In a recent educational video from the "AI, SaaS & Agentic Pricing with Monetizely" YouTube channel, the presenter shares valuable insights on using AI tools to estimate the financial impact of adding new features to a SaaS product. The video demonstrates how modern AI tools like ChatGPT can replace traditional pivot table analysis when making critical pricing and packaging decisions.
The Challenge of Pricing New SaaS Features
Adding new features to a SaaS platform presents a strategic pricing challenge for product managers and executives. When you develop a compelling new feature or module, you have multiple options for how to commercialize it:
- Offer it as an add-on exclusively to enterprise customers
- Make it available as an add-on across all customer tiers
- Bundle it into premium tiers to incentivize upgrades
- Include it in all tiers to increase win rates
The video presenter explains that the decision should be based on two key factors: popularity (how many customers want the feature) and willingness to pay (how much value customers place on the feature).
"You have two axis of decision, right? How popular is this new feature and how much are people willing to pay for it. That's basically the two things," the presenter explains in the video.
A Framework for Feature Pricing Decisions
The video introduces a decision-making rubric that helps product managers determine the optimal commercialization strategy based on popularity and willingness to pay:
- High popularity + High willingness to pay: Offer as a universal add-on across all tiers (Option B)
- Low popularity + High willingness to pay: Either offer as an enterprise-only add-on or bundle into the enterprise tier to drive upgrades (Options A or D)
- High popularity + Low willingness to pay: Bundle into all plans to increase win rates (Option C)
This framework provides a structured approach to what is often an intuitive decision, allowing teams to maximize revenue from new features.
Using AI to Model Financial Impact
The most valuable insight from the video is the demonstration of how AI can now handle the financial modeling that traditionally required complex spreadsheet work.
Using a hypothetical SaaS company with approximately $23 million in ARR by 2025, the presenter shows how to evaluate the potential impact of a new AI module called "AI Ways" across different pricing scenarios:
- Scenario A: 25% of enterprise customers purchase the add-on at 30% of their base ARR
- Scenario B: 40% of all customers purchase the add-on at 20% of their base ARR
- Scenario C: The feature is bundled into all plans, raising total ARR by 5%
- Scenario D: The feature is bundled into the enterprise tier, causing 15% of pro customers to upgrade at full list price
The presenter explains how ChatGPT can analyze CRM data containing customer information, tiers, user counts, and ARR figures to calculate the financial impact of each scenario.
"The need for you to run pivot tables and do your analysis on your own starts to become less and less and you can easily just do basic modeling analysis with GPT models and they're going to be pretty good," the presenter notes.
Surprising Results from AI Analysis
In the demonstration, ChatGPT analyzed the data and revealed that Scenario D (bundling the feature into enterprise tier to drive upgrades) would generate the highest incremental ARR at approximately $1.88 million, compared to $1.2 million for Scenario A and lesser amounts for the other options.
This result challenges conventional thinking, as the presenter points out: "Many times product managers default to bundle in a feature or default to having that be an on-plan, right? To just have it be in one plan and incentivize the upgrade is not something you would naturally come to."
The analysis reveals that driving upgrades through strategic feature placement could be more valuable than simply selling the feature as an add-on—information that might be counterintuitive, especially to sales teams who typically prefer to attach a direct price tag to new capabilities.
Combining AI Analysis with Business Intuition
While the AI can quickly provide financial projections, the presenter emphasizes that these calculations must be complemented by business intuition and customer knowledge:
"Your math is completely conjecture. Now you have to marry that with your intuition. Who are those customers? Do they need this feature? How do they value this feature? Perhaps you can do some studies with existing customers."
This balanced approach—using AI for rapid financial modeling while applying human judgment about customer needs and market dynamics—represents the future of SaaS pricing strategy.
Key Takeaways for SaaS Executives
- Leverage AI for pricing analysis: Tools like ChatGPT can quickly model financial outcomes of different pricing scenarios without complex spreadsheet work
- Consider all commercialization options: Don't default to add-on pricing; sometimes bundling features into specific tiers to drive upgrades can generate more revenue
- Use a structured framework: Base pricing decisions on the popularity of the feature and customers' willingness to pay
- Challenge sales team assumptions: The highest revenue option may not be the one that attaches a direct price tag to a new feature
- Balance data with intuition: Combine AI-generated financial projections with your understanding of customer needs and market dynamics
By applying these principles, SaaS executives and product managers can make more informed decisions about how to maximize the revenue impact of new features and modules in their offerings.