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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Below is a concise summary based on Price to Scale:
• In our book Price to Scale, we emphasize that there isn’t a one‐size‐fits-all answer to sample size requirements—it largely depends on your market and segment. For consumer settings, where you typically have higher traffic, you can more easily obtain large enough samples (often in the hundreds or thousands) to detect statistically significant signals.
• For small business software, while sample sizes may be lower than consumer products, experiments can still be effective if you fine-tune the design. However, as we point out, this method tends to fall short in enterprise software settings where the pool of potential testers or customers is much smaller.
• Rather than relying solely on a fixed numeric threshold, our pricing strategy book suggests designing your experiments by:
- Assessing the typical conversion rates or response volumes you have.
- Using tools like fixed trade-off screens or probing questions to supplement the quantitative data.
- Allowing for multiple scenarios or variations so that even if your sample size isn’t huge, you can still glean actionable insights about price sensitivities.
• For any statistically driven experiment, it is generally wise to conduct a power analysis (e.g., aiming for 80% power with a significance level of 5%) to determine the exact number of observations needed. This calculation can be adjusted based on the expected effect size and variance in your specific context.
In summary, our book Price to Scale doesn’t prescribe an exact number for sample sizes. Instead, it advises tailoring your approach based on the market segment—using larger samples where available (as in consumer settings) and supplementing with qualitative insights when sample sizes are inherently limited (as in enterprise contexts). This context-driven approach helps ensure that your pricing experiments yield robust, actionable data.