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Estd 4.22.2023
Introduction
Feature pricing remains one of the most challenging aspects of SaaS product management. When that feature doesn't even exist yet, the challenge compounds significantly. Yet, determining appropriate pricing early in the development cycle is crucial for resource allocation, ROI forecasting, and ensuring market alignment before significant investment. According to OpenView Partners' 2023 SaaS Benchmarks report, companies that establish pricing strategies before feature development are 35% more likely to hit their revenue targets for new offerings.
This guide walks SaaS executives through a systematic approach to pricing features that are still on the drawing board, combining market intelligence, customer insights, and strategic positioning to develop pricing that maximizes both adoption and revenue potential.
Why Pre-Development Pricing Matters
Pricing is not just a number—it's a strategic decision that communicates value. When you price a feature before it exists, you're essentially:
- Validating market demand: A pricing exercise forces you to quantify the feature's value proposition
- Setting development parameters: The target price point influences scope and investment
- Aligning stakeholders: Product, marketing, sales, and finance can align expectations early
- Creating ROI accountability: Establishing metrics for success before development begins
According to ProfitWell research, companies that determine pricing strategy before building features achieve 15-20% higher feature monetization compared to those who address pricing as an afterthought.
The Pre-Existence Pricing Framework
Step 1: Map the Value Metrics
Before discussing dollars, identify how the feature creates value for customers:
- Efficiency gains: Time saved, resources conserved
- Revenue enablement: How it helps customers make money
- Risk reduction: How it decreases exposure or liability
- Competitive advantage: How it positions customers ahead of their competitors
For each value metric, attempt quantification. For instance, if your feature automates a manual process, calculate the average labor hours saved multiplied by the typical hourly rate of professionals performing that task.
Step 2: Conduct Competitive Intelligence
Even when your feature is novel, competitors often offer adjacent or partial solutions:
- Direct alternatives: Similar features from competitors
- Workarounds: How customers currently solve the problem
- Complementary tools: Third-party solutions customers might integrate
Analyze their pricing structures, not just for amounts but for models:
- Is it included in core subscription?
- Offered as an add-on?
- Priced per user, per usage, or on value metrics?
According to a Gartner study, 64% of enterprises evaluate at least three competing solutions before purchasing new software capabilities, making competitive awareness critical to pricing strategy.
Step 3: Deploy the Van Westendorp Price Sensitivity Meter
This proven research methodology helps identify optimal price points through four simple questions to potential customers:
- At what price would you consider the feature to be so expensive that you would not consider buying it? (Too expensive)
- At what price would you consider the feature to be priced so low that you would question its quality? (Too cheap)
- At what price would you consider the feature to be getting expensive, but you would still consider buying it? (Expensive/High)
- At what price would you consider the feature to be a bargain—a great buy for the money? (Inexpensive/Bargain)
This approach creates four price points where lines intersect, helping identify:
- Point of Marginal Cheapness: Lower bound of acceptable price range
- Point of Marginal Expensiveness: Upper bound of acceptable price range
- Optimal Price Point: Where resistance to purchase and resistance to cheapness are equal
- Indifference Price Point: Where the same percentage find the product expensive as find it cheap
Even with a hypothetical feature, this exercise yields valuable insights when conducted with qualified prospective customers.
Step 4: Implement Conjoint Analysis
While seemingly complex, conjoint analysis helps understand how customers value different feature combinations. For unreleased features, present scenarios like:
"If our product included features A, B, and the new capability C at price point X, how likely would you be to purchase?"
By varying the combinations and price points across multiple respondents, patterns emerge about:
- The relative value of the new feature compared to existing ones
- Price sensitivity specifically associated with the new feature
- Different segment responses to various pricing models
According to research from Simon-Kucher & Partners, companies that use conjoint analysis in pricing decisions achieve 3-7% higher margins than those who don't.
Step 5: Structure Tiered Pilots
Once development begins, structure early access programs with different pricing tiers:
- Free tier: For feedback providers and beta testers
- Introductory tier: Early adopters with discounted pricing
- Premium tier: Full-featured access at target pricing
This approach validates price points with actual market behavior while generating early revenue to offset development costs.
Salesforce has famously used this approach for new feature launches, reporting that tiered pilots result in 40% higher adoption rates when features fully launch.
Pricing Models for Unreleased Features
Beyond determining the amount, consider how the pricing model itself influences perception:
Value-Based Pricing
Link pricing directly to the quantifiable benefit customers receive. For example, if your feature saves customers $100,000 annually, pricing at 10-20% of this value ($10,000-$20,000) often feels fair to customers while delivering strong margins.
Outcome-Based Pricing
Only charge when the feature delivers measurable results. This reduces adoption friction but requires robust tracking mechanisms. Mixpanel successfully used this approach when introducing advanced analytics features, charging only when insights led to measurable customer retention improvements.
Good-Better-Best Tiering
Create feature variants at different price points, even before full development. This helps identify which capabilities drive willingness to pay and where to focus engineering resources.
Bundling Strategy
Consider how the new feature fits into existing packages or if it warrants a new bundle. According to Price Intelligently, thoughtfully bundled features can increase average revenue per user by 15-30% compared to à la carte offerings.
Common Pitfalls to Avoid
The Cost-Plus Trap
Many executives default to pricing based on development costs plus margin. This approach ignores market dynamics and customer value perception. According to Harvard Business Review, cost-plus pricing leaves an average of 25% potential revenue on the table compared to value-based approaches.
The Competitive Matching Fallacy
Matching competitor pricing seems safe but undermines unique value propositions. If your feature solves problems more effectively than alternatives, pricing parity signals you don't believe in your differentiation.
Feature Commoditization Risk
Pricing too low initially makes future increases difficult. According to a study by Patrick Campbell of ProfitWell, SaaS companies that introduce features at low price points face 3x more customer resistance when attempting price increases later.
Case Study: Slack's Approach to Pricing Thread Feature Before Launch
Before launching Threads as a premium feature, Slack conducted extensive research to determine pricing. They:
- Identified that threads would save an estimated 15 minutes daily per user in communication efficiency
- Quantified this as $1,300 annual savings per knowledge worker
- Used conjoint analysis to determine that customers valued threads at approximately 5-8% of their overall Slack subscription value
- Implemented tiered pilot pricing with select enterprise customers
This methodical approach allowed Slack to successfully launch Threads as part of their premium tier, driving a 17% conversion rate from standard to premium plans within six months of launch.
Conclusion
Pricing a feature before it exists might seem like guesswork, but with structured approaches like value mapping, competitive analysis, the Van Westendorp method, and conjoint analysis, SaaS executives can develop pricing strategies based on evidence rather than intuition.
The most successful SaaS companies view pre-development pricing not as a final decision but as a testable hypothesis that guides development priorities and go-to-market strategy. By establishing a price target early, product teams gain clearer parameters for development scope, marketing teams can begin positioning work, and executives can set appropriate expectations for feature contribution to overall revenue.
Remember that pricing is iterative. Your initial pricing strategy provides a crucial starting point, but continuous market feedback will refine your approach as the feature moves from conception to reality.
Next Steps for SaaS Executives
- Identify your next major feature initiative and apply the pre-existence pricing framework before development begins
- Establish a cross-functional pricing committee including product, sales, marketing and finance stakeholders
- Develop a feature value calculator specific to your industry that helps quantify ROI for customers
- Create standardized pricing research protocols to ensure consistency across future feature development initiatives
By treating pricing as a strategic input to development rather than an output, you'll build features that don't just impress users but also drive predictable revenue growth.