
Can you put together materials
that go inside of, uh, a, a

513
00:26:24,085 --> 00:26:25,245
client facing presentation?

514
00:26:25,860 --> 00:26:27,510
You, you sure could, right?

515
00:26:27,510 --> 00:26:27,600
Mm-hmm.

516
00:26:27,840 --> 00:26:29,850
But how much do you want to edit that?

517
00:26:30,360 --> 00:26:35,670
And so I, I think that we, we need to
practice what we're preaching for one,

518
00:26:35,760 --> 00:26:40,260
uh, and, and two, um, it's ever evolving.

519
00:26:40,260 --> 00:26:43,240
And so that's one of those
things like a privacy policy.

520
00:26:44,190 --> 00:26:46,770
But you're gonna have to
go back and revisit, right?

521
00:26:46,770 --> 00:26:48,620
Like so, okay.

522
00:26:49,050 --> 00:26:49,340
Gdpr.

523
00:26:49,370 --> 00:26:49,660
Okay.

524
00:26:49,660 --> 00:26:51,960
Brexit, no UK's own thing.

525
00:26:52,020 --> 00:26:52,351
Uh, yeah.

526
00:26:52,710 --> 00:26:56,640
California's, CPRA, uh, Colorado
that I live in now all of a

527
00:26:56,640 --> 00:26:58,230
sudden has a policy, right?

528
00:26:58,230 --> 00:27:00,840
Like, and they just keep
piling on each other and.

529
00:27:01,335 --> 00:27:03,525
You're gonna have to figure
this out as a business.

530
00:27:03,855 --> 00:27:04,995
Yeah, it's crazy.

531
00:27:05,295 --> 00:27:08,565
Uh, part of me, I'm, I'm sort of sitting
here wondering, I wonder if I said

532
00:27:08,565 --> 00:27:12,855
to chat GPT write me a policy whether
it would actually create the policy.

533
00:27:13,125 --> 00:27:16,065
It probably would, uh, if
I put the right prompts in.

534
00:27:16,125 --> 00:27:20,625
Um, one of the things I found actually
with Chat GPT, um, and again, it, I

535
00:27:20,625 --> 00:27:25,305
guess it's the same with the other
platforms, is if I give it copy.

536
00:27:26,050 --> 00:27:30,130
Of, um, copies of, or chunks of content
that I have previously written in

537
00:27:30,130 --> 00:27:32,350
my sort of style, my tone of voice.

538
00:27:32,920 --> 00:27:34,750
Yeah, it can mimic that quite well.

539
00:27:34,750 --> 00:27:39,400
When it's output in the content,
it can sound quite, quite like me.

540
00:27:39,670 --> 00:27:43,390
Um, which minimizes my editing,
which I'm assuming will make it a bit

541
00:27:43,390 --> 00:27:45,100
more unique cuz it's, it's sort of.

542
00:27:46,035 --> 00:27:46,975
My Do, you know what I mean?

543
00:27:47,035 --> 00:27:47,905
My tone of voice.

544
00:27:47,905 --> 00:27:50,995
And I can, I can exactly throw that in
there, which is, which is quite handy.

545
00:27:51,235 --> 00:27:53,755
And it's quite nice because you can
do that with brand as well, can't you?

546
00:27:53,760 --> 00:27:56,035
You can throw brand voice in
there and brand values and it

547
00:27:56,095 --> 00:27:57,895
tends to output it in that way.

548
00:27:58,435 --> 00:28:00,475
Um, which i, I quite like.

549
00:28:00,475 --> 00:28:04,295
So what are some of the, the
creative, I mean you guys are

550
00:28:04,295 --> 00:28:05,695
using it a nimble gravity?

551
00:28:05,695 --> 00:28:08,455
I'm, I'm, do you use it
on behalf of your clients?

552
00:28:08,455 --> 00:28:11,455
Uh, what are some of the surprising
ways you've seen, I guess, uh,

553
00:28:11,725 --> 00:28:13,345
uh, generative AI being used?

554
00:28:14,455 --> 00:28:14,845
Yeah,

555
00:28:14,845 --> 00:28:18,625
so you know, the example that I
called out previously of, you know,

556
00:28:18,625 --> 00:28:23,755
rewriting, um, SEO friendly content
from, uh, one site to another, uh, is

557
00:28:23,755 --> 00:28:28,585
something that, that we've, uh, worked
with and, and created models around.

558
00:28:28,945 --> 00:28:32,455
Uh, and I would say that
written generative AI content

559
00:28:32,935 --> 00:28:34,495
is probably the largest area.

560
00:28:34,770 --> 00:28:35,220
Right now.

561
00:28:35,280 --> 00:28:35,370
Mm-hmm.

562
00:28:35,370 --> 00:28:35,670
Right.

563
00:28:35,910 --> 00:28:39,960
Uh, some of the, uh, photography
based kind of stuff is

564
00:28:39,960 --> 00:28:42,420
still a, a little bit new.

565
00:28:42,840 --> 00:28:42,960
Mm-hmm.

566
00:28:43,260 --> 00:28:46,620
We, we don't do graphic design or
anything like that, so we're not

567
00:28:46,620 --> 00:28:51,570
necessarily looking at some of those,
um, you know, brand or campaign

568
00:28:51,575 --> 00:28:56,070
level kind of AI that allow you to
create a, a whole bunch of templates.

569
00:28:56,130 --> 00:29:01,650
Um, but it seems like written
content is, is the largest thing

570
00:29:01,650 --> 00:29:02,790
that's happening right now.

571
00:29:02,795 --> 00:29:02,930
Yeah.

572
00:29:03,820 --> 00:29:05,590
It's for real applicable use.

573
00:29:05,650 --> 00:29:07,040
Yeah, yeah, yeah.

574
00:29:07,240 --> 00:29:10,390
And that goes to, that
goes to code too, Matt.

575
00:29:10,390 --> 00:29:13,660
So I just wanna throw that in
there, like actual programming

576
00:29:14,020 --> 00:29:15,940
code that goes live in websites.

577
00:29:16,030 --> 00:29:16,300
So

578
00:29:17,110 --> 00:29:19,990
Yeah, I, I know one or two of our
developers, they've started to use

579
00:29:20,020 --> 00:29:23,260
chat gpt to generate code for them,
which is quite fun, quite funny.

580
00:29:24,085 --> 00:29:24,375
Yeah.

581
00:29:24,475 --> 00:29:27,235
And what what's fascinating,
I mean obviously they still

582
00:29:27,235 --> 00:29:28,195
need to understand the code.

583
00:29:28,225 --> 00:29:31,525
My, uh, my friend who was
a copywriter still needs to

584
00:29:31,525 --> 00:29:33,475
understand is this copy right?

585
00:29:33,535 --> 00:29:34,165
Is it good?

586
00:29:34,170 --> 00:29:35,905
It still needs that human interaction.

587
00:29:35,905 --> 00:29:36,265
Right?

588
00:29:36,925 --> 00:29:41,185
Um, but the interesting thing, cuz when
I was talking to my, talking to Beth, a

589
00:29:41,185 --> 00:29:44,875
copywriter friend, at first you kind of
go, well chat GPT's gonna take over my

590
00:29:45,415 --> 00:29:47,125
job, it's people aren't gonna need me.

591
00:29:47,125 --> 00:29:48,325
And I'm like, well, not, no, not yet.

592
00:29:48,355 --> 00:29:48,955
Not quite.

593
00:29:49,585 --> 00:29:53,365
Um, and actually what I think you can do
is probably put your fees up quite a bit,

594
00:29:54,115 --> 00:29:57,925
um, for what you were charging by throwing
the fact in there that you're gonna use

595
00:29:58,105 --> 00:30:01,585
chat GPT and actually you're now prompt
engineering to get the best out of it.

596
00:30:01,590 --> 00:30:01,815
Right.

597
00:30:01,945 --> 00:30:02,295
Right.

598
00:30:02,435 --> 00:30:05,335
Um, because that's a highest
skillset, I think, because there

599
00:30:05,335 --> 00:30:08,605
are fewer and fewer people who,
who know how to write the prompts.

600
00:30:08,605 --> 00:30:10,435
It seems for AI to get
the stuff out of it.

601
00:30:11,395 --> 00:30:15,235
And so, yeah, it's a, it's a
fascinating one, this whole world of

602
00:30:15,265 --> 00:30:16,525
where do you think it's gonna go to?

603
00:30:16,555 --> 00:30:20,365
Because like you've now got Elon Musk
coming out and saying, oh, we need to stop

604
00:30:20,365 --> 00:30:22,315
everything and throw, slam the brakes on.

605
00:30:22,705 --> 00:30:25,225
Um, where do you think
it's all gonna go to?

606
00:30:26,005 --> 00:30:26,875
Yeah.

607
00:30:26,965 --> 00:30:27,175
Yeah.

608
00:30:27,175 --> 00:30:28,105
It's interesting, right?

609
00:30:28,105 --> 00:30:31,915
He, he wants to write his own thing
and he's also promoting, putting

610
00:30:31,915 --> 00:30:33,655
the breaks on everybody else's.

611
00:30:35,455 --> 00:30:36,295
Interesting.

612
00:30:36,355 --> 00:30:36,625
Um,

613
00:30:37,715 --> 00:30:39,055
Elon Musk for president.

614
00:30:39,055 --> 00:30:39,475
Yeah, sure.

615
00:30:41,915 --> 00:30:42,205
Yeah.

616
00:30:42,385 --> 00:30:45,140
Um, You know, where is it gonna go?

617
00:30:45,140 --> 00:30:47,630
It's ra, it's so rapidly advancing, right?

618
00:30:47,630 --> 00:30:47,720
Mm-hmm.

619
00:30:48,020 --> 00:30:54,080
I do think, I do think prompt
engineering is gonna be an actual skill.

620
00:30:54,170 --> 00:30:54,260
Mm-hmm.

621
00:30:54,500 --> 00:30:56,750
Probably an actual job, right?

622
00:30:57,110 --> 00:30:57,260
Yeah.

623
00:30:57,260 --> 00:30:57,770
And.

624
00:30:58,330 --> 00:31:03,760
You're not necessarily gonna be able to
be a good, prompt engineer for production

625
00:31:03,790 --> 00:31:10,360
code, as well as be a good, prompt
engineer for written, readable content.

626
00:31:10,420 --> 00:31:10,540
Mm-hmm.

627
00:31:10,930 --> 00:31:12,490
As well as like, right.

628
00:31:12,490 --> 00:31:15,580
Like this is almost like
you're the director of a movie.

629
00:31:15,850 --> 00:31:16,140
Yeah.

630
00:31:16,420 --> 00:31:19,820
And you're trying to tell the
entire crew what you want out of it.

631
00:31:20,695 --> 00:31:24,206
And some people are great at directing
westerns and other people mm-hmm.

632
00:31:24,260 --> 00:31:26,095
Are great at directing sci-fi.

633
00:31:26,515 --> 00:31:29,245
And some people do really
well with documentaries.

634
00:31:29,245 --> 00:31:29,545
Right.

635
00:31:29,545 --> 00:31:33,415
But you can't take a documentary director
and say, all right, we're gonna put

636
00:31:33,415 --> 00:31:38,065
you in this tent pole, blockbuster, uh,
you're directing the fifth Avengers film.

637
00:31:38,065 --> 00:31:41,185
Go like they're going to,
they're gonna fail at that.

638
00:31:41,215 --> 00:31:41,545
Right.

639
00:31:41,545 --> 00:31:44,455
So becoming a prompt engineer is gonna be.

640
00:31:44,910 --> 00:31:48,180
The job, and I think your
education is gonna have to shift

641
00:31:48,180 --> 00:31:49,320
in order to do that, right?

642
00:31:49,320 --> 00:31:53,550
Like this is causing unbelievable
upheaval in school boards

643
00:31:53,555 --> 00:31:55,080
and all kinds of stuff too.

644
00:31:55,530 --> 00:31:57,360
Oh my gosh, my students are cheating.

645
00:31:57,360 --> 00:31:58,380
What's going on?

646
00:31:58,410 --> 00:31:59,670
Like, how is this gonna happen?

647
00:31:59,790 --> 00:32:00,690
Stop that.

648
00:32:00,690 --> 00:32:03,750
Like stop it now, and start
teaching them how to be prompt

649
00:32:04,270 --> 00:32:07,590
engineers and start teaching them
how to be subject matter experts.

650
00:32:07,680 --> 00:32:07,770
Mm-hmm.

651
00:32:08,010 --> 00:32:11,970
Because regardless of of whatever
happens, you are gonna have to

652
00:32:11,975 --> 00:32:13,590
be a subject matter expert to be.

653
00:32:13,905 --> 00:32:15,465
Good at being a prompt engineer.

654
00:32:15,705 --> 00:32:16,035
Yeah.

655
00:32:16,095 --> 00:32:19,095
Can you use AI to help you
become a subject matter expert?

656
00:32:19,305 --> 00:32:20,405
You bet you can, right?

657
00:32:20,405 --> 00:32:20,605
Mm-hmm.

658
00:32:20,685 --> 00:32:23,055
Like you can go do research
like we were talking about, but

659
00:32:23,055 --> 00:32:24,855
again, it goes down to prompts.

660
00:32:24,975 --> 00:32:25,035
Yeah.

661
00:32:25,065 --> 00:32:29,685
So teach people how to use it versus
being afraid of it and thinking it's

662
00:32:29,690 --> 00:32:31,205
gonna steal and plagiarize everything.

663
00:32:31,215 --> 00:32:31,935
Right, right.

664
00:32:31,935 --> 00:32:36,195
Like teach them how to be good,
curious, inquisitive humans that

665
00:32:36,195 --> 00:32:41,415
find a passion around something and
then turn that passion into becoming.

666
00:32:42,010 --> 00:32:47,620
Somebody who can write fantastic prompts
and direct these tools to your advantage.

667
00:32:47,710 --> 00:32:47,770
Yeah.

668
00:32:48,010 --> 00:32:49,510
So that's what I would say.

669
00:32:50,200 --> 00:32:53,350
That's such a powerful point, Matt,
and I think it's very well said.

670
00:32:53,500 --> 00:32:57,190
And, um, I'm smiling because I
remember, you know, when I was back

671
00:32:57,190 --> 00:33:01,150
at school, uh, and they just invented
electricity, um, they were, they

672
00:33:01,420 --> 00:33:04,840
were, they were like, well, you know,
you, you, you've got calculators now.

673
00:33:04,840 --> 00:33:07,570
We had scientific calculators and I
remember when the first calculators

674
00:33:07,570 --> 00:33:08,770
came out that could draw graphs.

675
00:33:09,015 --> 00:33:09,305
Yeah.

676
00:33:09,310 --> 00:33:11,545
And they're like, well, you can't
take those into the exam cuz

677
00:33:11,550 --> 00:33:14,155
that's, you have to figure out how
to draw the graph for yourself.

678
00:33:14,695 --> 00:33:18,685
And it's, I think education when
it, when it realizes this is where

679
00:33:18,685 --> 00:33:22,495
technology is, how do we help our
kids utilize the technology to be

680
00:33:22,495 --> 00:33:24,355
smarter rather than try and stop them?

681
00:33:24,835 --> 00:33:25,465
Um, right.

682
00:33:25,470 --> 00:33:26,065
Because they're kids.

683
00:33:27,235 --> 00:33:30,385
I mean, you know, adults are using it
all the time to make their life easier.

684
00:33:30,535 --> 00:33:32,815
And we expect our kids to
go, well, I can't do that.

685
00:33:32,815 --> 00:33:34,645
That's just not right for my exams.

686
00:33:35,095 --> 00:33:37,495
It's just this ludicrous, isn't it really?

687
00:33:37,500 --> 00:33:38,185
In a lot of ways.

688
00:33:38,395 --> 00:33:38,695
Yeah.

689
00:33:38,695 --> 00:33:40,345
So, uh, it's fascinating.

690
00:33:40,345 --> 00:33:41,005
Fascinating.

691
00:33:41,485 --> 00:33:44,290
So you've got, um, Let's just start.

692
00:33:44,290 --> 00:33:47,740
I'm curious, uh, in the, in the last few
minutes of this show, uh, Matt, yeah.

693
00:33:47,740 --> 00:33:52,000
If I can dig into, I did look at your,
um, website, which is nimblegravity.com,

694
00:33:52,480 --> 00:33:56,380
and I was really curious by this phrase,
data science, which I, it is quite

695
00:33:56,380 --> 00:34:00,160
a generic phrase, but you use it in
quite a specific way in the sort of

696
00:34:00,165 --> 00:34:03,040
the, the junction of the three circles.

697
00:34:03,400 --> 00:34:04,060
Um, mm-hmm.

698
00:34:04,250 --> 00:34:06,060
What, what do they call that graph?

699
00:34:06,270 --> 00:34:08,250
Uh, a Venn diagram.

700
00:34:08,700 --> 00:34:09,120
Yeah.

701
00:34:09,210 --> 00:34:11,970
This is where I, I should have paid more
attention in Math rather than looking

702
00:34:11,970 --> 00:34:16,140
at the, the, the, the sine wave on
the calculator, uh, the Venn diagram.

703
00:34:16,140 --> 00:34:19,170
So just explain that and how
did you come up with this?

704
00:34:20,040 --> 00:34:20,490
Yeah.

705
00:34:20,495 --> 00:34:22,020
So you know, it.

706
00:34:22,900 --> 00:34:25,570
We talk about pragmatic
data science, right?

707
00:34:25,600 --> 00:34:29,800
And that's, you know, understanding
what you're trying to do From a, a

708
00:34:29,920 --> 00:34:35,290
general business hypothesis, what can
you do to test quickly, to be iterative,

709
00:34:35,290 --> 00:34:40,745
to, you know, extract something in a
very short period of time to then, Be

710
00:34:40,745 --> 00:34:44,765
intelligent about that and move on from
there, from whatever you learned and

711
00:34:44,915 --> 00:34:49,265
create the next phase and, and you know,
kind of creating this iterative cycle.

712
00:34:49,265 --> 00:34:49,325
Yeah.

713
00:34:49,505 --> 00:34:53,045
Just like you would in an
agile programming environment.

714
00:34:53,050 --> 00:34:53,495
Right.

715
00:34:53,555 --> 00:34:55,715
But doing it with data science tests.

716
00:34:55,815 --> 00:35:00,495
And projects and moving quickly and,
and forward and, uh, iteratively.

717
00:35:01,125 --> 00:35:04,275
And, you know, the venn diagram
that you're referring to on, on

718
00:35:04,275 --> 00:35:08,445
the site kind of shows, you know,
artificial intelligence, machine

719
00:35:08,450 --> 00:35:12,285
learning, math, and one circle, it
talks about technology in another.

720
00:35:12,705 --> 00:35:17,055
And then, you know, brings in business
and industry acumen into the other.

721
00:35:17,055 --> 00:35:19,755
And, you know, that's subject
matter expertise, right?

722
00:35:19,755 --> 00:35:19,845
Mm-hmm.

723
00:35:20,085 --> 00:35:24,035
And then that center overlapping
area is, Where data science

724
00:35:24,035 --> 00:35:25,655
actually happens, right?

725
00:35:25,655 --> 00:35:30,485
So you're, you're taking the knowledge
of the folks within the industry, within

726
00:35:30,485 --> 00:35:35,165
the business and their, their specialized
subject matter expertise over years.

727
00:35:35,165 --> 00:35:35,405
Right?

728
00:35:35,405 --> 00:35:40,175
And you're combining that with
technologies that allow you to move and

729
00:35:40,180 --> 00:35:43,145
analyze huge, large quantities of data.

730
00:35:43,415 --> 00:35:50,060
And then you're applying, you know, uh,
Mathematics, algorithms and equations

731
00:35:50,060 --> 00:35:54,620
with machine learning and AI on top
of that, and, bringing it all together

732
00:35:54,625 --> 00:35:56,810
into, into the world of data science.

733
00:35:57,080 --> 00:36:01,760
And then you're able to output
insights from an organization

734
00:36:01,760 --> 00:36:07,570
through their data that they might
not have really seen before, right?

735
00:36:07,570 --> 00:36:07,850
Mm-hmm.

736
00:36:07,930 --> 00:36:08,050
Like.

737
00:36:08,815 --> 00:36:13,045
They might have had gut feelings
that, you know, that the long-term

738
00:36:13,045 --> 00:36:16,345
employees that that really understood
the business in, in total might

739
00:36:16,350 --> 00:36:17,785
have been expressing these things.

740
00:36:17,790 --> 00:36:21,745
But you can actually put out the
goals and the percentages and, and

741
00:36:21,745 --> 00:36:23,215
these kinds of things you can do.

742
00:36:24,195 --> 00:36:29,955
Creation of propensity models, how likely
is a customer gonna be to buy from us?

743
00:36:29,955 --> 00:36:32,085
How likely are they gonna be
able to buy a second time?

744
00:36:32,415 --> 00:36:39,195
You can output, you know, actual,
um, modeling around, you know, people

745
00:36:39,200 --> 00:36:42,045
who interact with your business and
footfalls and what's going on there.

746
00:36:42,465 --> 00:36:44,055
Any number of things really.

747
00:36:44,415 --> 00:36:45,735
Um, and.

748
00:36:45,760 --> 00:36:47,200
It's, it's a lot of fun.

749
00:36:47,200 --> 00:36:52,300
You can take alternative data sets and
understand, um, you know, what are, what

750
00:36:52,300 --> 00:36:55,000
are people doing in their day, right?

751
00:36:55,000 --> 00:36:55,090
Mm-hmm.

752
00:36:55,330 --> 00:36:59,530
From foot traffic and stores and,
you know, where else are they going?

753
00:36:59,890 --> 00:37:04,600
Um, that we might be able to,
you know, modify our marketing

754
00:37:04,600 --> 00:37:07,690
messaging and placement in
order to capture these people.

755
00:37:07,690 --> 00:37:15,670
And you can unlock these kinds of super
interesting approaches to interacting with

756
00:37:15,670 --> 00:37:20,380
your customers that you probably wouldn't
have done through traditional tools.

757
00:37:20,410 --> 00:37:20,620
Mm-hmm.

758
00:37:20,680 --> 00:37:22,750
Yeah, that's really interesting, isn't it?

759
00:37:22,755 --> 00:37:25,960
And I, I, I, I get sucked into it, Matt.

760
00:37:25,965 --> 00:37:26,350
I do.

761
00:37:26,350 --> 00:37:31,030
Because it, you just get drawn into all
the, all the, all the possibilities that

762
00:37:31,090 --> 00:37:35,500
this sort of technology and thinking
now brings to you really, and the, and

763
00:37:35,500 --> 00:37:36,760
the possibilities that are out there.

764
00:37:36,760 --> 00:37:36,820
Yeah.

765
00:37:37,270 --> 00:37:42,760
I guess one thing, um, that sort of
springs to mind in all this is, It

766
00:37:42,760 --> 00:37:47,770
sounds expensive and complex, so if
I'm just starting out in e-commerce.

767
00:37:49,465 --> 00:37:51,895
Um, I'm curious to know your thoughts
on this, cause obviously you're dealing

768
00:37:51,895 --> 00:37:54,775
with companies doing billion dollar
turnovers and so it's kind of like mm-hmm.

769
00:37:55,195 --> 00:37:58,165
If you are starting out and you're
sitting there and you're listening to

770
00:37:58,165 --> 00:38:02,035
people going, well, we can, you know,
do this data and all, and all these sort

771
00:38:02,035 --> 00:38:05,215
of propensity models and all that sort
of wonderful stuff, but you're like,

772
00:38:05,255 --> 00:38:09,025
dude, I've got like a thousand people
coming to my website a year at the

773
00:38:09,025 --> 00:38:14,005
moment of which 15 are buying and I'm
trying to build the business, I think.

774
00:38:16,120 --> 00:38:20,080
It, it feels, I think it can
feel quite overwhelming for a

775
00:38:20,080 --> 00:38:21,040
startup Do you know what I mean?

776
00:38:21,040 --> 00:38:23,290
An entrepreneur to think, oh, I've
gotta have this, I've gotta have this,

777
00:38:23,290 --> 00:38:25,540
I've gotta have this, and, and, and
all these tools that the big guys

778
00:38:25,540 --> 00:38:27,340
have got, how am I even gonna compete?

779
00:38:28,000 --> 00:38:31,600
And I wonder what your thoughts
were on that, because it, I mean,

780
00:38:31,605 --> 00:38:34,120
in some respects, it's not actually
a million miles away from the guy

781
00:38:34,120 --> 00:38:37,420
that's starting up right now, but I,
I can understand that it, it feels

782
00:38:37,420 --> 00:38:39,430
like a massive world out there.

783
00:38:40,420 --> 00:38:43,120
And so I just wondered what your, your
thoughts were on that for the little guy.

784
00:38:44,125 --> 00:38:44,725
Yeah.

785
00:38:44,785 --> 00:38:47,365
So a couple things that
I'll say around that.

786
00:38:47,395 --> 00:38:52,015
There's a lot of platforms that have been,
that are data, data science platforms

787
00:38:52,020 --> 00:38:53,755
that have been productized, right?

788
00:38:53,755 --> 00:38:58,285
That allow you to do things
like, um, price testing and

789
00:38:58,285 --> 00:38:59,765
creating pricing models, right?

790
00:39:00,445 --> 00:39:05,125
And so as a smaller guy or
gal running a business, right?

791
00:39:05,155 --> 00:39:12,445
Um, it you don't necessarily have to, um,
Have a huge data science budget and hire

792
00:39:12,445 --> 00:39:16,795
a bunch of people and, you know, have
this massive team, or go to an agency and

793
00:39:16,795 --> 00:39:18,805
have them do that on your behalf, right?

794
00:39:19,135 --> 00:39:22,165
You can utilize some of these,
these tools that are out there.

795
00:39:22,465 --> 00:39:27,345
Uh, and you know, like the pricing model
that I'm talking about, um, there's

796
00:39:27,535 --> 00:39:30,955
a platform called Intelligence, uh,
that, you know, we've been talking

797
00:39:30,955 --> 00:39:33,865
with and, uh, they do this right?

798
00:39:33,865 --> 00:39:37,285
And, um, there, there's some
interesting things out there like that.

799
00:39:37,930 --> 00:39:39,700
But here's the other one, right?

800
00:39:39,700 --> 00:39:43,510
Like being a data scientist,
I would argue, right?

801
00:39:43,510 --> 00:39:49,810
Like I think we think of it as folks
who are, they have math degrees,

802
00:39:49,810 --> 00:39:53,200
they have computer science degrees,
they're working, you know, with

803
00:39:53,200 --> 00:39:57,130
huge data sets and environments like
Snowflake or whatever, and Right.

804
00:39:57,400 --> 00:40:01,330
Like those people are professionally
labeled as data scientists.

805
00:40:01,330 --> 00:40:01,331
Mm-hmm.

806
00:40:02,295 --> 00:40:07,485
A true data scientist is somebody who
is curious, who's willing to test and

807
00:40:07,485 --> 00:40:13,125
experiment and try things and create a
hypothesis and go out and execute against

808
00:40:13,125 --> 00:40:17,475
that and see what the results were,
and then come back and iterate on that.

809
00:40:17,475 --> 00:40:17,745
Right?

810
00:40:17,745 --> 00:40:17,805
Yeah.

811
00:40:17,985 --> 00:40:20,875
And you know where you can do that Excel.

812
00:40:21,880 --> 00:40:23,530
And Google Sheets, right?

813
00:40:23,530 --> 00:40:27,670
Like on the low, low end of
things, uh, you can go back and do

814
00:40:27,670 --> 00:40:32,230
regression studies in Excel, and
there are a thousand tutorials on

815
00:40:32,230 --> 00:40:33,760
the internet about how to do this.

816
00:40:33,760 --> 00:40:35,230
It's built into Excel.

817
00:40:35,560 --> 00:40:40,740
You can understand what happened with
your data in the past through a regression

818
00:40:41,020 --> 00:40:44,710
test, and you can even compare this to.

819
00:40:44,990 --> 00:40:48,770
Data sets that are freely available
from, say, the government, right?

820
00:40:48,800 --> 00:40:48,890
Mm-hmm.

821
00:40:49,280 --> 00:40:55,970
Uh, I did this back in, oh gosh, I
wanna say it was 2008, with, uh, a

822
00:40:55,970 --> 00:40:58,250
couple super smart business analysts.

823
00:40:58,400 --> 00:41:00,410
They were data scientists,
but at that point in time they

824
00:41:00,410 --> 00:41:01,640
were called business analysts.

825
00:41:02,180 --> 00:41:08,660
Um, and, you know, we did the, the
bulk of it, um, in, in Excel and.

826
00:41:08,995 --> 00:41:13,585
What we found at, at this point in time
is we were doing a regression analysis of

827
00:41:13,585 --> 00:41:20,215
appliance sales, uh, and comparing that
to housing starts, uh, which are available

828
00:41:20,215 --> 00:41:23,695
government metric here in the United
States that you can download for free

829
00:41:23,695 --> 00:41:25,755
off of, uh, off of government websites.

830
00:41:26,455 --> 00:41:32,125
And we were looking to understand, um,
how the correlation between housing

831
00:41:32,125 --> 00:41:34,915
starts in the areas we, we looked at.

832
00:41:35,215 --> 00:41:37,015
Met up with appliance sales.

833
00:41:37,015 --> 00:41:37,256
Right, right.

834
00:41:37,261 --> 00:41:38,185
Like that makes sense.

835
00:41:38,245 --> 00:41:41,515
You're building a new house, you gotta
build, you know, you gotta buy appliances.

836
00:41:41,815 --> 00:41:42,805
What's the gap?

837
00:41:42,835 --> 00:41:43,105
Right.

838
00:41:43,105 --> 00:41:47,425
Like that's really what we wanted to
understand and, and know when we should

839
00:41:47,430 --> 00:41:51,155
really be going to market and talking to
contractors and, and trying to mm-hmm.

840
00:41:51,235 --> 00:41:52,915
To land large sales deals.

841
00:41:53,535 --> 00:41:56,365
And that's a hundred
percent doable in Excel.

842
00:41:56,370 --> 00:41:56,555
Right.

843
00:41:56,845 --> 00:41:56,905
Wow.

844
00:41:56,905 --> 00:41:58,345
And I think it's more.

845
00:41:58,975 --> 00:42:00,865
Go figure out what your hypothesis is.

846
00:42:00,865 --> 00:42:00,955
Mm-hmm.

847
00:42:01,255 --> 00:42:05,155
And then figure out the environment that
you ha, you know, kind of need to utilize

848
00:42:05,155 --> 00:42:10,975
in order to do that and get yourself,
you know, somebody curious and, and

849
00:42:10,975 --> 00:42:15,565
smart and good with manipulating data in
multiple types of platforms and places.

850
00:42:15,865 --> 00:42:16,285
Mm-hmm.

851
00:42:17,215 --> 00:42:18,235
That's top advice.

852
00:42:18,445 --> 00:42:19,345
So let's start small.

853
00:42:19,405 --> 00:42:21,625
Just start doing it, I think, um, totally.

854
00:42:21,625 --> 00:42:24,505
Because e-commerce is both
art and science, right?

855
00:42:24,505 --> 00:42:28,585
It's not, and you, the, the sooner you
can get your head around understanding

856
00:42:28,585 --> 00:42:30,145
some of these things, the better, I think.

857
00:42:30,535 --> 00:42:30,865
Uh, yeah.

858
00:42:31,045 --> 00:42:35,095
And, uh, and, and if Excel can help you,
and Google Sheets can help you have at it.

859
00:42:35,355 --> 00:42:37,425
I mean, I've spent many an
hour in a Google spreadsheet.

860
00:42:40,905 --> 00:42:42,525
That should almost be a t-shirt slogan.

861
00:42:42,915 --> 00:42:47,895
Um, listen, uh, Matt, uh, I'm aware of
time, man, and, and I feel like we're

862
00:42:47,895 --> 00:42:49,605
just starting to scratch the surface.

863
00:42:49,605 --> 00:42:52,245
So let me ask you one
final question, if I may.

864
00:42:52,815 --> 00:42:52,905
Yeah.

865
00:42:52,905 --> 00:42:54,615
So you're a data scientist.

866
00:42:54,620 --> 00:42:58,035
You've got this company which
is helping, you know, all manner

867
00:42:58,035 --> 00:42:59,830
of different size organizations.

868
00:42:59,830 --> 00:43:01,330
You've seen a lot,
you've experienced a lot.

869
00:43:01,930 --> 00:43:08,440
What's the one opportunity you think that
e-commerce entrepreneurs are not taking

870
00:43:08,440 --> 00:43:11,740
advantage of on the whole, that they
probably should be doing right now to

871
00:43:11,745 --> 00:43:13,390
have the biggest impact on their business?

872
00:43:13,390 --> 00:43:16,210
From your point of view, from a data
science point of view, what, what do

873
00:43:16,215 --> 00:43:17,351
you, what do you see in all of this?

874
00:43:18,590 --> 00:43:19,960
Oh, wow.

875
00:43:20,080 --> 00:43:25,630
Um, That's, that's a great question and
obviously a big challenge, and I think

876
00:43:25,630 --> 00:43:29,260
it's kind of one of those overwhelming
things for, for e-commerce folks, but

877
00:43:30,040 --> 00:43:36,160
I'm gonna go back to a, a tried and
true standard that I unfortunately

878
00:43:36,160 --> 00:43:40,170
don't see enough people executing
well enough on a consistent basis.

879
00:43:40,670 --> 00:43:42,790
And that's AB testing.

880
00:43:43,090 --> 00:43:43,480
Right.

881
00:43:43,540 --> 00:43:44,050
Okay.

882
00:43:44,680 --> 00:43:52,090
And this is a surefire way to
increase the outputs of your

883
00:43:52,120 --> 00:43:54,190
e-commerce operation, right?

884
00:43:54,195 --> 00:43:54,540
Mm-hmm.

885
00:43:55,060 --> 00:43:56,110
Uh, yes.

886
00:43:56,110 --> 00:44:00,220
Sometimes that requires a, a good
amount of traffic, uh, and, and you

887
00:44:00,220 --> 00:44:04,780
need, you know, a certain number of,
of visits, uh, per year to utilize

888
00:44:04,780 --> 00:44:06,551
platforms that are gonna do that.

889
00:44:06,600 --> 00:44:11,200
And I think that that's where
people get a little bit scared too,

890
00:44:11,230 --> 00:44:12,670
from an AB testing perspective.

891
00:44:12,700 --> 00:44:12,730
Mm.

892
00:44:13,945 --> 00:44:19,465
So if you can utilize a platform, if your
website has enough traffic, uh, in order

893
00:44:19,465 --> 00:44:26,245
to be able to, to really get impact,
uh, from an AB testing platform, uh, a

894
00:44:26,245 --> 00:44:27,805
hundred percent you should be doing it.

895
00:44:27,810 --> 00:44:29,215
You should have a team around it.

896
00:44:29,215 --> 00:44:30,445
You should be working iteratively.

897
00:44:30,895 --> 00:44:35,905
You should be, you know, studying the
results and pushing live new features and

898
00:44:35,905 --> 00:44:39,475
functionality and layouts and user flows.

899
00:44:40,810 --> 00:44:43,750
All year long, like do
as many tests as you can.

900
00:44:44,665 --> 00:44:49,435
If your traffic isn't large enough to
support a platform like, you know, an

901
00:44:49,435 --> 00:44:56,275
optimizly convert.com, whatever it might
be, uh, I'm gonna go lofi again, right?

902
00:44:56,365 --> 00:44:59,045
And just be like, be brave.

903
00:44:59,635 --> 00:45:02,805
Start track what you're doing, like.

904
00:45:03,440 --> 00:45:07,310
You know, write it down somewhere,
put it in Jira, put it back into

905
00:45:07,310 --> 00:45:09,080
Excel, or a Google sheet of mm-hmm.

906
00:45:09,320 --> 00:45:11,840
You know, this is my
theory, here's my test.

907
00:45:11,840 --> 00:45:13,160
I launched it on this.

908
00:45:13,550 --> 00:45:15,230
And go watch your Google Analytics.

909
00:45:15,235 --> 00:45:15,290
Yeah.

910
00:45:15,320 --> 00:45:17,030
Or your free analytics platform.

911
00:45:17,090 --> 00:45:17,480
Right.

912
00:45:18,140 --> 00:45:23,900
Change, change some colors, add a second
checkout button above the, you know,

913
00:45:23,930 --> 00:45:27,500
the, the product not just down below
where you know, whatever it might be.

914
00:45:28,040 --> 00:45:30,110
And go watch your analytics.

915
00:45:30,470 --> 00:45:34,075
And did you increase things.

916
00:45:35,545 --> 00:45:36,025
Great.

917
00:45:36,025 --> 00:45:37,045
Keep it live.

918
00:45:37,195 --> 00:45:38,725
Did things stay flat?

919
00:45:39,055 --> 00:45:39,595
Great.

920
00:45:39,625 --> 00:45:40,585
Keep it live.

921
00:45:41,090 --> 00:45:43,195
Did you negatively impact things?

922
00:45:43,435 --> 00:45:43,735
Okay.

923
00:45:43,735 --> 00:45:45,925
Maybe go back and and take that out.

924
00:45:45,985 --> 00:45:46,465
Right.

925
00:45:46,735 --> 00:45:53,215
And see if that thing that you did was
the actual cause of the negative impact.

926
00:45:53,695 --> 00:45:53,905
If.

927
00:45:54,490 --> 00:46:00,100
If you rebound or things stay low or
whatever, maybe it wasn't right, like

928
00:46:00,340 --> 00:46:06,610
you can go lo-fi on your science and, and
kind of like just, just test and iterate.

929
00:46:06,610 --> 00:46:09,520
And in that scenario, I wouldn't
be doing a whole bunch of tests,

930
00:46:09,520 --> 00:46:11,960
but I'd be doing some tests and
I'd be being brave about it.

931
00:46:12,835 --> 00:46:13,105
Yeah.

932
00:46:13,375 --> 00:46:15,025
No, it's very good, very good.

933
00:46:15,025 --> 00:46:17,575
I, I love that, especially
your product page.

934
00:46:17,725 --> 00:46:21,205
Um, I don't feel like people product
test, uh, split test their product page

935
00:46:21,205 --> 00:46:25,195
enough and you can run different ads to
different versions of your product page.

936
00:46:25,200 --> 00:46:27,925
You know, you can, there's all
kinds of ways to play around

937
00:46:27,925 --> 00:46:30,655
with that and, and definitely do
that and get involved with it.

938
00:46:31,385 --> 00:46:34,615
Um, Uh, I like that.

939
00:46:34,640 --> 00:46:36,080
I like the simplicity of that.

940
00:46:36,080 --> 00:46:38,480
Matt, I'm not gonna lie, I was kind
of expecting you to say something in

941
00:46:38,480 --> 00:46:41,510
ai, but actually going back to split
testing makes an awful lot of sense.

942
00:46:41,510 --> 00:46:44,780
And then as you were talking, I'm going,
that's, that's actually the right answer.

943
00:46:44,870 --> 00:46:47,180
Uh, I I thought that was,
uh, very well said, sir.

944
00:46:47,270 --> 00:46:48,050
Very well said.

945
00:46:48,710 --> 00:46:48,920
Thank you.

946
00:46:48,920 --> 00:46:52,850
So, uh, no, listen, um, Matt, listen.

947
00:46:52,850 --> 00:46:54,710
How do people reach you?

948
00:46:54,710 --> 00:46:56,270
How do they get a hold of you?

949
00:46:56,270 --> 00:46:59,600
If they want to connect with you
guys, ask you some more questions,

950
00:46:59,600 --> 00:47:00,800
what's the best way to do that?

951
00:47:01,510 --> 00:47:05,060
Yeah, you, you already mentioned
the website, nimblegravity.com,

952
00:47:05,080 --> 00:47:07,690
so that's definitely a great
way to, to get ahold of us.

953
00:47:08,020 --> 00:47:10,150
The other easiest place
right, is LinkedIn.

954
00:47:10,210 --> 00:47:13,510
Uh, feel free to to reach out to
the company or follow us there

955
00:47:13,510 --> 00:47:15,250
or reach out to me directly.

956
00:47:15,310 --> 00:47:16,870
Uh, I'm on there, Matt Ranta.

957
00:47:17,230 --> 00:47:20,650
Uh, and happy to connect with
folks and chat with them about any

958
00:47:20,650 --> 00:47:23,680
of their e-commerce, digital AI
challenges, whatever it might be.

959
00:47:24,370 --> 00:47:25,090
Fantastic.

960
00:47:25,240 --> 00:47:27,970
Absolutely do go connect
with Matt on LinkedIn.

961
00:47:28,330 --> 00:47:32,110
I need to check, see if we are LinkedIn,
LinkedIn Linked, linked on LinkedIn.

962
00:47:32,140 --> 00:47:33,880
I know it's not easy to
say connected on LinkedIn.

963
00:47:34,540 --> 00:47:38,140
Um, uh, we we should definitely do that.

964
00:47:38,145 --> 00:47:41,951
Listen, we will of course link to
Matt's info in the show notes, which

965
00:47:42,130 --> 00:47:46,230
you can get along for free along with
the transcript at ecommercepodcast.net.

966
00:47:46,380 --> 00:47:49,210
Or it will be winging
its way into your inbox.

967
00:47:49,210 --> 00:47:54,115
If you've signed up for a newsletter,
Matt, listen, genuinely really

968
00:47:54,115 --> 00:47:55,735
appreciated the conversation.

969
00:47:56,005 --> 00:47:58,045
I'm loving this stuff right now.

970
00:47:58,435 --> 00:48:01,255
Um, I, I said at the start,
the show sponsored by the

971
00:48:01,285 --> 00:48:03,025
e-commerce cohort, which it is.

972
00:48:03,910 --> 00:48:06,550
And in the cohort, which is our
sort of monthly mastermind group,

973
00:48:07,090 --> 00:48:08,650
uh, we've just done a whole thing.

974
00:48:08,650 --> 00:48:13,150
I run, I, I do this thing on, on chat GPT,
on how to start an e-commerce business.

975
00:48:13,150 --> 00:48:17,410
How to use chat GPT to come up with
the name of the company, the products

976
00:48:17,410 --> 00:48:20,200
that we're gonna sell, what's gonna
differentiate it, how we do customer

977
00:48:20,200 --> 00:48:21,510
research, how we write product copy.

978
00:48:21,850 --> 00:48:24,460
We had a, we had a bit of
a laugh actually with that.

979
00:48:24,460 --> 00:48:24,670
Nice.

980
00:48:24,690 --> 00:48:29,550
And so, Yeah, if this is something which
has perked your interest, dear listener,

981
00:48:29,880 --> 00:48:32,050
do check out ecommercecohort.com.

982
00:48:32,280 --> 00:48:34,650
There is like a, a sort
of a freezed trace.

983
00:48:34,830 --> 00:48:36,690
It's like a dollar trial, I
think, or something like that.

984
00:48:36,690 --> 00:48:39,870
Check it out, uh, on the, on
the, the chat thing that we do.

985
00:48:39,870 --> 00:48:43,230
It was great fun actually, but
Matt, genuinely appreciate it.

986
00:48:43,235 --> 00:48:45,810
Love this stuff, love learning
lots of stuff about ai.

987
00:48:45,810 --> 00:48:47,310
I think it's a really interesting thing.

988
00:48:47,310 --> 00:48:49,050
So thank you so much for joining me, man.

989
00:48:49,675 --> 00:48:50,515
Thanks for having me.

990
00:48:50,515 --> 00:48:51,475
Great conversation.

991
00:48:51,475 --> 00:48:52,195
Really enjoyed it.

992
00:48:52,525 --> 00:48:53,245
No, it's been great.

993
00:48:53,485 --> 00:48:53,815
Great.

994
00:48:53,905 --> 00:48:57,595
So thanks again to Matt and also,
again, a big shout out to today's

995
00:48:57,595 --> 00:48:59,265
show sponsor, the e-commerce cohort.

996
00:49:00,055 --> 00:49:03,415
Uh, the free training is at
ecommercecycles.com that I mentioned.

997
00:49:03,420 --> 00:49:07,376
And if you want to check out more about
the, uh, chat GPT training that we did.

998
00:49:08,020 --> 00:49:10,070
That's at ecommercecohort.com.

999
00:49:10,270 --> 00:49:12,250
A lot of domains, but they're
all linked on the website.

1000
00:49:12,580 --> 00:49:16,000
Now, be sure to follow the e-commerce
podcast wherever you get your podcast

1001
00:49:16,000 --> 00:49:19,960
from because we've got yet more
great conversations lined up, and we

1002
00:49:19,960 --> 00:49:21,460
don't want you to miss any of them.

1003
00:49:21,460 --> 00:49:26,320
And before we wrap up today's episode,
let me take a little moment to invite

1004
00:49:26,320 --> 00:49:29,275
you to become a part of the show now.

1005
00:49:29,275 --> 00:49:32,245
If you're an e-commerce entrepreneur
or an expert and would like to share

1006
00:49:32,245 --> 00:49:35,515
your insights with our audience,
we would love to hear from you.

1007
00:49:35,935 --> 00:49:39,115
Or maybe you just know someone
who would make a great guest.

1008
00:49:39,115 --> 00:49:40,165
Do send 'em our way.

1009
00:49:40,375 --> 00:49:43,435
Just head over to the
website, ecommercepodcast.net

1010
00:49:43,465 --> 00:49:45,145
and get in touch with us.

1011
00:49:45,145 --> 00:49:49,435
All the information is on there,
and in case no one has told

1012
00:49:49,615 --> 00:49:52,615
you yet today, you are awesome.

1013
00:49:52,705 --> 00:49:53,245
Yes, you are.

1014
00:49:53,275 --> 00:49:54,235
Created awesome.

1015
00:49:54,355 --> 00:49:56,005
It's just a burden you have to bear.

1016
00:49:56,215 --> 00:49:57,115
I have to bear it.

1017
00:49:57,925 --> 00:49:59,305
The other Matt has to bear it.

1018
00:49:59,515 --> 00:50:01,645
And just about every Matt on
the planet has to bear it.

1019
00:50:01,645 --> 00:50:03,535
And if your name's not Matt,
you've also gotta bear it as well.

1020
00:50:03,595 --> 00:50:06,655
Now, the e-Commerce podcast
is produced by Aurion Media.

1021
00:50:06,985 --> 00:50:10,885
You can find our entire archive of
episodes on your favorite podcast app.

1022
00:50:10,885 --> 00:50:13,995
The team that makes this show
possible is Sadaf Beynon, Estella

1023
00:50:14,010 --> 00:50:15,955
Robin and Tanya Hutsuliak.

1024
00:50:15,955 --> 00:50:19,735
Our theme song was written by Josh
Edmundson, and as I mentioned, if

1025
00:50:19,855 --> 00:50:23,155
you would like to read the transcript
or show notes, head over to the

1026
00:50:23,155 --> 00:50:28,640
website, ecommercepodcast.net, uh,
where you can also sign up for our

1027
00:50:28,640 --> 00:50:31,580
weekly newsletter and get all of
this good stuff direct your inbox.

1028
00:50:31,970 --> 00:50:33,170
That's it from me.

1029
00:50:33,410 --> 00:50:34,340
That's it from Matt.

1030
00:50:34,340 --> 00:50:36,200
Thank you so much for joining us.

1031
00:50:36,200 --> 00:50:38,270
Have a fantastic week
wherever you are in the world.

1032
00:50:38,720 --> 00:50:39,470
I'll see you next time.

1033
00:50:40,070 --> 00:50:40,610
Bye for now.