Learn how to take value and translate that understanding of value into a determined price for your SaaS product
Author: Bryan Belanger
As you’ve likely noticed, we’ve been going deep on value-based pricing theory lately. Economic value estimation, the value cascade, all of it.
There are a few reasons for that. First, we are analysts by nature and thus gluttons for theory and frameworks. And second, “value-based” is a pricing buzzword.
Anybody tangentially associated with pricing SaaS products will tell you, “Oh, but yes, of course a value-based pricing strategy is the right approach to use.”
What we’ve found, however, as we’ve drilled into this topic, is that a trove of resources are available about the elements of value-based pricing, but there are few stories from pricing practitioners on how they connect all the elements together to derive an actual pricing strategy.
Thomas Nagle’s The Strategy and Tactics of Pricing and many other resources based on that work provide detailed viewpoints on how to analyze and quantify the different types of value for your product or service. There are even software tools that have been built, such as LeveragePoint and Ibakka Valio, to manage economic value estimation and link value estimation to pricing.
There are also plenty of resources out there about how to assess willingness to pay, and we previously shared our thoughts on that topic. Willingness to pay is typically seen as how you ultimately determine a price for your product.
But most of that content is missing both the tactical and the practical. We’re yearning for narratives on the other parts of pricing that aren’t on Nagle’s value cascade — how you get from economic value to perceived value to willingness to pay and then ultimately, and hopefully confidently, to an actual price that makes sense for your product and your target customers.
So how do you actually take value and translate that understanding of value into a determined price? To learn more about how this is done, we set about doing what we do best: applying our research approaches and scouring the internet.
Our assessment of this question spanned a variety of resources: B2B SaaS pricing consultants, pricing software vendors and, most critically, product marketing and pricing teams responsible for setting pricing at leading B2B SaaS companies. Here are a few interesting answers we found:
ProfitWell has provided the thought leadership foundation upon which a lot of B2B SaaS companies have built their price-setting strategies. Essentially, ProfitWell advocates for segmenting customers into personas, and then understanding the relative preference of features for your product and the willingness to pay for your product, using ProfitWell’s version of the Van Westendorp model. This approach yields a range of acceptable prices as well as an optimal price point, which ProfitWell suggests using to set initial pricing.
GitLab spells out its Pricing Methodology on its website. Key pillars include value proposition, quantifying value propositions, and willingness to pay through customer collaboration and understanding competitive landscape positions. GitLab’s approach to setting actual price levels appears to be largely based on willingness-to-pay research with its current and prospective customers.
BrowserStack is a Forbes Cloud 100 B2B SaaS vendor that provides software testing solutions. In a slightly dated post on Medium, Arpit Rai, formerly a product leader at BrowserStack, outlined how BrowserStack approached value-based pricing. Rai offered a concept that I hadn’t previously heard in reference to self-service value-based pricing in SaaS: the “10X rule.”
Here’s his description: “In this case, we decided to price our product at 10% of the value delivered. In general, SaaS companies tend to use the 10X rule where you deliver at least 10X RoI on the customer’s investment in your product.” Rai goes on to say, “The 10X does not necessarily have to be 10X. It could be 8X, 10X, 12X etc. depending on what you feel is right. If the value delivered is $1k per month, then you could choose to price your product anywhere in the range from $80 to $120, as an example.” Rai also referenced a post by Lincoln Murphy that also describes the “10x rule.”
Similar insight from a recent XaaS Pricing project
We recently completed a project focused on analyzing prevailing software pricing trends in a vertical that is heavily weighted to large, multiyear enterprise deals. The project involved interviewing a cross-section of vendors and customers. One respondent, a former account leader at one of the covered vendors, shared the following:
“Eighty percent to 90% of it [pricing] is based on budget and strategic nature of the deal as well as our competition. We might sell the same software to one customer for $1 million, and for another we might make a $3 million take-it-or-leave-it offer. It is often based on situational and subjective parameters. Let’s take a standard example for how we price if it is a new client and a new RFP. We always try to define and quantify the revenue that the operator will be making using the software. A safe rule of thumb is that we want to earn 1% to 3%, and typically no more than 5%, of that revenue value from our software.”
Marcos Rivera, Pricing I/O
Marcos Rivera is a well-known B2B SaaS pricing consultant and thought leader, and the founder of pricing consultancy Pricing I/O. I’ve been reading Marcos Rivera’s new book on B2B SaaS pricing, aptly named Street Pricing. In the book, Rivera writes: “You should aim to capture 40% of your net positive differentiated value [with pricing].” This refers to an economic value estimation approach, in which your pricing is equivalent to your competitors’ next best alternative price plus 40% of the total estimated net positive differentiation value of your product.
LeveragePoint is a software provider that offers a cloud platform for economic value estimation and price setting. For this post, we’re particularly interested in the methodologies that LeveragePoint provides to help customers with price setting.
Although it’s not clear exactly whether LeveragePoint uses a research-backed or just a model input, the system sets your product’s modeled price to equal your competitive alternative’s price, plus “50% of your offering’s differential value.” So, where Rivera says 40%, LeveragePoint is starting you at 50%.
There are essentially two ways that LeveragePoint allows users to set a price:
Option 1 involves simply adjusting the percentage from 50% to whatever percentage of differentiated value you feel that you should be capturing as a modeled price. How you land on a particular percentage is up to the research you’ve done, your gut feeling, and/or whatever else is informing this top-down figure.
Option 2 takes a more granular approach in which you can implement different “pricing factors” and then rate the price sensitivity of those factors against one of five standard sensitivity levels that the system allows, ranging from low to high sensitivity. A one-level change in sensitivity for a single factor corresponds to a 25% change in the percentage of the differentiation value that you are estimated to capture. This impact percentage reduces for any given change in sensitivity in equal measure to the amount of pricing factors added to the tool. For example, if you have one pricing factor, a one-step change in sensitivity would result in that 25% change, whereas if you have two factors, a one-step change in sensitivity would result in a 12.5% change. Changes are cumulative, so if you have one factor that increases sensitivity and another that reduces sensitivity by the same measure, the net impact to your pricing is 0%.
What does this all mean for setting B2B SaaS prices from value?
There are a few things going on here. There’s a range of “rules of thumb” about how to set your pricing relative to value. Some say capture 5% to 10% of the value you create, while others say capture 40% to 50% of the net positive differentiated value you create. There are also insights here and there about the different types of methodologies and tools you might use to get to a price point. Scratching your head on where to start? Us too!
An important (and positive) takeaway here is that regardless of methodology, all of these approaches are primarily anchoring pricing in some form to value, which is good. After all, that’s the point of value-based pricing. Where the discrepancies and confusions leak in are around the key question we posed at the start of this post — how do you actually use these methods to set pricing.
First, it’s important to note that rules of thumb are plentiful in B2B SaaS, including in pricing, and should be taken as such. The data points on ratio of pricing to value and percentage of positive differentiation value cited here are good benchmarks to know, and to use as references once you’ve set pricing, but they shouldn’t be a substitute for doing actual value and pricing research.
Which leads us to our next point: It’s pretty clear that all the approaches cited in this post are rooted in different price-setting methodologies, but where they seem consistent is in how they quantify value, primarily through using economic value estimation. If there’s one place that will set you off on the right foot with value-based pricing, it’s being able to truly understand value and develop competency in using economic value estimation approaches to quantify and manage value.
An important first step is customer research. It’s important to note that customer research here doesn’t mean jumping right into willingness to pay. You need to design customer research that is solely focusing on helping you understand value — jobs to be done, challenges and value drivers — so that you can build value quantification formulas to inform your economic value model. You’re not seeking at this stage to have customers define a solution or estimate pricing for a solution; you’re seeking to understand value. Whether it’s customer interviews, a customer survey, a competitor assessment, secondary market research or all of the above, that’s up to you based on the resources, time, budget and level of validation you need. But you need to start there, before getting into pricing.
Once you’ve established and quantified value, you’re ready to approach establishing pricing. Your economic value has established an upper bound for the maximum price of your offer, assuming a customer perceived value that was equivalent to your estimated economic value, and willingness to pay aligned to perceived value (this is a lofty assumption, but an important element of framing). For establishing price levels, we also like customer research, but we aren’t as reliant as others on quantitative methods such as Van Westendorp (see our previously referenced post on this topic). We like qualitative research or a hybrid approach, and research methods that are less narrow. It’s also really important that your research methods for price setting leverage the work done in the value estimation phase, so that you can adequately articulate value and position value drivers to the customers you’re asking to estimate pricing.
There are another couple of pieces that aren’t fodder for a deep dive in this post but cannot be understated in terms of their importance to this process:
Value communication: Value isn’t an effective frame for your pricing approach if it isn’t well understood, effectively managed, and communicated both internally and externally to your customers. Economic value shapes perceived value, which in turn shapes willingness to pay. Some of the value-based pricing tools that we’ve referenced in this post have modules and capabilities that are specifically designed to help you manage value with customers. It’s critical that any value-based price setting approach be complemented with a robust value communication and management strategy.
Value-based pricing as a process, not a project. We say this all the time, but it bears repeating — treat this as a process, not a project. Everyone says it, but it’s true: Pricing is art and science, where science informs the art. Marcos Rivera says that pricing is about “confidence,” meaning making decisions (art) based on incomplete data (science). At some point, you’ll be constricted based on the time, resources and budget you have available from doing the research you’d ideally want to do. You’ll have to do the best you can, and then commit to collecting more data on a recurring cadence to help you adapt to what you see in the market. If you establish a process and a cadence for reviewing pricing, you’ll be ready to iterate on price setting based on what your customers and your data tells you.
Want to talk SaaS pricing setting, or have a question about how these concepts apply to your product? You can find me on Twitter at @bbelangerTBR or send your feedback directly to [email protected]. I read all replies. And be sure to subscribe to get our content sent to your inbox once it publishes.