Price Testing

What is Price Testing?

Price testing is a systematic approach to finding the optimal price for your product or service by analyzing customer behavior, willingness to pay, and market demand at different price points. Rather than setting prices based on cost-plus calculations or competitor copying, price testing uses controlled experiments and customer research to determine what buyers will actually pay.

For SaaS companies, price testing typically involves methods like A/B testing different pricing tiers, surveying customers about willingness to pay, or analyzing how different segments respond to price changes. The goal is to maximize revenue without leaving money on the table through underpricing or losing customers through overpricing.

Why Price Testing Matters

Despite pricing being one of the most impactful levers for profitability, most companies don't invest in rigorous pricing research. According to OpenView's analysis of 2,200 SaaS companies, only 6% have conducted sophisticated pricing research on buyer needs and willingness to pay. Meanwhile, 48% haven't done any pricing research at all.

This gap matters because pricing optimization delivers measurable results. McKinsey's research across more than 1,000 pricing initiatives shows that systematic pricing improvements typically translate into a 2-7 percentage point increase in return on sales, depending on the sector. For the average S&P 1500 company, a 1% price increase generates an 8% increase in operating profits—nearly 50% more impact than a 1% reduction in variable costs.

The companies that invest in price testing gain several advantages. They discover actual willingness to pay rather than guessing. They can optimize for customer lifetime value by understanding price sensitivity across different segments. And they avoid competing solely on price by understanding the value customers place on specific features and benefits.

Common Price Testing Methods

Van Westendorp Price Sensitivity Meter

This survey-based method asks customers four key questions to map out acceptable price ranges:

1. At what price would you consider this too expensive to consider?

2. At what price would you consider this expensive but still worth considering?

3. At what price would you consider this a bargain?

4. At what price would you consider this too cheap, making you question the quality?

The intersections of these response curves reveal critical price points, including the optimal price point and the acceptable price range boundaries. Van Westendorp works well for new products where you need to establish initial pricing boundaries. It typically requires 100-200 respondents to generate reliable results.

Gabor-Granger Method

This approach directly tests willingness to pay by showing customers a specific price point and asking whether they'd purchase. Based on their response, you show them a higher or lower price until you find their maximum acceptable price.

The process is straightforward: show product at initial price, ask if they'd buy, then adjust the price up or down based on their response. This continues until you identify their price threshold. While simple to implement, Gabor-Granger provides clear data on demand curves and requires 200-300 respondents for statistical significance.

Conjoint Analysis

Conjoint analysis reveals how customers value different product attributes, including price. You show respondents various combinations of features and prices, then use their preferences to calculate the relative value of each attribute.

This method works particularly well for complex products with multiple features where you need to understand trade-offs. If you're deciding whether customers value advanced analytics more than priority support, conjoint analysis provides quantitative answers. The trade-off is complexity—you'll need 300-500 respondents and more sophisticated analysis tools.

A/B Testing

For companies with existing products and customer traffic, A/B testing lets you compare specific price points in real market conditions. You show different prices to randomly selected segments and measure conversion rates, revenue per customer, and long-term retention.

A/B testing provides the most realistic data since it's based on actual purchase behavior rather than stated intentions. However, it requires sufficient traffic volume and careful experiment design to avoid damaging customer trust through inconsistent pricing.

Building a Price Testing Framework

Define Clear Objectives

Before selecting a methodology, clarify what you're trying to achieve. Are you testing price elasticity for an existing product? Finding optimal pricing for a new feature? Understanding segment-specific willingness to pay? Evaluating new pricing models like usage-based or tiered pricing?

Your objective determines which testing method makes sense. A/B testing works for incremental adjustments to existing pricing. Van Westendorp helps establish initial price ranges for new products. Conjoint analysis clarifies feature-level value for complex packaging decisions.

Segment Your Audience

Different customer segments often have different willingness to pay. Enterprise customers evaluating contract management software care about integration capabilities and compliance features. Small businesses prioritize ease of use and transparent pricing. If you test pricing without accounting for these differences, you'll get muddy results.

Segment by criteria that correlate with value perception—company size, industry, use case, or existing spending patterns. Test pricing within each segment to understand how responses vary.

Design Controlled Experiments

Effective price tests isolate pricing as the variable while holding other factors constant. If you're A/B testing a price increase, both groups should see identical product descriptions, positioning, and feature sets.

Define clear success metrics beyond simple conversion rates. Consider customer acquisition cost payback period, lifetime value projections, and segment-specific responses. A successful price test might actually decrease conversion rates if it significantly increases average contract value and improves customer quality.

Plan for sufficient sample sizes. Statistical significance matters—you need enough data to distinguish real pricing signals from random variation. How much is enough depends on your baseline conversion rates and the size of effect you're trying to detect.

Measure Long-Term Effects

Price changes create ripple effects that appear over time. An increase might maintain initial conversion rates but increase churn three months later when renewal decisions happen. Conversely, lower pricing might boost initial conversions but attract customers with lower lifetime value.

Track cohorts through their full customer lifecycle. Compare not just conversion rates but also activation rates, expansion revenue, retention, and customer profitability across different price points.

Common Price Testing Pitfalls

Testing in isolation. Price doesn't exist in a vacuum. A change to your mid-tier pricing affects perception of both entry-level and premium tiers. Customers anchor on relative price differences across tiers, not just absolute prices. Test your full pricing structure, not individual price points.

Ignoring qualitative feedback. Numbers tell you what customers do, but conversations tell you why. Supplement quantitative testing with customer interviews. If a price point performs poorly, understanding whether customers see it as "too expensive" versus "surprisingly cheap, so probably low quality" completely changes your response.

Over-indexing on competitors. Competitive intelligence matters, but your unique value proposition should drive pricing. If you're delivering demonstrably more value than alternatives, you can price accordingly. Let competitive data inform your testing range, not determine your final price.

Changing prices too frequently. Price tests should inform strategic decisions, not create constant fluctuation. Frequent price changes erode customer trust and complicate financial forecasting. Test thoroughly before implementing changes, then give new pricing time to generate long-term data.

Implementation Considerations

For Usage-Based Pricing

When testing usage-based pricing, consider multiple variables: base rate per unit, volume discount curves, commitment incentives for annual versus monthly contracts, and overage handling. Each component affects customer perception and behavior differently.

Test not just the per-unit rate but also the discount structure. Linear discounts are simple but may not align with your cost structure. Tiered or graduated discounts can incentivize higher usage while protecting margins.

For Tiered SaaS Pricing

Beyond price points, test feature distribution across tiers. Should your popular analytics feature sit in the middle tier or premium tier? How many tiers optimize revenue versus creating decision paralysis?

Consider testing tier naming and positioning alongside prices. "Professional" versus "Business" conveys different value propositions even at identical price points. The number of tiers matters too—good/better/best structures simplify decisions, while more tiers allow finer-grained segmentation.

Regional Price Testing

Purchasing power and price sensitivity vary by geography. A price that works in San Francisco might be prohibitive in Prague. When expanding to new markets, test region-specific pricing rather than converting your US prices at current exchange rates.

B2B software buyers in the US often prioritize features and integrations over price. European buyers typically show more price consciousness and value transparent pricing structures. Test these differences rather than assuming uniform global willingness to pay.

Getting Started

Start with a pricing audit. When did you last validate your prices with actual customer data? If the answer is "never" or "when we launched," you have an opportunity.

Pick one methodology appropriate to your situation. If you have a new product without market data, start with Van Westendorp to establish acceptable price ranges. If you have an existing product with steady traffic, run an A/B test on a specific pricing change you're considering.

Run a pilot test with a constrained scope. Choose a specific customer segment or feature rather than testing your entire pricing structure simultaneously. Use the results to build internal understanding of price testing methodology before expanding to broader initiatives.

Price testing isn't a one-time project. Market conditions shift, competitive landscapes evolve, and customer expectations change. Companies that treat pricing as a core competency rather than a launch-day decision are better positioned to optimize revenue as their business grows.