August 20, 2024 • 43 mins
Can you imagine a world where traditional marketing rules no longer apply and user privacy reigns supreme? Join us on Planet Amazon as we sit down with Jeff Greenfield, CEO of Provalytics, to unravel the complexities of digital marketing in a cookie-less era. Jeff takes us on a journey from his early days in brand-side media buying to pioneering multi-touch attribution with C3 Metrics, providing an insider's look into the evolution of marketing analytics. Discover how Provalytics leverages cutting-edge technology to create accurate attribution models that respect user privacy and navigate the challenges posed by new privacy regulations.
Marketing attribution has always been a tricky business, especially when last-click attribution falls short in capturing long sales cycles. Jeff shares his insights on how marketing mix modeling and advanced mathematical predictions can revolutionize future sales strategies. By focusing on becoming "less wrong" over time, marketers can gain valuable insights without getting bogged down by complex algorithms. Jeff also addresses the skepticism marketers often feel towards machine-driven insights, emphasizing how Provalytics aims to shift their focus back to creative strategies and ultimately optimize media spend allocation.
The landscape of digital marketing is shifting rapidly, especially with the looming "cookie apocalypse." Learn how Provalytics is adapting to this new reality by employing innovative methods that maintain user anonymity while still delivering effective results. Jeff explores the synergy between different retail platforms like Amazon and Shopify, highlighting the importance of accurate sales attribution across multiple channels. We also discuss the significance of targeting specific demographics through platforms like TikTok and Facebook to maximize sales. Whether you're an Amazon-only store or a multi-platform retailer, this episode is packed with actionable insights to help you navigate the future of digital marketing.
For more information about Provalytics, please visit https://provalytics.com/
Want to chat with us about this podcast? Send us a text message here
The Planet Amazon podcast, brought to you by Phelps United, addresses all things Amazon and other eCommerce marketplaces. In each episode, we talk with Brands, Agencies, and Sellers about Amazon news, new features, policies, brand policies, logistics, marketing, issues, and challenges, among other topics.
To watch all Planet Amazon Podcast episodes, visit our YouTube channel.
To learn more about Phelps United, visit our website.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to the Planet Amazon podcast with Adam
Schaefer, where we explore theworld of Amazon and other
e-commerce marketplaces.
Join us as we delve into thelatest strategies and tactics
for successful selling on theworld's largest online
marketplace.
Speaker 2 (00:17):
Hello, I'm Adam Schaefer and welcome to Planet
Amazon, where we talk about allthings Amazon and e-commerce.
To Planet Amazon, where we talkabout all things Amazon and
e-commerce.
Today, we're excited to haveJeff Greenfield, ceo of
Provalytics, joining the podcast.
Jeff is the co-founder and CEOof Provalytics.
He has built the nextgeneration of attribution,
taking into account all the newprivacy regulations and which I
(00:40):
have a big question for himtoday the possible upcoming
cookie apocalypse.
Jeff is an entrepreneur withthree decades of strategy,
growth and marketing experience,building leadership teams with
an emphasis on innovativemarketing enabled by new
technology.
Welcome to the podcast, jeff,so excited to have you here,
adam, thank you so much forhaving me here.
Speaker 3 (01:01):
I'm very excited.
Yeah, everything cookies thesedays.
That's what's in the news formarketers.
Speaker 2 (01:07):
Yeah, I know we got to get to that.
I mean, as I'm talking about acookie-less world, I'm like, is
it or is it not?
But let's come back to that ina second and what we'd love to
know is more about you, man,Like tell us who you are and
what led you on this journey toget Provalytics off the ground
and going.
Speaker 3 (01:27):
Well, it wasn't anything like I went to school
for measurement.
To me, measurement reallydidn't seem to be like a sexy
thing.
It's sexy now that I'm in it,but it wasn't in the beginning.
I used to be on the brand side.
I used to buy media andeventually did large-scale
events, used to put products inTV and film, and what happened
(01:50):
to me is that, as digital wasstarting to grow, I had a client
, a publicly traded weight lossclient that was spending
millions of dollars a monthwhich back then was a lot of
money and doing billions ofimpressions every month.
And they had this big issuewhere, for every single sale,
they had multiple partnersclaiming credit it's what we
(02:10):
call now the deduplication issueand they were doing a lot of
deals that were CPA deals, wherethey're paying on a cost per
sale basis, and what ended uphappening is that we used to say
that for every sale, they hadfive mothers or fathers who were
claiming credit that that'stheir child.
And there seemed to be no wayaround this.
And I was already working onanother technology product.
(02:31):
So I had a team of engineersand I figured out a way.
I said, all right, we canfigure out a way to work around
this and we built a solution tosolve that deduplication.
And then every month somethingelse would come up.
Our media buyers would say well, you need to figure out who
gets credit for the sale.
And before I knew it, I hadbuilt out the first multi-touch
(02:51):
attribution platform called C3Metrics.
This is around late 2007, early2008.
And that's kind of what got meinto measurement.
Once I built it, I realized Ithink other people could use
this, built that up, scaled up,the company exited right before
COVID, thinking I was done withmeasurement, and then, like,
(03:13):
over the course of a year or two, the bottom dropped out.
All of these things startedhappening.
We went from a world ofmarketing where you could get
all sorts of information oneveryone, you could create all
these segments and all of asudden, privacy became the
number one rule of the day.
We ended up losing cookies andSafari and an iOS and we had all
(03:34):
those iOS updates that occurred.
And then it became, overnight aswell, an app world, and what
that means is that in the earlydays it used to be that
everything was websites and nowit was people going from app to
app, and people don't like toleave apps.
They like to stay in an app,and apps don't want you to leave
.
So it got to the point wherethe way that we used to do
(03:56):
tracking at C3 Metrics was nolonger going to work anymore,
and I saw an opportunity and Isaid you know, I think that we
can leverage the power ofsupercomputing meaning that
computers are so much faster nowthan they were even three or
four years ago and take datathat's not user level data, but
that's aggregated data and getto the same answers we got to
(04:20):
before meaning less data but thesame answers.
And we can do that now usingadvanced math and these
supercomputers.
Speaker 2 (04:28):
It's amazing, really.
I mean that is amazing.
But how does it address and I'mjumping ahead but how does it
address the privacy issues?
Speaker 3 (04:36):
Well, the way it addresses the privacy issues and
this is one of the big issueswith the way that marketing and
advertising has always been doneand it's not really a problem.
Remember, there was really noregulations or anything in the
early days, but all of marketingwith digital has all been at
the user level, meaning I cantarget you.
You go to a website, You'relooking at some shoes you don't
(04:57):
buy and then five minutes lateron Facebook you see those same
shoes in your Facebook feeds,the ability to have ads track
you around, and it's that verycool aspect of the internet, the
personalization that folksreally tend to like.
But marketing on digital hasalways been we target users and
we measure at that user level.
Because of all of these changes, we've kind of had to pan back
(05:21):
the camera.
We're actually from ameasurement standpoint of you
and a targeting.
We're really living in a worldvery similar to how things were
before digital, where we don'thave user level data.
It's just not available.
Things have to be anonymous,and so what we do is we take in
data that's not user level data.
We take in the data that youwould see when you go into a
(05:43):
report in Facebook or an Amazon.
There's no users there.
We're not interested inindividual orders or order
information.
I want to know how many ordersyou had each day.
So we take daily data.
That's non-PII and that's whatgoes into the platform and it's
from that daily data that we'reable to give marketers the same
types of insights.
(06:04):
At C3, we would take user leveldata, hundreds of billions of
data points every month.
We would take it at the userlevel but then we would kind of
aggregate it up to the dailylevel for insights.
Because when you're looking tomake decisions with budgets,
you're not going to do it at auser level, You're going to do
it at a large budget level.
So you need to see daily data.
(06:24):
We take in daily data atProvalytics, but it's not user
level, it's aggregated.
Speaker 2 (06:30):
So how would it work?
Okay, so we're going to go buysome Google advertising or
Facebook advertising, or let'sstay with Google.
How are you then selecting theaudience for your clients?
How does that work then?
Speaker 3 (06:46):
So clients come to us that have already bought media
or are in the process of buyingmore media, and so the data that
we get.
In a sense, in the case of likea Google, we would get granular
data that would be like youknow SEM, you bought it on
Google.
That's another level.
If you will, you've got acampaign.
(07:08):
Let's say we call a campaign1234, and it's audience, and you
have a name for the audience.
That's the information we wouldget.
But who those users are in thataudience?
We wouldn't get those.
Maybe that audience is taggedfor a particular keyword.
We would get that informationout.
Then, when you marry that withsome Facebook data from Facebook
, we would get that informationout.
And then, when you marry thatwith some Facebook data from
Facebook, we would get campaignad set and creative information
(07:33):
and an audience.
If you've got a specificaudience that's being hit as
well too.
Speaker 2 (07:37):
So you're looking at their historic advertising, the
historic spend.
Speaker 3 (07:41):
Right, so we take historical data up to like
yesterday and we use that datain order to make decisions about
where to spend your dollartomorrow.
It's kind of the old adageevery dollar I spend in
advertising at least half thedollar I spend in advertising is
wasted.
The problem is I don't knowwhich half.
So how do we make a decision onwhere to spend that next dollar
(08:03):
?
Well, the best indicator ofthat is what happened yesterday,
what happened last month andthen actually even what happened
over the last year, so you canlook at seasonality.
Are we coming into a month thatis a lower than usual month?
Is this a month that creates anopportunity for us?
What is Wednesday of the 23rdweek of the year look like
(08:25):
historically for us?
And we have that information bygetting a historical 12 months
worth of data on a client.
Speaker 2 (08:33):
That's amazing.
So I should have met you a fewyears ago.
Could have saved some money.
So I think about advertisingand what you said earlier about
the attribution, and I get itbecause I lived in that world
where everybody wants a piece ofthe pie, everybody's got a 14
day cookie I'm going to say theword cookie or something and so
(08:53):
you'll have an affiliate, google, facebook, all claiming that
they got the sale.
And so what was?
What was the silver bullet thatlets you figure out which is
the one that actually gets it?
Speaker 3 (09:05):
Well, that's that's a great question, because what we
did is we looked at all thoserules you know, the 14-day
view-through window, theseven-day click-through window
to try to see is there any likea semblance of truth, is there
anything that helps guide us?
And what we found is thatthere's these terms on IOs.
You know, back in the day,everything was purchased, not
(09:26):
programmatically but throughinsertion orders, and these were
essentially the rules ofengagement.
We're going to pay you basedupon these sets of rules.
And those rules were justsomewhat bogus.
They were just kind of made upand it was all a negotiation to
see what kind of deal you couldget.
But what led us in the beginningwas that everybody was last
click, and so the assumption waswell, for certain types of
(09:50):
brands and certain types ofbusiness, last click may be best
.
So if you're selling somethingthat people are always buying,
they're always in market forthat, last action may be a good
thing.
But then let's shift and let'slook at another product where
people are in market for ninemonths, like a new car In the US
(10:11):
.
It's about a nine-month salescycle.
Well, it would be absolutelypositively wrong to be judging
car sales based upon last click.
It would probably make moresense for the first action what
got someone in your sales funnel?
But then there's other productsthat are like seasonal and that
kind of gave us the idea thatmaybe something in the middle
(10:31):
would be there, and in the earlydays of C3 metrics that was
kind of our thinking.
But the reality is is that wedon't actually have to guess
about this stuff, becausemarketers have been using a
technique called marketing mixedmodeling for a very long time
and that's how they figured outthe effectiveness of ad
campaigns back before there wasdigital.
(10:53):
Now, back then it was somewhatlimited because they were
looking at correlations betweenmarketing channels not like
campaigns and not like creativesto sales, and that's all they
were looking at.
In the digital realm we havemultiple outcomes.
Typically, sometimes we'relooking at orders, sometimes
we're looking at revenue.
We may be looking at all orders.
(11:14):
We may be interested in justsubscription revenue.
So there's multiple outcomesand we also want to have
multiple levels of granularity,because if you tell a search
person, hey, cut search 3%,that's not going to help them.
You need to tell them campaign,ad, group, keyword, maybe even
match type to really help themout.
So we leverage the math of thepast to help us get to the
(11:37):
future.
We also leverage a techniquewhere we're simply looking at
how well does the mathematicalmodel predict the future, and
what I mean by that is that welook at the timeframe that we
have, that we're evaluating andwe train the model, just like
you hear about with AI, howwe're doing all of this training
(11:59):
of models.
You know authors arecomplaining that.
You know these AI models arereading their books and then
writing like them.
So what we do is we train themodel for a month.
When we have like a year'sworth of data, we will give, for
that first month, the model.
We will give it everything.
We'll give it all the marketingdata and all the sales data,
(12:19):
and then we will hold back thesales data and we want to see
how well the model, based uponimpressions, clicks and all of
the marketing data, how well canit predict the actual sales
volume or whatever conversionthat we're looking at.
That's how you can tell that amodel is working well is, if you
hold data back, it actuallypredicts 75, 85, sometimes 95%
(12:44):
of the time.
Now, remember, all models arewrong.
Some are useful, but you wantto bet on a model that's
predicting pretty well that it'smore times right than it's
wrong, and what I like to say isthat folks who are using Google
Analytics 4 or Adobe, who arespending significant sums of
(13:04):
money, they know that whatthey're using is wrong.
It's just wrong, and so ourphilosophy at Provalytics is
that you want to be less wrongtomorrow than you are today.
You don't need to shoot it upto the moon, but if you can
incrementally get a little lesswrong every day, you're going to
get closer to the ultimate,which is properly allocating
(13:27):
your media spend.
But you're never going to beperfectly right, but the key
here is to incrementally getClose to right, closer to right.
Yeah.
So you should look at a modeland it should be able to predict
pretty accurately what yoursales should be.
When you have a model that doesthat, based upon just how
you're doing things now, thenyou can say to the I want to do
(13:48):
better and I want my budget tobe the same.
I don't have any extra money tospend.
Figure out what is the best wayto reallocate this money.
Let's say I'm spending $5million a month.
All I've got is 5 million.
You have all my marketing data.
Show me the best possible plan.
And then it spits out the bestplan and said if you did
(14:09):
everything here, here's how muchmore money you would make.
And that gets people jazzed.
Speaker 2 (14:13):
They're like yeah, for sure.
Speaker 3 (14:15):
It's like I can actually have 50% more revenue
just by doing exactly this.
Now, does anyone do exactlythat?
Absolutely not, because we'rehumans and we don't trust the
machines a hundred percent yet.
It's just the way it is.
But what people will do isthey'll start testing.
They'll say, okay, I'm going totry five or six of those things
, and then they're amazed.
(14:35):
They're like, oh my God, Inever thought that it would have
this impact.
And then they start to trust ita bit more.
The most important thing is toremember is that, as marketers,
we're not math people.
We got into marketing becausewe wanted to be creative.
In fact, if they told you incollege when you studied
marketing that you're going tobe spending more time in a
spreadsheet as your accountingfriends, you would have never
(14:57):
gone into this business to beginwith.
We're creative people.
We haven't learned yet.
Even though we say we'redata-driven marketers, we have
not learned yet how to trust themachines.
Probolytics is the machine thatpoints you in the right
direction, so you can spend moretime doing the creative stuff
versus trying to figure out themath.
Speaker 2 (15:17):
That is awesome, and you're right.
I would never have done it if Iknew it was going to be math
all day long.
So thank you for what you'redoing.
So then let's go back to thisother thing.
First met, we were talkingabout you know, google and
cookies going away and howyou're going to help the world,
(15:38):
but then, all of a sudden, wehung up and I'm reading all
these articles saying, nah, wechanged our mind, we're not
going to do that.
So what actually happened?
Like did they stop?
Are they not doing it?
Speaker 3 (15:48):
I get iOS, but I don't understand the Google part
.
Yeah, the Google part is is thatin the Chrome browser they were
going to get rid of cookiesaltogether third-party cookies,
that is.
So you still would be able tolog in automatically to Amazon
and there would be thatpersonalization, but third-party
cookies the tracking cookies,were going to be gone and,
(16:10):
because of some litigation thatwas going on over in the EU,
they made a decision.
They're not going to be goneand, because of some litigation
that was going on over in the EU, they made a decision.
They're not going to do it.
They're going to put controlsin place, which a lot of us
believe are similar to thecontrols that happened in iOS,
where people opted out and saidthey asked people the question
do you want apps to track youacross things?
(16:33):
Do you want Facebook to be ableto track you across the web?
And most people selectedabsolutely not.
It all depends how you ask thequestion is what the answer is
going to be.
But the reality is that thewhole idea behind the cookie
apocalypse was a wake-up callfor marketers to let them know
that cookies haven't reallyworked for a very long time.
(16:57):
It's kind of a scenario of theemperor has no clothes.
It's this belief that there's away to track people and then
it's really effective.
But when you actually sit downand you look at the studies that
show how effective cookietracking is, it's really bad.
It used to be good, but it'snot as good as it was before.
(17:19):
I mean just looking at it thisway.
I mean the best way to thinkabout it is that if you're
running a store and you'reselling stuff, are you selling
to anyone that owns an iPhoneand are you noticing that
they're visiting your storeusing that iPhone?
If so, there's a good chancethat your ads are probably not
really targeting them well andyour ability to retarget them is
(17:41):
not that great and everyonelikes to believe of course I'm
selling to iPhone users becauseI have a high-end type item.
If that's the case, yourmeasurement and your targeting
is completely off, unless you'redoing some very sophisticated
stuff.
So the reality is, is that whatwe're going to find is that the
companies that have been movingtowards a cookie-less
(18:02):
environment anyway are on theright path, because you need to
have a separate identificationsystem that's anonymous on the
web, because there's not goingto be much oomph put in to the
further growth of cookies.
So the train left the station anumber of years ago.
It's just kind of slowed down abit, but it's on its way out,
(18:25):
without a doubt.
Speaker 2 (18:26):
You know it's weird.
I mean, maybe I'm a freak, butI actually like the targeted
advertising that I get, theremarketing I get.
Is that going to go awaybecause of this?
Speaker 3 (18:36):
It's not going to go away.
They've found ways to do it,like, for example, criteo, who's
one of the best known for theirbehavioral retargeting.
They've gone and they've setdirect deals with publishers,
which they've had for years andwhat they've been able to do
with them.
Because when you think about apublisher like the New York
Times or the Wall Street Journal, they have a large subscription
(18:58):
base and a majority of thepeople that are there are logged
in.
So any site where you'reauthenticated at, they know who
you are and there's a way forthem to hash your email ID and
sync up with other folks who arevisiting shopping websites.
So they have methodologies todo that.
The trade desk has createdtheir own unique, anonymous or
(19:20):
pseudo anonymous identifier, sothere's ways to link this stuff
up, which I think we're going tofind are going to be much more
reliable long-term and also beable to be measured to determine
how effective is it actually.
But the truth is that there'stheories Actually, it's not
theories.
There's research that has shownthat, as marketers have gotten
(19:45):
addicted to this user-leveltargeting where you get
hyper-targeted ad effectivenesshas actually declined sharply.
And here's why You've got an ad.
You've decided that you'regoing to have it on, let's say,
television.
So there's a cost for you todeliver that ad your advertising
(20:05):
fees, your TV ad fees.
Now you decide I want to targetit, instead of just running
during a certain timeframe, Iwant to run during a specific
show.
But when you do that, you'retargeting.
They're going to charge youmore money.
Now you add that to the digitalworld where, let's say, instead
of regular TV it's connectedtelevision, ctv.
(20:26):
And now you want to target acertain geo and you want to
target folks who drive Volvosand smoke cigars.
Every time you layer in acertain targeting criteria, it
increases your expense in termsof the ad delivery.
It can take it from the cost ofa 1X all the way up to a 10X to
(20:48):
deliver that ad.
What ends up happening is thateach time you target more, your
audience is smaller.
Now the idea here is that, well, I only want to target the
people that I think willactually buy.
Well, you're actuallydecreasing your risk.
You're hitting a smaller numberof people.
So if you imagine the oldmarketing concept of a funnel,
(21:10):
you're actually cutting off thetop of the funnel.
You're reaching fewer people.
You're reaching fewer people.
You have a less likelihood ofgetting fewer and fewer
conversions.
The idea behind a broadertargeting criteria is it's less
expensive to deliver each ad.
Yeah, you may hit some peoplewho aren't in your target market
(21:31):
, but at least you're gettingawareness out there, which is
the first thing you need inorder to sell stuff.
And there's some great studiesfrom Orlando Wood out of System
One in the UK, who wroteprobably one of the best books
on marketing ever that I've readright on this subject, called
Lemon how the Advertising BrainTurns Sour.
(21:52):
It's available on Amazon and itdoes a great job of
demonstrating how you need tohave a mix, both at that bottom
of the funnel, where you'retargeting and you're
hyper-targeting, but also theupper part of the funnel,
because what we've seenthroughout the years is
marketers have moved away frombranding all the way to
(22:12):
performance, which was a bigmistake.
Now we're seeing a shift backwhere marketers are going more
upper funnel.
I've been seeing ads, butthey've been doing it smart.
I've been seeing ads for Airbnbrecently that are encouraging me
to rent out my home because Ilive in somewhat of a touristy
area and they probably needhomes in this area.
(22:33):
Now that's targeted.
It's CTV, but it's more of abranding message versus a call
to action.
There wasn't a specific call toaction, it was just saying, hey
, you interested in this, we cantalk you through it, type of
thing.
But brands like Airbnb havemoved more to branding to fill
that funnel up for them.
Speaker 2 (22:53):
Cool.
So time to change the topic.
You talked a lot about thecookie-less world, but now let's
talk about Amazon, which iskind of what the podcast is more
about, and so what we've beenseeing is, first of all,
advertising on Amazon is justoff the charts.
People are investing a fortunein every type of advertising on
(23:15):
Amazon, but then it's becomingmuch more popular not to replace
but in addition to theadvertising on Amazon, to start
advertising more on Google andFacebook and other places to
drive traffic to Amazon, whereyour conversion rates should be
higher than maybe yourstandalone site, or maybe you
just sell on Amazon.
(23:36):
But Amazon's also helping bydiscounting or giving money back
as rebates, but it's becomereally popular to spend money
off of Amazon to put it back onAmazon, and what are your
thoughts on that?
I know that you spend a lot oftime in this area, and what are
your thoughts on that?
I know that you spend a lot oftime in this area.
Speaker 3 (23:52):
Yeah, we're living in this really fascinating
omni-channel world, if you will,and it's fascinating.
It's somewhat unfortunate for alot of retailers and businesses
, because when you have a brandthat starts to take off, you
kind of have to be in all ofthese places, which is
(24:15):
fascinating.
But what we're seeing now withAmazon especially companies that
have like their own dedicatedwebsite, like a Shopify store
and an Amazon store, and maybeeven in retail is you've got the
Amazon advertising, whereyou're spending on Amazon
because you've got this massiveaudience of people who have now
know that they've been trained.
(24:37):
It used to be a number of yearsago that whenever I heard of a
product, I would Google it,because that's what everybody
did, and back in those days, thefirst ad that was there was an
Amazon ad telling me to go toAmazon, and it didn't take long
(24:58):
after maybe 10 or 15 productsearches, that I would Google it
, click and go to Amazon, wherenow for products, I just go to
Amazon, which is what mostpeople do.
Amazon now controls worldwide35 to 38% of the entire search
market.
They pretty much own the searchmarket for products, especially
(25:19):
in the US.
So we've got this advertisingthat's going on there.
You've got an Amazon store,you've also got this other store
.
And what we're seeing now isthis halo effect, and what I
mean by that is that let's sayyou've got a hot product, you've
got the Shopify store andyou've got an Amazon store.
And let's say you'readvertising on TV and you're
doing digital everywheres.
(25:40):
Well, a lot of people are goingto see your ad and they're
going to go to your Shopifystore because that's what's
advertised in the ad and they'regoing to say this is really
cool.
And then they say, huh, Iwonder if it's on Amazon.
And then they go to Amazon,they check out Amazon and they
(26:00):
buy it on Amazon.
Other people will hear aboutthe product.
They'll do the search, like Italked about.
They'll search on it on Amazon,they'll check it out and
they'll say, huh, I wonder ifthey have their own store.
And then they go, do a searchand they go to the store.
So what we're seeing now iswe're seeing that when you spend
money to drive people to yourstore, a percentage of people
will automatically go to Amazon.
So a percentage of that revenueneeds to be attributed back to
(26:22):
the store, even though the salesare happening on Amazon, and
vice versa the money you spendon Amazon, even though it's
grabbing people that are onAmazon.
They're doing a search onAmazon and you're like, why,
people that are on Amazon,they're doing a search on Amazon
and you're like, why would theyleave Amazon?
Because they are, they'releaving Amazon and they're going
directly to the store.
So some of your dollars onAmazon have to be attributed
(26:43):
back to the Amazon, even thoughthe sales are happening in
Shopify or the regular store.
What I mean by that is thatanyone who's doing like anybody
who's doing sales or marketingfor a store, you have a certain
either cost per order or ROASnumber.
And if you have, you know,let's say, you're the Shopify
store and your cost per order,let's say, has to be $50 or less
(27:07):
.
And you're noticing it'sgetting to be $55, but Amazon
sales are going up.
It's getting to be 55, butAmazon sales are going up.
You actually have an argumentto say hey, our actual cost per
order is actually probably $45because we're driving a
percentage of that Amazon growth, and the same is true for the
Amazon marketers.
So it's a fascinating world.
Now let's add to it the Walmartteam as well too, and then
(27:29):
you've got the retail team, soyou've got the main advertising
which is driving people to thestore, to Amazon, to Walmart and
then to walk into Walmart toactually buy it.
So from an attributionperspective things are getting
more and more difficult, but inmy seat I'm loving it.
It's actually a pretty sexyproblem to figure out.
Speaker 2 (27:52):
And so this is something that you're helping
sellers with right now, orbrands with right now, is you're
helping them with their budgetand what the best way to convert
into sales, develop the brand,and you are recommending
spending money on Facebook toAmazon.
Speaker 3 (28:10):
Oh, a thousand percent, without a doubt,
because all of those Facebookbuyers, if you will, they're all
shopping on Amazon.
I mean, the truth is, is thatthe way to look at this is that
if you're selling a product andyou want to target men and women
in the US who are over 44,let's say, and you're not
advertising on meta, you'remissing a segment of your
(28:32):
audience.
Those people are also on Amazonshopping.
That's where they buy most oftheir stuff, but their awareness
of those products, those startin the meta and you could say
the metaverse, but they start inFacebook and Instagram.
If you're targeting youngeraudience, you need to be
advertising in TikTok to drivethose people to Amazon.
(28:52):
And then you have to start tothink well, maybe should I get a
TikTok shop.
If you will, should I haveother outlets that it's easier
for them to buy?
But remember that most of thesepeople are comfortable shopping
on Amazon.
It's part of a seamlessexperience for them.
So it's good to spend thosedollars off to drive back, and
you can accurately measure andshow the effectiveness of it,
(29:14):
which is what's amazing.
Speaker 2 (29:16):
What we found is we tested both and I love Shopify.
I think it's a great platformand I think there's a lot of
synergy between Amazon andShopify, so I think having both
is really important.
You can also have a much biggerassortment on your Shopify
store than you actually do onAmazon, and I definitely believe
that people that go to Amazondo find their way if they like
the brand to the Shopify storeto learn more about the brand
(29:37):
and other things that they'redoing and maybe get on the
newsletter.
So I think they really docomplement each other.
But what we did find when wetest is that the conversion
rates on Amazon were just betterand it's because people trust
it more.
They know that they couldreturn it.
Like they might not know howeasy it's going to be if they
have to return something to thisbrand Shopify store, but they
(30:00):
know if they don't like it, it'sgoing back to Amazon and
they're getting their money backand they also might have their
account set up and all theiraddresses in there and it's just
easy for them.
So I do actually think thatit's almost always not always,
but almost always a higherconversion play going to Amazon.
You just have to make sure themath works out with.
If you're in the program withAmazon where you can get your
(30:21):
money back like they'll give youeight to 15, I think it's 8% to
12% back on your advertisingthat gets attributed to Amazon.
So I think it's definitelysomething people should do.
But you just said TikTok.
Do you guys do TikTok?
Speaker 3 (30:36):
Oh, yeah, I mean, the platform is set up to look at
all platforms.
We built it out to befuture-proof in the sense that
TikTok wasn't around a couple ofyears ago definitely not for
advertisers.
Who knows what's going to bethat's being created today, that
, two years from now, is anotherplace for advertisers to spend
(30:57):
their dollars.
So we needed to build somethingout, because what's frustrating
for marketers is you get ameasurement platform in place
that's able to predict what yoursales will be, where you should
allocate, and then, all of asudden, you want to test a new
platform and you find out yeah,it's not going to work for that,
and so, in order to do that, wehad to look at what is the
(31:18):
minimum amount of data that youneed in order to measure, and
the minimum stuff that you canget is how much did I spend,
what was the creative or whatwas the ad or what was the
influencer, what did we callthat and how many views or how
many impressions did we get?
Because we're living in a worldnow where a lot of advertising
is not clickable.
(31:39):
That's one of the other bigproblems with GA4 is that, like,
for example, we're in a podcastright now.
Podcast advertising isexploding.
It's a great way to fill thetop of your funnel and build
awareness, but there's nothingfor anyone to click on, and so
there's no tab in GA4 that sayspodcast.
There's no tab there for TV orCTV and those advertising that
(32:03):
works, but there's nothing toclick on.
So we had to build it out insuch a way that we would be able
to handle anything new thatcomes down the pike and TikTok
is a great one and at the end ofthe day there's all these new
places that you need to be,depending upon who you're
selling to, what your audienceis.
You're in the beginning.
(32:24):
You kind of have to guess whoyour audience is and do a little
surveying and figure out.
Once you know who your idealcustomers are, their age, where
they live, their likes, thenit's a matter of just figuring
out where they spend their time,and it's pretty straightforward
these days where folks are at.
Speaker 2 (32:43):
So I wasn't planning on trying to market Provalytics
too much, but I'm quiteinterested.
So say, we're an Amazon onlystore.
We don't have a Shopify site,but we're really a hundred
percent Amazon.
We want to start doing more offAmazon advertising.
And we called Jeff up on thephone and we get things going.
(33:09):
How does it actually work?
How do we start?
How do you build a campaign andstart a campaign?
Do you need to get into myaccount?
Do you do it off our account?
How does it all work?
Speaker 3 (33:19):
Yeah.
So we're not building thecampaigns, we're not doing the
marketing.
That would be you or anyoneelse that would do that or an
agency that would do that.
We would get the data fromthose campaigns.
So, for example, on Amazon, wewould get an output from your
Amazon ads account that wouldgive us data such as for every
day I spent X amount of dollars,I got X number of impressions.
(33:43):
We're not really interested inclicks so much and here's what
the campaign made up thecampaign and the ad and maybe
the keywords that came with that.
That would be your Amazon adsdata.
We would also get informationabout your PDPs in terms of how
many views there were.
How many times did peopleactually land on it each day,
(34:04):
because that's a good sign ofengagement and when you think of
the Amazon environment, clicksare definitely important.
So you want to know how manytimes did that get viewed.
And then let's say you'readvertising in Facebook.
We would get output fromFacebook, such as campaign, your
ad set, maybe the ad itself,how much you spent, and then
also the impressions.
That data would come into ourteam.
(34:24):
We'd get put into the platformand then also the impressions
that data would come into ourteam would get put into the
platform and then the math wouldall happen and it would come
back and it would tell us what'sworking, what's not working,
and then it would giverecommendations on how we
reallocate to get more sales onAmazon.
Speaker 2 (34:41):
So you give that data back in a digestible form that
then we could go to the agencyor for ourselves and market and
advertise.
Speaker 3 (34:48):
Yeah, this is what gets interesting, adam, because
most folks back in the earlydays we built out our own
dashboards.
This is back in 2008.
There were no.
There was no like visualizationplatforms.
There were no tableaus oranything like that.
So back in the early days, webuilt out our own dashboards and
what we found today is thatpeople are exhausted with all of
(35:10):
the different logins that theyhave, especially in the
corporate environment and in theworld.
But most companies today itdoesn't matter how large or
small they are they have numbersthat they have to look at each
day and they have built outtypically their own internal
visualization system.
We're finding a lot of folks areusing Looker these days because
(35:31):
it's just so simple to use.
It's owned by Google now.
It's free, you can upload, youcan take your Google sheet where
you keep all your marketingdata and turn it into great pie
charts and graphs and look atthings historically, which is a
lot better than just an Excelchart.
So the output from our platformis actually.
The first step is a series ofCSV files.
(35:54):
Now we build out great lookingdashboards and looker reports
for all of our clients, but thereason it's CSV files is that we
have found that, in order fordata to be actionable for
marketers, it has to liveinternally.
Having a separate place for youto log into is not going to work
, and the big win, especiallyfor larger companies, is that
(36:16):
you want to have the C-level,particularly the CFO, the folks
that are handling the money,looking at the same numbers as
the marketing folks.
They're not going to be lookingat the level of detail.
They're not interested incampaign ad groups in Google.
They're interested in theaggregated numbers, the top line
(36:36):
numbers.
That's what they want, buteveryone needs to be looking at
the same number, and this is oneof the big issues today in
marketing.
One of the big frustrationsthat a lot of marketers have,
which is they keep saying theyjust don't get it, I just need
more money for this.
They just don't understand.
Well, they don't understandbecause they're looking at
different reports than you are,and so marketers have to make a
(36:56):
push for what we call a singlesource of truth.
In order for that to happen, weall need to be sharing the same
data, and so our data is madeavailable, so it can be shared
within an organization, becauseotherwise it's never going to
work.
Speaker 2 (37:15):
I wrote down Looker I want to try that.
I didn't even realize that wasavailable.
So thanks for that information.
And is that what you use?
Speaker 3 (37:22):
Yeah, that's what we build out, because for a lot of
our clients, we're finding themajority of them are either
using Looker or they're usingTableau, or they're using
something else like a Datarama,which is owned by Salesforce, or
they're using Power BI.
But at the end of the day, theinput to all of these platforms
is typically a CSV file, whichis an Excel file, and that's
(37:44):
what our output is.
Is that so for a lot of ourclients?
We have this data that'savailable on Google BigQuery and
you can have Google BigQuery bea source and looker.
Once you start playing aroundwith it, you're going to be like
this is amazing, because youcan click a couple of buttons
and have a really cool lookingreport that you can share easily
once someone's authenticatedthrough Google.
(38:06):
So if you're on the G Suitetype system and you're using
Google Mail as your email,there's no login.
It's very, very straightforwardand folks love it.
Speaker 2 (38:16):
That's pretty cool.
So, jeff, what you're saying isProvalytics is taking data and
they're analyzing data andthey're optimizing the
advertising you should be doingand you give it back through the
system and then ad agencies, orthe people that are placing the
(38:37):
advertising, are leveragingthis data to refine their
campaigns.
Is that really what's going onhere?
Speaker 3 (38:43):
That's exactly right, and the reports actually that
we build out in Looker arepretty straightforward.
It's really simple.
It's like how can you tellwhere to spend more and where to
spend less?
We have a green arrow going upwith a percentage and a red
arrow going down with apercentage.
So it's pretty straightforward.
We kind of make it dummy-proof,if you will, in terms of what
(39:05):
you should do.
So then I could use it.
Speaker 2 (39:06):
That, or we kind of make it dummy proof, if you will
, in terms of what you should do, Then I could use it.
That's great, Awesome.
So that is spectacular and, aswe're getting to the end of the
podcast, I'd love for you togive us some kind of your I hate
to say closing thoughts becauseI want to keep this
conversation going forever butreally your parting words to the
audience on what you think isreally important, what you
(39:28):
should be watching out for andhow to enhance your biz.
Speaker 3 (39:33):
I think the biggest thing for marketers to
understand is that the marketingworld, especially the digital
marketing world, has always beenfull of changes, and back in
the early days of the internet Iused to always say that you
know.
Back in the early days of theinternet, I used to always say
that you know, back in the earlydays of SEO, I had friends that
are some of the top SEO folksin the world and they would find
(39:54):
these like loopholes that wouldwork amazing for them, and my
rule of thumb was that if youwere on month three of a way
where things were working greatfor you, you were on borrowed
time in the internet.
So the world is constantlychanging.
So if you find something thatis working for you and you think
, my God, I'm going to shootthis to the moon, you just
(40:15):
better wait, Because the folksyou're buying ads from, the
folks that you're writingcontent for these are publicly
traded companies that areworking on a completely
different set of rules than youare, and they change on a
regular basis.
So the key is is that you haveto say to yourself well, how can
I get ahead?
How can I be aware of what'sgoing to come next?
(40:37):
Well, and the answer I'm goingto tell you is the answer you've
probably heard from your, likefourth grade teacher the only
way you're going to get ahead isyou have to understand the past
.
You have to study history inorder to know how you got to
where you are today, and that'sthe best way to understand
what's going to happen next.
You have to kind of strengthenyour foundation, and what we did
(41:00):
at Provalytics is we actuallydid all the work for you.
Last summer, we put together acourse on attribution
certification.
Now it's an attributioncertification because we look at
the world of measurement askind of the center of everything
.
We see everything that goes onacross all these platforms,
across every form of media.
(41:20):
We went back to the beginningof how we got to where we are.
It takes about an hour and ahalf to go through.
There's no cost for it and atthe end there's a little quiz.
You pass it, you get a greatcertificate to show on your
LinkedIn.
It's available for no cost atProvalyticscom.
Suggest everyone listening.
Go and check it out.
You'll see a link forattribution certification.
(41:43):
Those are my parting words.
Speaker 2 (41:45):
To understand what's going to happen next, you got to
study the past and you can doit all in about an hour and a
half, and I love history and Iwish everybody would look at the
past, because it's definitelypredictive of the future, for
sure.
Hey, so if people want to getin touch with you, jeff, what's
the best way to do it?
Speaker 3 (42:01):
The best way is to go to provoliticscom, or you can
also find me on LinkedIn as well.
I'm regularly posting there,love to talk to other
entrepreneurs or anyone who'sbuilding a business or building
a store on Amazon Always up forsharing information and sharing
insights.
Because we got to get throughthis thing together and it's a
fun ride.
Speaker 2 (42:22):
And is your target customer?
Small, medium, large, new?
What does it look like?
Speaker 3 (42:28):
Most of our customers are spending between $8 million
to $10 million or more per year.
In order for the Provolyticsplatform to really work for you
and get your ROI, if you'respending $8 million or more per
year, you would be a perfect fitfor us.
Okay, I?
Speaker 2 (42:44):
got to check my wallet.
Okay, well, that's awesome.
Well, with that, thank you verymuch.
I really enjoyed theconversation.
I learned a heck of a lot, andI'm sure everybody else did too.
So thank you for joining us andwe hope you come back.
Speaker 3 (42:58):
Thanks, adam, it's been a pleasure.
Speaker 2 (43:00):
Thank you.
Speaker 1 (43:02):
Thank you for watching another episode of the
Planet Amazon podcast, where wetalk all things Amazon.
If you want to learn about howto accelerate your sales on
Amazon, visit Phelps United'swebsite at phelpsunitedcom.
Welcome to the Planet Amazon podcast with Adam
Schaefer, where we explore theworld of Amazon and other
e-commerce marketplaces.
Join us as we delve into thelatest strategies and tactics
for successful selling on theworld's largest online
marketplace.
Speaker 2 (00:17):
Hello, I'm Adam Schaefer and welcome to Planet
Amazon, where we talk about allthings Amazon and e-commerce.
To Planet Amazon, where we talkabout all things Amazon and
e-commerce.
Today, we're excited to haveJeff Greenfield, ceo of
Provalytics, joining the podcast.
Jeff is the co-founder and CEOof Provalytics.
He has built the nextgeneration of attribution,
taking into account all the newprivacy regulations and which I
(00:40):
have a big question for himtoday the possible upcoming
cookie apocalypse.
Jeff is an entrepreneur withthree decades of strategy,
growth and marketing experience,building leadership teams with
an emphasis on innovativemarketing enabled by new
technology.
Welcome to the podcast, jeff,so excited to have you here,
adam, thank you so much forhaving me here.
Speaker 3 (01:01):
I'm very excited.
Yeah, everything cookies thesedays.
That's what's in the news formarketers.
Speaker 2 (01:07):
Yeah, I know we got to get to that.
I mean, as I'm talking about acookie-less world, I'm like, is
it or is it not?
But let's come back to that ina second and what we'd love to
know is more about you, man,Like tell us who you are and
what led you on this journey toget Provalytics off the ground
and going.
Speaker 3 (01:27):
Well, it wasn't anything like I went to school
for measurement.
To me, measurement reallydidn't seem to be like a sexy
thing.
It's sexy now that I'm in it,but it wasn't in the beginning.
I used to be on the brand side.
I used to buy media andeventually did large-scale
events, used to put products inTV and film, and what happened
(01:50):
to me is that, as digital wasstarting to grow, I had a client
, a publicly traded weight lossclient that was spending
millions of dollars a monthwhich back then was a lot of
money and doing billions ofimpressions every month.
And they had this big issuewhere, for every single sale,
they had multiple partnersclaiming credit it's what we
(02:10):
call now the deduplication issueand they were doing a lot of
deals that were CPA deals, wherethey're paying on a cost per
sale basis, and what ended uphappening is that we used to say
that for every sale, they hadfive mothers or fathers who were
claiming credit that that'stheir child.
And there seemed to be no wayaround this.
And I was already working onanother technology product.
(02:31):
So I had a team of engineersand I figured out a way.
I said, all right, we canfigure out a way to work around
this and we built a solution tosolve that deduplication.
And then every month somethingelse would come up.
Our media buyers would say well, you need to figure out who
gets credit for the sale.
And before I knew it, I hadbuilt out the first multi-touch
(02:51):
attribution platform called C3Metrics.
This is around late 2007, early2008.
And that's kind of what got meinto measurement.
Once I built it, I realized Ithink other people could use
this, built that up, scaled up,the company exited right before
COVID, thinking I was done withmeasurement, and then, like,
(03:13):
over the course of a year or two, the bottom dropped out.
All of these things startedhappening.
We went from a world ofmarketing where you could get
all sorts of information oneveryone, you could create all
these segments and all of asudden, privacy became the
number one rule of the day.
We ended up losing cookies andSafari and an iOS and we had all
(03:34):
those iOS updates that occurred.
And then it became, overnight aswell, an app world, and what
that means is that in the earlydays it used to be that
everything was websites and nowit was people going from app to
app, and people don't like toleave apps.
They like to stay in an app,and apps don't want you to leave
.
So it got to the point wherethe way that we used to do
(03:56):
tracking at C3 Metrics was nolonger going to work anymore,
and I saw an opportunity and Isaid you know, I think that we
can leverage the power ofsupercomputing meaning that
computers are so much faster nowthan they were even three or
four years ago and take datathat's not user level data, but
that's aggregated data and getto the same answers we got to
(04:20):
before meaning less data but thesame answers.
And we can do that now usingadvanced math and these
supercomputers.
Speaker 2 (04:28):
It's amazing, really.
I mean that is amazing.
But how does it address and I'mjumping ahead but how does it
address the privacy issues?
Speaker 3 (04:36):
Well, the way it addresses the privacy issues and
this is one of the big issueswith the way that marketing and
advertising has always been doneand it's not really a problem.
Remember, there was really noregulations or anything in the
early days, but all of marketingwith digital has all been at
the user level, meaning I cantarget you.
You go to a website, You'relooking at some shoes you don't
(04:57):
buy and then five minutes lateron Facebook you see those same
shoes in your Facebook feeds,the ability to have ads track
you around, and it's that verycool aspect of the internet, the
personalization that folksreally tend to like.
But marketing on digital hasalways been we target users and
we measure at that user level.
Because of all of these changes, we've kind of had to pan back
(05:21):
the camera.
We're actually from ameasurement standpoint of you
and a targeting.
We're really living in a worldvery similar to how things were
before digital, where we don'thave user level data.
It's just not available.
Things have to be anonymous,and so what we do is we take in
data that's not user level data.
We take in the data that youwould see when you go into a
(05:43):
report in Facebook or an Amazon.
There's no users there.
We're not interested inindividual orders or order
information.
I want to know how many ordersyou had each day.
So we take daily data.
That's non-PII and that's whatgoes into the platform and it's
from that daily data that we'reable to give marketers the same
types of insights.
(06:04):
At C3, we would take user leveldata, hundreds of billions of
data points every month.
We would take it at the userlevel but then we would kind of
aggregate it up to the dailylevel for insights.
Because when you're looking tomake decisions with budgets,
you're not going to do it at auser level, You're going to do
it at a large budget level.
So you need to see daily data.
(06:24):
We take in daily data atProvalytics, but it's not user
level, it's aggregated.
Speaker 2 (06:30):
So how would it work?
Okay, so we're going to go buysome Google advertising or
Facebook advertising, or let'sstay with Google.
How are you then selecting theaudience for your clients?
How does that work then?
Speaker 3 (06:46):
So clients come to us that have already bought media
or are in the process of buyingmore media, and so the data that
we get.
In a sense, in the case of likea Google, we would get granular
data that would be like youknow SEM, you bought it on
Google.
That's another level.
If you will, you've got acampaign.
(07:08):
Let's say we call a campaign1234, and it's audience, and you
have a name for the audience.
That's the information we wouldget.
But who those users are in thataudience?
We wouldn't get those.
Maybe that audience is taggedfor a particular keyword.
We would get that informationout.
Then, when you marry that withsome Facebook data from Facebook
, we would get that informationout.
And then, when you marry thatwith some Facebook data from
Facebook, we would get campaignad set and creative information
(07:33):
and an audience.
If you've got a specificaudience that's being hit as
well too.
Speaker 2 (07:37):
So you're looking at their historic advertising, the
historic spend.
Speaker 3 (07:41):
Right, so we take historical data up to like
yesterday and we use that datain order to make decisions about
where to spend your dollartomorrow.
It's kind of the old adageevery dollar I spend in
advertising at least half thedollar I spend in advertising is
wasted.
The problem is I don't knowwhich half.
So how do we make a decision onwhere to spend that next dollar
(08:03):
?
Well, the best indicator ofthat is what happened yesterday,
what happened last month andthen actually even what happened
over the last year, so you canlook at seasonality.
Are we coming into a month thatis a lower than usual month?
Is this a month that creates anopportunity for us?
What is Wednesday of the 23rdweek of the year look like
(08:25):
historically for us?
And we have that information bygetting a historical 12 months
worth of data on a client.
Speaker 2 (08:33):
That's amazing.
So I should have met you a fewyears ago.
Could have saved some money.
So I think about advertisingand what you said earlier about
the attribution, and I get itbecause I lived in that world
where everybody wants a piece ofthe pie, everybody's got a 14
day cookie I'm going to say theword cookie or something and so
(08:53):
you'll have an affiliate, google, facebook, all claiming that
they got the sale.
And so what was?
What was the silver bullet thatlets you figure out which is
the one that actually gets it?
Speaker 3 (09:05):
Well, that's that's a great question, because what we
did is we looked at all thoserules you know, the 14-day
view-through window, theseven-day click-through window
to try to see is there any likea semblance of truth, is there
anything that helps guide us?
And what we found is thatthere's these terms on IOs.
You know, back in the day,everything was purchased, not
(09:26):
programmatically but throughinsertion orders, and these were
essentially the rules ofengagement.
We're going to pay you basedupon these sets of rules.
And those rules were justsomewhat bogus.
They were just kind of made upand it was all a negotiation to
see what kind of deal you couldget.
But what led us in the beginningwas that everybody was last
click, and so the assumption waswell, for certain types of
(09:50):
brands and certain types ofbusiness, last click may be best
.
So if you're selling somethingthat people are always buying,
they're always in market forthat, last action may be a good
thing.
But then let's shift and let'slook at another product where
people are in market for ninemonths, like a new car In the US
(10:11):
.
It's about a nine-month salescycle.
Well, it would be absolutelypositively wrong to be judging
car sales based upon last click.
It would probably make moresense for the first action what
got someone in your sales funnel?
But then there's other productsthat are like seasonal and that
kind of gave us the idea thatmaybe something in the middle
(10:31):
would be there, and in the earlydays of C3 metrics that was
kind of our thinking.
But the reality is is that wedon't actually have to guess
about this stuff, becausemarketers have been using a
technique called marketing mixedmodeling for a very long time
and that's how they figured outthe effectiveness of ad
campaigns back before there wasdigital.
(10:53):
Now, back then it was somewhatlimited because they were
looking at correlations betweenmarketing channels not like
campaigns and not like creativesto sales, and that's all they
were looking at.
In the digital realm we havemultiple outcomes.
Typically, sometimes we'relooking at orders, sometimes
we're looking at revenue.
We may be looking at all orders.
(11:14):
We may be interested in justsubscription revenue.
So there's multiple outcomesand we also want to have
multiple levels of granularity,because if you tell a search
person, hey, cut search 3%,that's not going to help them.
You need to tell them campaign,ad, group, keyword, maybe even
match type to really help themout.
So we leverage the math of thepast to help us get to the
(11:37):
future.
We also leverage a techniquewhere we're simply looking at
how well does the mathematicalmodel predict the future, and
what I mean by that is that welook at the timeframe that we
have, that we're evaluating andwe train the model, just like
you hear about with AI, howwe're doing all of this training
(11:59):
of models.
You know authors arecomplaining that.
You know these AI models arereading their books and then
writing like them.
So what we do is we train themodel for a month.
When we have like a year'sworth of data, we will give, for
that first month, the model.
We will give it everything.
We'll give it all the marketingdata and all the sales data,
(12:19):
and then we will hold back thesales data and we want to see
how well the model, based uponimpressions, clicks and all of
the marketing data, how well canit predict the actual sales
volume or whatever conversionthat we're looking at.
That's how you can tell that amodel is working well is, if you
hold data back, it actuallypredicts 75, 85, sometimes 95%
(12:44):
of the time.
Now, remember, all models arewrong.
Some are useful, but you wantto bet on a model that's
predicting pretty well that it'smore times right than it's
wrong, and what I like to say isthat folks who are using Google
Analytics 4 or Adobe, who arespending significant sums of
(13:04):
money, they know that whatthey're using is wrong.
It's just wrong, and so ourphilosophy at Provalytics is
that you want to be less wrongtomorrow than you are today.
You don't need to shoot it upto the moon, but if you can
incrementally get a little lesswrong every day, you're going to
get closer to the ultimate,which is properly allocating
(13:27):
your media spend.
But you're never going to beperfectly right, but the key
here is to incrementally getClose to right, closer to right.
Yeah.
So you should look at a modeland it should be able to predict
pretty accurately what yoursales should be.
When you have a model that doesthat, based upon just how
you're doing things now, thenyou can say to the I want to do
(13:48):
better and I want my budget tobe the same.
I don't have any extra money tospend.
Figure out what is the best wayto reallocate this money.
Let's say I'm spending $5million a month.
All I've got is 5 million.
You have all my marketing data.
Show me the best possible plan.
And then it spits out the bestplan and said if you did
(14:09):
everything here, here's how muchmore money you would make.
And that gets people jazzed.
Speaker 2 (14:13):
They're like yeah, for sure.
Speaker 3 (14:15):
It's like I can actually have 50% more revenue
just by doing exactly this.
Now, does anyone do exactlythat?
Absolutely not, because we'rehumans and we don't trust the
machines a hundred percent yet.
It's just the way it is.
But what people will do isthey'll start testing.
They'll say, okay, I'm going totry five or six of those things
, and then they're amazed.
(14:35):
They're like, oh my God, Inever thought that it would have
this impact.
And then they start to trust ita bit more.
The most important thing is toremember is that, as marketers,
we're not math people.
We got into marketing becausewe wanted to be creative.
In fact, if they told you incollege when you studied
marketing that you're going tobe spending more time in a
spreadsheet as your accountingfriends, you would have never
(14:57):
gone into this business to beginwith.
We're creative people.
We haven't learned yet.
Even though we say we'redata-driven marketers, we have
not learned yet how to trust themachines.
Probolytics is the machine thatpoints you in the right
direction, so you can spend moretime doing the creative stuff
versus trying to figure out themath.
Speaker 2 (15:17):
That is awesome, and you're right.
I would never have done it if Iknew it was going to be math
all day long.
So thank you for what you'redoing.
So then let's go back to thisother thing.
First met, we were talkingabout you know, google and
cookies going away and howyou're going to help the world,
(15:38):
but then, all of a sudden, wehung up and I'm reading all
these articles saying, nah, wechanged our mind, we're not
going to do that.
So what actually happened?
Like did they stop?
Are they not doing it?
Speaker 3 (15:48):
I get iOS, but I don't understand the Google part
.
Yeah, the Google part is is thatin the Chrome browser they were
going to get rid of cookiesaltogether third-party cookies,
that is.
So you still would be able tolog in automatically to Amazon
and there would be thatpersonalization, but third-party
cookies the tracking cookies,were going to be gone and,
(16:10):
because of some litigation thatwas going on over in the EU,
they made a decision.
They're not going to be goneand, because of some litigation
that was going on over in the EU, they made a decision.
They're not going to do it.
They're going to put controlsin place, which a lot of us
believe are similar to thecontrols that happened in iOS,
where people opted out and saidthey asked people the question
do you want apps to track youacross things?
(16:33):
Do you want Facebook to be ableto track you across the web?
And most people selectedabsolutely not.
It all depends how you ask thequestion is what the answer is
going to be.
But the reality is that thewhole idea behind the cookie
apocalypse was a wake-up callfor marketers to let them know
that cookies haven't reallyworked for a very long time.
(16:57):
It's kind of a scenario of theemperor has no clothes.
It's this belief that there's away to track people and then
it's really effective.
But when you actually sit downand you look at the studies that
show how effective cookietracking is, it's really bad.
It used to be good, but it'snot as good as it was before.
(17:19):
I mean just looking at it thisway.
I mean the best way to thinkabout it is that if you're
running a store and you'reselling stuff, are you selling
to anyone that owns an iPhoneand are you noticing that
they're visiting your storeusing that iPhone?
If so, there's a good chancethat your ads are probably not
really targeting them well andyour ability to retarget them is
(17:41):
not that great and everyonelikes to believe of course I'm
selling to iPhone users becauseI have a high-end type item.
If that's the case, yourmeasurement and your targeting
is completely off, unless you'redoing some very sophisticated
stuff.
So the reality is, is that whatwe're going to find is that the
companies that have been movingtowards a cookie-less
(18:02):
environment anyway are on theright path, because you need to
have a separate identificationsystem that's anonymous on the
web, because there's not goingto be much oomph put in to the
further growth of cookies.
So the train left the station anumber of years ago.
It's just kind of slowed down abit, but it's on its way out,
(18:25):
without a doubt.
Speaker 2 (18:26):
You know it's weird.
I mean, maybe I'm a freak, butI actually like the targeted
advertising that I get, theremarketing I get.
Is that going to go awaybecause of this?
Speaker 3 (18:36):
It's not going to go away.
They've found ways to do it,like, for example, criteo, who's
one of the best known for theirbehavioral retargeting.
They've gone and they've setdirect deals with publishers,
which they've had for years andwhat they've been able to do
with them.
Because when you think about apublisher like the New York
Times or the Wall Street Journal, they have a large subscription
(18:58):
base and a majority of thepeople that are there are logged
in.
So any site where you'reauthenticated at, they know who
you are and there's a way forthem to hash your email ID and
sync up with other folks who arevisiting shopping websites.
So they have methodologies todo that.
The trade desk has createdtheir own unique, anonymous or
(19:20):
pseudo anonymous identifier, sothere's ways to link this stuff
up, which I think we're going tofind are going to be much more
reliable long-term and also beable to be measured to determine
how effective is it actually.
But the truth is that there'stheories Actually, it's not
theories.
There's research that has shownthat, as marketers have gotten
(19:45):
addicted to this user-leveltargeting where you get
hyper-targeted ad effectivenesshas actually declined sharply.
And here's why You've got an ad.
You've decided that you'regoing to have it on, let's say,
television.
So there's a cost for you todeliver that ad your advertising
(20:05):
fees, your TV ad fees.
Now you decide I want to targetit, instead of just running
during a certain timeframe, Iwant to run during a specific
show.
But when you do that, you'retargeting.
They're going to charge youmore money.
Now you add that to the digitalworld where, let's say, instead
of regular TV it's connectedtelevision, ctv.
(20:26):
And now you want to target acertain geo and you want to
target folks who drive Volvosand smoke cigars.
Every time you layer in acertain targeting criteria, it
increases your expense in termsof the ad delivery.
It can take it from the cost ofa 1X all the way up to a 10X to
(20:48):
deliver that ad.
What ends up happening is thateach time you target more, your
audience is smaller.
Now the idea here is that, well, I only want to target the
people that I think willactually buy.
Well, you're actuallydecreasing your risk.
You're hitting a smaller numberof people.
So if you imagine the oldmarketing concept of a funnel,
(21:10):
you're actually cutting off thetop of the funnel.
You're reaching fewer people.
You're reaching fewer people.
You have a less likelihood ofgetting fewer and fewer
conversions.
The idea behind a broadertargeting criteria is it's less
expensive to deliver each ad.
Yeah, you may hit some peoplewho aren't in your target market
(21:31):
, but at least you're gettingawareness out there, which is
the first thing you need inorder to sell stuff.
And there's some great studiesfrom Orlando Wood out of System
One in the UK, who wroteprobably one of the best books
on marketing ever that I've readright on this subject, called
Lemon how the Advertising BrainTurns Sour.
(21:52):
It's available on Amazon and itdoes a great job of
demonstrating how you need tohave a mix, both at that bottom
of the funnel, where you'retargeting and you're
hyper-targeting, but also theupper part of the funnel,
because what we've seenthroughout the years is
marketers have moved away frombranding all the way to
(22:12):
performance, which was a bigmistake.
Now we're seeing a shift backwhere marketers are going more
upper funnel.
I've been seeing ads, butthey've been doing it smart.
I've been seeing ads for Airbnbrecently that are encouraging me
to rent out my home because Ilive in somewhat of a touristy
area and they probably needhomes in this area.
(22:33):
Now that's targeted.
It's CTV, but it's more of abranding message versus a call
to action.
There wasn't a specific call toaction, it was just saying, hey
, you interested in this, we cantalk you through it, type of
thing.
But brands like Airbnb havemoved more to branding to fill
that funnel up for them.
Speaker 2 (22:53):
Cool.
So time to change the topic.
You talked a lot about thecookie-less world, but now let's
talk about Amazon, which iskind of what the podcast is more
about, and so what we've beenseeing is, first of all,
advertising on Amazon is justoff the charts.
People are investing a fortunein every type of advertising on
(23:15):
Amazon, but then it's becomingmuch more popular not to replace
but in addition to theadvertising on Amazon, to start
advertising more on Google andFacebook and other places to
drive traffic to Amazon, whereyour conversion rates should be
higher than maybe yourstandalone site, or maybe you
just sell on Amazon.
(23:36):
But Amazon's also helping bydiscounting or giving money back
as rebates, but it's becomereally popular to spend money
off of Amazon to put it back onAmazon, and what are your
thoughts on that?
I know that you spend a lot oftime in this area, and what are
your thoughts on that?
I know that you spend a lot oftime in this area.
Speaker 3 (23:52):
Yeah, we're living in this really fascinating
omni-channel world, if you will,and it's fascinating.
It's somewhat unfortunate for alot of retailers and businesses
, because when you have a brandthat starts to take off, you
kind of have to be in all ofthese places, which is
(24:15):
fascinating.
But what we're seeing now withAmazon especially companies that
have like their own dedicatedwebsite, like a Shopify store
and an Amazon store, and maybeeven in retail is you've got the
Amazon advertising, whereyou're spending on Amazon
because you've got this massiveaudience of people who have now
know that they've been trained.
(24:37):
It used to be a number of yearsago that whenever I heard of a
product, I would Google it,because that's what everybody
did, and back in those days, thefirst ad that was there was an
Amazon ad telling me to go toAmazon, and it didn't take long
(24:58):
after maybe 10 or 15 productsearches, that I would Google it
, click and go to Amazon, wherenow for products, I just go to
Amazon, which is what mostpeople do.
Amazon now controls worldwide35 to 38% of the entire search
market.
They pretty much own the searchmarket for products, especially
(25:19):
in the US.
So we've got this advertisingthat's going on there.
You've got an Amazon store,you've also got this other store
.
And what we're seeing now isthis halo effect, and what I
mean by that is that let's sayyou've got a hot product, you've
got the Shopify store andyou've got an Amazon store.
And let's say you'readvertising on TV and you're
doing digital everywheres.
(25:40):
Well, a lot of people are goingto see your ad and they're
going to go to your Shopifystore because that's what's
advertised in the ad and they'regoing to say this is really
cool.
And then they say, huh, Iwonder if it's on Amazon.
And then they go to Amazon,they check out Amazon and they
(26:00):
buy it on Amazon.
Other people will hear aboutthe product.
They'll do the search, like Italked about.
They'll search on it on Amazon,they'll check it out and
they'll say, huh, I wonder ifthey have their own store.
And then they go, do a searchand they go to the store.
So what we're seeing now iswe're seeing that when you spend
money to drive people to yourstore, a percentage of people
will automatically go to Amazon.
So a percentage of that revenueneeds to be attributed back to
(26:22):
the store, even though the salesare happening on Amazon, and
vice versa the money you spendon Amazon, even though it's
grabbing people that are onAmazon.
They're doing a search onAmazon and you're like, why,
people that are on Amazon,they're doing a search on Amazon
and you're like, why would theyleave Amazon?
Because they are, they'releaving Amazon and they're going
directly to the store.
So some of your dollars onAmazon have to be attributed
(26:43):
back to the Amazon, even thoughthe sales are happening in
Shopify or the regular store.
What I mean by that is thatanyone who's doing like anybody
who's doing sales or marketingfor a store, you have a certain
either cost per order or ROASnumber.
And if you have, you know,let's say, you're the Shopify
store and your cost per order,let's say, has to be $50 or less
(27:07):
.
And you're noticing it'sgetting to be $55, but Amazon
sales are going up.
It's getting to be 55, butAmazon sales are going up.
You actually have an argumentto say hey, our actual cost per
order is actually probably $45because we're driving a
percentage of that Amazon growth, and the same is true for the
Amazon marketers.
So it's a fascinating world.
Now let's add to it the Walmartteam as well too, and then
(27:29):
you've got the retail team, soyou've got the main advertising
which is driving people to thestore, to Amazon, to Walmart and
then to walk into Walmart toactually buy it.
So from an attributionperspective things are getting
more and more difficult, but inmy seat I'm loving it.
It's actually a pretty sexyproblem to figure out.
Speaker 2 (27:52):
And so this is something that you're helping
sellers with right now, orbrands with right now, is you're
helping them with their budgetand what the best way to convert
into sales, develop the brand,and you are recommending
spending money on Facebook toAmazon.
Speaker 3 (28:10):
Oh, a thousand percent, without a doubt,
because all of those Facebookbuyers, if you will, they're all
shopping on Amazon.
I mean, the truth is, is thatthe way to look at this is that
if you're selling a product andyou want to target men and women
in the US who are over 44,let's say, and you're not
advertising on meta, you'remissing a segment of your
(28:32):
audience.
Those people are also on Amazonshopping.
That's where they buy most oftheir stuff, but their awareness
of those products, those startin the meta and you could say
the metaverse, but they start inFacebook and Instagram.
If you're targeting youngeraudience, you need to be
advertising in TikTok to drivethose people to Amazon.
(28:52):
And then you have to start tothink well, maybe should I get a
TikTok shop.
If you will, should I haveother outlets that it's easier
for them to buy?
But remember that most of thesepeople are comfortable shopping
on Amazon.
It's part of a seamlessexperience for them.
So it's good to spend thosedollars off to drive back, and
you can accurately measure andshow the effectiveness of it,
(29:14):
which is what's amazing.
Speaker 2 (29:16):
What we found is we tested both and I love Shopify.
I think it's a great platformand I think there's a lot of
synergy between Amazon andShopify, so I think having both
is really important.
You can also have a much biggerassortment on your Shopify
store than you actually do onAmazon, and I definitely believe
that people that go to Amazondo find their way if they like
the brand to the Shopify storeto learn more about the brand
(29:37):
and other things that they'redoing and maybe get on the
newsletter.
So I think they really docomplement each other.
But what we did find when wetest is that the conversion
rates on Amazon were just betterand it's because people trust
it more.
They know that they couldreturn it.
Like they might not know howeasy it's going to be if they
have to return something to thisbrand Shopify store, but they
(30:00):
know if they don't like it, it'sgoing back to Amazon and
they're getting their money backand they also might have their
account set up and all theiraddresses in there and it's just
easy for them.
So I do actually think thatit's almost always not always,
but almost always a higherconversion play going to Amazon.
You just have to make sure themath works out with.
If you're in the program withAmazon where you can get your
(30:21):
money back like they'll give youeight to 15, I think it's 8% to
12% back on your advertisingthat gets attributed to Amazon.
So I think it's definitelysomething people should do.
But you just said TikTok.
Do you guys do TikTok?
Speaker 3 (30:36):
Oh, yeah, I mean, the platform is set up to look at
all platforms.
We built it out to befuture-proof in the sense that
TikTok wasn't around a couple ofyears ago definitely not for
advertisers.
Who knows what's going to bethat's being created today, that
, two years from now, is anotherplace for advertisers to spend
(30:57):
their dollars.
So we needed to build somethingout, because what's frustrating
for marketers is you get ameasurement platform in place
that's able to predict what yoursales will be, where you should
allocate, and then, all of asudden, you want to test a new
platform and you find out yeah,it's not going to work for that,
and so, in order to do that, wehad to look at what is the
(31:18):
minimum amount of data that youneed in order to measure, and
the minimum stuff that you canget is how much did I spend,
what was the creative or whatwas the ad or what was the
influencer, what did we callthat and how many views or how
many impressions did we get?
Because we're living in a worldnow where a lot of advertising
is not clickable.
(31:39):
That's one of the other bigproblems with GA4 is that, like,
for example, we're in a podcastright now.
Podcast advertising isexploding.
It's a great way to fill thetop of your funnel and build
awareness, but there's nothingfor anyone to click on, and so
there's no tab in GA4 that sayspodcast.
There's no tab there for TV orCTV and those advertising that
(32:03):
works, but there's nothing toclick on.
So we had to build it out insuch a way that we would be able
to handle anything new thatcomes down the pike and TikTok
is a great one and at the end ofthe day there's all these new
places that you need to be,depending upon who you're
selling to, what your audienceis.
You're in the beginning.
(32:24):
You kind of have to guess whoyour audience is and do a little
surveying and figure out.
Once you know who your idealcustomers are, their age, where
they live, their likes, thenit's a matter of just figuring
out where they spend their time,and it's pretty straightforward
these days where folks are at.
Speaker 2 (32:43):
So I wasn't planning on trying to market Provalytics
too much, but I'm quiteinterested.
So say, we're an Amazon onlystore.
We don't have a Shopify site,but we're really a hundred
percent Amazon.
We want to start doing more offAmazon advertising.
And we called Jeff up on thephone and we get things going.
(33:09):
How does it actually work?
How do we start?
How do you build a campaign andstart a campaign?
Do you need to get into myaccount?
Do you do it off our account?
How does it all work?
Speaker 3 (33:19):
Yeah.
So we're not building thecampaigns, we're not doing the
marketing.
That would be you or anyoneelse that would do that or an
agency that would do that.
We would get the data fromthose campaigns.
So, for example, on Amazon, wewould get an output from your
Amazon ads account that wouldgive us data such as for every
day I spent X amount of dollars,I got X number of impressions.
(33:43):
We're not really interested inclicks so much and here's what
the campaign made up thecampaign and the ad and maybe
the keywords that came with that.
That would be your Amazon adsdata.
We would also get informationabout your PDPs in terms of how
many views there were.
How many times did peopleactually land on it each day,
(34:04):
because that's a good sign ofengagement and when you think of
the Amazon environment, clicksare definitely important.
So you want to know how manytimes did that get viewed.
And then let's say you'readvertising in Facebook.
We would get output fromFacebook, such as campaign, your
ad set, maybe the ad itself,how much you spent, and then
also the impressions.
That data would come into ourteam.
(34:24):
We'd get put into the platformand then also the impressions
that data would come into ourteam would get put into the
platform and then the math wouldall happen and it would come
back and it would tell us what'sworking, what's not working,
and then it would giverecommendations on how we
reallocate to get more sales onAmazon.
Speaker 2 (34:41):
So you give that data back in a digestible form that
then we could go to the agencyor for ourselves and market and
advertise.
Speaker 3 (34:48):
Yeah, this is what gets interesting, adam, because
most folks back in the earlydays we built out our own
dashboards.
This is back in 2008.
There were no.
There was no like visualizationplatforms.
There were no tableaus oranything like that.
So back in the early days, webuilt out our own dashboards and
what we found today is thatpeople are exhausted with all of
(35:10):
the different logins that theyhave, especially in the
corporate environment and in theworld.
But most companies today itdoesn't matter how large or
small they are they have numbersthat they have to look at each
day and they have built outtypically their own internal
visualization system.
We're finding a lot of folks areusing Looker these days because
(35:31):
it's just so simple to use.
It's owned by Google now.
It's free, you can upload, youcan take your Google sheet where
you keep all your marketingdata and turn it into great pie
charts and graphs and look atthings historically, which is a
lot better than just an Excelchart.
So the output from our platformis actually.
The first step is a series ofCSV files.
(35:54):
Now we build out great lookingdashboards and looker reports
for all of our clients, but thereason it's CSV files is that we
have found that, in order fordata to be actionable for
marketers, it has to liveinternally.
Having a separate place for youto log into is not going to work
, and the big win, especiallyfor larger companies, is that
(36:16):
you want to have the C-level,particularly the CFO, the folks
that are handling the money,looking at the same numbers as
the marketing folks.
They're not going to be lookingat the level of detail.
They're not interested incampaign ad groups in Google.
They're interested in theaggregated numbers, the top line
(36:36):
numbers.
That's what they want, buteveryone needs to be looking at
the same number, and this is oneof the big issues today in
marketing.
One of the big frustrationsthat a lot of marketers have,
which is they keep saying theyjust don't get it, I just need
more money for this.
They just don't understand.
Well, they don't understandbecause they're looking at
different reports than you are,and so marketers have to make a
(36:56):
push for what we call a singlesource of truth.
In order for that to happen, weall need to be sharing the same
data, and so our data is madeavailable, so it can be shared
within an organization, becauseotherwise it's never going to
work.
Speaker 2 (37:15):
I wrote down Looker I want to try that.
I didn't even realize that wasavailable.
So thanks for that information.
And is that what you use?
Speaker 3 (37:22):
Yeah, that's what we build out, because for a lot of
our clients, we're finding themajority of them are either
using Looker or they're usingTableau, or they're using
something else like a Datarama,which is owned by Salesforce, or
they're using Power BI.
But at the end of the day, theinput to all of these platforms
is typically a CSV file, whichis an Excel file, and that's
(37:44):
what our output is.
Is that so for a lot of ourclients?
We have this data that'savailable on Google BigQuery and
you can have Google BigQuery bea source and looker.
Once you start playing aroundwith it, you're going to be like
this is amazing, because youcan click a couple of buttons
and have a really cool lookingreport that you can share easily
once someone's authenticatedthrough Google.
(38:06):
So if you're on the G Suitetype system and you're using
Google Mail as your email,there's no login.
It's very, very straightforwardand folks love it.
Speaker 2 (38:16):
That's pretty cool.
So, jeff, what you're saying isProvalytics is taking data and
they're analyzing data andthey're optimizing the
advertising you should be doingand you give it back through the
system and then ad agencies, orthe people that are placing the
(38:37):
advertising, are leveragingthis data to refine their
campaigns.
Is that really what's going onhere?
Speaker 3 (38:43):
That's exactly right, and the reports actually that
we build out in Looker arepretty straightforward.
It's really simple.
It's like how can you tellwhere to spend more and where to
spend less?
We have a green arrow going upwith a percentage and a red
arrow going down with apercentage.
So it's pretty straightforward.
We kind of make it dummy-proof,if you will, in terms of what
(39:05):
you should do.
So then I could use it.
Speaker 2 (39:06):
That, or we kind of make it dummy proof, if you will
, in terms of what you should do, Then I could use it.
That's great, Awesome.
So that is spectacular and, aswe're getting to the end of the
podcast, I'd love for you togive us some kind of your I hate
to say closing thoughts becauseI want to keep this
conversation going forever butreally your parting words to the
audience on what you think isreally important, what you
(39:28):
should be watching out for andhow to enhance your biz.
Speaker 3 (39:33):
I think the biggest thing for marketers to
understand is that the marketingworld, especially the digital
marketing world, has always beenfull of changes, and back in
the early days of the internet Iused to always say that you
know.
Back in the early days of theinternet, I used to always say
that you know, back in the earlydays of SEO, I had friends that
are some of the top SEO folksin the world and they would find
(39:54):
these like loopholes that wouldwork amazing for them, and my
rule of thumb was that if youwere on month three of a way
where things were working greatfor you, you were on borrowed
time in the internet.
So the world is constantlychanging.
So if you find something thatis working for you and you think
, my God, I'm going to shootthis to the moon, you just
(40:15):
better wait, Because the folksyou're buying ads from, the
folks that you're writingcontent for these are publicly
traded companies that areworking on a completely
different set of rules than youare, and they change on a
regular basis.
So the key is is that you haveto say to yourself well, how can
I get ahead?
How can I be aware of what'sgoing to come next?
(40:37):
Well, and the answer I'm goingto tell you is the answer you've
probably heard from your, likefourth grade teacher the only
way you're going to get ahead isyou have to understand the past
.
You have to study history inorder to know how you got to
where you are today, and that'sthe best way to understand
what's going to happen next.
You have to kind of strengthenyour foundation, and what we did
(41:00):
at Provalytics is we actuallydid all the work for you.
Last summer, we put together acourse on attribution
certification.
Now it's an attributioncertification because we look at
the world of measurement askind of the center of everything
.
We see everything that goes onacross all these platforms,
across every form of media.
(41:20):
We went back to the beginningof how we got to where we are.
It takes about an hour and ahalf to go through.
There's no cost for it and atthe end there's a little quiz.
You pass it, you get a greatcertificate to show on your
LinkedIn.
It's available for no cost atProvalyticscom.
Suggest everyone listening.
Go and check it out.
You'll see a link forattribution certification.
(41:43):
Those are my parting words.
Speaker 2 (41:45):
To understand what's going to happen next, you got to
study the past and you can doit all in about an hour and a
half, and I love history and Iwish everybody would look at the
past, because it's definitelypredictive of the future, for
sure.
Hey, so if people want to getin touch with you, jeff, what's
the best way to do it?
Speaker 3 (42:01):
The best way is to go to provoliticscom, or you can
also find me on LinkedIn as well.
I'm regularly posting there,love to talk to other
entrepreneurs or anyone who'sbuilding a business or building
a store on Amazon Always up forsharing information and sharing
insights.
Because we got to get throughthis thing together and it's a
fun ride.
Speaker 2 (42:22):
And is your target customer?
Small, medium, large, new?
What does it look like?
Speaker 3 (42:28):
Most of our customers are spending between $8 million
to $10 million or more per year.
In order for the Provolyticsplatform to really work for you
and get your ROI, if you'respending $8 million or more per
year, you would be a perfect fitfor us.
Okay, I?
Speaker 2 (42:44):
got to check my wallet.
Okay, well, that's awesome.
Well, with that, thank you verymuch.
I really enjoyed theconversation.
I learned a heck of a lot, andI'm sure everybody else did too.
So thank you for joining us andwe hope you come back.
Speaker 3 (42:58):
Thanks, adam, it's been a pleasure.
Speaker 2 (43:00):
Thank you.
Speaker 1 (43:02):
Thank you for watching another episode of the
Planet Amazon podcast, where wetalk all things Amazon.
If you want to learn about howto accelerate your sales on
Amazon, visit Phelps United'swebsite at phelpsunitedcom.
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy And Charlamagne Tha God!
The Joe Rogan Experience
The official podcast of comedian Joe Rogan.