Startup Explore

Peter Moot (00:00)
Yeah, so we are building Promi and really in a nutshell, it's a platform for personalizing discounts for e-commerce retailers.

We, myself, my co-founder and team are kind of coming from Uber. And so we're taking a lot of the same approach

I usually think of discounts as a bit of the Wild West of pricing.

It's the merchants thinking, hey, I just want to boost my revenue this month. What's the easiest way to do that? And usually discounts are kind of the answer there.

a lot of merchants out there tend to think, I think they kind of underestimate the amount that personalization can kind of bring to their business.

Ali | Startup Explore (00:41)
Today I'm joined by Peter Moot, CEO and co-founder of ProMe AI, a seed stage YC backed startup that is using AI to personalize pricing discounts for e-commerce retailers. Their goal, boost revenue while protecting margins by offering the right discount to the right shopper at the right time. Let's

Ali | Startup Explore (01:03)
Peter, thank you so much for coming to the show. Very excited to have you.

Peter Moot (01:07)
Thanks for having me, Ali.

Ali | Startup Explore (01:08)
All right, well, let's dive in. So can you tell me what you're building and what is that big problem that you're trying to solve?

Peter Moot (01:16)
Yeah, so we are building Promi and really in a nutshell, it's a platform for personalizing discounts for e-commerce retailers.

⁓ The most popular product, the reason that most merchants will use us is our AI model that will actually automatically kind of adjust and send discounts out to the visitors on a website that we deem most price sensitive ⁓ and also avoiding sending that discount to people who are price insensitive.

The idea being we can basically make more money for merchants by improving the conversion rate for those people really on the edge without giving way too much margin to the other people who were already going to place the order in.

Ali | Startup Explore (01:52)
Now I know the pricing decisions are pretty strategic and important for any company. Maybe at a high level, what's the methodology and logic behind those optimizations?

Peter Moot (02:04)
Yeah, the optimizations that we're making or typically how merchants kind of think about it.

Ali | Startup Explore (02:09)
that you guys are making.

Peter Moot (02:11)
Yep. So personalization in a lot of ways is kind of a new dimension that a lot of merchants are not kind of diving into right now, just mostly because they don't really have a platform in order to do that. We, myself, my co-founder and team are kind of coming from Uber. And so we're taking a lot of the same approach that we used at Uber and the tools we built at Uber for personalizing discounts and making it available to those merchants.

Kind of in nutshell, what we're doing is basically looking at each visitor when they jump on the website, ⁓ collecting data on those sessions. So broadly, it's kind of three different categories of data. There's the contextual data, ⁓ time of day, day week, device type, source of traffic, things like that. Then there's individual data. So have we seen this person session before? How many times? What products have they viewed? What's their transaction history with the website?

And then the last category is just store metadata. So things like what's the relative conversion rate of the different products on the store? What's the profit margin of the different products in the store? And what we can kind of do with all that is basically pull it up into a model and come out with a prediction of a price sensitivity for each of those, those visitors. If you are more price sensitive and less likely to place the order anyway, then we'll give you a higher discount and vice versa. That's kind of

Ali | Startup Explore (03:10)
Mm-hmm.

Peter Moot (03:33)
a little bit of how the decisioning process goes.

Ali | Startup Explore (03:37)
Got it. That's pretty interesting. ⁓ usually I feel like a lot of the merchants ⁓ approach discounting just kind of with a static approach and maybe some guesswork. But it feels like with Promi, you kind of take that guesswork out of it and it's all like data or science driven.

Peter Moot (03:55)
It is. Yeah. I usually think of discounts as a bit of the Wild West of pricing.

A lot of these merchants, especially the larger merchants, might have actually a pricing team that are actually doing some of these either switchbacks or A-B tests to kind of understand what the best price is for each product. But when you get to discounts, a lot of that kind of know-how is throughout the door. And kind of for good reason, a lot of times discounts are temporary. Maybe you're doing kind of a stunt campaign for a certain

holidays. ⁓ You may have kind of some of these evergreen campaigns for things like abandoned carts. ⁓ You may do email blasts every once in a while. But there's not really a good way to do A-B testing for them. And a lot of times it's kind of an afterthought. It's the merchants thinking, hey, I just want to boost my revenue this month. What's the easiest way to do that? And usually discounts are kind of the answer there.

And so we are definitely trying to take a lot.

that kind of sophistication and bring it to the market. ⁓ Both in terms of, you know, measurability and like our whole platform is built on kind of an A-B testing suite. So anything we do, we can measure the precise impacts of, ⁓ but then also just kind of a new framework for who you should be sending discounts to and how large of a discount that type of that person should be getting. So yes.

Ali | Startup Explore (05:20)
got it. And when you say who you should be sending those discounts to, can you give me an example? Like, can it be someone who is maybe coming to the website from like Facebook or TikTok?

Peter Moot (05:30)
Yeah. So one of the stronger signals in our model is traffic source, for instance. ⁓ This is actually a trend that holds kind of shockingly well across different types of merchants and industries. ⁓ Things like if someone is typing in the URL of a website directly to go there, their intent to purchase is way higher than if they are coming from a Facebook, Instagram ad, things like that.

It's usually kind of that browsing mindset when you're kind of on Facebook and you see kind of an ad and you click into it. ⁓ So if your intent to purchase is way lower, those are the types of people that typically make sense to give more of a discount to ⁓ because they wouldn't have bought otherwise versus if someone's going to the website directly, then they likely have more of an intent to purchase and you don't need to discount in order to win that order.

that's one example. There are a bunch of others. Obviously it's a machine learning model. So there are dozens and dozens of different features and we can kind of pick up on a lot of these types of trends. ⁓ Another easy one is new customer versus existing customers. Typically new customers are a little bit more price sensitive. They're not familiar with the brand, existing customers, they trust the brand a little bit more. They know kind of the product, the quality of the stuff that they're getting. So they're a little less price sensitive.

Ali | Startup Explore (06:43)
Yeah, those are good examples. That's very helpful. ⁓ so if I'm a merchant and I install Promi let's say today, ⁓ can you walk me through high level what that experience is going to be for that merchant once he or she installs it?

Peter Moot (06:57)
Yeah. So the platform itself is actually very, flexible to depending on what the merchant wants to do. ⁓ We do recommend kind of a starting point for the merchant. And what we think will generate the most profit in revenue bumps for them is really this kind of evergreen campaign that will we basically identify the most price sensitive customers and then only give a few of those very, very price sensitive customers a discount.

Ali | Startup Explore (07:01)
Mm-hmm.

Peter Moot (07:26)
⁓ That's going to happen kind of in the background all the time When a merchant will first kind of install us in order to kind of launch that campaign There's a few kind of components they have to put on their store that starts collecting data for us Usually takes about a week for us to collect enough data to then actually train our models for that store specifically and then

Ali | Startup Explore (07:26)
Mm-hmm.

Peter Moot (07:48)
Once the model is actually trained, you can go in and launch. We have just a draft campaign available to these merchants with kind of our recommended configurations for the AI model. They can click that. It'll launch as an A-B test and they can come back a couple of weeks later and see exactly how much more revenue or profit they're generating with this tool.

Ali | Startup Explore (08:07)
Yeah, that's pretty, I think, going to be interesting for merchants to watch, just kind of to see what the impact of the discounting program is. Okay. And I know you've mentioned you kind of watch for multiple parameters or dimensions as you kind of set the discounting and pricing for the product. Can one of those parameters be like a timeline? Let's say I'm a merchant who wants to liquidate a certain seasonal product, let's say within the three, four months.

Can the timeline be a factor in coming up the discounts for ProMate?

Peter Moot (08:42)
Yeah, so we do actually have a like very kind of fledgling product is one of our new ones that will look at basically we call kind of a liquidation schedule ⁓ for a lot of the products that you're holding. ⁓ So if you are trying to basically and we typically will look at kind of sell through rates. ⁓ And so we'll say, hey, if you want to sell X number of products in like per week or something along these lines, then we can look at saying

Ali | Startup Explore (08:51)
Mm-hmm.

Mm-hmm.

Peter Moot (09:08)
you're not selling fast enough right now, so you need to kind of bump up that discount value, or you're selling a little bit too slowly, ⁓ or selling a little bit too quickly. We actually think we can make you a little bit more margin, so we bump down the discount in those cases.

Ali | Startup Explore (09:21)
⁓ Nice.

can probably be installed on any websites today or is it just the Shopify powered platform today?

Peter Moot (09:32)
As of today, it's just for Shopify merchants. We definitely have plans to expand past that, but it's been a good kind of go-to-market strategy for us.

Ali | Startup Explore (09:35)
Okay.

Mm-hmm.

And another question I had that I was curious to hear is, so I know you tried to optimize the discount and pricing and basically entice the customer to make the purchase and while maintaining the margin for the company. ⁓ That being said, I'm curious when a customer logs onto a website, do you guys, does Promi AI take into account prices of similar products on, let's say competing websites at that moment?

Peter Moot (10:08)
As of right now,

we're just looking at your specific website. And for that reason, a lot of kind of like the ICP for us is people who are selling unique goods ⁓ and people who kind of have their own brand and are willing to kind of fluctuations outside of that brand don't impact kind of the conversion rate of that brand specifically or quite as much. I think that like,

Ali | Startup Explore (10:12)
Okay.

Mm-hmm.

got it. Okay.

Peter Moot (10:31)
know, iterations in the future will hopefully be able to take into account, you know, prices of similar products, like a ton of different features.

Ali | Startup Explore (10:37)
Mm-hmm.

Yeah, that was actually one of my questions down below, but we can maybe cover that now, like in terms of the future product developments. So I know this would be one of them. Are there any other key features that you're most excited about to see?

Peter Moot (10:52)
Yeah, so ⁓ integrations are kind of a big category for us. I think things like piping through our personalized discounts into Google ads, Facebook ads, Instagram ads is something that would be very interesting to us and very impactful for our merchants. So that's kind of a big one in my mind. ⁓ There are a ton of things we can do with the model and a ton of new types of data that we can kind of ingest. Right now, it's very much kind of based on what we're collecting from the merchants. ⁓

the merchants website, but it could be things, you know, you think about kind of like geographic information or ⁓ like other sales information across other websites. ⁓ There's kind of this interesting idea of like, could we even kind of build these profiles of visitors when they jump on one website ⁓ and we know kind of how price sensitive they are because they're buying only sales items or non-sales items and things like that.

can we then port that over to other websites that are using us and kind of jumpstart the model that way, ⁓ which might be kind of an interesting trend and we'll have to kind of test and see how well that might work.

Ali | Startup Explore (12:05)
Yeah, I feel like that would be very kind of a new and novel approach. now let's talk about some of the use cases and ⁓ if you can share your most favorite use case or most recent one.

Peter Moot (12:16)
⁓ Yeah, so we have, you talk about just merchants that kind of use us, right?

Ali | Startup Explore (12:22)
Right. Right.

Peter Moot (12:24)
So we were working with a ⁓ shirt seller right now. And one of my favorite use cases was they were looking to launch some volume discounts. basically, hey, can we incentivize people to place larger orders? ⁓ But they didn't want to necessarily offer the discounts to everyone because they wanted to go pretty high, around 20 % off for a lot of these. And so we ran originally an A-B test with them, just saying if you buy three shirts,

or more will give you kind of a personalized discount. Some people might not even get any discount if we just deem them kind of relatively price insensitive. And what we tested it against, and usually what the A-B tests that I think are most valid in understanding the impact of the model is when we, for instance, in this, we're kind of ranging the discount between what's called like a 15 to 25 % on the personalization side versus basically this fixed 20 % off.

that everyone gets. ⁓ And we A-B test that and we can see precisely what the average discount per order is on each side just to make sure that we're not spending way more or less with the personalization model. But then looking at the revenue and gross profit, that's really where you can see how much more personalization gave to you versus just giving out this discount to everyone. ⁓ And that experiment itself showed, I think, like a 60 %

Ali | Startup Explore (13:23)
Mm-hmm.

Peter Moot (13:49)
5 % increase in overall revenue per visitor ⁓ with the personalization. So it can really be impactful if you're doing it right.

Ali | Startup Explore (13:59)
Yeah, those are pretty impressive kind of double digit growth in revenue per user, I would say. ⁓ I went through a couple other examples and use cases on your website. Yeah, it was pretty impressive to see the results that you guys are getting for those companies. All right, let's talk about the, ⁓ maybe if you can provide some other key insights or key metrics around market traction, what the market traction has been for Promi so far.

Peter Moot (14:27)
Yeah, so I won't go into revenue, but I'm having to go into more kind of our like experiments and like so far where we've gotten today. ⁓ In general, what we see is usually the high 20 % kind of bump in average revenue per visitor. And fortunately, we've actually seen that kind of go up over time, or we're able to improve the model, add additional features and things like that. Recently, then we also decided ⁓

to build in kind of tracking of gross profit into our AV testing suite. ⁓ And typically what we see there is around the kind of 20 % growth. And this is all store wide. Now the assumption is that the merchant is applying us across a good kind of cross section of the store ⁓ instead of just like a few items here and there. ⁓ Those items specifically when we're deploying these personalization models, as I mentioned before,

Ali | Startup Explore (15:14)
Mm-hmm.

Peter Moot (15:23)
On British shirts, that's kind of a 65 % lift in average revenue per visitor. We saw another one on Supplement Store that was over, I think it was a 51, 52 % lift in revenue per visitor. And then have had a couple others. There's a kind of secondhand appliance store that we launched on that's store wide. I think that it was close to like a 27 % lift in average revenue per visitor.

Ali | Startup Explore (15:48)
Yeah, that's pretty meaningful impact. That's awesome to hear. All right, let's switch gears and let me take you back a little bit. Can you talk about your background, your education, your history, and maybe something that people can't find on LinkedIn?

Peter Moot (16:04)
Yeah. So I went to school at Harvard. I studied economics, which lends itself well to pricing optimization. I guess you could imagine from there. I jumped into consulting, didn't like it at all. I'm just not that the consulting type, I guess it always bothered me kind of the deliverable being a stack of papers with ideas in it and not actually seeing if it would work or not. So I think the

From there, I decided to jump out into product management ⁓ with the idea being that I'll get a little bit more of that feedback loop and understand what actually makes impacts in the market. And so I worked at jet.com. I went directly into the pricing team there. ⁓ And that was kind of the beginning of a lot of my pricing work. ⁓ But from Jet, I moved over to Uber and ended up leading their discounting team on the product side.

Ali | Startup Explore (16:47)
Okay.

Peter Moot (17:01)
both for Uber Eats and Uber Rides. So a lot of the discounts that you would see on a weekly basis ⁓ would be coming from our team and we would be kind of deciding what the right discount value is. ⁓ From there, I mean, just looking at the kind of magnitude of impact that a lot of this personalization had, ⁓ that was the inspiration for jumping out and really starting And it is a little bit of that kind of

Ali | Startup Explore (17:12)
Y'all.

sense.

Peter Moot (17:30)
I guess, founder market fit for Promi. I've always been a very kind of quantitative person. I've loved getting into the details of, you know, how to optimize and ⁓ pricing has been a great fit for that. ⁓ And so, yeah, it's worked out well for me so far.

Ali | Startup Explore (17:32)
Mm-hmm.

Nice, and what about your co-founder?

Peter Moot (17:51)
He was at Uber actually with me. We overlapped for two years. He was working on an adjacent team. ⁓ He's a tech lead and has a lot more experience in AI products and actually building them. And while our products at Uber were AI products, as a product manager, you're thinking about very different things than as a tech lead. So I usually think it's worked out actually very well. And some advice I usually give people who are looking for co-founders is, ⁓

Ali | Startup Explore (17:53)
Okay.

Mm-hmm.

Peter Moot (18:21)
Find someone with very complimentary skill set to your own for us, know you I need to entirely trust him to build the best architecture of our software possible and he really does entirely need to trust me to that there is a need for this and Working with merchants and building something that's ⁓ And so that level of trust and that level of kind of complimentary skill sets I think has really set us up well for ⁓

working together and not stepping on each other's toes.

Ali | Startup Explore (18:52)
Yeah, that totally makes sense having those complimentary skills to each other. ⁓ With that, I know you guys went through YC program summer 2024 badge. I'm always curious to ask questions around that topic. Can you tell me maybe what was the most challenging part in the application process and how you guys dealt with it? Because I know YC is very selective. It's pretty hard to get into that school.

Peter Moot (19:17)
⁓ Yes, the interview process, a lot of people will realize is it's actually kind of shockingly brief in a lot of ways. So you fill out ⁓ this application and the application itself takes us a while because we want to make sure that we are putting our best foot forward, thinking about all of these things and it kind of pushes you to realize that, maybe I should go out and talk to more merchants too.

Ali | Startup Explore (19:27)
Okay, okay.

Mm-hmm.

Peter Moot (19:47)
⁓

really validate our ideas before jumping into YC. ⁓ But once you do that, you submit, ⁓ you really just get a 15 minute interview with one of the partners. So it's very quick. And for us, ⁓ we went through that. We didn't hear anything typically by the end of the day, you'll basically get a call from the partner if you're in. And if not, you get an email saying, hey, like we couldn't decide not to accept you. ⁓

We ended up getting an email and I was thinking, ⁓ that's too bad. Like we didn't get in, we opened it up and it was basically the partner saying, hey, I want to talk to you again tomorrow. And so was like, I don't know what this means. This is hadn't heard this online at all. ⁓ We jumped on the phone with the partner the next day. He grilled us a little bit more, but I think it was kind of just like a friendly grilling and then it was like, okay, I'll punch you guys. You're in. ⁓ And it's funny, like that it was very quick process, the whole thing.

Ali | Startup Explore (20:26)
Okay.

Okay. Okay.

Peter Moot (20:44)
⁓ Talking to a lot of the other YC companies, it turns out that's like a pretty common way for them to admit people is ⁓ email and ask them to jump on the phone again. ⁓ so that was kind of the the entrance process for us.

Ali | Startup Explore (20:54)
Mm-hmm.

That's

Got it. That's good to know. And then maybe one of the most memorable advice that you guys got from YC school that you apply to Promi these days.

Peter Moot (21:09)
I think, I don't know if it's like a, I have a specific quote for you, but I think that one of the biggest kind of misconceptions of a company and like startups in general is when you start, I would say it's probably 75 % chance that you will pivot into a different idea. ⁓ and maybe should even be higher. The people who are not pivoting are the ones who are more likely to.

Um, and I think it's a little bit of this misconception of, know, you get into YC and everyone's asking kind of like what you're building and such. And, uh, which you'll realize is like the partner interviews. A lot of times it's kind of more about your backgrounds and they're investing in teams. They're not necessarily investing in ideas. Um, and so I think it's this, a little bit of a shift in mentality of, you know, validate quickly. How do you kind of build something quickly, get, uh, some interest from.

Ali | Startup Explore (21:52)
Okay.

Peter Moot (22:06)
potential customers. ⁓ And if it doesn't work, quickly to ⁓ it's YC also instilled in us definitely kind of a fast paced mentality around testing, validating, building a little bit more testing, validating, building a little bit more kind of instead of building that vacuum. So

Ali | Startup Explore (22:25)
⁓

Got it. And Curious, the idea for Promi that you have today, was that the same idea that you went to YC with? Or did you have to pivot?

Peter Moot (22:36)
It pivoted, we pivoted a little bit. ⁓ I had wanted to do something in kind of like the pricing space just because I knew that space so well. And I was pretty confident that some of the features and things that we had built at Uber would also help other merchants. We did not start with kind of personalization. We started with basically, could we just adjust discounts like across different items? ⁓ But we quickly realized there's a large trend towards personalization across the e-comm industry right now.

and there was a big appetite for it for e-commerce retailers, so we moved into that.

Ali | Startup Explore (23:11)
Okay, that's interesting. Now let me switch gears to ideal customer profile for you guys. Who are you targeting

Peter Moot (23:19)
Yeah, I mentioned it a little bit briefly before, so we're working with Shopify merchants. ⁓ That mostly is just because it streamlines a lot of the onboarding and standardizes the data for us to ingest and use in our model. But typically we're looking at, we've been targeting the SMB segment as of right now, which is usually kind of like the one to $10 million range customers. We do have some that are a little bit larger than that too.

and also like a couple that are a little bit smaller than that. ⁓ As far as industry goes, we're actually pretty agnostic. What we find is our model works pretty well if, for instance, as I mentioned before, you are using ads or different acquisition channels for your customers, and there's a good mix of kind of returning customers and existing customers and things like that. You can just kind of exploit a lot of that heterogeneity and the types of visitors coming to the website. ⁓ That's really what makes the

Ali | Startup Explore (23:51)
Okay.

Mm-hmm.

Peter Moot (24:18)
the model work. ⁓ Typically, I think it works a little bit better for the ⁓ AOVs of around average order values around maybe $100, $200 or less. ⁓ That just allows us to get more order data. The more orders we have, the more confident we are in training our model. ⁓ And then as I mentioned before, a lot of times it's more effective if you are basically ⁓ have your own brand.

⁓ and are selling unique items instead of ⁓ maybe like drop shipplers or resellers ⁓ who are selling more established brands.

Ali | Startup Explore (24:57)
when it comes to competition, I assume you have other tools in the market that provide similar services. I guess my question would be what makes Promi different and stand out from them.

Peter Moot (25:09)
Yeah, so there is quite a bit of...

like other competitors in, I would call it the marketing automation space and really pulling in AI into that to automate the types of touch points that you're sending out, whether it be emails, SMS, notification, things like that to customers. As a part of that, there are other players who are personalizing the discounts that you may get in those emails and push notifications. Traditionally, a lot of how

like companies out there will personalize their discounts. And this is how Uber did it too, is you're sending out this kind of randomized data. So you basically, let's call it, take 20 % of the market over your users and basically split them half, half of them will get, let's say like a 20 % discount, half of them will get no discount. And then you can use that to train your model and say, okay, who reacts well when given a discount? The issue with that, and I think that that's the approach that a lot of the competitors are taking, the issue with that is it's,

It's very bulky. It's a little bit difficult to kind of turn on and off. It's very expensive because you're basically burning money to send out these discounts to then train a model in order to generate some uplift. ⁓ If you don't want to send out discounts for a few months, you're going to have kind of relatively stale training data. And so you might want to do the process over again. And you need to have a lot of data.

Ali | Startup Explore (26:31)
Mm-hmm.

I say.

Peter Moot (26:37)
like a lot of customers, it's basically that ratio of customers, the kind of minimum quantity of them needed to train the model needs to be small enough as a share of your overall user population for you to really get the benefit of a lot of these personalization models. And we've taken a very different approach that kind of helps open up a lot of the SMB market to us. We're training on organic, just data of people coming to the website. ⁓

Ali | Startup Explore (27:06)
Mm-hmm.

Peter Moot (27:06)
not necessarily with a discount at all. And it's a different approach, it's a little bit kind of our secret sauce of like how we make that work. ⁓ But I think it's a much leaner, much more convenient kind of approach to personalization ⁓ than a lot of the other competitors out there.

Ali | Startup Explore (27:21)
Mm-hmm.

Got it, got it. I ⁓ would assume because of your approach, your ending optimization would probably be more, I would say, don't know if the word accurate would be the right word to use, better strategy to implement probably.

Peter Moot (27:38)
It's a little bit interesting.

I am 100 % sold that it's a better strategy to implement because I've been working at discounting for the last five years of my life and I've seen both approaches and have a good sense of the pros and cons of each. One of the issues with the other approach that a lot of the competitors are taking is it's difficult to trade models and predict on, for instance, like an item level because you...

Ali | Startup Explore (27:44)
You

Peter Moot (28:06)
need so much data that looking at the orders of a specific item are not necessarily going to be sufficiently large enough for you to train a model to say, this item specifically should be discounted at this level. We are taking a very different approach where we need so much less data that now we can actually predict and say what's the best value for this item specifically.

Ali | Startup Explore (28:11)
Mm-hmm.

Peter Moot (28:31)
And that allows us to take into account the variations in profit margin across the user store. And so those types of things, think, are really like allow our model to perform so well compared to a lot of the other competitors.

Ali | Startup Explore (28:47)
That's pretty unique, interesting approach. ⁓ Now, looking into the future, let's say three to five years, what's the North Star for the company? Will you guys go beyond discounting, maybe to like loyalty program spaces and other spaces? ⁓ Give me some color there.

Peter Moot (29:07)
Yeah, so I think another adjacency that might make sense for us is the broader pricing optimization kind of piece. So instead of just personalizing discounts, we can also kind of optimize prices for a lot of these merchants. ⁓ I know that there's like lot of ways that we can kind of bring value there. think discounts has been a good kind of starting area.

particularly personalization because there's not really a good way for a lot of those merchants to be able to personalize the discounts that they have today. They just don't have like the technical platform to do it versus pricing. A lot of these merchants can do the analysis that they want to kind of on their own and then adjust those prices for the items. And so that's kind of one of the reasons we decided, like let's build something completely new that will benefit a lot of these merchants.

Ali | Startup Explore (29:53)
Mm-hmm.

Peter Moot (30:04)
And there have been a lot of other kind of like pricing specific tools out there that are doing some of that price optimization. We're kind of like sidestepping that market for the time being in order to really provide something that we think is a little bit more valuable and will drive a lot of revenue growth.

Ali | Startup Explore (30:23)
got it. Cool. Yeah. ⁓ I guess as you grow the company, you will need to hire more and more people. I'm curious to hear your thoughts on like the hiring approach that you take, what specific qualities you look for in candidates usually.

Peter Moot (30:24)
least to start.

Yeah. So culturally we try to build a very open, transparent environment and instill a ship of a sense of ownership for, to each of our employees. ⁓ I like to hire people who are genuinely curious about this topic, who think it's a fun problem to work on ⁓ and kind of bring their whole self to work. ⁓

Ali | Startup Explore (31:07)
Mm-hmm.

Peter Moot (31:09)
So I want someone who is relatively autonomous in the way they work, ⁓ who is basically using me for ideas to shape whatever product that they are working on. ⁓ And I think a lot of times that means they are kind of coming to me and asking for things instead of me kind of coming to them and asking for kind of project updates and things like that. ⁓ So far, we're very much in hiring engineers, more engineers. ⁓

but also the people who can kind of wear multiple hats. So primarily they're going to be doing engineering, but you know, as we can pivot a little bit, we might need more salespeople and things like that. So that's kind of the starting points. Obviously as we get bigger and bigger, we might actually build out that sales team. We might build out ⁓ other teams, marketing teams as well.

Ali | Startup Explore (32:02)
Now question outside of work, ⁓ what activities do you like to do? What hobbies do you have?

Peter Moot (32:09)
Yeah. Um, so I've been in a pickleball league, um, a little, a little bit, you know, I think I've enjoyed it more than I thought I would. Um, my fiance has been playing pickleball more than I have, and she kind of dragged me and some of our neighbors dragged me into the league. Um, so that's been fun. I do try to surf when I have an opportunity to, um, I hate the cold weather or cold water up in Northern California, but.

Ali | Startup Explore (32:13)
nice.

Peter Moot (32:38)
It's still worth it. have a wetsuit. ⁓ I'm not very good, but I generally think it's a very difficult sport to be good at, but I'm putting in the time, so hopefully we'll soon get better at it. ⁓

Ali | Startup Explore (32:51)
Yeah, as long as you

enjoy it, I think that's most important thing.

Peter Moot (32:54)
Yeah, yeah, exactly. I think those are the two big things. ⁓ I love to travel. So whenever I have an opportunity, we'll take kind of short trips here and there. Obviously, that's difficult now that I have a one year old startup, but ⁓ we'll try to do it as much as I can.

Ali | Startup Explore (33:12)
Yeah, startup is like your baby,

Peter Moot (33:16)
Yeah, exactly.

Ali | Startup Explore (33:18)
All right, and my last question, Peter, is, ⁓ is there anything else about the product, team, company, future vision that we didn't touch on that you want to share with the audience?

Peter Moot (33:29)
⁓ maybe one thing I would say is a lot of merchants out there tend to think, I think they kind of underestimate the amount that personalization can kind of bring to their business. They're thinking of it as your typical kind of, discounting campaigns and maybe they're benchmarking off of these like

new discounting apps like a new type of bundle, a new type of BOGO or something. And really kind of what we want people to think about this as is like a whole new dimension of how to optimize your store. You you've been thinking about building kind of the right inventory and types of products to offer or the right kind of marketing channels and things. But now you should really also be thinking about like how like personalizing your store and discounts particularly can bring a lot more.

I think that

Over time, we're going to get a lot better and we've already seen kind of like huge lift in the amount of revenue that we can generate for merchants with just pretty simple personalization so far.

Ali | Startup Explore (34:41)
Yeah, that's awesome to hear. Thank you. Well, Peter, thank you so much. ⁓ I hope that we will speak soon sometime again and you will have even bigger ⁓ successes to share.

Peter Moot (34:53)
Yeah, that was fun. Thanks for having me, Ali.

Ali | Startup Explore (34:56)
Thank you.