Pricing as a Strategic Lever with Peter Moot

Introduction to Pricing in Product Management

Productside | 00:02–00:47

Hi everyone. And welcome to Productside stories, the podcast where we reveal the very real and raw lessons learned from product leaders and thinkers all over the world. I’m your host, Rina Alexin, CEO of Productside. And today I have the pleasure to be speaking with Peter Moot, co-founder of Promi, a Y Combinator backed company from summer 2024 that automates pricing in e-commerce with dynamic incentives and promotions.

Prior to founding Promi, Peter spent four years at Uber as lead product manager for incentives and promotions across rides and eats and held a product manager role at Jet.com, also focused on pricing. So welcome to the podcast Peter.

Peter Moot | 00:47–00:50

Thanks for having me. I’m excited to be here.

Productside | 00:50–01:22

I love this topic because we often talk about it at Productside, that product managers really need to be in the room and have a seat at the table for pricing discussions. And that’s why we wanted to talk to you because you’re at the heart, you’ve been a product manager involved in pricing, and now you’re at the heart of changing how AI can automate pricing for e-commerce brands. Peter, I usually start asking why product management, but for you, I want to know more.

Why pricing? Why did you go in this direction?

Peter Moot | 01:22–02:14

Yeah, it’s funny, I actually get the question a decent amount. I never really sought out pricing as the industry to create a career in for myself. I think I just drifted into it over time. For myself, think it’s a great fit. I have always been a quantitative person. I studied economics in college. As I was moving more towards tech with my career,

pricing product management was a good stepping stone, having kind more of a business background and such. And so it made sense. I started in Jet.com. I actually really loved it. It was very quantitative, very experiment driven and kind of have doubled down ever since. Yeah, I think that’s kind of the way that I found myself into pricing.

Productside | 02:14–02:41

You know, we often talk about how different aspects of product management are somewhat art and science. And pricing definitely, I feel like, has a good flavor of both. But you mentioned it’s very quantitative. So at the end, data can truly drive some decisions. Talk to me about how you are designing Promi to use data to essentially create an AI way to drive decisions around pricing.

Peter Moot | 02:41–04:11

Yeah, yeah, exactly. So I think you’re exactly right in the fact that a lot of times pricing can be either science or art. I think a lot of times it really depends on the size of the organization. Obviously, if you have the luxury of big data, you can make it more scientific. You can run A-B tests. You can run switchbacks, all these types of things. For the smaller organizations, that’s not always possible. And so it is a little bit more art.

The process we’re taking at Promi actually, we’re trying to bring a lot of this automated A-B testing and things that we were able to develop it at a place like Uber, it was a very big data environment and actually bring it and make it available to the smaller merchants also. And what we find a lot of times is pricing is one of those muscles that I think a lot of merchants develop a little bit later on while they’re scaling.

You know, once they get to the maybe hundred million dollar range above, you start actually building out like a pricing team and having more sophisticated pricing strategies and maybe even pricing engineers and that matter. but you can get a lot of benefits even in a much smaller stage too. And that’s kind of what we see with Promi. we’re really just trying to bring some of the very low hanging fruit that we saw at Uber that’s, that’s easily.

replicate in a smaller data environment and making it available to other merchants. And specifically for us, that’s personalization, understanding what types of people react well to discounts and who doesn’t.

Productside | 04:11–04:34

Okay, so interesting. So your AI tool also takes into account the other side of the transaction, essentially the buyer profile, in order to come up. Okay, that’s really interesting. because normally I would think that you need a certain level of transactions, like a certain level of volume of transactions in order to do proper A-B testing, as I think you’re kind of alluding to. Uber has so many transactions per second, so it can do a lot of that.

Peter Moot | 04:34–04:40

Mm-hmm.

Yes.

Productside | 04:40–05:02

but w if I’m thinking about an e-commerce store that might not have that many transactions, there’s still a lot you can learn from buyer profiles. So, okay. So tell me, how do you get that kind of data sets? Are you aggregating data sets on buyers and their behaviors and then applying that to a store? Talk me through that.

Peter Moot | 05:02–07:37

Yeah, yeah. So the data we collected at Promi and a lot of the stuff that we saw that was very predictive of how someone will buy an Uber really broadly kind of falls into three categories of data. There’s contextual information. So we’re gathering information on the session itself, time of day, day of week, device type, referral URL. Although those are actually pretty predictive already on like whether someone will buy or not. Good example in that data category would be referral URL where

If someone is typing in the URL of your website directly, they are typically on a mission to go purchase something. They have a high conversion rate. On the flip side, if someone is coming from Google Ads, Facebook, Instagram, things like that, they’re typically more in the browsing mindset. Maybe an ad caught their attention and they’re going in there just taking a look through. And you see that conversion rate is typically much lower for that category.

And so we can exploit those types of things. And that’s kind of just the contextual data category. We’re also using things like personal data. So what’s the view history of this person when they jump on the website? What’s the transaction history? What types of products have they bought and things along those lines? Those are, I think for a lot of people, a little bit more intuitive in the fact that they would be predictive of elasticities and whether or not someone will purchase. Obviously returning users,

typically have a higher conversion rate than new users. And then the last data category that we look at is just like the product information itself. So what’s the profit margin, the price, any of that metadata? We’re able to kind of string all of that together to get a good understanding of the conversion rate for different types of people when they jump on the website. For a little bit of your point on, know, hey, this can work for smaller merchants.

I think that like the approach that we’ve taken is a little bit different than for a lot of these large merchants and that we’re, we’re curating it for these lower data environments. And so much as, we’re really, you know, the traditional machine learning approach is throwing out basically kind of randomized differences in prices. And so you can get really rich training data to, train that AI model and exactly estimate the elasticities of different people.

we’re actually saying, hey, let’s just look at the existing organic traffic that someone has and try to train a model on that that can get 85, 90% of the way that a model with all that really rich data can get and just work for a lot smaller merchants too.

Understanding Buyer Profiles and Personalization

Productside | 07:37–07:47

Interesting, Peter, you’re making me reflect right now a lot on an example is that my husband would get very frustrated with me and with Uber actually, because I would get all of the discounts like nonstop. It would be always something 20% off, 20% off your next five rides, whatever you order. And I enjoyed it. And I definitely purchased more on Uber. They probably knew my buyer profile is that I had some elasticity, whereas he probably didn’t. So.

Peter Moot | 07:47–08:06

Hehehe.

Yeah.

Productside | 08:06–08:20

It’s interesting for me when I reflect on that. Also, what you’re saying is if someone’s typing in with the intent to buy, it’s almost like we are not penalizing them, but we know they’re going to convert so we don’t try as hard. Even though they are maybe more of our more loyal customers. Does that ever get into, I don’t know, some moral quandary here?

Peter Moot | 08:20–09:22

Mm-hmm.

Like more quadrant, yes. think that some of the biggest like pushback that we see from merchants and retailers in general is just like the concept of personalized pricing. know, are people going to feel bad if one person sees one price and another person sees another price? And I can totally believe that. I think it really is up to each merchant strategy on how they want to shape the relationship with their customers. At Uber, I think

you know, we were looking at the magnitude of the gains that we were able to generate by personalizing things like discounts. And we made a business decision that was was definitely worth it. I think on average, we saw something like a 35 percent lift in the efficiency of the discounts that we sent out with personalization versus just flat static discounts out there. And so it is it’s pretty significant and changing for the business. But it’s a trade off that, you know, every every merchant should make for themselves.

Productside | 09:22–09:28

Yeah, and even though my husband was frustrated, he wasn’t getting the discounts. He certainly was getting the benefit when I ordered the car. So it worked for our family. OK, so interesting on this that everything that you’re saying makes a lot of sense. And I think that with even more data, Promi can actually unlock some unique pricing knowledge for

Peter Moot | 09:28–09:46

Yeah.

Mm-hmm.

Productside | 09:46–10:03

for businesses that didn’t have or wouldn’t have this large data set. So it’s kind of a huge value add to have that kind of perspective. Can you speak maybe, you know, we’re talking about, I think a lot of B2C, like in, and that is your background with Uber and also Jet.com. But is there anything that B2B product managers can learn from the B2C space around personalized pricing?

Peter Moot | 10:03–11:20

Mm-hmm.

Yeah, I I think that like, I have less experience in B2B in general. So I’ll caveat this with saying that, but at least in my mind, a lot of the same principles apply to B2B that apply to the B2C space. In many ways to me, pricing in B2B is just a kind of maybe like lower in example, as in lower number of customers, maybe a higher touch, higher ticket size versus a B2C, but…

they’re still going to behave in a very similar way. So if you’re thinking about like the relative elasticity is the types of business customers and their willingness to pay, there’s definitely a lot of learning there. Even just like running a startup as Promi, it’s we have to think about how do we price our products? Not only how do we price the products of our merchants? And there’s a lot that goes into that as to.

You know, should we have tiered pricing for different size businesses, which I think is pretty common. think that’s an example of segmentation as well and personalized pricing effectively. that makes a lot of sense in certain cases. Some merchants can afford only so much, and especially if they’re small and starting out.

Productside | 11:20–11:43

Yeah, I feel in the B2B space, because we have products, I very much have both B2C, like where we sell the individual courses, but also to sell to teams. When we get to enterprise, it does end up being very much personalized pricing from willingness to pay budgets and every single account has a, I mean, we have a discount table, right? So we use an approach, but in the end it does become.

Peter Moot | 11:43–11:45

Mm-hmm.

Productside | 11:45–12:00

you know, one-to-one contracts and there’s just a lot of leverage in the contract negotiations. But I’ve never actually thought about approaching B2B also from the perspective of what is this buyer profile? Like what is the profile of the enterprise that we’re going for? are you, is that the way that maybe people should be thinking differently about pricing?

Peter Moot | 12:00–12:51

Mm-hmm. Mm-hmm.

Yeah, I definitely think so. In some ways, B2B is a little bit more ripe for personalization, as you mentioned before. The channels through which you’re advertising the pricing typically are a little bit more restricted. You might not even have your pricing on your website. And a lot of times, the B2C approach is much more like the price is listed everywhere. You go on Uber or Uber Eats. You can see the price right there in front of you. We have a little bit of the luxury of the fact that people are logged into a specific app.

And so we have that logged in profile that we could show personalized discounts to. But any of kind of like your out of home advertising, things like that, if you’re to put pricing on there, it really restricts what you’re able to do with the personalization. And that I think is a little bit different on the B2B side that you’re able to do that more. But yeah, I definitely agree with.

Challenges in Pricing Optimization

Productside | 12:51–13:36

Yeah. We have

found, and I think there was a study done of about over 500 SaaS companies on pricing data and impact of pricing optimization on profitability. And we teach it in our digital course because it’s fascinating. A 1% improvement in acquisition yields something like a 4% improvement in profitability.

1% optimization and monetization. the pricing strategy, 1% improvement of that leads to 12% and over 12%. I think it was 12.7% in profitability. And so it just also, I think pains me a little bit when I know a lot of companies, they don’t actually review pricing more than once a year, if that. And I feel like it behooves them to also consider

Peter Moot | 13:36–13:42

Mm-hmm.

Productside | 13:42–14:00

what’s next with pricing, because if they can optimize pricing, if they can do better job at identifying who has more elasticity, convert those better, it just benefits people, get more access to better products, and then the company obviously grows. So there’s a lot of incentive to get this right.

Peter Moot | 14:00–14:24

Yeah, huge. mean, pricing changes can be massive changes and levers for companies out there. I mean, exactly like you said, right? Like, you know, a one dollar increase in the price of your item, that dollar flows directly through to bottom line profit. And so a lot of times that is why it’s so impactful to kind of like change the prices, figure out what’s optimal.

Productside | 14:24–14:27

So why do you think companies don’t do this more often?

Peter Moot | 14:27–15:36

I don’t think it’s an easy problem necessarily to solve. I think that a lot of them have difficulty measuring the price changes that they offer, whether it’s too small, the A-B test isn’t set up correctly. There’s a lot of intuition-based, even at companies the size of Uber, we would have internal conversations about what is the right…

kind of investment to make if you think of it that way. We can make these kind of short-term pricing changes that show a bump in revenue or bump in profit, but we’re worried about kind of the long-term impact, how it will shape LTV for the customers. And I think that you can really get caught up in just.

impossible conversations to solve if you’re hypothesizing on like, does this impact this one cohort that we saw shrink in this test, even though we saw a net positive and profit everywhere else. And that’s a strategic cohort. So how much should we be valuing that money from those people versus money from other people? And so things like that, especially

Productside | 15:36–15:46

I think what you’re talking about

is essentially the difficulty in really assessing how much value you’re creating for the customer as well as for yourself, because that’s what you’re trying to do is you’re playing a balancing act here. What I heard you mentioned was that there could be a bump, you know, short-term, but long-term the LTV can go down because maybe that customer felt they’re not really getting as much value from your product or service. And so they’re not willing to come back. And so it’s, it’s, you never really then know.

Peter Moot | 15:46–16:03

Mm-hmm.

Exactly.

Productside | 16:03–16:13

But you can predict, I think that’s what you’re saying. You need a good enough data set, an understanding of the customer behavior to be able to predict pricing.

Peter Moot | 16:13–16:53

Mm hmm. Yes, exactly. And I mean, even in a place like Uber, the size and scale of that, we still had questions and we weren’t able to solve for like the long, long term customer lifetime value for a lot of these changes that we were making. And so it was very much a kind of like some of them are leaps of faith. saying, hey, we’re seeing short term results. We think that that will stick. There’s no reason to believe that this doesn’t, you know.

carry through in the long run. And so we might put that through and basically kind of claim that in back and say like, hey, we made this much money for the business because of this change.

Productside | 16:53–17:03

So I’m curious, Peter, let’s just say, let’s play this out. You raised prices and got a little bump, but you started seeing more turn. And so maybe that decision was wrong. What do you do? you lower, like, you lower pricing? Prices, is that hard or do you just play with promotions and discounts more?

Peter Moot | 17:03–18:44

Mm-hmm.

I mean, it depends. fortunately, Uber, we kind of had a little bit of a framework that we would work with on something like this. It’s kind of an investment framework. I think actually applies really anywhere. It’s so, you know, what is the ROI of the pricing change? Right. And really was if we’re giving a discount, you know, what’s the profit cost of that discount versus how much revenue on the the top line that we’re actually generating?

For most businesses, think you’re really just trying to optimize profit. For very growth centered businesses, that might not always be true. You might actually prefer to get a little bit, you know, sacrifice some profit if you’re generating enough revenue in exchange. And so a lot of the changes that we would make, we would look at basically the efficiency of those investment levers. Whereas like promotions, for instance, were considered an investment lever. If we raise prices and we saw that actually turn was going up,

We would think of, in that moment of time, what is the marginally most effective lever or most efficient lever? If, for instance, there was one that was profitable and it was just we’d make more money without sacrificing too much revenue and that trade-off was the most favorable, we would go through that lever. That might be lowering prices. That might be, though, actually running more discounts. Typically, what we found is like discounts were relatively efficient in terms of

compared to just price changes too.

Navigating Pricing Decisions and Customer Value

Productside | 18:44–19:02

Yeah, I find that if people were to think about all of the alternatives that they can have, let’s say if they make a decision on price, increase the price, it’s not the end of the world. They can always reduce price or, as you’re saying, have other levers. I do wonder how much that stops people from making increases, especially I think in the

Peter Moot | 19:02–19:08

Mm-hmm.

Productside | 19:08–19:31

Maybe not so much in the startup space where people are really educated about pricing, but in the small businesses that run America, I think there’s a lot less education there. In your experience, what would you say people get wrong the most about setting prices?

Peter Moot | 19:31–20:56

Well, so I think that a lot of times there’s a lot of intuition that comes into pricing and maybe harkening back to the discussion we had on what’s the right timeline over which to measure the impact that you’re making. Just in my experiences, both at Jet and in Uber, there were…

endless teams that had ideas on, you know, strategic investments that we should make because theoretically they would pay off in the long run and they weren’t necessarily data driven and such. I think that that kind of intuition, a lot of times personally, I think it’s wrong and people are just, you know, kind of coming up and strategizing their mind without a good foundation. I think that that is something that people get wrong a lot. I think that it I would stress people be as data driven as possible, you know.

I bet I could convince a lot of people here that, like we should be giving discounts, for instance, to our most loyal customers because, you know, they are the ones that we need coming back more and more. They generate a lot of profitability for us when in reality, if we give discounts to our most loyal customers, we’re just carving more into margin because they were already going to place the order at that higher price anyway. And so like that is an example of like, that’s, that’s just inefficient. That was a intuition that just didn’t work out.

And so I think that’s like a common one. Maybe another.

Productside | 20:56–21:12

Yeah, I think that goes

to the point, Peter, just to kind of emphasize that point. What we’re talking about here is pricing is a reflection of the value creation. So if you have loyal customers, they’re getting a lot of value from your product and service. And so it’s actually okay to charge them more because you’re charging for your value. Whereas new customers, and that’s why we see a lot with new customer acquisition, you haven’t proved the value yet. And so that does incentivize or…

Peter Moot | 21:12–21:27

Mm-hmm.

Mm-hmm.

Productside | 21:27–21:32

It gives them another reason to just say yes, because it do risks that choice.

Peter Moot | 21:32–21:47

Yeah, exactly. That’s totally right. Yeah, and I think it’s just something that people don’t think about a lot, or they’re kind of getting it backwards a little bit in their mind as far as what the efficiency, at least in the short term, what the efficiency will be for those pricing changes. The other point that I was going to make is just that the things that people get wrong a lot, I just see kind of a spectrum on the

Productside | 21:47–22:00

Yeah.

Peter Moot | 22:00–23:21

development, just increasing sophistication of pricing strategies out there with retailers. think that I wouldn’t even necessarily say it’s people getting it wrong. It’s just they don’t have the resources to pour into improving their pricing strategy at the stage of the business that they’re at yet. A lot of times what you see is like those when you’re even pricing a brand new product out there that you haven’t even started the business. A lot of times that’s either going to be just cost plus pricing. Maybe you’re doing some competitor research and looking at

what has similar value props and then pricing yourself kind of relative to that out there. And that’s fine. mean, like that works for new products, definitely. As you start the business, you should really be kind of doing those pricing A-B tests to just validate that that is actually what your value prop is. And that’s probably kind of the next stage of sophistication. Then you can get into some more of those like dynamic pricing updates.

Then you can get into more of that personalization as you get bigger and bigger and you get more data. But yeah, I think that like probably just checking yourself and saying, Hey, where’s my stage of the business? Are we still kind of two steps behind where we should be? Do we have the data? Do we have the size to be able to get a little bit more sophisticated with some of those more advanced techniques or not?

Productside | 23:21–23:28

So I was going to ask you what you mentioned a lot. We’ve been talking a lot about the importance of having data behind your decisions. But if you are starting a new product, you don’t quite have as much data. And so is your advice, then, to start with a cost plus? Like, just get some margin, because at that point, there’s just too much you don’t know that you kind of have to start somewhere.

Peter Moot | 23:28–24:30

That’s it.

Yeah, I mean, I don’t think you need to kill yourself over deciding what the price should be. And like really, like a lot of the know-how and advice on starting up startup in general, just like try something, see if it works, if it doesn’t, you can do that with prices. As you mentioned before, it’s a two-way door. But I probably would take more of the approach of like, what are the competitors out there and what is their pricing model look like? And you can say, hey, but we offer maybe

a more exhaustive feature set than the competitors. So we think we can justify a little bit more of a higher price. And that’s totally fine too. Or vice versa, you know, if you’re a stripped down version and you want to go after kind of the volume market or just be a cheaper alternative, then yeah.

Advice for Product Managers on Pricing

Productside | 24:30–24:45

Yeah, you could be the, know, what is that, challenger pricing approach if you’re starting out and there’s incumbents so that you can kind of take, right? Essentially, the lower your price, the more market share you’re trying to kind of acquire. But then all the companies go through that where at some point they want to be the premium brand. so pricing also signals. It has a power of signaling when something is kind of more valuable, right? It’s worth more. So you should be willing to pay more.

Peter Moot | 24:45–25:01

Mm-hmm.

Yeah.

Yeah, definitely.

Productside | 25:01–25:08

So

I mentioned at the start of this that a lot of times product managers don’t get a seat at the table for pricing discussions. And clearly, I mean, you have had quite a seat at a few tables for pricing. What can product managers do today if they’re perhaps in a situation where they’re not having as much influence over price?

Peter Moot | 25:08–26:34

Mm-hmm.

Mm-hmm.

Yeah. Well, maybe the first thing I say is I totally think that product managers should have a seat at the table. actually, you know, coming from, I think a Jet.com and a Walmart.com where it was a little bit more strategy driven versus product driven. you had teams of like consultants or ex consultants actually on the, pricing team and it was a strategy team and the product managers will work with them to decide what the pricing should be versus Uber, where it was all driven.

by product managers. There is kind of a much smaller strategy and planning function, but they don’t decide the roadmap for the product managers on the pricing team. And I actually very much believe the latter is a more effective approach because it is more keen to that. You can move quickly if the product manager is the one making the decisions on the pricing as far as…

running experiments, A-B tests and such. And I think it really just increases the velocity of what you’re able to do versus relying on a bigger kind of pricing strategy team. But I don’t know if I exactly answered your question. It’s kind of what can can product managers do to get a seat at the table?

Productside | 26:34–26:41

Well,

you know, I always think about it this way, Peter, that product managers are responsible for value creation. So they can’t really understand the value created of their decisions if they don’t have a pricing lever. So it makes the most sense to me from a company standpoint to have that major value voice at the table in helping drive those decisions. You also set a benefit of product managers also have that skill set of experimentation. And so

Peter Moot | 26:41–27:03

Mm-hmm.

Mm-hmm.

is us.

Productside | 27:03–27:08

being data-driven, having experimentation, that’s also quite necessary for pricing. Are there other, I guess the question I’m asking is, are there other benefits? Let’s say you’re a product manager in a situation where you don’t have that much influence, who do you talk to and how do you create that influence in the organization?

Peter Moot | 27:08–28:08

Yes.

Yeah, I mean, that’s a great question. I think that a lot of times it really does have to come from leadership as far as like an org change and actually willingness to kind of get the tech team involved. I think that there can be examples where showing very successful experiments that are driven by the product team or the engineering team and such can kind of shift mindsets.

for the executives and just make sure that the PM or someone from the tech team actually has more of a seat at the table. It’s kind of maybe like grassroots way of getting there. Otherwise, it’s up to leadership.

Productside | 28:08–28:21

I’m here with you. In the end, you’re right. It’s a leadership decision. if it’s not, I always wonder, it’s like somebody is in these larger organizations, someone feels like they are going to lose power. There’s like all the politics involved, but at the end of the day, it’s like, what’s right for the business and the people who are responsible for product success and value creation should, I think it’s a duh question, right? They should be part of it.

Peter Moot | 28:21–28:35

Mm-hmm.

Yeah.

Productside | 28:35–28:49

What are you most excited about with Promi? Because it sounds like you’re building a product that allows for companies that don’t necessarily have as much data to get access to personalized data strategies.

Peter Moot | 28:49–29:38

Yeah, it’s very much that. I think that there was a ton of impact that we saw from personalization at Uber and being able to kind of make that available to other merchants is pretty exciting. You know, this area in general for me, I think actually has a lot of founder market fit as well. Just as I mentioned, being in the pricing space for such a long time, having so much interest in the pricing space. And so kind of like that optimization mindset, like just

tinkering with very sophisticated problems and such of like, how do you build these machine learning models? How do you collect the right data? How do you address the discount value accordingly based on all these things? Like what’s the objective function? It’s just all really, really interesting. And just a lot of fun, honestly, to figure out and work on.

Productside | 29:38–29:49

Well, I can’t wait to see what happens because I think it’s just fascinating because as we started talking at the beginning, there’s art and science and I think AI is going to have a massive impact. And if we can, I can imagine a future where it’s not just Uber that has the ability to send someone like me personalized discounts and maybe get my business went a little bit more. But you know, I’m a typical consumer, right? Like we all get influenced by.

Peter Moot | 29:49–30:07

Mm-hmm.

Mm-hmm.

Yeah.

Productside | 30:07–30:08

having a good deal and good value. Yeah.

Peter Moot | 30:08–30:47

Yeah, no,

definitely. like, there are examples of personalized discounts out there today that I think that are probably more prevalent than most people realize. There’s the concept of just your typical marketing segmentation and, you know, marketers will stick kind of one discount value in an email to this segment and a different one to this other segment or.

You know, first order discounts are another good example, where there’s a pop-up on a website and that’s really only if you haven’t placed an order before or things like that. We’re just trying to make it a little bit better and smarter and data-driven instead of intuition-based.

Productside | 30:47–30:57

I even heard with marketers, there’s also a discussion around, you show percent off or do show the dollar value off? Do you guys play with that?

Peter Moot | 30:57–31:43

Well, so we don’t actually right now, we’re planning on building that in. So it’s definitely on our roadmap. We did a lot of that experimentation at Uber actually. Really kind of where it came down to is probably 90 % of the time we just ended up using percent off discounts. They were a little bit more flexible. We didn’t find great success with some of the more like creative.

structures that we were using in the past. So we tried things like almost a punch card system. So it was like ride this many times this week and get 20% off next week. And other things like Uber cash back. If you ride this today, then we’ll give you $5 for next week. Yeah, yeah.

Productside | 31:43–31:47

I remember that.

Peter Moot | 31:47–32:28

And it like, this is maybe another good example of like intuition saying like these things obviously would make sense, right? It like leads to more stickiness. You’re incentivizing the next order instead of this order. But something we saw with Uber cash back is just that, and maybe this is unique to Uber, just so many people would take that second trip anyway, that, you know, our utilization, our burn or like redemption rate on that Uber cash was near a hundred percent. And it turns out that just the concept of getting

a discount in the future was just so much less compelling than getting a discount right now on this order that you just didn’t really see that kind of revenue and trip lift. And so it ended up not being very efficient.

Productside | 32:28–32:33

Yeah, I can imagine, especially with Uber.

It makes sense to me why you would also do the percent off because it’s the dollar amount is not as impressive as that percent off. So it’s like the psychology of that. And it’s interesting to me, like the timing between the reward generation, you know, I guess it just depends on the frequency of the use, right? The frequency of the use and then the total dollar value. Okay, can you end a,

Peter Moot | 32:33–33:00

I think.

Mm-hmm.

Productside | 33:00–33:08

controversial discussion. Does it matter if you charge someone 20 bucks or $19.95?

Peter Moot | 33:08–33:37

I don’t know if I’m like the definitive one to weigh in on this. I’ve heard so much conflicting data also. I think there were some studies I’ve seen like, there is like kind of the sense value of this. But then I also saw some other study that was like, actually that first study was not effectively structured. And so we don’t think that there’s that much of an impact.

I don’t know. Maybe I’ll leave it. I don’t know. The jury is still out, you know, but it’s still controversial.

Productside | 33:37–33:52

Hahaha

Still controversial. I know, I

go back and forth on it too. At some point it was supposed to be psychologically, but then now are people kind of wise to it, and now you’re trying to trick me and I don’t trust you as much, who the hell knows? All right, so if you have one piece of advice for product managers stepping into their hopefully not annual, more frequent pricing review committee, what would it be?

Peter Moot | 33:52–34:46

Yeah. Yeah.

Mm-hmm.

I think it’s being data-driven, challenging your intuitions, being experiment-focused, and really kind of iterating quickly through that. I think a lot of the mistakes that I’ve seen operations teams in the past do is kind of take a strategy because they think theoretically it should pay off and it’s intuitive and they never test it. And they’re running with this for years and it turns out that it’s actually

not the best thing for the business. So always challenging yourself to the extent that you can, you know, with the data that you have available in your organization.

Productside | 34:46–34:54

feel like that advice can be applied to just about everything in product management. Experiment, learn, and don’t have an ego about it. So I love that advice. Thank you so much, Peter. How can people find you after this episode has aired?

Peter Moot | 34:54–35:16

Yeah.

Yeah, so we have our Promi website, usepromi.com. You can go on there, get in contact, reach out on LinkedIn. Obviously, I have a presence there as well. Yeah, I think this would be the best way.

Productside | 35:16–36:03

Awesome. Well, everyone, if you found our conversation valuable today, I always like to say, don’t keep it to yourself. Share it with a friend and subscribe to Productside Stories so you don’t miss a future episode. And thank you all so much for tuning into today’s episode. I hope the insights inspire you and propel you forward on your product journey. Remember, every challenge is a lesson just waiting to be learned.

Visit us at Productside for more free resources, including webinars, templates, and other product wisdom repackaged for you. I’m Rina Alexin, and until next time, keep innovating, keep leading, and keep sharing stories. I’m going have to redo that one. That’s okay. They have it already recorded. Yeah, I’ve that so many times. But that’s okay. I was supposed to say keep creating.

Peter Moot | 36:03–36:10

I’m sure you’ve done it, yeah, a thousand times, right?

Productside | 36:10–36:10

Let me just say one word. I’m Rina Alexin and until next time, keep innovating, keep leading and keep creating stories worth sharing.