Ian Ash: (00:08)
Hi, I'm Ian, co-founder at Dig insights and President of Dig’s innovation insights platform Upsiide. Welcome to Dig In. Dig In is the place to stay up to date on what's happening in the world of innovation, research and technology, find inspiration from today's business and innovation leaders, and to properly dig into hot topics that matter for consumer brands right now. And when applicable we’ll bring our own research to that conversation.

Ian Ash: (00:35)
Welcome back to Dig In this week. We're talking to Ajit Ghuman. He's a subject matter expert in SaaS pricing strategy. I know it sounds exciting, but it's a big deal. It's really hard to try to price SaaS products. And that's exactly why he's written his wildly popular book Price to Scale. It's gonna be the inspiration behind this conversation today. And in Price to Scale, he deals with a whole bunch of different issues, including how they should price the “good, better, best” dogma, how they should do testing around the different pricing strategies that they're coming to market with, and this just in general, how seriously you should take your pricing strategy. And that leads to one of the first questions that I'd like to ask. Thanks so much for joining me today, Ajit. Before we get into why you wrote the book, I just talk about how seriously you think most companies are taking, and how they price their SaaS products.

Ajit Ghuman: (01:37)
Ian, thanks so much for having me speak to you today. I actually, I've had this observation about many software companies that pricing is for the most part for them an afterthought. They create the product, they sell it. And at the end of the moment they have to sell it to a customer, they're like, “oh, well, how do we price it? Okay, well, here's a ballpark.” And they keep pointing that problem down the road until it becomes a really big problem. And you have to do a massive strategic overhaul and bring some consultants in because the pricing doesn't scale and it creates further bottlenecks for sales. So, I would say 80% to 90% of companies consider it exceptionally hard and as it is it remains an afterthought for them.

Ian: (02:31)
So is that what led you to write this book Price to Scale?

Ajit: (02:35)
That's partly why I wrote the book. One of the other reasons was that I was in the midst of this overhaul for my own company. And that was one of the key reasons that I was even hired when I was exploring the topic of pricing in more detail. I saw that in the industry today… And just search on Google, “SaaS pricing strategy”, you'll find 20 articles that go something like this, “the top seven pricing strategies” or “the top most common pricing strategies”. And then you open and it gives you vague names of “cost plus”, and “this plus” or “segmented”. And there are all these names that somebody's just come up with no context on marketing strategy, with no understanding on how this is actually to be implemented. And I learned that a lot of the people who said and who created those articles did not even know themselves. So I got super confused. And when I implemented pricing and I learned about it, I basically approached it from a first principal's perspective. And then I felt like a basic book needs to exist because that's the book I was trying to find when I was doing this project.

Ian: (03:51)
Great. You talked a little bit about what you were facing. I kept your intro really short because I wanted to get into this question, but I mean, you've helped firms like Medallia, Helpshift, Feedzai. And right now you're at Narvar. Tell me a little bit about what you're doing at Narvar.

Ajit: (04:13)
Yeah, so in Narvar my role is broader. I'm looking over all of product marketing, so not just product pricing. I look into three product lines. And so I've always approached the product marketing role that I've been in for the last eight plus years as a role that is about differentiation. So how do we differentiate our product in the market? How do we talk about value in the context of everybody else, every other competitor pricing in the market, and how do we gain competitive advantage? That's the value based framework that I even took to the pricing work when I did do pricing.

Ian: (04:53)
Excellent. And so you talked a little bit about that company, it's an afterthought. I don't know anybody who would've done that. I think we all fall into that trap. I know when we designed Upsiide, when we originally designed the platform, we didn't even have tiering of features. I wish I'd picked up your book while we were still in the build phase. But beyond just making sure that you're building a piece of software that can actually have variance in price, what are the other things that business owners should be doing to take their pricing more seriously?

Ajit: (05:31)
Yeah, I think, there are two things that basically anybody can start doing to get a little bit smarter. One is just look at how your pricing is performing. So basic analytics and math, look at your past two or three quarters of information. Look at what deals you've sold. If you have a discounting policy, just see how the discounting has gone over the packages that you do have. You can calculate a per unit metric. So if you are selling in batches of units, do a dollar per unit and see how that curve scales through your own customer base. That may give you some basic understanding. If you see a lot of discounting, that may point that you need to be strict in your discounting rules, such that you have more predictable pricing.

If the size of the companies you sell to, and the ARR that they bring in are well correlated, well, then that's good news. But if they're not correlated and sales is just selling ad hoc, that means you have a lot of room for improvement. In either case having a sense of the data will just help you understand where you are, put you on some firm footing. That's one thing.
And the second thing is, um, when you're doing product development discussions, just try to make a hypothesis around pricing for every new product that you work on to say, well, we think this is the value it's going to generate 4X more ROI for customers, a hundred percent more conversions, whatever, be the case, whatever you think your product will do. And come up with a hypothetical number for the new product, the price point when you are developing it. But not at the end, don't leave it for the salesperson to decide.

Ian: (07:19)
Don't leave it up to the salesperson and decide. That's a good point. We had a whole bunch of people in our meeting today about what our pricing strategy is gonna be Jan 1st 2022, how are we gonna change it? And there were a lot of voices in that room. Who do you think the right people to have in that room are? Who are the people in the company who should really be involved in pricing these discussions and decisions.

Ajit: (08:01)
There are people who you definitely need feedback from. So I would not say sales should not be considered in the pricing strategy discussions. In fact, many times the sales team is the team that gets you the closest ear to the ground feedback from the market. But in terms of leads, such a project is generally a strategic function that does not have quarter and over quarter numbers tied to their backs. So marketing or maybe a biz ops function. Mostly it's marketing or biz ops that I've seen. Product management could also be there, they have similar incentives. But it's just that sometimes product teams lack the experience. But either of these three teams would work under some executive sponsorship from the CEO or COO.
And then what is critical is to have feedback councils from the mix of the customers, customer success people as well as sales people, so that you're testing and you're learning from the market. And from your customer success teams and sales teams, your testing whatever you want to do, you can actually do it, so that you can scale your sales engine, which is also sometimes not considered.
Well, you can also equally end up in complete strategy land and not be able to consider the operational ramifications of this process, which are many. And many times it takes a lot more time than the development of the strategy itself.

Ian: (09:31)
So Narvar are very lucky that they happen to have a pricing expert as their head of product marketing, but in other companies where does the buck stop? Like who's the person who ultimately makes the call because as with all these kinds of really strategic and important decisions, particularly competitive pricing strategy, what's gonna go on your website? How are you gonna communicate it? Is it gonna be a “good, better, best” model? Are you gonna have a modular model. By the way, read Ajit’s book, if you're not sure what those things mean. But who's normally in charge of that pricing decision in your experience?

Ajit: (10:07)
I think marketing is generally marketing and product marketing is generally the quarterback. I would say that the CEO really is the decider, or at least the person who needs to sign off. As much as the CEO understands that this is a high leverage item that they can use to go back to the board, markup the valuation of the company with little change in any other system in the organization or any other hiring of people. So if the CEO understands that over, not only at one time, but over time, this is a very high leverage area. And that understanding of leverage is there, I think they're gonna be more interested in looking at what the team comes up with. And I think between product management teams and marketing teams, the options can definitely be presented. But I do think that the CEO needs to have skin in the game.

Ian: (11:03)
So let's talk about what I just touched on there a moment ago. You put forward two main models. I mean, there's other ways that people can price obviously, but there were two main models that you focused on in your book. One was this “good, better, best”, and the other one was a modular approach. Can you just lay that out for people really simply what those two things mean?

Ajit: (11:27)
Yeah. That is my model on just the packages. I would say, if you let me zoom out for one moment, the key decision that one has to make in their pricing strategy, or anytime you're looking at pricing is four decisions: packaging, pricing, metric, pricing structure, and then the price point. So now we're starting with the packaging. Within packaging, there is the model that we hear quite a lot, “Good, better, best”. We hear it all the time. And then I call the alternative a more simpler, modular approach or what other people call a Chinese menu approach, which is just really one plan with a lot of add-ons. And then there is a white spectrum of area between that, right? You can have five plans, you can have two plans and so on. But the idea is to make you understand the trade offs.
And the thing with “good, better, best” is that it has become an unnecessary dogma in the industry because you see a lot of companies start pricing and they say, “oh, well, because I need a pricing page that needs to have a “good, better, best” plan. And okay, here are the features, here is my essential, pro, premium pricing plan, and I'm gonna distribute features in some sort of ad hoc way.”
So that's a backwards way of doing it. I think what companies need to understand is that you have to start with the number of market segments. You have to design the right offers for these market segments that really fit them well. And that automatic will tell you how many plans you need. If you're purely an enterprise company that is selling to verticals that may pay you very different amounts of money. Let's say financial services and retail: one is very cost-conscious, price sensitive. The other is not, the other may need much more security features. So you may create slightly different offerings with very different price points. And I saw that happening at Medallia: one company will sell roughly the same product to half a million dollars and $15 million from a financial services company. So that's the art of source pricing. You can make a lot of money if you do it a little bit intelligently.
So if you create a “good, better, best” plan in that market, you're just anchoring yourself in one position and you may end up leaving money on the table. A case in point, when I was writing the book, I spoke to a lot of pricing leaders. And I spoke to Johnny Chang. What he saw with the “good, better, best” model at gain site was that only one of the plans was even being used on a quarterly basis, which was their middle plan. And, the sales team were selling both three plans to the mid-market. So you had three plans for one segment. As a result, a lot of the features were shelfware. They could not be absorbed and they weren't able to make the right amount of money when it was sold the first time. So he was able to fix that at that company. And that just points out that it's not always gonna work out well, but you start from plus principles, if you start looking at the market segments first, design the right offer for every segment, and then look at the packages, then look at the addons, that's going to get you get you much further than just starting from a final product in mind.

Ian: (14:51)
That's really interesting. I think there's a whole bunch of things there that you said that I'd like to touch on really quickly. One was the example that you talked about where it's a similar product, but you're, you're selling to two very different market segments. You use the example of retail pricing and finance pricing. In that case, I'm assuming that they would both have been enterprise level and that probably wouldn't have been a published price on the website anyways, is that correct?

Ajit: (15:18)
Yes. That it was not a published price. And it's not always obvious that it should be published.

Ian: (15:24)
I think you made a really good case about that in your book as well. You said in many cases it's gonna work against you to publish prices for all your levels. But let's talk about the “good, better, best” for a second there because I'm gonna get back to something that I found particularly impressive as somebody who's a market researcher in SaaS but for years I’ve been a market researcher. You talked about two different testing pricing methods at a very in depth level, by the way, very impressive in your book, which was a) a pricing methodology where you basically try to get this range of acceptable prices where you say, what would be a such a low price that you would doubt if it’s of high quality, what's the high price, etc. And then the other one is conjoint, which is a very, very robust, yet complex method to say, what's the right combination of attributes or features of in this case where people would pay for it, and you're gonna end up with utilities around the features so that you can actually move features between buckets, let's say it's “good, better, best pricing model”, in a way that's meaningful. And I thought that was a really interesting insight because I think the challenge and I know we've had it, is how do you create meaningful differentiation between your three levels? And I think that's where a lot of companies go wrong in assuming they can do three levels. Do people really value that particular feature of your software, enough to put it in a different bucket? Do you wanna talk a little bit about the importance of a) getting those buckets right and b) how you even ascertain what should be important enough to talk about in terms of a tiered feature.

Ajit: (17:35)
Yeah, you're, you're right. Once you have a hypothesis for your plans, you have to test it. And there are many options to test it. If you are a company like Netflix or Zoom, you're gonna have a very large sample size of customers. You'll probably wanna do a survey approach. It is only going to give you visibility into things like price points, maybe not the packages. Conjoint will help you get information on what is the right package and what's the take rate and which package might be able to get you more revenue. That's at a large scale. But I've been running more of primary research decks where I've taken in front of my own clients my hypothetical packages. And I force them to choose how to spread it.
Assume you have a hundred bucks, spend a hundred bucks across three plans, choose the one that fits you the best. Let's take one plan. Now tell me the feature that if I removed it, you wouldn't mind at all. Tell me the feature that you would really have a problem with if I removed it. Now, how many times you have a gut feeling and intuition if you have a bunch of new customers and you have customer empathy, it's really at the edge of where you're testing that out. You're really not testing it at the very base level. And very soon with not a lot of sample size in some B2B companies that I've worked at, I've been able to gauge where the value is. So that's one way to test and make sure that there is differentiation and make sure that there's not gonna be shelf ware.
And they're not the only people to test with. I would also test with our sales team. And you ask similar things, but the questions may be a little different because many times they can sell the same product without three more features, right? Sales is really good at positioning products. You give them a package to sell, they'll sell it. But if you remove three features, they may still sell it for the same price. And then you can reserve those three features for your upsell flows. So there, you're understanding what should be the first set of features to be offered. What is generally the need, because those are the only features they're showing in a demo. They're not showing all of the features to the prospect. Some features can be introduced in your upsell flows and that's something you want. You don't want to give the whole house away the first time you make a sale. So many ways to test, but testing is definitely required, whether you do it in survey form or or a manual form and different people to test with as well.

Ian: (20:24)
That's great. Thank you. I'm gonna talk about something that's a little bit complicated. We're getting to the weeds a little bit, but it's something that it's close to us in terms of something that we've had to deal with, which is the idea of usage based pricing. You talk in your book about things where, let's say, you plug in another piece of software that has a variable cost within your software, like you're plugging into an API or something, and you're paying for a call. To the point where it becomes meaningful, right. In res tech, which is market research tech, our variable cost is sample. It's the people who go in to take this. Can you talk a little bit about usage based pricing. And when you need to use it and what that means in terms of how you're gonna price.

Ajit: (21:19)
There is one very often case where you will see a lot of companies use usage based pricing, like you were just alluding to, where the cost of delivering the service does not follow a diminishing return pattern. That means if your sample is, let's say a thousand respondents or 10,000 respondents, you're still going to pay every respondent. And that means you cannot offer volume discounts. It's not like any other piece of software because with any other piece of software, you pay just a fixed cost for hosting and all of the cloud fees. And after that, it just runs. You can sell the same thing to a hundred thousand or a million people, and it's a marginal difference in cost. So, when your cost of production scales like that, many companies will want to do usage based pricing.
Think about Amazon. They offer infrastructure layer products, their production costs scale. In your case, your cost also scales. So that's one area where usage based pricing is the only way you can actually sell that piece of the service and also make money on your end. So that's in one way, cost plus pricing. That's the most obvious time when you use it.
The other time when you should use it, where you can get a lot of leverage is when the usage is directly correlated to how the customer measures ROI. So think about video games that are played by millions and tens of millions of people. At Helpshift, which was the company I was at prior, their head of revenue changed the metric of the product from users to per hundred thousand MAU off the gaming application. That simple change caused us to make up to 10X more revenue from these accounts. And they were happy to pay because they had tens and hundreds of millions of users in MAU. And they had small customer service teams. So had we priced like other customer service solutions by agent or user, we would not have made much money and they don't really have big customer service teams to begin with. But they have a lot of consumers and consumer demand. And if you price proportionate to them, and as much as you can measure it, you can monetize a lot of that value. That's the other way where usage based pricing is really helpful is for you to scale with the customer.
The old way of doing usage based pricing is just by sizing a company, right? You're a big company, big size, you'll pay more, you're small company, small size pay less, but you, you couldn't architect or instrument. You were not able to collect the data. And now with all these digital products, you can. So, it just gives us more ways to develop competitive pricing better and more granularity to do with it.
The only caveat I would say on usage based pricing is that it's not the A and B. Today, I'm seeing a lot of noise in the market and people are talking about usage based pricing, they're creating buzzwords out of everything. And again, it's one tool in your toolbox capability-based pricing is very helpful too. Let’s say you have a survey product and you have areas where you are maybe collaborating internally. Maybe you have analytics, those all are capabilities, right? You can still price those things with capability and you can still charge usage based pricing for your survey respondents. So you can have a mix. If you look at Zuora, they do the subscription newsletter and they talk about their insights. They also say that a mix of capability and usage is probably when you get you the best results. I'm prominent in not being dogmatic, but there are certain tools that get you certain results.

Ian: (25:12)
It's an interesting tension, right? Because again, I'll fall back on res tech, cause it's the world I know. I've read your book, but I haven't talked to all the people you've spoken to. The case of the price per seat model. I don’t know if it's a trend in general, but I mean, it’s a real turnoff for a lot of people when they want to be able to share things across the organization, but not all those people are gonna be using the software to the same level. And so particularly if you've got a usage based pricing model, it seems like the price per seat model, those two things don't necessarily go together. Do you, do you think that's the case?

Ajit: (25:52)
I agree. If your differentiator is more adoption inside an organization, you may not want to price per seat. Again,, but at the same time a company like Slack does that. That's how they monetize. They don't monetize by a number of messages. So the caveat there is, it also has to be easy for your target audience to understand, that's when you can do it. I used to work at a company called Medallia. We used to sell market research products for large hotel chains and retailers. We used to price based on the number of locations these businesses had that let us support up to 50,000 users at a company like Hilton or 2000 users like in a financial services company for a similar amount of prices, because it was like per location or per unit. And we had to define units in a specific way. Just like usage based pricing per user pricing is another dogma that you have to really look at, does it get you to where you call?

Ian: (27:03)
Mean, where do you see the trends going in terms of these things? Are you noticing some models becoming more, less popular or have things not really changed? Is it still “good, better, best”? And that's what it's gonna be for the next few years?

Ajit: (27:15)
No, actually things have been changing. There is a company called Matrix Partners. They did a survey recently. And in that survey, basically companies say 41% of companies are still doing perceived value pricing. And 25% are using some sort of usage-based metric. And then there are all other types like database sizes, total employees, or model user functionality. So I think that's an interesting mix. I would anticipate the perceived model to reduce even further because a lot of our products are automation products. And if it's an automation product, you are not really expanding your size too much. You are maybe adding some other value. So it's about the value that we are delivering at the end of the day and usage-based pricing isn't really always proportional to the value, but it is a reasonable proxy for sizing, if you have nothing else.

Ian: (28:26)
Last thing I want to talk about before I let you go is the role of discounting? Cause I think we have a perception that we're putting pricing up on a website. We probably aren't disclosing our enterprise price and then we've gotta make sure that the sales team has the leeway to discount because it plays an important role. You talk about it in your book, you say that discounting can be up to 80% in some cases. Can you talk a little bit about how you properly empower a sales team to discount appropriately and properly?

Ajit: (29:13)
I think the first thing to notice is that discounting is a benefit that we have in B2B sales that is not available in B2C. B2C, you really have to get your price points right across your segments. In B2B, you can use the power of discounting to find the right price point within a certain band. So that's why from a strategic standpoint, I feel discounting really helps some wider bands, so that you can discover your price points.
Now that being said, the other thing that discounting adds in your sales process is a little bit of friction. Let’s say you had a model that had an infinite discounting sale price. Whatever they would be, they would be really putting a lower price at the end of the quarter to make the deal just so that they're compensated.
With discounting now that adds friction and an approval process so that they only do it when it is really required. That's really the goal of discounting the level at which they're able to do it generally, SMB deals, not a lot of discounting enterprise deals. Like I was saying, it can go as high as 80%, really depending on what the situation is. Something like 80% is likely something a CEO or a CFO somewhere approved, but at least it forces the sales person to make a case to their executive team that this is why the discount needs to happen. And so that's why it's a feature. It's not a bug. And allowing for discounting that is not too strict or not too loose is gonna help you in running a good sales process.

Ian: (31:02)
Right. Something just to leave on because I just thought it was a really interesting insight that you called out was the idea that you put forward that you should charge, even if it's a nominal amount, even a small amount for trial plans to weed out low potentials. I think the number that you quoted was only about 4% of free trials actually converted.

Ajit: (31:40)
Right. Now, if you go to different places, the statistics do change, but not by that much. So somebody will say two, somebody will say four, somebody may say eight, I mean, eight is probably really high. If you have a large market share and you serve the free customers at a lower cost, then maybe that's fine. Maybe that's where you build a product-led growth engine. But for many other cases, it may just overburden you. You will have your whole marketing team focusing on the leads from this segment. This segment will have a high cost to serve. You'll be fixing the needs of the free segment. Your engineering team will work on it. And then if the conversion is not good enough, and you also have an enterprise segment to cater to, well, then you just spend too much time and too much money on a segment that may not convert for you.

I think this is where you're referencing this from. I liked the example of Ahrefs, which is an SEO company and they charge like a seven bucks fee. I guess they're able to charge that because they also have a pretty good product. They're known to have a good SEO product. And even for their trial, they charge, I think one bucket a day for seven or something like that. So if you like it, if you have some conviction, that's why you will use the free trial. If you're just a passerby they're not gonna offer it. Maybe that's because they were probably just spending too much investment in making sure there is a good experience for all the free customers. And if they get just too many of that, economics just change. So I found that that was interesting. It was not totally free. It could be something nominal to qualify through interest.

Ian: (33:34)
Yeah. I love that point. I mean, you had a lot of really great points in your book. That one, that one alone was, is worth the price for me, Ajit. And are you gonna write another book? I still have some questions.

Ajit: (34:16)
I hope to. I have some plans for the future. Some of those relate to pricing operations, which I feel is also a little bit overlooked and probably more if you don't get that right. All of the good work done here sometimes can fall flat. So I have some ideas.

Ian: (34:38)
Great. Thanks so much for your time today.

Ajit (34:40)
Thank you, Ian.

Ian (34:42)
Thanks for joining us for this week's episode of Dig In. If you want more information about Dig Insights or Upsiide, please check a set on LinkedIn or our website at diginsights.com or upside.com. If you have any ideas for future episodes or would like to be a guest, please feel free to message me through the LinkedIn app.