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Prittam: The value you're delivering through rapid product innovation should match the pricing that you're charging for your product. And in order to keep them in sync, you need to have the infrastructure to test different pricing scenarios and specific ports of customer. And that's not easy to do unless you have a very agile experimentation infrastructure.

Welcome to Launch Pod, the show from Log Rocket, where we sit down with top product and digital leaders. Today we're talking with pre Tom Pagani. VP of product at Chargebee, where they have insight into how over 10,000 customers are optimizing their pricing models. In this episode, we discuss how AI is forcing companies to rethink pricing and why usage, outcome, and agent-based models are the future, and how to build the necessary technical and product infrastructure to test pricing models in real time across millions of users.

So here's our episode with Pre Tom Bogani.

Jeff: All right, Tom, how are you today? I'm excited for this one 'cause we are talking about something I think super timely. You have great background in, you know, time at Chargebee. There you have 10,000 customers and insight [00:01:00] into what they're doing pricing wise and this world where pricing is suddenly becoming a hot topic.

That is changing quickly. Welcome to the show. How you doing man? And thanks for joining.

Prittam: Hey Jeff. Thank you so much for having me. I'm super excited to be here. Yeah, like you were saying. I said at the intersection of building our own AI product capabilities and kind of re-imagining our own pricing strategy. At the same time, witnessing like over 10,000 customers experiment with their pricing strategies. I'm really excited to be here and to share this uh, with the peer group.

Jeff: it's an interesting time to be in pricing, right? 'cause I mean, I remember doing pricing exercises at past companies and I always knew the pricing was important, but it always seemed a little bit like, how much is this really gonna help? Doesn't it seem like we should just make a better product or focus on other things?

But now. It's no longer just, do you do seat based or do you do some kind of like, capacity based or something? , there's so many options and this is becoming [00:02:00] massively important. Both, you know, people are saying AI products are underpricing themselves. People are saying, some of them, some other things are overpriced or, you know, you're gonna find that you're getting innovated on if you're just using legacy pricing mechanisms.

'cause someone else could come out. And scoop up your industry with a more innovative, kind of better focused on the customer pricing. So what are you seeing? Like, let's just dive right into it here. Why has this become so important suddenly, , like, what, what the hell is going on?

Prittam: Yeah, great question, Jeff. So, You know, There are different types of pricing strategies that have been used by different industries for a long time. Like for

example, we've been using cell phones for a long time and you know, initially we were used to paying by the number of minutes and the number of SMSs we sent, but then it became all you can eat type of plans. What's happening now is things are profoundly changing because of AI within the software industry. Everybody's aware of it, so I don't have to talk anything specific about it because the underlying models are being used left, right, and center by many [00:03:00] products. Those underlying models come with its own cost, and now if you are providing product and services to your customer. You also have to start thinking about the cost that you are going to incur to provide them the service beyond the infrastructure that you're running your product on because of the foundational models. And that fundamentally changes the equation. So that's one big point. The second really big point is that because of the AI, people are starting to think about agent based pricing models. Right. There are products that are trying to replicate the entire persona. So you can see products like Cursor and others that are building agents for development.

So now you are essentially replacing or to a great extent the activities. That an engineer is supposed to do using an agent. So now you have to start thinking about like how to price and package that capability. And if you are pricing it as an agentic [00:04:00] model, you are likely to tap into the budget that is 10 times bigger than your typical software budget. So just to give you an example, you know, give you some examples of the pricing models. We have three, you know, primarily three or four main pricing strategies that are. You know, showing up a lot. Now the first one is usage based. So essentially, you know, if you are using open AI and you're using their different models that they provide, depending on the questions that you ask the number of tokens that you consume, that es how much you're gonna pay uh, to open ai, right?

Jeff: \ it seems like this is pretty akin to seat based, you know, with Salesforce or analytics tools. You might be events based or you know, in our case like session based. But it's starting to get one little bit closer. To, you know, you it's usage versus you know, just kind of seats or some, one step removed.

But it seems like the common thread across all these, and we'll get into other ones, is this concept, right, of like, we've always known this was [00:05:00] a better way to do it, right? Like, how do you, in reality, the goal is always how do you charge? As close to the value of what you deliver as possible. Right. And is, am I missing something?

This seems to be just like something we've always known is we wanna move closer to that. And now just because the ai, some of these tools can do more and can do more of the complete workflow, they're not a tool that helps you be a little bit faster to workflow. They can do a lot of it. You can start to charge more for like what you would pay the human for that workflow.

You can tap into that like personnel budget almost instead of just the software budget. is That the right way to think about it?

Prittam: that's one way of thinking about it. There are multiple models, so like usage based, for example, like we're talking about is one model. The other one is outcome based.

So there are companies like Intercom, for example, who are now charging based on the resolutions.

That they're able to do for their, you know, support products, right? Instead of just the number of conversations they're charging based on resolution. So it's very outcome based. And then there are companies that are charging completely agent [00:06:00] based. So like, you would basically get a sales support agent and instead of paying $60,000 to a person who's doing sales support, you would pay $10,000 for that agent. That will, you know, perform a part of the role of the support agent. So there are lots of interesting things that are happening and you know, it's really exciting time to be in the space. And AI is really changing everything and it's making things really fast. And people have to adjust.

Every day there's something new that's happening in the space.

Jeff: The one thing that always kinda had me wondering is outcome-based pricing especially, right? Like it makes sense in that, again, back to it is in theory, connected the most to ultimately what we're trying to get out of buying software, which is solving a problem. But there's kind of two pieces that, that I was always a little bit curious about.

One looking at, you know, you have intercom, like you said, with their fin product Deagon. You have charge flow, which helps with like recovering payments and loss payments. Are these companies doing better as a result of this? or is [00:07:00] it just.

It's better because they're gonna get more customers. Like where? Where does the value accrue from this kind of pricing model?

Prittam: Yeah, I think it's very closely connected to the go-to market models that will actually succeed. You know, it's as simple as if you go to a customer and if you tell them that I guarantee you that you know you are gonna get X number of outcome or X amount of outcome from our product, and then we are going to only charge you based on outcome, that would become a much simpler sale. As compared to like, Hey, here's the product. You have to implement it, you have to pay us, like for the platform, then you have to pay us , for using the platform. And then there is a subscription fee on top of that and so on and so forth. Obviously, the other model is much simpler for the buyer to make a decision, and that's why it has this disrupting power. 

Jeff: it kinda removes the risk, right?

Prittam: it removes the risk for the buyer, and it's also like, it elevates the amount. Of product value that you have to deliver because you have to now [00:08:00] quantify exactly the value that you'll be delivering to the customer. And, it's also like harder to build products that can actually truly deliver that.

Jeff: Right. So I guess if you're looking at it like in general, it's probably one of those things that if you can do that, you almost shouldn't. Worry, like short term, is this gonna accrue more value for you? It's more, can you use this to win more market because you are gonna deliver a better outcome for the customer.

Is it, you know, you can over time move people in maybe higher average contract volumes because they'll pay more when they can tie it directly to outcomes. I am curious though, like the one thing I still continue to wonder about this is. On outcome based one, if you're not delivering that and you kinda move to, this seems like a risk with it is if you move to it too hastily, you might find yourself without much revenue.

Or if you have a kind of like wishy-washy outcome, is there best practice around like proving the outcome? How does that work where a customer goes, yep, this is what I want. Like, do people ever dispute these things?

Prittam: Great question, Jeff. [00:09:00] So, you know the way most companies are doing it, especially the ones that are introducing outcome-based pricing or agent-based pricing or usage-based pricing, it all starts with capturing the usage information, the granular. Events data that allow you to know exactly how the product is getting used.

Then once you capture that data, you build value metrics on top of it. For example, let's say you are building a customer support product, so with the usage events, you will start capturing the number of conversations that the agent is having. But then you will go one step further and you will say like. It's not just the number of conversations that matter, it's also how many conversations are resulting into a support ticket getting resolved. So you then filter down the outcome, and that's called your value metric. And once you define the value metric, then you start charging based on the value metric.

So,

This is, you know, this is the, best strategy, but even the [00:10:00] best companies find it very hard to really like. Get it right. So for example, you know, if you look at Cursor, cursor is a hugely popular AI based IDE and fairly recently they introduced a new pricing model.

They had a standard $20. Subscription pricing for their pro plans. And that gave you a certain amount of you know, calls that you could make to like the paid models, paid underlying models. They changed their pricing. And now they had like a component of monthly subscription, but then also usage based. And there was a huge backlash when they rolled out the pricing because. Overnight, some of the engineers using the product started seeing like very significant amount of bills, like $500 and $400. So these are the types of risks that exist as you kind of embark upon the journey to change the pricing and packaging. So what the best strategies is by first like capturing detailed usage data and [00:11:00] analyzing the patterns, then identifying the value metrics. Price, you know, and then introducing the SKU alongside your existing pricing in a controlled way through pricing, experimentation. And only when you understand all the nuances like, you know, scaling it broadly.

Jeff: it's funny because it's kind of flies to the face of, you know, how typically new kind of software industries or verticals would work. Where each to be you introduce a new. Kind of tool set. It was just get usage. Don't worry about being perfect on pricing right away.

Like get a general, the right direction and then really work on user experience and delivering a great product. And then, you know, as you mature, you know, perfect your pricing model. But it seems like a lot of the AI tools. Almost went into it with that mindset.

And the problem they've run into is they were delivering so much value for so cheap, like you said, cursor for instance, right? There's no way that $20 a month was a fair price for the value it was bringing, [00:12:00] but people were used to it. And then you suddenly start to charge anywhere near what it's actually delivering.

And it's just such a huge cognitive dissonance that people are shocked. I just had my finance meeting with our controller today, going through budget stuff.

And you know, he's like, and this is gonna be this much every month, and this could be this much and any variation. He's like, well, what happened here? And if you run into these outcome-based things, this could be a lot more conversations explained to, controllers. What happened here and

Prittam: Yeah,

Jeff: why this month did it fly up?

And all that kinda stuff.

Prittam: that's also another thing that's worth highlighting, which is. The agility that is needed, right? Like the market is moving so fast. You know, if you think about like the agen AI native products like Cursor lovable, and the others, they are. Evolving their product extremely rapidly.

There's this tool that takes you back in time and gives you all the changes that happen to a public website.

So I was looking at that and I saw like how many times OpenAI was making changes to their pricing page.

And they made over like 128 pricing [00:13:00] changes just in like one year to their pricing page

changes to their pricing page. So, so it basically ties back to the point that you are making that the value you are delivering through rapid product innovation should match the value.

.

Or the pricing that you're charging for your product. And in order to keep them in sync, you need to have the infrastructure that allows you to test different pricing scenarios and run like specific, you know, cohorts of experimentation on specific cohorts of customer and understand the nuances of pricing and valuing your. Product in the right way. And that's not easy to do unless you

have a very agile experimentation infrastructure.

Jeff: so on that, like how do you do that, how do you kinda nail that out the gate where you're not, you know, cursor delivering, I don't know, 500 bucks a month of value, if not more for 20 bucks. But at the same time, you don't wanna go the other way and come in, you know, starting it.

It might have been tougher for them to start though if they came in at $500 a month [00:14:00] and couldn't get anyone to try it in the first place.

Prittam: That's right. That's absolutely right. So it varies a lot, you know, depending on the company and the type of business. But I think there is an absolute need of changing the mindset, the product mindset around pricing. So what used to be a loosely coupled function, you know, in terms of being collaborative with the go-to-market team and, you know, most of the decisions being made by the sales and , the pricing team within the go-to-market function, it's changing from that to becoming almost a weekly exercise, right?

One are the days when you would price your product once a year or once every six months that those, that time is gone. The time now is to be able to run these pricing experiments almost like you run your sprints,

because the value being delivered because of all the AI advancement is really rapid.

And then also you have to adjust to the changes that are happening. You know, [00:15:00] all of a sudden Deepsea comes up with their model. How do you respond as an open ai? Right.

Jeff: Who are already shipping, what pricing changes every three days. So, I mean, clearly they mean they've done all right if they, if you wanna model yourself after someone I would happily take even half the results OpenAI has had with growth. So, you know, that's probably something to look at there. I realize one thing though around this is you brought up cursor, took a lot of flack when they substantively raised their prices, I guess at some level.

It doesn't seem like they were hurt by it, though. There were some people frustrated. But the great thing about having a product as good as that is, is I would wager most of those people maybe complain for a minute and then kept using it. Like it's almost, we've seen this time and time again too, right?

Like, remember way back in the day, Netflix. They tried to bifurcate the things and then they brought it and they said, nevermind, we're not gonna do that. Brought it back together. And they took a lot of black people stayed with it 'cause the streaming was a better service. And then [00:16:00] ultimately, even like recently they started to shut down account sharing and their business was not hurt by that.

Everyone's predicting it's the end of Netflix, but because it was. Something people really loved and got, you know, value out of, everyone complained about it and then they kept using it. Or cursor, same thing. People got mad about that, but I don't think cursor is exactly hurting for revenue right now.

Like does this kinda go back to, like you said, outcome-based pricing or this kind of high value? The closer you can get to value, people can complain. But if you're getting your pricing all right, they're gonna complain and then keep using your product and keep paying you.

Prittam: I mean, yeah, of course. I mean, if you have a great product, if you're delivering the value, customers are going to be okay with you know, having some troubles here and there if it, you know. But the key here is being able to provide the transparency.

So in, in cursor's case, I don't think users would mind paying them more because they're getting so much value. The problem happened when the expectation mismatch happened.

You were paying $20 and all of a sudden the bill shows up with [00:17:00] $500 with, and you were not communicated that it's getting changed. And that was the challenge. Right? So being extremely transparent about pricing changes providing estimates of what's gonna happen to your pricing next month, you know, if you look at AWS and other products that have been doing this usage based model for some time, , you are able to like set up thresholds and alerts on. Your usage and you get alerts based on like, Hey, you have hit 70% of your usage limit, right? And you're projected to hit this amount during this billing cycle. So those are some of the things that that really matter in terms of providing a great customer experience.

Jeff: Yeah, it seems like you're right. Communication is probably a big part of it. Just get ahead of it. Warm people. Make sure they don't accidentally find themselves expecting a $20 bill and getting a $500 bill. And then the other is make sure that, you know, when you are raising prices, when prices are gonna go up, it should be connected to something where people see, you know, oh, that makes sense.

I'm getting a lot of value on this lever. So I should pay more as that [00:18:00] count goes up. That makes sense. I pay more for each of those because they're valuable and you know, if you were to tie it to a non-consequential metric, you're probably gonna get a lot more pushback and a lot less acceptance of price.

To, to kinda think about that, like how as a product person and, and someone who has seen, you know, so much kinda across so many companies, how do companies do that, right? Like, what do you need to do to make sure. You're capturing the data and you have the insight, and if you're doing pricing experiments, that you're moving it along the right, you know you are using the right variables for that.

Prittam: Yeah. See, I think like the most important thing I think, if I would like to share that with my peer group is. A different mindset is needed on pricing. Pricing now is a lot more product thing

than it used to be. The reason for that is if you really want to adopt some of these modern pricing strategies you have to really implement a great solution,

technically a great solution because imagine. If you are trying to do agent-based pricing or [00:19:00] outcome-based pricing or usage-based pricing, the scale of events that you have to ingest

easily goes into like billions. So, you know, recently I've been working with a customer of ours. They have 50 million subscribers.

It's an AI native company. Just to give you the complexity of

how these pricing works behind the scenes, there are 50 million subscribers. They have the ability to in real time track your usage. And if you are generating content using AI and you've just hit your threshold or your limit that is allowed for you in every month, they have the ability to, in real time, stop and duration of that AI specific content. Right? So imagine like capturing the billions of events for 50 million users and then in real time. Stopping the generation of the content image or whatever you're creating with AI and showing a paywall that says like, Hey, you just hit the limit. [00:20:00] Either upgrade or like, you know, buy additional credits, something like that. It's a very technically sophisticated problem, so that's why it's becoming more of a product thing and a mindset change is really essential, like product, getting more involved. Into pricing, building the right infrastructure to be able to capture the usage information, capture the events, and then building these types of real time experiences is a hard problem. And I'll give you a real world example. Another customer of ours they started building this in-house. And the technical complexity of it is so heavy that they started pouring resources into it.

And the resources that were working on it are all backend engineers. Okay? These engineers will be much better used if they're building their own product instead of working on, a suggestion for the audience or my peer group of product people here would be that. Don't underestimate the technical complexity of it.

You might start small, but it gets [00:21:00] really complicated really quickly.

So I've seen time and again, people getting into it, getting overwhelmed by it, pouring like huge amount of resources in it, and then basically like still not being able to deliver the type of experiences that they should deliver.

Jeff: . And what is the opportunity cost of that, right? Because you have, like you said, you have a bunch of engineers working on this problem when they could be building your great AI product and moving you forward and allowing you to deliver more value, but also, I can tell you, like if I'm in the middle of, you know, creating a piece of content or, you know, lovable got, I think, dinged me twice for this real quick, when I was building a bunch of stuff there and you hit a usage threshold it's really compelling if you're in the middle of something and it's like, oh, you hit this and I knew I was coming up to it 'cause they communicated well.

But I hit it and it was kinda like, all right, well I guess I gotta, you know, whack my credit card again. And it was just, is not a thought. 'cause I was prepped for it 'cause they communicated it well and then it hit and I was like, well I'm in the middle of getting this. This is valuable and I'm almost there.

But I. [00:22:00] I went up, I think twice in one month. I upgraded to get more credits there. This is before they had age agentic pricing and everything like that, but I was happy with it. I didn't mind 'cause I was getting value of creating the thing I needed there. But if they had, you know, just shut me down for a day and asked me two days later or had they maybe been like, oh, hey, you were over and you need to upgrade.

I probably wouldn't like Right. Capture me right there in the moment in workflow when it was relevant. Was so powerful and it just made it a zero, a no brainer to make that decision to pay more.

Prittam: Absolutely. And There are so many ex examples of monetization throughout the customer lifecycle. And you really need a great product and the great infrastructure to actually be

able to do that. That's a competitive differentiator. The other thing that I think would be really helpful for the peer group is to understand that the predictability of pricing. Is very important for the product and the business. So like for example, it's a real headache for the [00:23:00] finance organization if we are not able to predict what the revenue is going to be for this quarter or this

month. Right? So what has also worked, and this is probably going to be the dominant model going forward for most companies, is. What we call as hybrid model

where you have a certain monthly subscription cost. Like for example, you know, cursor charging $20 a month. But then when you go beyond the threshold usage limit, then you get charged based on like pay as you go based on the amount of excess usage that you do. This is a good strategy because it gives you the revenue predictability through the subscription that is stable every month. And then you can figure out like what would be the additional revenue based on the unit economics. So

it's really like an interesting way to bring predictability into your revenue stream also. So hybrid is likely going to be the model that most companies will [00:24:00] adopt.

Jeff: and it's great too because. As you make the product better and deliver more value, you know you're gonna find people doing more in it or more willing to pay there. There's, I would wager, more direct correlation between, we made the product better and we can charge more. Not even, we can charge more. Like you said, people are using it more and therefore paying that overage.

So, you know, ideally you can, it's always been a bit of a hassle, maybe sometimes to justify, you know. Upgrade and the product from making it better if you were already at a certain threshold of , customer goodwill or customer outcomes, or customer experience because, you know, it was tough to kind of go in and raise price regularly and stuff like that.

But a, I guess people are more used to pricing flux now, and b, if it's usage based and people just start using it more, like it's already baked in, so you can just, you're incented to make the product even better, which. I think it's really great for, you know, users potentially then too.

Prittam: Yeah, that's true. But there are also like more [00:25:00] complexities. So like so far, like what we have been discussing our use cases where we're talking about like a user using product, you know, maybe like a

consumer in a more like a B2C type of setting. But then. There are lots of customers that we work with who also have a fairly sophisticated sales led motion

that you are doing like multi-year contracts right now.

, we recently onboard a customer. They're in the infrastructure space. They're extremely successful and you know, the, one of the marquee AI companies and they're growing like crazy now. In their path to growth, they did a root cause analysis

and they wanted to understand like what is, what are some of the things that will hold us back from scaling, like extremely rapidly. And you know, as you know, like some of these AI companies, they're growing like triple digits, you know,

even more, right? So the root cause analysis told them that the billing system was holding them back. [00:26:00] And the reason for that was. They were not just doing product led growth motion, they were also doing sales led motion complexity was significant and a lot of manual work was happening.

Like, you know, people, like salespeople were creating quotes from the ERP system. A lot of manual entry was happening, you know, and you really have to start thinking about whether you have the right infrastructure. To be able to move fast. As you introduce not only new pricing models, but also new go-to market strategies,

you might go from product led motion to sales led motion. You might go from sales led motion to marketplaces. You know, you might sell your product to AWS or other marketplaces, and you have to start thinking about all of those go to market angles as you think about your pricing and packaging strategy.

Jeff: it's interesting that this kind of brings forward, I feel like a lot of things that were already known in pricing, it just. If they were good, it makes them better. And if they were pain points and pricing, it kind of [00:27:00] exacerbates them potentially because, you know, predictable pricing and enabling sales to move was something we knew you had to have, but you could kind of fake it.

Like you said, you could kinda like price from the ERP. You could for, especially for bigger things, you could, you know, slow roll a little bit and go get custom pricing for every deal, but it was always better to have. You know, well understood structured pricing that just worked. But now this just like rips the bandaid off of you just can't do it that way anymore.

You're far too slow, you're not agile enough, and it makes that just infinitely worse to try to do that. So it's kinda like in some ways forcing us to have better habits in some ways. One, one thing I'm curious about, have you seen though to switch a little bit? Is. This can put more burden on the supplier as well.

When it comes to, you have to provide, like you said, there's costs around these models that are sometimes not that low, right? There's infrastructure costs there's a lot of compute going on here and a lot of power behind these things. What have you seen for what [00:28:00] companies can do when they're providing these tools to make sure that they're not gonna run into a situation where they're providing, you know, infrastructure and compute and really highly expensive things without recouping some of that.

Prittam: So the unit economics are changing completely. That's basically what's happening. So imagine this situation, right? We are just talking a lot about Cursor here, but let's just talk about Cursor. So let's

say that 

Jeff: everyone knows it. It's a good example.

Prittam: example. People know it, people like using it. So let's take an example of Cursor, right? I use Cursor, but I'm not like a, you know, I'm not basically a startup founder

who's cranking through cursor like all day. I use it when I need it, you know, not a heavy user. I might be, and I'm paying like $20 a month and I might be using like 10% of the usage limit that is offered. On the other hand, there could be like a startup founder who's, you know, burning the mid light oil ranking through cursor building, like almost like entire modules of the product in a day. [00:29:00] And they might be blowing past the limits that cursor has. So at a per user level, I am a much more profitable user for Cursor, right? And the other user is like basically a loss making user. So that's where unit economics gets really interesting and you have to really understand the unit economics. And the only way to do that is to be able to capture the usage and then connect it with, at the user level. And then based on that figure out your pricing and packaging strategy.

So it's still early innings, but at the end of the day, it all starts with capturing that data,

organizing your value metrics, understanding the unit economics, running very careful, experiments with small cohorts of customers, and then rolling it out more broadly And then being, being super agile, right?

Like being

very agile, changing the mindset that pricing is like every sprint type of a thing, rather than like a quarterly event. Because imagine, right, like [00:30:00] the model prices are going down substantially

right now, if the model prices go down by 50% and your product is mostly using the models to deliver the value, you would want to translate that, you know, cost benefit to your users. One of the biggest problem for like these gen AI companies is also a lot of retention issues.

Like people come join, they find another tool, they jump to a different tool. So you still want to make sure that the lifetime value of customers is significant and pricing is a major lever to make sure that the lifetime value is significant as well.

So you translate the savings back to the subscribers.

Jeff: one thing I've noticed is, is a lot of what you talked about here around how to determine pricing and where the right things lie, and how do you know, not lose your hat, but also how do you not price yourself outta the market? Are. Probably it's a more complex analysis than it used to be, but it's all based around the product and how you deliver it and how it works.

Like what are you seeing from a [00:31:00] standpoint of, you know, I think historically product is probably somewhat involved in pricing and it's company to company, but like are you seeing more companies where product is more heavily or completely, you know, much more in charge of pricing? Is that kinda moving that direction?

Prittam: Yeah, definitely. We interact a lot with the chief technology officer, chief Product

Officer, personas within our customer base,

and many of our customers are. Either rolling out, have already rolled out or are experimenting with usage based models. And I'll give you an example of what we are doing internally within Chargebee. So I am collaborating very closely with our business technology team. So business technology team is the team that manages all the applications from CRM systems to ERP systems, to accounting systems, to billing systems and all the infrastructure that runs the company. I'm deeply involved with and working very closely with them because we are also as an organization introducing [00:32:00] skews that will be usage based and outcome based. So we are tracking the usage, we are identifying the value metric. We are running the experiments. We are using our own product to do that. So because of the technical nature of it, I was not this much involved, like let's say one year ago, but now I'm heavily involved, you know, meeting the business technology teams two times every week at that level. So, I think most companies, and as I said like, you know, we interact a lot with CPOs and CTOs because when it comes to usage based pricing, either, whether it is agent agentic model or whether it is outcome-based model, it does require that technical persona to build the entire infrastructure that can actually run smoothly.

Jeff: It really has become, it's a much more strategic level. But it's more to it too. If you wanna be able to pivot, you know, every three days, like open ai you're gonna need a much more sophisticated setup than if you just, you know, can hard code pricing once every two years or once a [00:33:00] year or something like that.

You don't, you can kinda set it and forget it. But also the understanding of what, you know, experimentation, driving tests, like you said, can you test a different pricing model on a small subset of your users? This almost starts to get into the world of, you know, even like growth product people or growth teams of, you know, it becomes a lever for can that help you grow faster which is even one more step removed.

From that. So it's interesting to see where that world of pricing is going. I guess a topic I wanna make sure we cover that kind of relates to this is, are you seeing you know, with your exposure to kind of all the number of companies through where you are at Chargebee, are you seeing that there are winners and loses here?

Like, have you seen companies kind of get their lunch eaten? From under them when they were slow here? Or have you seen, conversely, maybe people, you know, who, who weren't as successful selling rapidly ascend because they got, you know, they got this thing right. Like, how is that looking competitively as a disruption lever?

Prittam: I think it's more than ever [00:34:00] before, like

pricing is becoming a strategic differentiator. I, that's how I would put it. Any new company when they're introducing a product, you know, there was a significant emphasis like in the prior years on getting the product right and then slowly getting the product market fit.

But with the growth that some of these AI native companies are seeing, you know, you are talking about like going from zero to a hundred million in just a matter of six, or, you know, eight months,

Jeff: Yeah, it's absurd.

Prittam: We have been fortunate to be working with companies that have grown from like 2 million to 30 million in a year. Like that type of growth, when that happens, you have to get a lot of things right.

Okay. You don't have the time like it traditionally, you typically would take like a couple of years to get the product market fit. Right Now you don't have the time. You have to get it right. So what we are also seeing is. These companies are firing on all cylinders.

So the companies that are really [00:35:00] differentiating and are able to scale at that speed, they're firing on all cylinders from a pricing and go-to market standpoint. So I'll give you an example, right? First of all, they'll have some very innovative pricing model, which is like absolutely no-brainer.

You know, either outcome-based or usage based. You can actually translate how you use the product directly to the value and then

directly to the amount of money you're paying. Then what we are also seeing is like customers would start, typically would start like in the prior years, they would start with the PLG motion, and then once they get the PLG motion, right, then they would introduce a like sales led motion, right? That's what Chargebee did. Like we got the PLG motion, right? And then we started moving up market

and then introducing more high touch sales led motion rate

Jeff: same here. So.

Prittam: for you guys. So. Now if you look at some of these high growth companies they're introducing the product led and sales led motion at the same time

that exponentially increases the [00:36:00] complexity of your billing system. So the example that I was giving to you, this high growth infrastructure company, the number one reason of blocker for their growth was billing because they introduced SLG motion

And all of a sudden it became extremely complicated for them. Right? So. I think the companies that are doing it right, they understand the complexity associated with having multiple motions at the same time in a hypergrowth mode. Are able to move quickly, experiment and change pricing in an agile way, but also in a very data-driven way. Like not just changing the pricing because you know, we've all done pricing in the past and we know how it works. Like we just come up with a price and then if it works it works, otherwise we change it.

Like that was the way things were happening for quite some time, like years. If you think about like a few years back now, that's not the case. Like people are extremely like the winners. Are extremely data driven. They're doing experiments. They know exactly what's gonna happen, what will be the uptake in [00:37:00] terms of revenue. They are very transparent in terms of pricing, so they really take it very seriously, 

Jeff: Yeah. Well, I love that taking pricing seriously has changed so much because I, I remember a day when, you know, a pricing exercise was included probably a six figure engagement with some consulting firm that came in, and you would spend six. Months, nine months on a pricing engagement with this incredibly expensive consulting agency.

And now the way that you know, these people, you know, companies are making super data informed, really successful pricing choices, but they're, like you said, they're experimenting every couple days they're driving change, you know, weekly, biweekly like we said, open AI every three days on average it seems like.

Requires, you know, and seems these companies are doing better. So it puts more power into your hands, but you have to do the work to understand it. You have to do the work to make sure you're delivering value, but the potential is just so high. If you get the stuff right where you know if you can deliver the right moment, the right value, the right time to [00:38:00] upgrade, you're gonna have people just throwing money at you.

But you have to do the work. To understand it, you can't offload that to some six-figure consultant and you know, wait nine months.

Prittam: And I cannot, like, I cannot tell you how many of our customers. Are asking us to be able to support even dynamic

pricing. Like imagine the pricing for you to be different than somebody else in your office using the same product. Like

it's going to get to that level of understanding usage at the user level, the unit

economics are changing, right? And the other thing is also like you have to really think from ground up.

So right from when you are building your product. Right from creating the infrastructure to do the feature management and entitlements like feature provisioning and entitlements. Right. From there, this whole monetization journey starts.

So you have to first get the feature analytics, entitlements [00:39:00] and provisioning system, right? Then you have to have the right. Way of setting up your infrastructure so you can capture these usage events, identify the value metrics, understand the unit economics, and then on top of that you have to have this rapid fire experimentation capabilities so you can make data driven decisions, and it becomes like a biweekly thing and not like a six month or a yearly thing.

Jeff: Yeah. I mean, so to kind of put it all together, it seems like the nice thing about these right couple pricing models, you know, you can do usage based, outcome based agent based. In the end, they're all moving us closer. To a pricing lever that more closely mirrors what is the actual thing you get value out of this?

Product doing. And that's probably something companies should look at, our team should look at, is like, how can we get as close as we as possible to this, you know, ideal pricing lever. And that's more possible with AI now, but you have to, like you said, you [00:40:00] have to be able to understand behavior better.

You have to be able to understand the data and the events and what's driving it so you can get the, you know, get the wedge in at the right time to pop it up. And the people who do that well. Are seeing this huge growth, if you can do it quickly and agilely and with the right data behind it the, you know, it can be this huge thing.

It turns retention to a huge growth lever. But you gotta do the work to get there. And you, like you said, you have to be able to do it agilely. You have to be able to deliver the experience and. It can't be, you know, slow. It can't be behind eight ball. It can't be delayed. It's doing it quickly, doing it the right time, and really having the right cause behind it.

If you can do that, it's powerful. Which is great 'cause I think it, it brings home more value to users in the end where you're gonna pay more if you're gain more value. And that's probably. Good for everyone. I mean, I could see a world right where, like you said, you, you know, your $20 a month to cursor probably is a little bit over the value you're actually getting and someone else is getting probably a major loss leader deal from them.

[00:41:00] In the future we could move to a world where it's possible, where, you know, you're paying less, they're paying more and everyone's super happy for what they're getting which would be great. But there is also the piece of this where. You have a job at Chargebee. And as much as I'm having fun nerding out with you about pricing, I can't steal you too long from them.

'cause then I'll be getting too much value out of this exchange. And you know, I'll find a way to, to get equilibrium there. So, you know, thank you so much for coming on the show. I think this was really educational, really good to kind of take time to focus on how does pricing work in this new world?

How should people be looking at it, understanding it. And I hope people learned a lot. I know I did. So thank you so much for coming on. Really appreciate you coming on, man. Thank you. Thank you so much. Yeah.

Prittam: Thank you, Jeff. I really enjoyed coming here and talking about pricing. It's a topic that's very close to my heart, and I hope that you know, the peer group watching this gets some value out of it. Happy to to also help if somebody has questions and they wanna reach out more than happy to be a sounding board and just discuss pricing related topics.

Jeff: Nice. I was gonna say is LinkedIn the best place [00:42:00] to, to reach out? I think that's where we met actually.

Prittam: Yeah, LinkedIn is perfectly fine.

Jeff: Awesome. Yeah. So, p Tom Bani VP of Product at Chargebee. Thank you so much for coming on. Check him out. Give him a, hit him up. He's a good guy. I've had a lot of fun getting to talk to you and I learned a lot, so I know always will too.

If you learned a lot from the show, if. You're on, you know, YouTube, give it a, like, give it a follow, subscribe. If you're on I don't know what, Spotify, apple Podcast, any of those things, subscribe to the show, please write us a review. It really helps us get the info out about this and helps get the word out.

And if you like this content, help us make more of it, and that's how we're gonna do it. So, appreciate everyone. Listen free. Tom, thank you so much for coming on, man. This was a real joy and let's stay in touch and hope to talk to you again soon.

Prittam: Thank you, Jeff.