In the video "What Is Pricing Power in SaaS? How To Calculate It" from the channel "AI, SaaS & Agentic Pricing with Monetizely," the speaker breaks down a straightforward, data-driven methodology for measuring pricing power in software companies. This approach moves beyond simple discounting metrics or competitor comparisons to provide actionable insights that even Warren Buffett would appreciate when evaluating investment opportunities.
What Exactly Is Pricing Power?
Pricing power is a term frequently referenced in business contexts, particularly when discussing company valuation. As the speaker notes, "Warren Buffett looks at the pricing power of a company in order to invest in it." But for many SaaS executives, quantifying this concept remains challenging.
The fundamental question is straightforward but often difficult to answer: "What is the pricing power? What do we bear in the market?" With varying discount ranges and inconsistent reporting metrics across organizations, many leaders resort to guesswork or vague competitive assessments.
A Simple Method to Calculate Your Pricing Power
The solution proposed in the video is refreshingly straightforward and relies on data you likely already have:
"In your company, you are going to have CRM sales data, right? And you're gonna have data of opportunities that you have closed. Now, the opportunities that you have closed, you're going to have the total contract value and you're going to have the number of units that you have sold."
These units could be anything from user seats to gigabytes of data or server compute capacity. The key is to normalize your data by calculating a dollar-per-unit metric.
Normalizing Your Contract Data
The process involves two primary data points:
- Total contract value per customer
- Units sold upfront or consumed later
By dividing the first by the second, you create a standardized dollar-per-unit metric that provides much more insight than raw discounting data.
"What this will do is let you normalize all of your customer data into this dollar per unit metric. You can say my pro plan averages a $60 per user, my elite plan averages a $90 per user, and you can track that over time."
This approach is superior to tracking discounts because list prices frequently change, making discount percentages an unreliable metric over time. The dollar-per-unit figure, however, tends to remain relatively stable throughout a company's lifecycle unless there's a major business model pivot.
Tracking Upfront Value vs. Expansion Potential
The speaker emphasizes how this methodology also reveals expansion potential:
"People on average pay $60 per seat for the Pro plan, and then those cohorts end up expanding another $30 per seat. Something happens to the Elite plan, right? And you may observe that, hey, it may be the case that our Elite plan is only sold for $80 per seat upfront, but these type of customers end up paying $150 per seat more after the fact."
This creates a compelling narrative about both initial pricing power and expansion opportunity, giving executives a clearer picture of lifetime value potential across different customer segments.
What If You Don't Have Formal Pricing Tiers?
For companies without established pricing plans that have been selling on an ad hoc basis, the speaker recommends a quartile analysis approach:
"What you wanna do is you want to quartile out your customers. You want to see what the top quartile is doing, what the third quartile, second and first quartile are doing, and have some way to segment out based on the units consumed as to where these customer quartiles live, and then create pseudo packages."
This quartile segmentation creates artificial tiers that allow for the same type of analysis, even without formal pricing structures.
Understanding Your Revenue Curve
When visualizing your pricing data, you'll likely discover an important pattern:
"When you look at your data, you're most likely going to find some sort of an exponential function where your top enterprise customers tend to pay you a lot, but the other times this curve immediately rapidly falls and the units consumed tend to fall."
The steepness of this curve reveals crucial information about your business model. A steeper curve indicates that a small number of enterprise customers drive most of your revenue, suggesting you should focus on optimizing those enterprise plans and ensuring your sales cycle accommodates them properly.
Conversely, a flatter curve indicates more equal contribution across customer segments, which might call for maximizing market reach rather than focusing exclusively on enterprise deals.
"The less steep this curve is and the more higher prices across the board customers are going to pay you, that is really good for you. But regardless, you will need to understand the nature of your business."
Next Steps for SaaS Executives
To implement this pricing power analysis in your organization:
- Extract your CRM data on closed deals
- Identify your unit metric (seats, usage, etc.)
- Calculate the dollar-per-unit value across customers
- Segment by plan tier or by quartile if no formal tiers exist
- Track these metrics over time
- Analyze your revenue curve to determine where your pricing power truly lies
This approach provides a data-backed foundation for pricing decisions that can significantly impact your company's growth trajectory and valuation. Instead of relying on gut feeling or arbitrary competitor comparisons, you'll have concrete metrics to guide strategic pricing decisions and to communicate pricing power to investors and board members.
Your pricing power isn't just about what you can charge—it's about understanding the true value dynamics across your customer base and optimizing your pricing strategy accordingly.