November 14, 2024 • 25 mins
In this episode of The D2Z Podcast, Brandon Amoroso interviews Noam Szpiro, co-founder and CEO of Monocle, an AI-powered incentive platform for e-commerce brands.
What if you could revolutionize your e-commerce brand's promotional strategy with the power of AI? Join us as we uncover the secrets behind Monocle's groundbreaking incentive platform with Noam Sphero, the visionary co-founder and CEO. From his beginnings in the Israeli Navy to his pivotal role in Silicon Valley, Noam's journey is nothing short of inspiring. At Lyft and Instacart, he discovered the art of optimizing promotions through machine learning and causal inference. Now, he's focused on helping e-commerce brands grow sustainably, ditching traditional discount methods for AI-driven incentives that preserve brand integrity.
Prepare to have your perspective on e-commerce promotions challenged and transformed. Noam shares how Monocle is educating brands to rethink their promotional spending, offering smarter alternatives that integrate seamlessly with existing platforms like Klaviyo. We delve into the strategic decision to specialize in a niche, addressing specific pain points before considering broader expansions. This approach not only meets immediate needs but also positions Monocle as a leader in AI-driven promotional optimization. Tune in for insights that could redefine your approach to e-commerce marketing and set your brand on a path to sustainable growth.
Here's what you'll learn:
📈 Understanding customer behavior is crucial for effective promotions.
💰 Promotions can significantly drive customer engagement and retention.
📦 It's important to validate business ideas before launching.
💸 E-commerce brands often lack awareness of their promotional spending.
🚀 Future expansion should focus on comprehensive promotional solutions.
💼 Finding the right co-founder can impact business dynamics.
🤖 Integrating with existing tools can ease customer onboarding.
Timestamps:
00:00 Introduction to AI-Powered Incentives
02:31 Noam's Journey: From Ride-Sharing to E-Commerce
05:59 Understanding Promotions and Customer Behavior
10:10 Building Monocle: Lessons from Previous Ventures
14:05 Navigating the E-Commerce Landscape
18:06 Customer Education and Market Fit
22:01 Future Expansion and Integration Strategies
Noam Szpiro:
LinkedIn - https://www.linkedin.com/in/noamszpiro/
Monocle - https://usemonocle.com/
Brandon Amoroso:
LinkedIn - https://www.linkedin.com/in/brandonamoroso/
Web - https://brandonamoroso.com/
Instagram - https://www.instagram.com/bamoroso11/
X - https://twitter.com/AmorosoBrandon
Scalis.ai - https://scalis.ai/
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Hey everyone, thanks for tuning in to D2Z, a podcast
about using the Gen Z mindset togrow your business.
I'm Gen Z entrepreneur, brandonAmoroso, founder and president
of retention as a service agencyElectric, as well as the
co-founder of Scaleless, andtoday I'm talking with Noam
Sphero, who's the co-founder andCEO at Monocle, which is an AI
powered incentive platform thatgenerates profits for high
(00:22):
growth e-com brands.
Thanks for coming on theshowcom brands.
Thanks for coming on the show.
Of course, thanks for having me.
So, before we dive into theworld of AI-powered incentives,
can you give everybody a quickbackground on yourself and sort
of your founding journey here?
Speaker 2 (00:36):
Yeah, happy to.
So I'm from Israel, originallyWent to the Navy there a really
long time ago, then studiedcomputer science in Cambridge in
England.
After that I decided to move tothe Valley, kind of worked on
for a few startups there,eventually started my own
(00:57):
company in the ride sharingindustry.
I thought it was a good idea atthe time to compete with Uber
and Lyft Turns out it's not avery good idea.
So about a year later or so Iended up getting acquired by
Lyft.
So I joined the Lyft team there, worked a little bit on a few
(01:19):
different products there, butmost of my time there was around
passenger growth and a lot ofwhat we did there was around
promotion optimizationsbasically.
So that's maybe where you cansee the theme of the
conversation of where we'll go,but at a high level.
What we found at Lyft waspromotions are super valuable
(01:40):
for people and if you're usingit right, it's a great tool for
growth.
And so what we did initiallywas we said, hey, let's give
everyone a coupon who hasn'ttaken a ride in the last few
weeks and let's see what happens.
Ends up being super profitable.
People come back when you givethem discounts and take more
rides, but eventually theystopped using the service.
(02:03):
So what we really wanted tofind out was how do we target
the right people to give themdiscounts, not only the people
who will just come back and usethe coupon, but ones that will
use the coupon and then stay onand ones that will otherwise not
use the service, basically.
So really trying to find out,dissect those people who are
(02:26):
very promo sensitive and theones that we can make them
change their behavioressentially.
And so that's where we startedinvesting a lot of effort into
this and we built asophisticated system around
machine learning that reallyuses what we call causal
inference, so reallyinvestigating the cause and
(02:46):
effect of promotions fordifferent individuals and then
only targeting those people whohave a very large effect,
essentially.
So you don't want to give acoupon to someone who will take
a ride anyway and will use thecoupon.
You want to give it to peoplewho are unlikely to take a ride
unless you give them that coupon.
And so when we started usingcausal inference, we saw massive
(03:08):
gains and we basicallyincreased the ROI by about 50%.
So with the same budget, wewere able to get 50% more rides.
Essentially so super successfulended up doing that same thing
over for the driver side of thebusiness, thinking about it more
generally, around uh incentivesand not necessarily promotion.
(03:28):
So, uh, for drivers we havesimilar stuff where you know we
might give uh a bonus if they'vetaken five rides in a row,
something like that.
Uh, so use the same technologyover there.
Uh, after lyft ended up joiningInstacart.
So we worked at Instacart for afew years.
I was a director of engineeringthere working on conversion
(03:52):
optimizations, basket sizeoptimization, so kind of similar
stuff on a really large scaleessentially.
And then about close to twoyears, I almost started Monaco,
raised a large seed round at thebeginning of last year and then
(04:12):
off to the races after that.
Speaker 1 (04:15):
Got it.
That's cool.
That's something that was athought in our mind at the
agency for a while.
When, know, when you're sendingout all these campaigns and
promotions and they're sort ofjust you know one-to-many versus
being one-to-one, and you knowthere's definitely a lot of
brands that we've worked with inthe past who would then become
over-reliant upon you know,discounting and you know sort of
(04:37):
hurting the, you know the brandreputation along the way and
getting customers toocomfortable with waiting for
that code versus you know themshopping and if they had not
gotten that, that code, um.
So how did you, how did yousort of transition into um, the,
the Shopify ecosystem from, uh,you know, ride sharing and then
(04:58):
into into Instacart and thenand then e-com?
Speaker 2 (05:01):
Yeah.
So I think, uh, in general, youknow, general, I think there's
a lot of similarities betweenLyft, instacart and generally
the Shopify ecosystem.
They're all about basicallygetting people to place that
order book, that ride,essentially.
So it's fairly similar in thatsense.
The difference is it's a littlebit of a different scale for,
(05:23):
uh, a lyft, uh, uber, all theride sharing uh and services
like that, and then instacart aswell, uh, so for e-commerce
it's a little bit different.
Um, it's the industry, and inshopify is interesting because
you can really get to massivescale with almost no, no team or
(05:44):
maybe a 10-person team.
Sometimes you see brands doover 100 million in sales
basically, and so obviously thatwould never happen for a
startup like Lyft and Instacart,things like that.
They would need much biggerteams.
And so when I was looking atthat, I saw, okay, well, uber,
lyft, all those they havemassive teams around them that
(06:07):
help them really figure out whatis the right pricing, how do
you do discounts, all of that.
So lots of talent there aroundmachine learning, data
scientists, people like that,and you know, for the Shopify
ecosystem, you know it's reallyhard to do it yourself, really
hard to do it yourself.
(06:27):
Basically, you have to eitherhire a bunch of people, invest a
lot of time, or you maybe havesome tools out there today, but
they're not very advanced interms of that and typically the
tools in Shopify, or a lot ofthe tools in Shopify, are
basically tools.
They're not really working onoptimizations or basically going
beyond that, just providingtools but actually implementing
(06:47):
the solution essentially.
So that's where I realized,okay, there's a little bit of a
gap here where, with mybackground and the people I've
worked with in the past, I thinkwe can build something that
could help tailor it forbasically all the Shopify stores
that are out there and wouldsave them the effort of actually
(07:08):
building these thingsthemselves, but at a high level.
Yeah, we looked at how peopledo promotions on Shopify and you
saw there are a lot ofsimilarities, like initially
they're just doing simplesegmentation, like you mentioned
, essentially.
But I saw that there's a lot ofopportunities for improvements
(07:30):
here, because it's mostly aroundmaybe simple A-B tests, things
like that, but nothing aroundpersonalizing discounts, really
all the way to the end,essentially.
Speaker 1 (07:41):
Are you able to utilize the data from multiple
brands?
For, let's say, one customershops at five of the companies
that are using Monocle and thenyou have a six company that
starts using Monocle and I go tothat website and am also a
customer of that.
Is it possible to leverage theway that I respond and engage
(08:05):
with the discounts from theother five to inform sort of the
sixth, or is it much?
Is it more siloed, you know,for that particular brand and
that particular customer?
Speaker 2 (08:15):
Yeah, so.
So right now it is siloedBasically.
So each brand I kind of we lookat them as a unique brand and
figure out what, uh, how dopromotions work for them?
And you know there's some.
I agree that there's somenotion around people being a
little bit more price sensitiveand maybe some people just
(08:38):
generally need coupons topurchase.
Uh, what we found is that it'snot entirely true.
Basically so the way peopleactually interact is more it
depends on the interaction withthe actual brand.
Essentially.
So there are some purchases, uh, that you know you just want to
make that purchase and you'renot waiting for a coupon because
(08:58):
you need this item, basically,and it doesn't really matter if
you're generally you likereceiving discounts or not.
What really matters is okay,how did you interact with the
brand?
How many times have you visitedthe site?
And through that we typicallyhave a much stronger signal on
(09:20):
how are you likely to purchasebasically, or how likely is it
that a coupon will change yourbehavior.
Essentially, so we don't reallydo any cross-store matching.
What we do do is try tounderstand what are the type of
features.
So for each user we build somesort of profile.
(09:41):
Essentially that's unique tothe brand.
But what we find is thosefeatures tend to be fairly
similar between the brands.
So, for example, one that wetypically look at that's a very
strong indicator is how longyour session has been.
So if you have a really shortsession, promotion is unlikely
(10:03):
to change your behavior,basically, if you have a really
long session, conversely, youmight have already made up your
mind and a discount is not goingto change that, basically.
So that's also not an idealperson to give a discount to.
And the people in the middlethat are kind of on the fence
still, those are the type ofpeople that you typically want
(10:24):
to give discounts to, and so wefind that that is fairly similar
between brands.
But what is the definition of ashort session or long session
is vastly different betweenbrands, and that's why we don't
have, right now at least, amodel that basically looks at
everything and decides for everyuser.
(10:45):
Let's do this, but rather forthis specific brand.
Here's what it looks like andhere's how it might be best to
represent these sessions,basically, Got it.
Speaker 1 (10:56):
Okay, that makes sense.
On the side of actuallybuilding and growing the
business, are there any sort oflessons or sort of gotchas that
you took from the first businessthat you started that you kept
in mind when going at it for asecond round here?
Speaker 2 (11:16):
Yeah, good question.
Yeah, I think two things.
One is just the people you workwith is super important.
I think hiring a right team iscritical for the business.
Finding the right co-founder isalso super important.
(11:39):
I personally, like I know a lotof people, tend to say you know,
co-founders need to complementeach other.
Make sure you havecomplementary skills.
I don't necessarily agree withthat statement.
What I think is you need tofind someone that you just like
to work with, even if yourskills are fairly similar.
That's okay.
(12:00):
The goal of having co-foundersat least you can bounce ideas
off each other, talk to them.
Sometimes things don't go rightand your co-founder is the one
that's helping you, maybepulling you up or telling you OK
, here's maybe one other way wecan look at this situation or
save this deal, or anything likethat.
So I think people you get alongwith and people it's fun to
(12:25):
work with I think is moreimportant than complementary
skills.
Basically that's my opinionwith.
I think is more important thancomplimentary skills.
Basically that's my opinion.
And then it is fun to just youknow, have, make sure you have a
good team that you're excitedto go to work with.
And then we we tend to work inin person.
So it just the energy isdifferent and making sure you
(12:48):
have those people who are wholike that energy of bouncing
ideas off each other, buildingthings really quickly.
I think that's super important.
So that's in terms of peoplelearned uh over time.
(13:13):
Is that?
Uh?
Before I started monica, I said, okay, I'm not gonna start
another business until I know.
Okay, this is uh a businessthat I know can explode,
basically.
And so before we uh we hiredanyone, before we wrote any line
of code, uh, basically wetalked to, I want to say like 50
brands or so and kind ofinterviewed them around how they
do promotions, all that stuffDifferent segments, also in the
(13:36):
e-commerce industry and then,right before we even had a
product, we started doing somemanual analysis for them.
And then, once we did that andwe saw that they were excited by
it, that's when I realized,okay, this is a good area to
dive into and take this thing tothe next level.
So I do think, before jumpinginto things, making sure you're
(14:01):
in the right industry with theright idea, basically.
Speaker 1 (14:05):
Yeah, not building a ton and then launching it
without having the initialcustomer discovery.
Yeah, exactly, that makes sense.
Interesting on the co-foundernote, though, I think people
probably do prioritize somethings beyond just the basics.
(14:27):
Beyond just like the basics.
Like, do you actually even likethis person and you know having
to go to work with them everyday and having some sort of
camaraderie, whether you'recomplimentary or not, you know,
do you actually want to show upand be a partner with that
person?
Speaker 2 (14:40):
Yeah, and like, wherever you end up choosing,
you're gonna it's not alwaysgoing to be easy Like you're
always gonna, or you'reinevitably going to argue about
certain things.
And I think the important thingis that you know you respect
each other and you know you canargue a little bit, but then you
take that to the next level,basically, and solve that
(15:01):
argument and and and build ontop of that, rather than you
know, maybe not talk to eachother than for the next few
weeks, yeah, how do you thinkabout breaking into you?
Speaker 1 (15:09):
know for the next uh, a few weeks.
Basically, yeah, how do youthink about breaking into uh,
you know the ecosystem with awith a product that you know
there's not a lot out there whenit comes to what, what you're
doing, whereas you know if youhad launched a reviews app I
don't know in like 2019 or 2020?
You know there's, like you know, hundreds of review apps that
(15:29):
are out there, but brands arealso more familiar with what
that is.
How have you gone about thecustomer education and then
followed through with actuallygetting them to sign on?
Speaker 2 (15:44):
Yeah, good question.
In general, I really likethere's a product market fit
framework that Sequoia publisheda while back.
So the way they think about itis they split it into three
areas.
There's the hair on fire, hardfact and future vision.
Hair on fire is kind of whatyou described as like the
(16:08):
reviews app, so somethingeverybody knows they need.
It's a matter of building thebest solution out there.
Essentially, hard fact isbasically what we're doing where
people don't know there's analternative to the status quo,
essentially, so the biggestcompetitor we have is just using
(16:30):
what you're doing today andsticking with it.
This future vision is, you know, maybe you know, building
flying cars or things like that,essentially, uh, or a store
that, uh, you know marketsitself, or something along those
lines uh so with you know, withour area, our, our specific uh
product market, fit it.
(16:51):
It is a little.
The challenges are a little bitdifferent.
So it's not about, you know,talking to a customer and then
convincing them, hey, like youshould move off this platform
and onto our platform, butrather, you know, when we talk
to brands, it's about moreeducation.
So, basically understanding,okay, how much money have you
(17:13):
spent on promotions in the lastyear?
Most brands don't really knowbecause in a way, it doesn't
really matter, because they justlook at the bottom line
basically.
And when you're thinking aboutpromotion, sometimes you don't
even see it in your P&L,basically because it might just
be the actual cost is baked intothe actual discount is baked
(17:35):
into that cost.
So there's a little bit moreeducation around that.
So there's less competition forus.
When we talk to brands, we haveto tell them hey, there's a huge
opportunity here and we have tosell them on the vision a
little bit, that promotions canjust be something that an AI can
(17:57):
manage on its own and optimizeand adapt over time to trends
and seasonality, all that stuffbasically.
But the idea is that in thisspecific area, once you've done
enough education in the market,then, because there's no
competition, you become a leaderreally easily.
So right now we're, I think, inthe beginning of people
(18:20):
understanding, okay, there are alot of AI tools out there and
brands are now looking to expandinto that and figure out, okay,
where should they add AI ormachine learning into their
store?
And this is kind of one areathat we see that brands Kind of
always think about what are thedifferent ways to innovate in
(18:45):
that?
Speaker 1 (18:45):
area essentially.
Speaker 2 (18:48):
So it's interesting.
The other thing I would saythat we're doing that helps is
basically, we didn't want to getpeople to migrate onto our
platform, so one thing we couldhave done, which was easy.
So, in general, when we lookabout, when we think about
promotions, we split it into afew different categories, but
(19:09):
one category is basically thedelivery mechanism.
So how are you actuallyinforming the user that they
have a discount on their account?
So that's the deliverymechanism.
We decided not to touch thatdelivery mechanism because
there's just so much competitionthere and there's so many great
tools out there essentiallythat do this.
(19:30):
So for pop-ups, it might be Amp, it might be Klaviyo,
postscript all of those brands,all of those products have great
pop-ups, basically, and there'sno reason why we should build
another one.
That is more or less the same.
Rather, what we decided iswe're just going to plug into
(19:53):
those tools, essentially, andthen we're going to basically
essentially be powering thepop-up, or the brains behind the
scenes of the sides.
When is the right time to showa pop-up, what should the
discount be?
All that stuff basically.
And the advantage there is thatnow, when we are onboarding a
new brand, we don't need them tooff-board from Klaviyo onto us.
(20:17):
They just keep the same UIelements that they have there
and then we just plug into thatto really decide when and what
to show basically to the user.
Speaker 1 (20:30):
Got it.
No, that makes a lot of senseand probably makes the process
significantly easier, not havingto deal with migrating anybody
off of anything.
But I know there's a lot ofcompanies that are out there
right now that are buildingproduct that already exists, but
they're getting wider with sortof what they're offering is, as
it feels like a lot of a lot ofbusinesses are started doing.
(20:53):
It was going for that sort oflike HubSpot approach, where
there's a bunch of differentproducts underneath sort of one
platform.
What are your thoughts aroundgoing that route versus being a
(21:14):
very specific sort of Swiss Armyknife for a particular use case
, which is effectively I guessyou guys are doing with when it
comes to like the, the pricingoptimization, because I don't
think it works all the time,because you can't do that with
like reviews, because reviewshave now been commoditized.
So how do you, how do you thinkabout that?
Speaker 2 (21:34):
yeah, um, yeah.
So I think when you're startinga company, uh, in my opinion,
the best way to get it off theground is to be that Swiss army
knife or not exactly the Swissarmy knife, but really finding
that one specific pain point andreally solving that really well
(21:56):
.
Basically, and that's a reallygreat way in where you know,
you're talking about a painpoint that brands have, you're
solving it, they're coming toyou for that specific thing and
then ultimately you want toexpand and then provide more
solutions to other pain pointsin that same specific area.
Now I think Yotpo is a greatexample of a lot of different
(22:21):
products in different kind of indifferent categories.
So reviews is a little bitdifferent than you know, sending
emails, for example, or sendingSMS but it makes sense to
expand into that because that'sanother product that brands
really want For us.
I don't like we'll never expandinto reviews, for example, but
(22:45):
for us, expanding into differentareas that have promotions in
them is very natural for us, forexample.
So right now we typicallycontrol I wanna say with some
brands about 50% of thepromotional spend.
So that's through pop-ups,emails, on-site deciding when
(23:08):
people should receive discounts.
Basically, we sometimes triggerwhen a user should receive a
discount in a win-back campaign,things like that, and that all
accounts for about 50% of thepromotions.
We want to also tackle theother 50%, so that a lot of
times is through strikethroughs,so just items that are on sale,
(23:32):
for example, or bundle offers,things like that.
So those are areas that wehaven't gotten into and that's
kind of the area that we want toexpand to.
I know, once you have controlover all the promotional spend,
that's where there are greatinteraction effects, where you
know, if you know this item ison sale and someone has it on
their cart, maybe you can givethem an even larger discount,
(23:55):
for example.
Or maybe there's a bundle offerfor three items.
The user has two of them.
Maybe you can give them anadditional discount and tell
them, hey, like, add this itemand get this additional offer.
But once everything isconnected, it's much easier to
play around with it.
For example, black Friday iscoming up right now.
(24:18):
It's a little tricky to get toa point where everything is
synced, so a lot of brands justdecide to shut down their emails
, maybe turn on something new.
The idea is, with Monocle, inthe future, you're just going to
be able to say, well, I justwant to give 40% off for
everyone, and then you'll do itfrom one platform and we'll
(24:39):
update everything.
So all of your email marketingplatforms, all of the pop-ups,
everything on the site basicallywill update everything.
So all of your email marketingplatforms, all of the pop-ups,
everything on the site,basically.
Speaker 1 (24:48):
So you don't need to go and manage everything
independently, essentially Gotit.
That makes sense.
Well, I really appreciate youtaking the time and coming on.
I think it's a huge need and itmakes a ton of sense.
So definitely a lot of valuethat e-com brands can get out of
their customers and I thinkeverything's moving towards that
.
You know one-to-onepersonalization versus the
general, like you know,segmentation and whatnot.
(25:09):
But before we, before we wrapup, can you let everybody know
where they can, you know, findyou guys online and how they can
connect with you if they, ifthey want to learn more?
Speaker 2 (25:18):
yeah, sure, so our site is.
Use monoclecom.
There's a book demo link there.
Feel free to fill it out andwe'll reach out Awesome.
Speaker 1 (25:28):
Well, again, I really appreciate you taking the time
for everybody listening.
As always, this is BrandonAmoroso.
You can find me atbrandonamorosocom or scalistai.
Thanks for listening and we'llsee you next time.
Speaker 2 (25:41):
Thanks, Brandon.
Hey everyone, thanks for tuning in to D2Z, a podcast
about using the Gen Z mindset togrow your business.
I'm Gen Z entrepreneur, brandonAmoroso, founder and president
of retention as a service agencyElectric, as well as the
co-founder of Scaleless, andtoday I'm talking with Noam
Sphero, who's the co-founder andCEO at Monocle, which is an AI
powered incentive platform thatgenerates profits for high
(00:22):
growth e-com brands.
Thanks for coming on theshowcom brands.
Thanks for coming on the show.
Of course, thanks for having me.
So, before we dive into theworld of AI-powered incentives,
can you give everybody a quickbackground on yourself and sort
of your founding journey here?
Speaker 2 (00:36):
Yeah, happy to.
So I'm from Israel, originallyWent to the Navy there a really
long time ago, then studiedcomputer science in Cambridge in
England.
After that I decided to move tothe Valley, kind of worked on
for a few startups there,eventually started my own
(00:57):
company in the ride sharingindustry.
I thought it was a good idea atthe time to compete with Uber
and Lyft Turns out it's not avery good idea.
So about a year later or so Iended up getting acquired by
Lyft.
So I joined the Lyft team there, worked a little bit on a few
(01:19):
different products there, butmost of my time there was around
passenger growth and a lot ofwhat we did there was around
promotion optimizationsbasically.
So that's maybe where you cansee the theme of the
conversation of where we'll go,but at a high level.
What we found at Lyft waspromotions are super valuable
(01:40):
for people and if you're usingit right, it's a great tool for
growth.
And so what we did initiallywas we said, hey, let's give
everyone a coupon who hasn'ttaken a ride in the last few
weeks and let's see what happens.
Ends up being super profitable.
People come back when you givethem discounts and take more
rides, but eventually theystopped using the service.
(02:03):
So what we really wanted tofind out was how do we target
the right people to give themdiscounts, not only the people
who will just come back and usethe coupon, but ones that will
use the coupon and then stay onand ones that will otherwise not
use the service, basically.
So really trying to find out,dissect those people who are
(02:26):
very promo sensitive and theones that we can make them
change their behavioressentially.
And so that's where we startedinvesting a lot of effort into
this and we built asophisticated system around
machine learning that reallyuses what we call causal
inference, so reallyinvestigating the cause and
(02:46):
effect of promotions fordifferent individuals and then
only targeting those people whohave a very large effect,
essentially.
So you don't want to give acoupon to someone who will take
a ride anyway and will use thecoupon.
You want to give it to peoplewho are unlikely to take a ride
unless you give them that coupon.
And so when we started usingcausal inference, we saw massive
(03:08):
gains and we basicallyincreased the ROI by about 50%.
So with the same budget, wewere able to get 50% more rides.
Essentially so super successfulended up doing that same thing
over for the driver side of thebusiness, thinking about it more
generally, around uh incentivesand not necessarily promotion.
(03:28):
So, uh, for drivers we havesimilar stuff where you know we
might give uh a bonus if they'vetaken five rides in a row,
something like that.
Uh, so use the same technologyover there.
Uh, after lyft ended up joiningInstacart.
So we worked at Instacart for afew years.
I was a director of engineeringthere working on conversion
(03:52):
optimizations, basket sizeoptimization, so kind of similar
stuff on a really large scaleessentially.
And then about close to twoyears, I almost started Monaco,
raised a large seed round at thebeginning of last year and then
(04:12):
off to the races after that.
Speaker 1 (04:15):
Got it.
That's cool.
That's something that was athought in our mind at the
agency for a while.
When, know, when you're sendingout all these campaigns and
promotions and they're sort ofjust you know one-to-many versus
being one-to-one, and you knowthere's definitely a lot of
brands that we've worked with inthe past who would then become
over-reliant upon you know,discounting and you know sort of
(04:37):
hurting the, you know the brandreputation along the way and
getting customers toocomfortable with waiting for
that code versus you know themshopping and if they had not
gotten that, that code, um.
So how did you, how did yousort of transition into um, the,
the Shopify ecosystem from, uh,you know, ride sharing and then
(04:58):
into into Instacart and thenand then e-com?
Speaker 2 (05:01):
Yeah.
So I think, uh, in general, youknow, general, I think there's
a lot of similarities betweenLyft, instacart and generally
the Shopify ecosystem.
They're all about basicallygetting people to place that
order book, that ride,essentially.
So it's fairly similar in thatsense.
The difference is it's a littlebit of a different scale for,
(05:23):
uh, a lyft, uh, uber, all theride sharing uh and services
like that, and then instacart aswell, uh, so for e-commerce
it's a little bit different.
Um, it's the industry, and inshopify is interesting because
you can really get to massivescale with almost no, no team or
(05:44):
maybe a 10-person team.
Sometimes you see brands doover 100 million in sales
basically, and so obviously thatwould never happen for a
startup like Lyft and Instacart,things like that.
They would need much biggerteams.
And so when I was looking atthat, I saw, okay, well, uber,
lyft, all those they havemassive teams around them that
(06:07):
help them really figure out whatis the right pricing, how do
you do discounts, all of that.
So lots of talent there aroundmachine learning, data
scientists, people like that,and you know, for the Shopify
ecosystem, you know it's reallyhard to do it yourself, really
hard to do it yourself.
(06:27):
Basically, you have to eitherhire a bunch of people, invest a
lot of time, or you maybe havesome tools out there today, but
they're not very advanced interms of that and typically the
tools in Shopify, or a lot ofthe tools in Shopify, are
basically tools.
They're not really working onoptimizations or basically going
beyond that, just providingtools but actually implementing
(06:47):
the solution essentially.
So that's where I realized,okay, there's a little bit of a
gap here where, with mybackground and the people I've
worked with in the past, I thinkwe can build something that
could help tailor it forbasically all the Shopify stores
that are out there and wouldsave them the effort of actually
(07:08):
building these thingsthemselves, but at a high level.
Yeah, we looked at how peopledo promotions on Shopify and you
saw there are a lot ofsimilarities, like initially
they're just doing simplesegmentation, like you mentioned
, essentially.
But I saw that there's a lot ofopportunities for improvements
(07:30):
here, because it's mostly aroundmaybe simple A-B tests, things
like that, but nothing aroundpersonalizing discounts, really
all the way to the end,essentially.
Speaker 1 (07:41):
Are you able to utilize the data from multiple
brands?
For, let's say, one customershops at five of the companies
that are using Monocle and thenyou have a six company that
starts using Monocle and I go tothat website and am also a
customer of that.
Is it possible to leverage theway that I respond and engage
(08:05):
with the discounts from theother five to inform sort of the
sixth, or is it much?
Is it more siloed, you know,for that particular brand and
that particular customer?
Speaker 2 (08:15):
Yeah, so.
So right now it is siloedBasically.
So each brand I kind of we lookat them as a unique brand and
figure out what, uh, how dopromotions work for them?
And you know there's some.
I agree that there's somenotion around people being a
little bit more price sensitiveand maybe some people just
(08:38):
generally need coupons topurchase.
Uh, what we found is that it'snot entirely true.
Basically so the way peopleactually interact is more it
depends on the interaction withthe actual brand.
Essentially.
So there are some purchases, uh, that you know you just want to
make that purchase and you'renot waiting for a coupon because
(08:58):
you need this item, basically,and it doesn't really matter if
you're generally you likereceiving discounts or not.
What really matters is okay,how did you interact with the
brand?
How many times have you visitedthe site?
And through that we typicallyhave a much stronger signal on
(09:20):
how are you likely to purchasebasically, or how likely is it
that a coupon will change yourbehavior.
Essentially, so we don't reallydo any cross-store matching.
What we do do is try tounderstand what are the type of
features.
So for each user we build somesort of profile.
(09:41):
Essentially that's unique tothe brand.
But what we find is thosefeatures tend to be fairly
similar between the brands.
So, for example, one that wetypically look at that's a very
strong indicator is how longyour session has been.
So if you have a really shortsession, promotion is unlikely
(10:03):
to change your behavior,basically, if you have a really
long session, conversely, youmight have already made up your
mind and a discount is not goingto change that, basically.
So that's also not an idealperson to give a discount to.
And the people in the middlethat are kind of on the fence
still, those are the type ofpeople that you typically want
(10:24):
to give discounts to, and so wefind that that is fairly similar
between brands.
But what is the definition of ashort session or long session
is vastly different betweenbrands, and that's why we don't
have, right now at least, amodel that basically looks at
everything and decides for everyuser.
(10:45):
Let's do this, but rather forthis specific brand.
Here's what it looks like andhere's how it might be best to
represent these sessions,basically, Got it.
Speaker 1 (10:56):
Okay, that makes sense.
On the side of actuallybuilding and growing the
business, are there any sort oflessons or sort of gotchas that
you took from the first businessthat you started that you kept
in mind when going at it for asecond round here?
Speaker 2 (11:16):
Yeah, good question.
Yeah, I think two things.
One is just the people you workwith is super important.
I think hiring a right team iscritical for the business.
Finding the right co-founder isalso super important.
(11:39):
I personally, like I know a lotof people, tend to say you know,
co-founders need to complementeach other.
Make sure you havecomplementary skills.
I don't necessarily agree withthat statement.
What I think is you need tofind someone that you just like
to work with, even if yourskills are fairly similar.
That's okay.
(12:00):
The goal of having co-foundersat least you can bounce ideas
off each other, talk to them.
Sometimes things don't go rightand your co-founder is the one
that's helping you, maybepulling you up or telling you OK
, here's maybe one other way wecan look at this situation or
save this deal, or anything likethat.
So I think people you get alongwith and people it's fun to
(12:25):
work with I think is moreimportant than complementary
skills.
Basically that's my opinionwith.
I think is more important thancomplimentary skills.
Basically that's my opinion.
And then it is fun to just youknow, have, make sure you have a
good team that you're excitedto go to work with.
And then we we tend to work inin person.
So it just the energy isdifferent and making sure you
(12:48):
have those people who are wholike that energy of bouncing
ideas off each other, buildingthings really quickly.
I think that's super important.
So that's in terms of peoplelearned uh over time.
(13:13):
Is that?
Uh?
Before I started monica, I said, okay, I'm not gonna start
another business until I know.
Okay, this is uh a businessthat I know can explode,
basically.
And so before we uh we hiredanyone, before we wrote any line
of code, uh, basically wetalked to, I want to say like 50
brands or so and kind ofinterviewed them around how they
do promotions, all that stuffDifferent segments, also in the
(13:36):
e-commerce industry and then,right before we even had a
product, we started doing somemanual analysis for them.
And then, once we did that andwe saw that they were excited by
it, that's when I realized,okay, this is a good area to
dive into and take this thing tothe next level.
So I do think, before jumpinginto things, making sure you're
(14:01):
in the right industry with theright idea, basically.
Speaker 1 (14:05):
Yeah, not building a ton and then launching it
without having the initialcustomer discovery.
Yeah, exactly, that makes sense.
Interesting on the co-foundernote, though, I think people
probably do prioritize somethings beyond just the basics.
(14:27):
Beyond just like the basics.
Like, do you actually even likethis person and you know having
to go to work with them everyday and having some sort of
camaraderie, whether you'recomplimentary or not, you know,
do you actually want to show upand be a partner with that
person?
Speaker 2 (14:40):
Yeah, and like, wherever you end up choosing,
you're gonna it's not alwaysgoing to be easy Like you're
always gonna, or you'reinevitably going to argue about
certain things.
And I think the important thingis that you know you respect
each other and you know you canargue a little bit, but then you
take that to the next level,basically, and solve that
(15:01):
argument and and and build ontop of that, rather than you
know, maybe not talk to eachother than for the next few
weeks, yeah, how do you thinkabout breaking into you?
Speaker 1 (15:09):
know for the next uh, a few weeks.
Basically, yeah, how do youthink about breaking into uh,
you know the ecosystem with awith a product that you know
there's not a lot out there whenit comes to what, what you're
doing, whereas you know if youhad launched a reviews app I
don't know in like 2019 or 2020?
You know there's, like you know, hundreds of review apps that
(15:29):
are out there, but brands arealso more familiar with what
that is.
How have you gone about thecustomer education and then
followed through with actuallygetting them to sign on?
Speaker 2 (15:44):
Yeah, good question.
In general, I really likethere's a product market fit
framework that Sequoia publisheda while back.
So the way they think about itis they split it into three
areas.
There's the hair on fire, hardfact and future vision.
Hair on fire is kind of whatyou described as like the
(16:08):
reviews app, so somethingeverybody knows they need.
It's a matter of building thebest solution out there.
Essentially, hard fact isbasically what we're doing where
people don't know there's analternative to the status quo,
essentially, so the biggestcompetitor we have is just using
(16:30):
what you're doing today andsticking with it.
This future vision is, you know, maybe you know, building
flying cars or things like that,essentially, uh, or a store
that, uh, you know marketsitself, or something along those
lines uh so with you know, withour area, our, our specific uh
product market, fit it.
(16:51):
It is a little.
The challenges are a little bitdifferent.
So it's not about, you know,talking to a customer and then
convincing them, hey, like youshould move off this platform
and onto our platform, butrather, you know, when we talk
to brands, it's about moreeducation.
So, basically understanding,okay, how much money have you
(17:13):
spent on promotions in the lastyear?
Most brands don't really knowbecause in a way, it doesn't
really matter, because they justlook at the bottom line
basically.
And when you're thinking aboutpromotion, sometimes you don't
even see it in your P&L,basically because it might just
be the actual cost is baked intothe actual discount is baked
(17:35):
into that cost.
So there's a little bit moreeducation around that.
So there's less competition forus.
When we talk to brands, we haveto tell them hey, there's a huge
opportunity here and we have tosell them on the vision a
little bit, that promotions canjust be something that an AI can
(17:57):
manage on its own and optimizeand adapt over time to trends
and seasonality, all that stuffbasically.
But the idea is that in thisspecific area, once you've done
enough education in the market,then, because there's no
competition, you become a leaderreally easily.
So right now we're, I think, inthe beginning of people
(18:20):
understanding, okay, there are alot of AI tools out there and
brands are now looking to expandinto that and figure out, okay,
where should they add AI ormachine learning into their
store?
And this is kind of one areathat we see that brands Kind of
always think about what are thedifferent ways to innovate in
(18:45):
that?
Speaker 1 (18:45):
area essentially.
Speaker 2 (18:48):
So it's interesting.
The other thing I would saythat we're doing that helps is
basically, we didn't want to getpeople to migrate onto our
platform, so one thing we couldhave done, which was easy.
So, in general, when we lookabout, when we think about
promotions, we split it into afew different categories, but
(19:09):
one category is basically thedelivery mechanism.
So how are you actuallyinforming the user that they
have a discount on their account?
So that's the deliverymechanism.
We decided not to touch thatdelivery mechanism because
there's just so much competitionthere and there's so many great
tools out there essentiallythat do this.
(19:30):
So for pop-ups, it might be Amp, it might be Klaviyo,
postscript all of those brands,all of those products have great
pop-ups, basically, and there'sno reason why we should build
another one.
That is more or less the same.
Rather, what we decided iswe're just going to plug into
(19:53):
those tools, essentially, andthen we're going to basically
essentially be powering thepop-up, or the brains behind the
scenes of the sides.
When is the right time to showa pop-up, what should the
discount be?
All that stuff basically.
And the advantage there is thatnow, when we are onboarding a
new brand, we don't need them tooff-board from Klaviyo onto us.
(20:17):
They just keep the same UIelements that they have there
and then we just plug into thatto really decide when and what
to show basically to the user.
Speaker 1 (20:30):
Got it.
No, that makes a lot of senseand probably makes the process
significantly easier, not havingto deal with migrating anybody
off of anything.
But I know there's a lot ofcompanies that are out there
right now that are buildingproduct that already exists, but
they're getting wider with sortof what they're offering is, as
it feels like a lot of a lot ofbusinesses are started doing.
(20:53):
It was going for that sort oflike HubSpot approach, where
there's a bunch of differentproducts underneath sort of one
platform.
What are your thoughts aroundgoing that route versus being a
(21:14):
very specific sort of Swiss Armyknife for a particular use case
, which is effectively I guessyou guys are doing with when it
comes to like the, the pricingoptimization, because I don't
think it works all the time,because you can't do that with
like reviews, because reviewshave now been commoditized.
So how do you, how do you thinkabout that?
Speaker 2 (21:34):
yeah, um, yeah.
So I think when you're startinga company, uh, in my opinion,
the best way to get it off theground is to be that Swiss army
knife or not exactly the Swissarmy knife, but really finding
that one specific pain point andreally solving that really well
(21:56):
.
Basically, and that's a reallygreat way in where you know,
you're talking about a painpoint that brands have, you're
solving it, they're coming toyou for that specific thing and
then ultimately you want toexpand and then provide more
solutions to other pain pointsin that same specific area.
Now I think Yotpo is a greatexample of a lot of different
(22:21):
products in different kind of indifferent categories.
So reviews is a little bitdifferent than you know, sending
emails, for example, or sendingSMS but it makes sense to
expand into that because that'sanother product that brands
really want For us.
I don't like we'll never expandinto reviews, for example, but
(22:45):
for us, expanding into differentareas that have promotions in
them is very natural for us, forexample.
So right now we typicallycontrol I wanna say with some
brands about 50% of thepromotional spend.
So that's through pop-ups,emails, on-site deciding when
(23:08):
people should receive discounts.
Basically, we sometimes triggerwhen a user should receive a
discount in a win-back campaign,things like that, and that all
accounts for about 50% of thepromotions.
We want to also tackle theother 50%, so that a lot of
times is through strikethroughs,so just items that are on sale,
(23:32):
for example, or bundle offers,things like that.
So those are areas that wehaven't gotten into and that's
kind of the area that we want toexpand to.
I know, once you have controlover all the promotional spend,
that's where there are greatinteraction effects, where you
know, if you know this item ison sale and someone has it on
their cart, maybe you can givethem an even larger discount,
(23:55):
for example.
Or maybe there's a bundle offerfor three items.
The user has two of them.
Maybe you can give them anadditional discount and tell
them, hey, like, add this itemand get this additional offer.
But once everything isconnected, it's much easier to
play around with it.
For example, black Friday iscoming up right now.
(24:18):
It's a little tricky to get toa point where everything is
synced, so a lot of brands justdecide to shut down their emails
, maybe turn on something new.
The idea is, with Monocle, inthe future, you're just going to
be able to say, well, I justwant to give 40% off for
everyone, and then you'll do itfrom one platform and we'll
(24:39):
update everything.
So all of your email marketingplatforms, all of the pop-ups,
everything on the site basicallywill update everything.
So all of your email marketingplatforms, all of the pop-ups,
everything on the site,basically.
Speaker 1 (24:48):
So you don't need to go and manage everything
independently, essentially Gotit.
That makes sense.
Well, I really appreciate youtaking the time and coming on.
I think it's a huge need and itmakes a ton of sense.
So definitely a lot of valuethat e-com brands can get out of
their customers and I thinkeverything's moving towards that
.
You know one-to-onepersonalization versus the
general, like you know,segmentation and whatnot.
(25:09):
But before we, before we wrapup, can you let everybody know
where they can, you know, findyou guys online and how they can
connect with you if they, ifthey want to learn more?
Speaker 2 (25:18):
yeah, sure, so our site is.
Use monoclecom.
There's a book demo link there.
Feel free to fill it out andwe'll reach out Awesome.
Speaker 1 (25:28):
Well, again, I really appreciate you taking the time
for everybody listening.
As always, this is BrandonAmoroso.
You can find me atbrandonamorosocom or scalistai.
Thanks for listening and we'llsee you next time.
Speaker 2 (25:41):
Thanks, Brandon.
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