In a recent educational video titled "How To: Basic SaaS Pricing Analysis" from the Monetizely channel, viewers are provided with a practical approach to evaluate pricing performance without requiring complex statistical studies. The presenter demonstrates how SaaS executives can leverage existing CRM data to identify pricing inefficiencies and make informed adjustments to their pricing strategy.
Why Most Companies Miss Critical Pricing Insights
Most SaaS companies have abundant pricing performance data sitting unused in their systems. Despite this wealth of information, many executives make pricing decisions based solely on limited metrics without analyzing the complete picture.
"In most companies you're going to have a lot of pricing performance information that is just not being measured," the presenter explains. "Executives going to have an opinion and surprisingly nobody's looking at the pricing performance at all. They're just looking at topline revenue numbers. Did we make the number? Did we miss the number? What is the margin?"
This narrow focus creates blind spots in understanding how different pricing tiers are actually performing in the market. Instead of running complex pricing studies like conjoint analysis or Van Westendorp methods, the presenter suggests beginning with a simpler empirical approach using data you already have.
Creating a Basic CRM Dataset for Pricing Analysis
The first step in conducting an empirical pricing analysis is organizing your CRM data into a usable format. The presenter demonstrated this using an anonymized dataset that contained:
- Account names
- Total contract value
- Number of seats sold
- Dollar per seat (derived by dividing contract value by seats)
- Package/tier purchased
- Discount percentage from list price
- Sales quarter
- Sales cycle length
This structured approach provides a foundation for identifying patterns and potential pricing problems across your different tiers.
Spotting Pricing Red Flags in Your Data
Once your data is organized, several key metrics can reveal pricing inefficiencies. In the example provided, the analysis revealed that:
- The middle-tier "Pro" package generated significantly less revenue ($657K) compared to the entry-level "Essentials" tier ($1.5M) and premium "Elite" tier ($6.5M).
- Only eight deals included the Pro package, while Essentials and Elite had roughly equal adoption.
- The Elite tier showed unusually high discount rates.
- The Pro tier had longer sales cycles than both the entry-level and premium tiers.
"Pro is not selling and while pro is not selling elite has high discounting which is a symptom that pro is not fitting customers and they end up buying elite and then discounting it a lot which is not really the original intention we had," the presenter notes.
These patterns suggest a problem with the middle tier's value proposition, forcing customers to purchase the Elite tier at steep discounts to access needed features.
The Downstream Impact of Ineffective Pricing
Ineffective pricing tiers don't just impact immediate revenue—they create long-term problems for customer retention and growth. When customers purchase higher tiers primarily for one or two features (while getting many unused features), it creates several issues:
"Now they're going to have a lot of shelf wear in those plans and adding on usage data to this over time will help validate whether there is a lot of shelf wear and if you sell the plan like this it also blocks your upsell later on because you cannot keep up with the growth of the account. This creates churn problems and net retention problems."
The ideal scenario is having well-designed packages tailored to specific customer segments that allow for natural growth and expansion over time.
Combining Quantitative Analysis with Qualitative Insights
The power of this approach comes from combining the data analysis with qualitative insights from your team. After identifying potential issues, the presenter recommends:
"What I can do is have a conversations with my sales team, have a conversation with my strategy functions, marketing as well as the CEO and have a hypothesis for what they think might be happening and provide this data to either validate or invalidate some of those conversations."
This collaborative approach ensures that the data isn't analyzed in isolation but is instead enriched with frontline insights from those closest to customers.
Next Steps After Your Initial Analysis
This empirical pricing analysis serves as a foundation for more targeted pricing adjustments. The presenter suggests this approach should precede more complex pricing studies:
"At least you have some data to go off of to refine your pricing and packaging after which you will do further market studies."
By starting with this basic analysis of existing data, you can identify the most pressing issues and develop informed hypotheses before investing in more extensive pricing research.
Conclusion
A data-driven approach to pricing doesn't necessarily require sophisticated market studies, at least not initially. By systematically analyzing your existing CRM data and looking for patterns in pricing tier performance, discount rates, and sales cycles, you can uncover valuable insights to optimize your pricing strategy.
This straightforward analysis provides a practical starting point for SaaS executives looking to improve pricing performance without immediately jumping to complex statistical methods. As the presenter concludes, "This is a very simple way for you to figure this out, figure out what's happening based on your sales data."